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177 Great Artificial Intelligence Research Paper Topics to Use

In this top-notch post, we will look at the definition of artificial intelligence, its applications, and writing tips on how to come up with AI topics. Finally, we shall lock at top artificial intelligence research topics for your inspiration.

What Is Artificial Intelligence?

It refers to intelligence as demonstrated by machines, unlike that which animals and humans display. The latter involves emotionality and consciousness. The field of AI has gained proliferation in recent days, with many scientists investing their time and effort in research.

How To Develop Topics in Artificial Intelligence

Developing AI topics is a critical thinking process that also incorporates a lot of creativity. Due to the ever-dynamic nature of the discipline, most students find it hard to develop impressive topics in artificial intelligence. However, here are some general rules to get you started:

Read widely on the subject of artificial intelligence Have an interest in news and other current updates about AI Consult your supervisor

Once you are ready with these steps, nothing is holding you from developing top-rated topics in artificial intelligence. Now let’s look at what the pros have in store for you.

Artificial Intelligence Research Paper Topics

The role of artificial intelligence in evolving the workforce
Are there tasks that require unique human abilities apart from machines?
The transformative economic impact of artificial intelligence
Managing a global autonomous arms race in the face of AI
The legal and ethical boundaries of artificial intelligence
Is the destructive role of AI more than its constructive role in society?
How to build AI algorithms to achieve the far-reaching goals of humans
How privacy gets compromised with the everyday collection of data
How businesses and governments can suffer at the hands of AI
Is it possible for AI to devolve into social oppression?
Augmentation of the work humans do through artificial intelligence
The role of AI in monitoring and diagnosing capabilities

Artificial Intelligence Topics For Presentation

How AI helps to uncover criminal activity and solve serial crimes
The place of facial recognition technologies in security systems
How to use AI without crossing an individual’s privacy
What are the disadvantages of using a computer-controlled robot in performing tasks?
How to develop systems endowed with intellectual processes
The challenge of programming computers to perform complex tasks
Discuss some of the mathematical theorems for artificial intelligence systems
The role of computer processing speed and memory capacity in AI
Can computer machines achieve the performance levels of human experts?
Discuss the application of artificial intelligence in handwriting recognition
A case study of the key people involved in developing AI systems
Computational aesthetics when developing artificial intelligence systems

Topics in AI For Tip-Top Grades

Describe the necessities for artificial programming language
The impact of American companies possessing about 2/3 of investments in AI
The relationship between human neural networks and A.I
The role of psychologists in developing human intelligence
How to apply past experiences to analogous new situations
How machine learning helps in achieving artificial intelligence
The role of discernment and human intelligence in developing AI systems
Discuss the various methods and goals in artificial intelligence
What is the relationship between applied AI, strong AI, and cognitive simulation
Discuss the implications of the first AI programs
Logical reasoning and problem-solving in artificial intelligence
Challenges involved in controlled learning environments

AI Research Topics For High School Students

How quantum computing is affecting artificial intelligence
The role of the Internet of Things in advancing artificial intelligence
Using Artificial intelligence to enable machines to perform programming tasks
Why do machines learn automatically without human hand holding
Implementing decisions based on data processing in the human mind
Describe the web-like structure of artificial neural networks
Machine learning algorithms for optimal functions through trial and error
A case study of Google’s AlphaGo computer program
How robots solve problems in an intelligent manner
Evaluate the significant role of M.I.T.’s artificial intelligence lab
A case study of Robonaut developed by NASA to work with astronauts in space
Discuss natural language processing where machines analyze language and speech

Argument Debate Topics on AI

How chatbots use ML and N.L.P. to interact with the users
How do computers use and understand images?
The impact of genetic engineering on the life of man
Why are micro-chips not recommended in human body systems?
Can humans work alongside robots in a workplace system?
Have computers contributed to the intrusion of privacy for many?
Why artificial intelligence systems should not be made accessible to children
How artificial intelligence systems are contributing to healthcare problems
Does artificial intelligence alleviate human problems or add to them?
Why governments should put more stringent measures for AI inventions
How artificial intelligence is affecting the character traits of children born
Is virtual reality taking people out of the real-world situation?

Quality AI Topics For Research Paper

The use of recommender systems in choosing movies and series
Collaborative filtering in designing systems
How do developers arrive at a content-based recommendation
Creation of systems that can emulate human tasks
How IoT devices generate a lot of data
Artificial intelligence algorithms convert data to useful, actionable results.
How AI is progressing rapidly with the 5G technology
How to develop robots with human-like characteristics
Developing Google search algorithms
The role of artificial intelligence in developing autonomous weapons
Discuss the long-term goal of artificial intelligence
Will artificial intelligence outperform humans at every cognitive task?

Computer Science AI Topics

Computational intelligence magazine in computer science
Swarm and evolutionary computation procedures for college students
Discuss computational transactions on intelligent transportation systems
The structure and function of knowledge-based systems
A review of the artificial intelligence systems in developing systems
Conduct a review of the expert systems with applications
Critique the various foundations and trends in information retrieval
The role of specialized systems in transactions on knowledge and data engineering
An analysis of a journal on ambient intelligence and humanized computing
Discuss the various computer transactions on cognitive communications and networking
What is the role of artificial intelligence in medicine?
Computer engineering applications of artificial intelligence

AI Ethics Topics

How the automation of jobs is going to make many jobless
Discuss inequality challenges in distributing wealth created by machines
The impact of machines on human behavior and interactions
How artificial intelligence is going to affect how we act accordingly
The process of eliminating bias in Artificial intelligence: A case of racist robots
Measures that can keep artificial intelligence safe from adversaries
Protecting artificial intelligence discoveries from unintended consequences
How a man can stay in control despite the complex, intelligent systems
Robot rights: A case of how man is mistreating and misusing robots
The balance between mitigating suffering and interfering with set ethics
The role of artificial intelligence in negative outcomes: Is it worth it?
How to ethically use artificial intelligence for bettering lives

Advanced AI Topics

Discuss how long it will take until machines greatly supersede human intelligence
Is it possible to achieve superhuman artificial intelligence in this century?
The impact of techno-skeptic prediction on the performance of A.I
The role of quarks and electrons in the human brain
The impact of artificial intelligence safety research institutes
Will robots be disastrous for humanity shortly?
Robots: A concern about consciousness and evil
Discuss whether a self-driving car has a subjective experience or not
Should humans worry about machines turning evil in the end?
Discuss how machines exhibit goal-oriented behavior in their functions
Should man continue to develop lethal autonomous weapons?
What is the implication of machine-produced wealth?

AI Essay Topics Technology

Discuss the implication of the fourth technological revelation in cloud computing
Big database technologies used in sensors
The combination of technologies typical of the technological revolution
Key determinants of the civilization process of industry 4.0
Discuss some of the concepts of technological management
Evaluate the creation of internet-based companies in the U.S.
The most dominant scientific research in the field of artificial intelligence
Discuss the application of artificial intelligence in the literature
How enterprises use artificial intelligence in blockchain business operations
Discuss the various immersive experiences as a result of digital AI
Elaborate on various enterprise architects and technology innovations
Mega-trends that are future impacts on business operations

Interesting Topics in AI

The role of the industrial revolution of the 18 th century in A.I
The electricity era of the late 19 th century and its contribution to the development of robots
How the widespread use of the internet contributes to the AI revolution
The short-term economic crisis as a result of artificial intelligence business technologies
Designing and creating artificial intelligence production processes
Analyzing large collections of information for technological solutions
How biotechnology is transforming the field of agriculture
Innovative business projects that work using artificial intelligence systems
Process and marketing innovations in the 21 st century
Medical intelligence in the era of smart cities
Advanced data processing technologies in developed nations
Discuss the development of stelliform technologies

Good Research Topics For AI

Development of new technological solutions in I.T
Innovative organizational solutions that develop machine learning
How to develop branches of a knowledge-based economy
Discuss the implications of advanced computerized neural network systems
How to solve complex problems with the help of algorithms
Why artificial intelligence systems are predominating over their creator
How to determine artificial emotional intelligence
Discuss the negative and positive aspects of technological advancement
How internet technology companies like Facebook are managing large social media portals
The application of analytical business intelligence systems
How artificial intelligence improves business management systems
Strategic and ongoing management of artificial intelligence systems

Graduate AI NLP Research Topics

Morphological segmentation in artificial intelligence
Sentiment analysis and breaking machine language
Discuss input utterance for language interpretation
Festival speech synthesis system for natural language processing
Discuss the role of the Google language translator
Evaluate the various analysis methodologies in N.L.P.
Native language identification procedure for deep analytics
Modular audio recognition framework
Deep linguistic processing techniques
Fact recognition and extraction techniques
Dialogue and text-based applications
Speaker verification and identification systems

Controversial Topics in AI

Ethical implication of AI in movies: A case study of The Terminator
Will machines take over the world and enslave humanity?
Does human intelligence paint a dark future for humanity?
Ethical and practical issues of artificial intelligence
The impact of mimicking human cognitive functions
Why the integration of AI technologies into society should be limited
Should robots get paid hourly?
What if AI is a mistake?
Why did Microsoft shut down chatbots immediately?
Should there be AI systems for killing?
Should machines be created to do what they want?
Is the computerized gun ethical?

Hot AI Topics

Why predator drones should not exist
Do the U.S. laws restrict meaningful innovations in AI
Why did the campaign to stop killer robots fail in the end?
Fully autonomous weapons and human safety
How to deal with rogues artificial intelligence systems in the United States
Is it okay to have a monopoly and control over artificial intelligence innovations?
Should robots have human rights or citizenship?
Biases when detecting people’s gender using Artificial intelligence
Considerations for the adoption of a particular artificial intelligence technology

Are you a university student seeking research paper writing services or dissertation proposal help ? We offer custom help for college students in any field of artificial intelligence.

65+ Topics In Artificial Intelligence: A Comprehensive Guide To The Field

Jane Ng • 24 Jul 2023 • 6 min read

Welcome to the world of AI. Are you ready to dive into the 65+ best topics in artificial intelligenc e and make an impact with your research, presentations, essay, or thought-provoking debates?

In this blog post, we present a curated list of cutting-edge topics in AI that are perfect for exploration. From the ethical implications of AI algorithms to the future of AI in healthcare and the societal impact of autonomous vehicles, this “topics in artificial intelligence” collection will equip you with exciting ideas to captivate your audience and navigate the forefront of AI research.

Artificial intelligence research topics, artificial intelligence topics for presentation, ai projects for the final year, artificial intelligence seminar topics, artificial intelligence debate topics, artificial intelligence essay topics, interesting topics in artificial intelligence.

Key Takeaways

FAQs About Topics In Artificial Intelligence

Here are topics in artificial intelligence that cover various subfields and emerging areas:

AI in Healthcare: Applications of AI in medical diagnosis, treatment recommendation, and healthcare management.
AI in Drug Discovery : Applying AI methods to accelerate the process of drug discovery, including target identification and drug candidate screening.
Transfer Learning: Research methods to transfer knowledge learned from one task or domain to improve performance on another.
Ethical Considerations in AI: Examining the ethical implications and challenges associated with the deployment of AI systems.
Natural Language Processing: Developing AI models for language understanding, sentiment analysis, and language generation.
Fairness and Bias in AI: Examining approaches to mitigate biases and ensure fairness in AI decision-making processes.
AI applications to address societal challenges.
Multimodal Learning: Exploring techniques for integrating and learning from multiple modalities, such as text, images, and audio.
Deep Learning Architectures: Advancements in neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Here are topics in artificial intelligence suitable for presentations:

Deepfake Technology: Discussing the ethical and societal consequences of AI-generated synthetic media and its potential for misinformation and manipulation.
Cybersecurity: Presenting the applications of AI in detecting and mitigating cybersecurity threats and attacks.
AI in Game Development: Discuss how AI algorithms are used to create intelligent and lifelike behaviors in video games.
AI for Personalized Learning: Presenting how AI can personalize educational experiences, adapt content, and provide intelligent tutoring.
Smart Cities: Discuss how AI can optimize urban planning, transportation systems, energy consumption, and waste management in cities.
Social Media Analysis: Utilizing AI techniques for sentiment analysis, content recommendation, and user behavior modeling in social media platforms.
Personalized Marketing: Presenting how AI-driven approaches improve targeted advertising, customer segmentation, and campaign optimization.
AI and Data Ownership: Highlighting the debates around the ownership, control, and access to data used by AI systems and the implications for privacy and data rights.

AI-Powered Chatbot for Customer Support: Building a chatbot that uses natural language processing and machine learning to provide customer support in a specific domain or industry.
AI-Powered Virtual Personal Assistant: A virtual assistant that uses natural language processing and machine learning to perform tasks, answer questions, and provide recommendations.
Emotion Recognition : An AI system that can accurately recognize and interpret human emotions from facial expressions or speech.
AI-Based Financial Market Prediction: Creating an AI system that analyzes financial data and market trends to predict stock prices or market movements.
Traffic Flow Optimization: Developing an AI system that analyzes real-time traffic data to optimize traffic signal timings and improve traffic flow in urban areas.
Virtual Fashion Stylist: An AI-powered virtual stylist that provides personalized fashion recommendations and assists users in selecting outfits.

Here are the topics in artificial intelligence for the seminar:

How Can Artificial Intelligence Assist in Natural Disaster Prediction and Management?
AI in Healthcare: Applications of artificial intelligence in medical diagnosis, treatment recommendation, and patient care.
Ethical Implications of AI: Examining the ethical considerations and responsible development of AI Systems.
AI in Autonomous Vehicles: The role of AI in self-driving cars, including perception, decision-making, and safety.
AI in Agriculture: Discussing AI applications in precision farming, crop monitoring, and yield prediction.
How Can Artificial Intelligence Help Detect and Prevent Cybersecurity Attacks?
Can Artificial Intelligence Assist in Addressing Climate Change Challenges?
How Does Artificial Intelligence Impact Employment and the Future of Work?
What Ethical Concerns Arise with the Use of Artificial Intelligence in Autonomous Weapons?

Here are topics in artificial intelligence that can generate thought-provoking discussions and allow participants to critically analyze different perspectives on the subject.

Can AI ever truly understand and possess consciousness?
Can Artificial Intelligence Algorithms be Unbiased and Fair in Decision-Making?
Is it ethical to use AI for facial recognition and surveillance?
Can AI effectively replicate human creativity and artistic expression?
Does AI pose a threat to job security and the future of employment?
Should there be legal liability for AI errors or accidents caused by autonomous systems?
Is it ethical to use AI for social media manipulation and personalized advertising?
Should there be a universal code of ethics for AI developers and researchers?
Should there be strict regulations on the development and deployment of AI technologies?
Is artificial general intelligence (AGI) a realistic possibility in the near future?
Should AI algorithms be transparent and explainable in their decision-making processes?
Does AI have the potential to solve global challenges, such as climate change and poverty?
Does AI have the potential to surpass human intelligence, and if so, what are the implications?
Should AI be used for predictive policing and law enforcement decision-making?

Here are 30 essay topics in artificial intelligence:

AI and the Future of Work: Reshaping Industries and Skills
AI and Human Creativity: Companions or Competitors?
AI in Agriculture: Transforming Farming Practices for Sustainable Food Production
Artificial Intelligence in Financial Markets: Opportunities and Risks
The Impact of Artificial Intelligence on Employment and the Workforce
AI in Mental Health: Opportunities, Challenges, and Ethical Considerations
The Rise of Explainable AI: Necessity, Challenges, and Impacts
The Ethical Implications of AI-Based Humanoid Robots in Elderly Care
The Intersection of Artificial Intelligence and Cybersecurity: Challenges and Solutions
Artificial Intelligence and the Privacy Paradox: Balancing Innovation with Data Protection
The Future of Autonomous Vehicles and the Role of AI in Transportation

Here topics in artificial intelligence cover a broad spectrum of AI applications and research areas, providing ample opportunities for exploration, innovation, and further study.

What are the ethical considerations for using AI in educational assessments?
What are the potential biases and fairness concerns in AI algorithms for criminal sentencing?
Should AI algorithms be used to influence voting decisions or electoral processes?
Should AI models be used for predictive analysis in determining creditworthiness?
What are the challenges of integrating AI with augmented reality (AR) and virtual reality (VR)?
What are the challenges of deploying AI in developing countries?
What are the risks and benefits of AI in healthcare?
Is AI a solution or a hindrance to addressing social challenges?
How can we address the issue of algorithmic bias in AI systems?
What are the limitations of current deep learning models?
Can AI algorithms be completely unbiased and free from human bias?
How can AI contribute to wildlife conservation efforts?

Key Takeaways

The field of artificial intelligence encompasses a vast range of topics that continue to shape and redefine our world. In addition, AhaSlides offers a dynamic and engaging way to explore these topics. With AhaSlides, presenters can captivate their audience through interactive slide templates , live polls , quizzes , and other features allowing for real-time participation and feedback. By leveraging the power of AhaSlides, presenters can enhance their discussions on artificial intelligence and create memorable and impactful presentations.

As AI continues to evolve, the exploration of these topics becomes even more critical, and AhaSlides provides a platform for meaningful and interactive conversations in this exciting field.

What are the 8 types of artificial intelligence?

Here are some commonly recognized types of artificial intelligence:

Reactive Machines
Limited Memory AI
Theory of Mind AI
Self-Aware AI
Superintelligent AI
Artificial Superintelligence

What are the five big ideas in artificial intelligence?

The five big ideas in artificial intelligence, as outlined in the book “ Artificial Intelligence: A Modern Approach ” by Stuart Russell and Peter Norvig, are as follows:

Agents are AI systems that interact with and impact the world.
Uncertainty deals with incomplete information using probabilistic models.
Learning enables AI systems to improve performance through data and experience.
Reasoning involves logical inference to derive knowledge.
Perception involves interpreting sensory inputs like vision and language.

Are there 4 basic AI concepts?

The four fundamental concepts in artificial intelligence are problem-solving, knowledge representation, learning, and perception.

These concepts form the foundation for developing AI systems that can solve problems, store and reason with information, improve performance through learning, and interpret sensory inputs. They are essential in building intelligent systems and advancing the field of artificial intelligence.

Ref: Towards Data Science | Forbes | Thesis RUSH

A writer who wants to create practical and valuable content for the audience

12 Best Artificial Intelligence Topics for Research in 2024

Explore the "12 Best Artificial Intelligence Topics for Research in 2024." Dive into the top AI research areas, including Natural Language Processing, Computer Vision, Reinforcement Learning, Explainable AI (XAI), AI in Healthcare, Autonomous Vehicles, and AI Ethics and Bias. Stay ahead of the curve and make informed choices for your AI research endeavours.

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Table of Contents

1) Top Artificial Intelligence Topics for Research

a) Natural Language Processing

b) Computer vision

c) Reinforcement Learning

d) Explainable AI (XAI)

e) Generative Adversarial Networks (GANs)

f) Robotics and AI

g) AI in healthcare

h) AI for social good

i) Autonomous vehicles

j) AI ethics and bias

2) Conclusion

Natural Language Processing

Natural Language Processing (NLP) is centred around empowering machines to comprehend, interpret, and even generate human language. Within this domain, three distinctive research avenues beckon:

1) Sentiment analysis: This entails the study of methodologies to decipher and discern emotions encapsulated within textual content. Understanding sentiments is pivotal in applications ranging from brand perception analysis to social media insights.

2) Language generation: Generating coherent and contextually apt text is an ongoing pursuit. Investigating mechanisms that allow machines to produce human-like narratives and responses holds immense potential across sectors.

3) Question answering systems: Constructing systems that can grasp the nuances of natural language questions and provide accurate, coherent responses is a cornerstone of NLP research. This facet has implications for knowledge dissemination, customer support, and more.

Computer Vision

Computer Vision, a discipline that bestows machines with the ability to interpret visual data, is replete with intriguing avenues for research:

1) Object detection and tracking: The development of algorithms capable of identifying and tracking objects within images and videos finds relevance in surveillance, automotive safety, and beyond.

2) Image captioning: Bridging the gap between visual and textual comprehension, this research area focuses on generating descriptive captions for images, catering to visually impaired individuals and enhancing multimedia indexing.

3) Facial recognition: Advancements in facial recognition technology hold implications for security, personalisation, and accessibility, necessitating ongoing research into accuracy and ethical considerations.

Reinforcement Learning

Reinforcement Learning revolves around training agents to make sequential decisions in order to maximise rewards. Within this realm, three prominent Artificial Intelligence Topics emerge:

1) Autonomous agents: Crafting AI agents that exhibit decision-making prowess in dynamic environments paves the way for applications like autonomous robotics and adaptive systems.

2) Deep Q-Networks (DQN): Deep Q-Networks, a class of reinforcement learning algorithms, remain under active research for refining value-based decision-making in complex scenarios.

3) Policy gradient methods: These methods, aiming to optimise policies directly, play a crucial role in fine-tuning decision-making processes across domains like gaming, finance, and robotics.

Introduction To Artificial Intelligence Training

Explainable AI (XAI)

The pursuit of Explainable AI seeks to demystify the decision-making processes of AI systems. This area comprises Artificial Intelligence Topics such as:

1) Model interpretability: Unravelling the inner workings of complex models to elucidate the factors influencing their outputs, thus fostering transparency and accountability.

2) Visualising neural networks: Transforming abstract neural network structures into visual representations aids in comprehending their functionality and behaviour.

3) Rule-based systems: Augmenting AI decision-making with interpretable, rule-based systems holds promise in domains requiring logical explanations for actions taken.

Generative Adversarial Networks (GANs)

The captivating world of Generative Adversarial Networks (GANs) unfolds through the interplay of generator and discriminator networks, birthing remarkable research avenues:

1) Image generation: Crafting realistic images from random noise showcases the creative potential of GANs, with applications spanning art, design, and data augmentation.

2) Style transfer: Enabling the transfer of artistic styles between images, merging creativity and technology to yield visually captivating results.

3) Anomaly detection: GANs find utility in identifying anomalies within datasets, bolstering fraud detection, quality control, and anomaly-sensitive industries.

Robotics and AI

The synergy between Robotics and AI is a fertile ground for exploration, with Artificial Intelligence Topics such as:

1) Human-robot collaboration: Research in this arena strives to establish harmonious collaboration between humans and robots, augmenting industry productivity and efficiency.

2) Robot learning: By enabling robots to learn and adapt from their experiences, Researchers foster robots' autonomy and the ability to handle diverse tasks.

3) Ethical considerations: Delving into the ethical implications surrounding AI-powered robots helps establish responsible guidelines for their deployment.

AI in healthcare

AI presents a transformative potential within healthcare, spurring research into:

1) Medical diagnosis: AI aids in accurately diagnosing medical conditions, revolutionising early detection and patient care.

2) Drug discovery: Leveraging AI for drug discovery expedites the identification of potential candidates, accelerating the development of new treatments.

3) Personalised treatment: Tailoring medical interventions to individual patient profiles enhances treatment outcomes and patient well-being.

AI for social good

Harnessing the prowess of AI for Social Good entails addressing pressing global challenges:

1) Environmental monitoring: AI-powered solutions facilitate real-time monitoring of ecological changes, supporting conservation and sustainable practices.

2) Disaster response: Research in this area bolsters disaster response efforts by employing AI to analyse data and optimise resource allocation.

3) Poverty alleviation: Researchers contribute to humanitarian efforts and socioeconomic equality by devising AI solutions to tackle poverty.

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Autonomous vehicles

Autonomous Vehicles represent a realm brimming with potential and complexities, necessitating research in Artificial Intelligence Topics such as:

1) Sensor fusion: Integrating data from diverse sensors enhances perception accuracy, which is essential for safe autonomous navigation.

2) Path planning: Developing advanced algorithms for path planning ensures optimal routes while adhering to safety protocols.

3) Safety and ethics: Ethical considerations, such as programming vehicles to make difficult decisions in potential accident scenarios, require meticulous research and deliberation.

AI ethics and bias

Ethical underpinnings in AI drive research efforts in these directions:

1) Fairness in AI: Ensuring AI systems remain impartial and unbiased across diverse demographic groups.

2) Bias detection and mitigation: Identifying and rectifying biases present within AI models guarantees equitable outcomes.

3) Ethical decision-making: Developing frameworks that imbue AI with ethical decision-making capabilities aligns technology with societal values.

Future of AI

The vanguard of AI beckons Researchers to explore these horizons:

1) Artificial General Intelligence (AGI): Speculating on the potential emergence of AI systems capable of emulating human-like intelligence opens dialogues on the implications and challenges.

2) AI and creativity: Probing the interface between AI and creative domains, such as art and music, unveils the coalescence of human ingenuity and technological prowess.

3) Ethical and regulatory challenges: Researching the ethical dilemmas and regulatory frameworks underpinning AI's evolution fortifies responsible innovation.

AI and education

The intersection of AI and Education opens doors to innovative learning paradigms:

1) Personalised learning: Developing AI systems that adapt educational content to individual learning styles and paces.

2) Intelligent tutoring systems: Creating AI-driven tutoring systems that provide targeted support to students.

3) Educational data mining: Applying AI to analyse educational data for insights into learning patterns and trends.

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Conclusion

The domain of AI is ever-expanding, rich with intriguing topics about Artificial Intelligence that beckon Researchers to explore, question, and innovate. Through the pursuit of these twelve diverse Artificial Intelligence Topics, we pave the way for not only technological advancement but also a deeper understanding of the societal impact of AI. By delving into these realms, Researchers stand poised to shape the trajectory of AI, ensuring it remains a force for progress, empowerment, and positive transformation in our world.

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Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

Research topics and ideas about AI and machine learning

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

Developing AI algorithms for early detection of chronic diseases using patient data.
The use of deep learning in enhancing the accuracy of weather prediction models.
Machine learning techniques for real-time language translation in social media platforms.
AI-driven approaches to improve cybersecurity in financial transactions.
The role of AI in optimizing supply chain logistics for e-commerce.
Investigating the impact of machine learning in personalized education systems.
The use of AI in predictive maintenance for industrial machinery.
Developing ethical frameworks for AI decision-making in healthcare.
The application of ML algorithms in autonomous vehicle navigation systems.
AI in agricultural technology: Optimizing crop yield predictions.
Machine learning techniques for enhancing image recognition in security systems.
AI-powered chatbots: Improving customer service efficiency in retail.
The impact of AI on enhancing energy efficiency in smart buildings.
Deep learning in drug discovery and pharmaceutical research.
The use of AI in detecting and combating online misinformation.
Machine learning models for real-time traffic prediction and management.
AI applications in facial recognition: Privacy and ethical considerations.
The effectiveness of ML in financial market prediction and analysis.
Developing AI tools for real-time monitoring of environmental pollution.
Machine learning for automated content moderation on social platforms.
The role of AI in enhancing the accuracy of medical diagnostics.
AI in space exploration: Automated data analysis and interpretation.
Machine learning techniques in identifying genetic markers for diseases.
AI-driven personal finance management tools.
The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

Machine learning in cybersecurity threat detection and response.
AI applications in virtual reality and augmented reality experiences.
Developing ethical AI systems for recruitment and hiring processes.
Machine learning for sentiment analysis in customer feedback.
AI in sports analytics for performance enhancement and injury prevention.
The role of AI in improving urban planning and smart city initiatives.
Machine learning models for predicting consumer behaviour trends.
AI and ML in artistic creation: Music, visual arts, and literature.
The use of AI in automated drone navigation for delivery services.
Developing AI algorithms for effective waste management and recycling.
Machine learning in seismology for earthquake prediction.
AI-powered tools for enhancing online privacy and data protection.
The application of ML in enhancing speech recognition technologies.
Investigating the role of AI in mental health assessment and therapy.
Machine learning for optimization of renewable energy systems.
AI in fashion: Predicting trends and personalizing customer experiences.
The impact of AI on legal research and case analysis.
Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
Machine learning in genomic data analysis for personalized medicine.
AI-driven algorithms for credit scoring in microfinance.
The use of AI in enhancing public safety and emergency response systems.
Machine learning for improving water quality monitoring and management.
AI applications in wildlife conservation and habitat monitoring.
The role of AI in streamlining manufacturing processes.
Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
Machine Learning in Tourism (Rugge, 2022)
Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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106 Artificial Intelligence Essay Topics & Samples

In a research paper or any other assignment about AI, there are many topics and questions to consider. To help you out, our experts have provided a list of 76 titles , along with artificial intelligence essay examples, for your consideration.

💾 Top 10 Artificial Intelligence Essay Topics

🏆 best essay topics on artificial intelligence, 🖱️ interesting artificial intelligence topics for essays, 🖥️ good ai essay titles, ❓ artificial intelligence research questions.

AI and Human Intelligence.
Computer Vision.
Future of AI Technology.
Machine Learning.
AI in Daily Life.
Impact of Deep Learning.
Natural Language Processing.
Threats in Robotics.
Reinforcement Learning.
Ethics of Artificial Intelligence.
The Problem of Artificial Intelligence The introduction of new approaches to work and rest triggered the reconsideration of traditional values and promoted the growth of a certain style of life characterized by the mass use of innovations and their integration […]
Artificial Intelligence: The Helper or the Threat? To conclude, artificial intelligence development is a problem that leaves nobody indifferent as it is closely associated with the future of the humanity.
Artificial Intelligence: Positive or Negative Innovation? He argues that while humans will still be in charge of a few aspects of life in the near future, their control will be reduced due to the development of artificial intelligence.
Artificial Intelligence Managing Human Life Although the above examples explain how humans can use AI to perform a wide range of tasks, it is necessary for stakeholders to control and manage the replication of human intelligence.
Artificial Intelligence and Related Social Threats It may be expressed in a variety of ways, from peaceful attempts to attract attention to the issue to violent and criminal activities.
Artificial Intelligence and Humans Co-Existence Some strategies to address these challenges exist; however, the strict maintenance of key areas under human control is the only valid solution to ensure people’s safety.
Artificial Intelligence Reducing Costs in Hospitality Industry One of the factors that contribute to increased costs in the hospitality industry is the inability of management to cope with changing consumer demands.
Application of Artificial Intelligence in Business The connection of AI and the business strategy of an organization is displayed through the ability to use its algorithm for achieving competitive advantage and maintaining it.
Artificial Intelligence and Future of Sales It is assumed that one of the major factors that currently affect and will be affecting sales in the future is the artificial intelligence.
Artificial Intelligence: Pros and Cons Artificial intelligence, or robots, one of the most scandalous and brilliant inventions of the XX century, causing people’s concern for the world safety, has become one of the leading branches of the modern science, which […]
Artificial Intelligence and People-Focused Cities The aim of this research is to examine the relationship between the application of effective AI technologies to enhance urban planning approaches and the development of modern smart and people focused cities.
What Progress Has Been Made With Artificial Intelligence? According to Dunjko and Briegel, AI contains a variety of fields and concepts, including the necessity to understand human capacities, abstract all the aspects of work, and realize similar aptitudes in machines.
Artificial Intelligence: A Systems Approach That is to say, limitations on innovations should be applied to the degree to which robots and machine intelligence can be autonomous.
Turing Test: Real and Artificial Intelligence The answers provided by the computer is consistent with that of human and the assessor can hardly guess whether the answer is from the machine or human.
Saudi Arabia Information Technology: Artificial Intelligence The systems could therefore not fulfill the expectations of people who first thought that they would relieve managers and professionals of the need to make certain types of decisions.
Artificial Intelligence and Video Games Development Therefore, in contrast to settings that have been designed for agents only, StarCraft and Blizzard can offer DeepMind an enormous amount of data gathered from playing time which teaches the AI to perform a set […]
Artificial Intelligence System for Smart Energy Consumption The proposed energy consumption saver is an innovative technology that aims to increase the efficiency of energy consumption in residential buildings, production and commercial facilities, and other types of structures.
Artificial Intelligence in Healthcare Delivery and Control Side Effects This report presents the status of AI in healthcare delivery and the motivations of deploying the technology in human services, information types analysed by AI frameworks, components that empower clinical outcomes and disease types.
Artificial Intelligence for Diabetes: Project Experiences At the end of this reflective practice report, I plan to recognize my strengths and weaknesses in terms of team-working on the project about AI in diabetic retinopathy detection and want to determine my future […]
Artificial Intelligence Company’s Economic Indicators On the other hand, it is vital to mention that if an artificial intelligence company has come of age and it is generally at the level of a large corporation, it can swiftly maneuver the […]
Apple’s Company Announcement on Artificial Intelligence This development in Apple’s software is a reflection of the social construction of technology theory based on how the needs of the user impact how technological development is oriented.
Artificial Intelligence Threat to Human Activities Despite the fictional and speculative nature of the majority of implications connected to the supposed threat that the artificial intelligence poses to mankind and the resulting low credibility ascribed to all such suggestions, at least […]
Artificial Intelligence and the Associated Threats Artificial Intelligence, commonly referred to as AI refers to a branch of computer science that deals with the establishment of computer software and programs aimed at the change of the way many people carry out […]
Artificial Intelligence Advantages and Disadvantages In the early years of the field, AI scientists sort to fully duplicate the human capacities of thought and language on the digital computer.
Artificial Intelligence in the Documentary “Transcendent Man” The artificial intelligence is becoming a threat to the existence of humanity since these machines are slowly but steadily replacing the roles of mankind in all spheres of life.
Non Experts: Artificial Intelligence Regardless of speed and the complexity of mathematical problems that they can solve, all that they do is to accept some input and generate desired output. This system is akin to that found in a […]
Autonomous Controller Robotics: The Future of Robots The middle level is the Coordination level which interfaces the actions of the top and lower level s in the architecture.
Exploring the Impact of Artificial Intelligence: Prediction versus Judgment
Maintaining Project Networks in Automated Artificial Intelligence Planning
The Effects Artificial Intelligence Has Had On Society And On Business
What Role Will Artificial Intelligence Actually Play in Human Affairs in the Next Few Decades?
How Artificial Intelligence and Machine Learning Can Impact Market Design
The Use of Artificial Intelligence in Today’s Technological Devices
The Correlation of Artificial Intelligence and the Invention of Modern Day Computers and Programming Languages
How Artificial Intelligence Will Affect Social Media Monitoring
Artificial Intelligence and Neural Network: The Future of Computing and Computer Programming
The Foundations and History of Artificial Intelligence
Comment on Prediction, Judgment, and Complexity: A Theory of Decision Making and Artificial Intelligence
Artificial Intelligence And Law: A Review Of The Role Of Correctness In The General Data Protection Regulation Framework
Artificial Intelligence: Compared To The Human Mind’s Capacity For Reasoning And Learning
A Comparison Between Two Predictive Models of Artificial Intelligence
Artificial Intelligence as a Positive and Negative Factor in Global Risk
Search Applications, Java, and Complexity of Symbolic Artificial Intelligence
Integrating Ethical Values and Economic Value to Steer Progress in Artificial Intelligence
Computational Modeling of an Economy Using Elements of Artificial Intelligence
The growth of Artificial Intelligence and its relevance to The Matrix
The Impact of Artificial Intelligence on Innovation
The Potential Negative Impact of Artificial Intelligence in the Future
An Overview of the Principles of Artificial Intelligence and the Views of Noam Chomsky
How Artificial Intelligence Technology can be Used to Treat Diabetes
Artificial Intelligence and the UK Labour Market: Questions, Methods and a Call for a Systematic Approach to Information Gathering
An Overview of Artificial Intelligence and Its Future Disadvantage to Our Modern Society
Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions
Comparing the Different Views of John Searle and Alan Turing on the Debate on Artificial Intelligence (AI)
A Comparison of Cognitive Ability and Information Processing in Artificial Intelligence
Improvisation Of Unmanned Aerial Vehicles Using Artificial Intelligence
Artificial Intelligence and Its Implications for Income Distribution and Unemployment
The Application of Artificial Intelligence in Real-Time Strategy Games
Advancement in Technology Can Someday Bring Artificial Intelligence to Reality
Artificial Intelligence Based Congestion Control Mechanism Via Bayesian Networks Under Opportunistic
Artificial Intelligence Is Lost in the Woods a Conscious Mind Will Never Be Built Out of Software
An Analysis of the Concept of Artificial Intelligence in Relation to Business
The Different Issues Concerning the Creation of Artificial Intelligence
Traditional Philosophical Problems Surrounding Induction Relating to Artificial Intelligence
The Importance of Singularity and Artificial Intelligence to People
Man Machine Collaboration And The Rise Of Artificial Intelligence
What Are the Ethical Challenges for Companies Working In Artificial Intelligence?
Will Artificial Intelligence Have a Progressive or Retrogressive Impact on Our Society?
Why Won’t Artificial Intelligence Dominate the Future?
Will Artificial Intelligence Overpower Human Beings?
How Does Artificial Intelligence Affect the Retail Industry?
What Can Artificial Intelligence Offer Coral Reef Managers?
Will Artificial Intelligence Replace Computational Economists Any Time Soon?
How Can Artificial Intelligence and Machine Learning Impact Market Design?
Can Artificial Intelligence Lead to a More Sustainable Society?
Will Artificial Intelligence Replace Humans at Job?
How Can Artificial Intelligence Help Us?
How Will Artificial Intelligence Affect the Job Industry in the Future?
Can Artificial Intelligence Become Smarter Than Humans?
How Would You Define Artificial Intelligence?
Should Artificial Intelligence Have Human Rights?
How Do Artificial Intelligence and Siri Operate in Regards to Language?
What Are the Impacts of Artificial Intelligence on the Creative Industries?
How Can Artificial Intelligence Help Us Understand Human Creativity?
When Will Artificial Intelligence Defeat Human Intelligence?
How Can Artificial Intelligence Technology Be Used to Treat Diabetes?
Will Artificial Intelligence Replace Mankind?
How Will Artificial Intelligence Affect Social Media Monitoring?
Can Artificial Intelligence Change the Way in Which Companies Recruit, Train, Develop, and Manage Human Resources in Workplace?
How Does Mary Shelley’s Depiction Show the Threats of Artificial Intelligence?
Why Must Artificial Intelligence Be Regulated?
Will Artificial Intelligence Devices Become Human’s Best Friend?
Does Artificial Intelligence Exist?
Can Artificial Intelligence Be Dangerous?
Why Do We Need Artificial Intelligence?
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Artificial Intelligence Research Topics, Future Perspectives and Innovations in AI

Artificial Intelligence (AI) has become increasingly significant in our modern world, transforming various industries and shaping our lifestyles and work. Artificial intelligence can revolutionize crucial fields such as healthcare, transportation, finance, and education.

Given its profound impact, discussing and exploring artificial intelligence topics and answering AI research questions is essential to understand its capabilities, challenges, and ethical implications. This article aims to provide valuable insights into artificial intelligence topics, inspiring researchers, college students, and professionals to delve into different themes within artificial intelligence and contribute to its growth and development.

How to Write Research Papers on Artificial Intelligence Topic?

Writing a research paper on an artificial intelligence topic requires a systematic approach and careful consideration of key elements. If you’re not sure how to write a research paper and achieve success, follow these steps:

Choose a specific artificial intelligence research question or problem by focusing on a particular aspect of artificial intelligence, such as machine learning or computer vision.
Conduct a comprehensive literature review to familiarize yourself with existing research and identify gaps in knowledge and areas requiring further investigation.
Formulate a clear research objective by defining the purpose of your study and crafting a research question or hypothesis to guide your efforts.
Collect and analyze data by gathering relevant datasets, conducting experiments, or working with existing data sources, and then applying appropriate Artificial Intelligence techniques to derive meaningful insights.
Present your findings effectively within your research paper, organizing the topics in artificial intelligence with clear sections like introduction, literature review, methodology, results, discussion, and conclusion. Use visual aids like tables, graphs, and figures for enhanced presentation.
Conclude and discuss implications by drawing conclusions based on your analysis and discussing your research’s significance and potential impact on computer science.
Include references and citations properly to acknowledge the contributions of other researchers and ensure adherence to the appropriate citation style, such as APA or MLA.

By following these guidelines, you can effectively write research work with artificial intelligence topics that contribute to existing knowledge and advances computer science.

Interesting Artificial Intelligence Topics

Artificial intelligence topics cover several subfields. Here are some intriguing artificial intelligence research topics with different subthemes:

Innovative AI Research Ideas

Artificial Intelligence holds promise in this age of technological advancement, including being a big help to write your research , which links to the ability to generate new research ideas for groundbreaking innovations.

Some innovative AI research ideas include:

Explore the Potential of Artificial Intelligence in Augmenting Human Creativity by Using AI Systems and Machine Learning to Assist Creative Minds in Generating Novel and Imaginative Works.
Develop Artificial Intelligence Models for Art Generation, Music Composition, and Innovative Product Design.
Research Artificial Intelligence’s Role in Renewable Energy, Climate Remodeling and Disaster Prediction.
Develop Artificial Intelligence-Powered Virtual Assistants for Personalized Healthcare by Implementing Artificial Intelligence Algorithms and Natural Language Processing.
Investigate Artificial Intelligence Techniques Like Anomaly Detection, Behavior Analysis, Fraud Detection, and Threat Intelligence to Improve Cybersecurity.
Develop AI-Based Systems to Strengthen Cybersecurity Defenses and Identify and Mitigate Cyber Threats in Real-Time.
Examine Artificial Intelligence’s Impact on Autonomous Vehicles, Traffic Management, and Transportation Optimization.
Develop Artificial Intelligence Algorithms to Enhance Vehicle Navigation, Improve Traffic Flow, and Ensure Safer Transportation Systems.
Utilize Artificial Intelligence to Analyze Financial Data, Identify Patterns, and Make Accurate Predictions.
Develop Artificial Intelligence Models to Assist in Stock Market Forecasting, Risk Assessment, and Portfolio Optimization.

Artificial Intelligence Topics For High School Students

Engaging high school students in Artificial Intelligence project research can foster interest and curiosity in emerging technologies. The experts from Edusson developed some Artificial Intelligence topics for research papers suitable for high school students interested in computer science and quantum com.

Take a look:

Explore the Ethical Implications of Artificial Intelligence, Investigating Ethical Frameworks for Artificial Intelligence Development, Machine Learning, and Usage.
Address Issues Such as Bias, Transparency, Accountability, and the Replacement of Human Functions by Robots.
Study Artificial Intelligence’s Application in Education and Personalized Learning Through Adaptive Learning Environments, Large-Scale Deep Learning, and Big Data Analytics.
Develop Artificial Intelligence Models That Adapt to Student’s Learning Styles, Provide Tailored Feedback, and AI Intelligence Support Personalized Learning Paths.
Investigate the Development and Implementation of Artificial Intelligence-Driven Chatbots in Various Fields Such as Customer Service, E-Commerce, and Virtual Assistants.
Develop Chatbot Systems That Can Understand and Respond to Natural Language Queries, Enhancing Customer Experiences and Efficiency.
Analyze the Potential of Artificial Intelligence and Computer Science in Environmental Sustainability and Climate Change Mitigation.
Study Artificial Intelligence’s Role in Monitoring Environmental Resources, Using Deep Learning Systems for Analyzing Environmental Data, and Promoting Sustainability.
Study the Effects of Artificial Intelligence and Automation on the Job Market and Workforce, Analyzing the Potential for Job Displacement.
Investigate Strategies for Reskilling and Upskilling to Adapt to a Changing Job Landscape Impacted by Artificial Intelligence and Automation.

Machine Learning Research Topics

Machine Learning (ML) is a key subfield of artificial intelligence with diverse research areas. Here are some compelling machine-learning research papers:

Explore Deep Reinforcement Learning Algorithms for Autonomous Robotics, Developing Advanced Algorithms in Dynamic Environments.
Research the Use of Machine Learning in Machine Translation and Sentiment Analysis.
Develop New Techniques for Ensuring Fairness in Artificial Intelligence and ML Models to Promote Ethical and Unbiased Decision-Making.
Explore the Use of Generative Adversarial Networks (Gans) For Creating Realistic Synthetic Data to Augment Limited Datasets in Machine Learning Tasks.
Study the Integration of ML With Internet of Things (IoT) Devices to Create Autonomous Systems for Applications Such as Smart Homes and Healthcare.
Create Artificial Intelligence Models That Allow Robots to Perform Complex Tasks and Interact With the Environment Autonomously.
Develop Explainable Artificial Intelligence Techniques to Enhance the Interpretability and Transparency of AI Models.
Investigate Methods for Visualizing the Decision-Making Processes of a Complex AI System, Making Them More Understandable and Trustworthy.
Investigate Transfer Learning Techniques in Machine Learning to Improve AI Knowledge Across Domains.
Develop Meta-Learning Approaches That Help AI Models Learn Human Cognition Efficiently.

Deep Learning Research Topics

Deep learning, a subfield of machine learning, focuses on training deep neural networks to learn complex patterns and representations from data. If you need to write about this issue and find it too complex, remember to contact a research paper writing service . Here are some intriguing artificial intelligence research paper topics:

Explore Convolutional Neural Networks for Image Recognition and Computer Vision Tasks.
Investigate Techniques to Enhance the Accuracy and Efficiency of Deep Learning Models for Visual Recognition.
Investigate the Potential of Gans for Realistic Image Synthesis, Including Image Editing, Virtual Content Creation, and Data Augmentation.
Explore Using Autoencoders for Unsupervised Representation Learning, Enabling Efficient Data Compression and Feature Extraction Across Various Domains.
Discover Recurrent Neural Networks for Language Processing and Text Generation.
Create AI Models That Can Understand and Generate Natural-Sounding Text.
Investigate Attention Mechanisms in Deep Learning to Improve the Interpretability, Performance, and Robustness of AI Models.
Explore Methods Like Self-Attention and Transformer Architectures for Human-Language Processing and Computer Vision Tasks.
Apply Deep Learning Techniques to Medical Image Analysis and Disease Diagnosis, Focusing on X-Rays, MRIs, and CT Scans.
Develop AI Models to Assist in Disease Detection, Diagnosis, and Prognosis, Enhancing Accuracy and Efficiency in Healthcare.

Computer Science Artificial Intelligence Topics

Computer science plays a crucial role in advancing AI research and development. Consider these artificial intelligence topics for paper within the domain of computer technology:

Design and Develop Intelligent Systems With AI Capabilities to Learn, Adapt, and Make Intelligent Decisions Using Complex Software Systems.
Explore Knowledge Representation, Reasoning, and Planning Techniques for Building Robust and Efficient Intelligent Systems.
Investigate High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware.
Explore the Utilization of Gpus, Tpus, and Other Architectures for Training and Inference Tasks.
Study the Integration of Artificial Intelligence Models and Algorithms Into Cloud High Performance Computing Infrastructure.
Explore Scalable and Cost-Effective AI Deployments, Including Distributed Training, Model Serving, and Real-Time Inference on Human Tasks.
Discover the Intersection Between AI and Robotics in Developing Autonomous Systems for Use in the Human World and Cloud Computing.
Determine AI Applications in Technical Spheres Such as Office Organization and Security.
Investigate Computational Intelligence Models Inspired by Natural Intelligence, Such as Evolutionary Algorithms and Artificial Neural Networks.
Explore Their Applications in Optimization, Pattern Recognition, and Problem-Solving Domains.

Artificial Intelligence Ethics Topics

As artificial intelligence becomes increasingly pervasive, addressing ethical considerations is crucial. Consider these Artificial Intelligence topics in ethics research:

Examine the Ethical Implications and Challenges of AI Computer Technology, Investigating Frameworks and Guidelines for Responsible AI Perspective.
Ensure Fairness, Transparency, and Accountability in Artificial Intelligence Through Data Science.
Investigate the Use of AI in Environmental Monitoring and Prevention of Avoidable Hazards.
Develop Methods to Enhance the Interpretability of AI Systems, Using Rule Extraction and Model Visualization to Understand AI Decision-Making Processes.
Explore the Responsible Use of AI in Balancing Public Safety With Individual Freedoms.
Analyze the Potential Impact of AI on Social Inequality, Addressing the Unequal Distribution of AI Technologies and Opportunities.
Create New Interests in Protecting Students’ Privacy Using AI for Personalized Learning.
Investigate Ways to Bridge the Digital Divide and Promote Inclusive Benefits of Artificial Intelligence Through Large-Scale Machine Learning and AI Engineering.
Analyze the Ethical Implications of Using AI as Autonomous Vehicles Needed for Regulations on Public Safety.
Consider the Responsible Use of AI in Ensuring Fairness in Law-Making and Judicial Processes.

AI Natural Language Processing (NLP) Research Topics on Artificial Intelligence

NLP is a vital area of artificial intelligence in computational science. Consider these artificial intelligence topics:

Develop Techniques for Sentiment Analysis and Opinion Mining in Text Data, Including Sentiment Classification and Opinion Summarization.
Investigate AI Perspective for Named Entity Recognition and Classifying Entities Using Rule-Based, and Explain Deep Learning Approaches.
Explore Methods for Text Summarization, Generating Concise Summaries of Long Documents or Articles Through Extractive and Abstract Summarization.
Investigate Advanced Techniques for Machine Translation, Enabling Automatic Text Translation Between Different Languages With Neural Machine Translation Models.
Study the Integration of Multimodal Information Systems for Human Behavior in Language Processing of AI Machines.
Develop AI Models That Allow User Personalization Through Changes in Tones and Writing Styles While Preserving the Original Contents.
Explore Intelligent Question-Answering Systems, Capable of Understanding and Responding to Human Intelligence and Generated Questions.
Discover the Use of AI in Information Retrieval, Question Classification, and Passage Ranking Methods.
Generate AI Models for Sentiment-Aware Systems With Considerations for Personalized Content Suggestions.
Consider Cross-Lingual Natural Language Processing Which Improves Language Accessibility and Diversity.

Controversial Topics in AI

AI may have simplified mundane living but arguably have privacy risks, algorithm biases, and negative impacts on the workforce and job markets. These make for excellent controversial AI topics. Here are controversial topic ideas:

Take a Critical Analysis of AI and Privacy, Determining Risks and Challenges in Computational Science Concerning Personal Privacy and Data Protection.

Explore Privacy-Preserving Software Development and Regulations to Address Privacy Concerns.
Determine the Implication of Ai-Driven Surveillance on Civil Rights and Freedoms.
Examine Biases in AI Algorithms, Quantum Computing, and Cloud Computing, and Assess Their Potential Impact on Decision-Making, Fairness, and Discrimination.
Develop New Methods for Bias Detection, Mitigation, and Algorithmic Transparency.
Analyze the Potential for AI to Manipulate Public and Personal Information and Its Implication on Democracy and Governance.
Explore the Ethical Implications of Developing Autonomous Weapons Systems Using Machine Learning.
Discuss the Potential Consequences of AI in Warfare and International Security, Emphasizing the Need for Regulations and Ethical Guidelines.
Analyze the Impact of AI Projects and Automation on the Job Market and Workforce.
Develop Strategies to Mitigate Job Displacement, Including Retraining, Upskilling, and Promoting Collaboration Between AI and Human Intelligence.

Artificial intelligence offers a vast array of research topics across various themes and subfields. By delving into these topics, researchers can contribute to advancing artificial intelligence knowledge and address significant challenges and ethical considerations. Engaging in artificial intelligence fosters innovation promotes responsible development, and ensures AI technologies align with societal needs and values.

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Artificial intelligence and machine learning research: towards digital transformation at a global scale

Published: 17 April 2021
Volume 13 , pages 3319–3321, ( 2022 )

Cite this article

Akila Sarirete 1 ,
Zain Balfagih 1 ,
Tayeb Brahimi 1 ,
Miltiadis D. Lytras 1 , 2 &
Anna Visvizi 3 , 4

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Artificial intelligence (AI) is reshaping how we live, learn, and work. Until recently, AI used to be a fanciful concept, more closely associated with science fiction rather than with anything else. However, driven by unprecedented advances in sophisticated information and communication technology (ICT), AI today is synonymous technological progress already attained and the one yet to come in all spheres of our lives (Chui et al. 2018 ; Lytras et al. 2018 , 2019 ).

Considering that Machine Learning (ML) and AI are apt to reach unforeseen levels of accuracy and efficiency, this special issue sought to promote research on AI and ML seen as functions of data-driven innovation and digital transformation. The combination of expanding ICT-driven capabilities and capacities identifiable across our socio-economic systems along with growing consumer expectations vis-a-vis technology and its value-added for our societies, requires multidisciplinary research and research agenda on AI and ML (Lytras et al. 2021 ; Visvizi et al. 2020 ; Chui et al. 2020 ). Such a research agenda should oscilate around the following five defining issues (Fig. 1 ):

Source: The Authors

An AI-Driven Digital Transformation in all aspects of human activity/

Integration of diverse data-warehouses to unified ecosystems of AI and ML value-based services

Deployment of robust AI and ML processing capabilities for enhanced decision making and generation of value our of data.

Design of innovative novel AI and ML applications for predictive and analytical capabilities

Design of sophisticated AI and ML-enabled intelligence components with critical social impact

Promotion of the Digital Transformation in all the aspects of human activity including business, healthcare, government, commerce, social intelligence etc.

Such development will also have a critical impact on government, policies, regulations and initiatives aiming to interpret the value of the AI-driven digital transformation to the sustainable economic development of our planet. Additionally the disruptive character of AI and ML technology and research will required further research on business models and management of innovation capabilities.

This special issue is based on submissions invited from the 17th Annual Learning and Technology Conference 2019 that was held at Effat University and open call jointly. Several very good submissions were received. All of them were subjected a rigorous peer review process specific to the Ambient Intelligence and Humanized Computing Journal.

A variety of innovative topics are included in the agenda of the published papers in this special issue including topics such as:

Stock market Prediction using Machine learning

Detection of Apple Diseases and Pests based on Multi-Model LSTM-based Convolutional Neural Networks

ML for Searching

Machine Learning for Learning Automata

Entity recognition & Relation Extraction

Intelligent Surveillance Systems

Activity Recognition and K-Means Clustering

Distributed Mobility Management

Review Rating Prediction with Deep Learning

Cybersecurity: Botnet detection with Deep learning

Self-Training methods

Neuro-Fuzzy Inference systems

Fuzzy Controllers

Monarch Butterfly Optimized Control with Robustness Analysis

GMM methods for speaker age and gender classification

Regression methods for Permeability Prediction of Petroleum Reservoirs

Surface EMG Signal Classification

Pattern Mining

Human Activity Recognition in Smart Environments

Teaching–Learning based Optimization Algorithm

Big Data Analytics

Diagnosis based on Event-Driven Processing and Machine Learning for Mobile Healthcare

Over a decade ago, Effat University envisioned a timely platform that brings together educators, researchers and tech enthusiasts under one roof and functions as a fount for creativity and innovation. It was a dream that such platform bridges the existing gap and becomes a leading hub for innovators across disciplines to share their knowledge and exchange novel ideas. It was in 2003 that this dream was realized and the first Learning & Technology Conference was held. Up until today, the conference has covered a variety of cutting-edge themes such as Digital Literacy, Cyber Citizenship, Edutainment, Massive Open Online Courses, and many, many others. The conference has also attracted key, prominent figures in the fields of sciences and technology such as Farouq El Baz from NASA, Queen Rania Al-Abdullah of Jordan, and many others who addressed large, eager-to-learn audiences and inspired many with unique stories.

While emerging innovations, such as Artificial Intelligence technologies, are seen today as promising instruments that could pave our way to the future, these were also the focal points around which fruitful discussions have always taken place here at the L&T. The (AI) was selected for this conference due to its great impact. The Saudi government realized this impact of AI and already started actual steps to invest in AI. It is stated in the Kingdome Vision 2030: "In technology, we will increase our investments in, and lead, the digital economy." Dr. Ahmed Al Theneyan, Deputy Minister of Technology, Industry and Digital Capabilities, stated that: "The Government has invested around USD 3 billion in building the infrastructure so that the country is AI-ready and can become a leader in AI use." Vision 2030 programs also promote innovation in technologies. Another great step that our country made is establishing NEOM city (the model smart city).

Effat University realized this ambition and started working to make it a reality by offering academic programs that support the different sectors needed in such projects. For example, the master program in Energy Engineering was launched four years ago to support the energy sector. Also, the bachelor program of Computer Science has tracks in Artificial Intelligence and Cyber Security which was launched in Fall 2020 semester. Additionally, Energy & Technology and Smart Building Research Centers were established to support innovation in the technology and energy sectors. In general, Effat University works effectively in supporting the KSA to achieve its vision in this time of national transformation by graduating skilled citizen in different fields of technology.

The guest editors would like to take this opportunity to thank all the authors for the efforts they put in the preparation of their manuscripts and for their valuable contributions. We wish to express our deepest gratitude to the referees, who provided instrumental and constructive feedback to the authors. We also extend our sincere thanks and appreciation for the organizing team under the leadership of the Chair of L&T 2019 Conference Steering Committee, Dr. Haifa Jamal Al-Lail, University President, for her support and dedication.

Our sincere thanks go to the Editor-in-Chief for his kind help and support.

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King Abdulaziz University, Jeddah, 21589, Saudi Arabia

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Sarirete, A., Balfagih, Z., Brahimi, T. et al. Artificial intelligence and machine learning research: towards digital transformation at a global scale. J Ambient Intell Human Comput 13 , 3319–3321 (2022). https://doi.org/10.1007/s12652-021-03168-y

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208 Artificial Intelligence Essay Topics & Research Questions about AI

If you’re looking for interesting AI research questions or essay topics, you’ve come to the right place! In this list, we’ve compiled the latest trending essay topics on artificial intelligence, research questions, and project ideas. It doesn’t matter if you’re a high school student or a Ph.D. holder: here, you will find research questions about artificial intelligence for beginners as well as professionals.

🏆 Best Essay Topics on Artificial Intelligence

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Artificial Intelligence Pros and Cons: Essay Sample
Artificial Intelligence and Unemployment
Ethics of Artificial Intelligence
Artificial Intelligence and Its Impact on the Future
Artificial Intelligence in Healthcare
Artificial Intelligence in the Workplace
Impact of Artificial Intelligence on the Education System
Artificial Intelligence Versus Human Intelligence Since the discovery of electricity in the 1890s, current advances in Artificial Intelligence have set the stage for the greatest cultural boom in human history.
Artificial Intelligence and Its Role in Business The paper looks into the peculiarities of replacing human work with AI to define its potential for development and issues associated with such implementation.
Artificial Intelligence: Effects on Business Artificial intelligence is a wide range of technological advancements that deal with current and future effects on the business sector to enhance profitability.
Artificial Intelligence in the Labor Market The paper states that the continuous improvements in terms of developing artificial intelligence make render this technology close to reality.
Artificial Intelligence and Music The paper discusses use of Artificial Intelligence is rapidly expanding, with several innovative companies adopting it to create music.
Will Artificial Intelligence Replace Humans? The paper discusses whether artificial intelligence replaces humans. There is no single answer to the question because it affects several areas of human life.
Artificial Intelligence as a Potential Threat to Humanity Artificial intelligence (AI) demonstrates immense potential in terms of improving society as long it is developed and implemented properly.
Artificial Intelligence and Effects of Its Rise This work will address unemployment and humanity’s ethical dilemmas, conceived because of the rise of artificial intelligence as a globalization consequence.
Artificial Intelligence in Hospitality Industry The purpose of this paper is to explore the use application of AI in the hospitality industries. The paper focuses on the utilization of booking engines and hotel software systems.
Artificial Intelligence Economy This annotated bibliography aims to discuss seven articles devoted to the topic of the artificial intelligence economy.
Artificial Intelligence in the Hotel Industry The results of the analysis have shown that, overall, Artificial Intelligence proves to have a positive impact on the quality of hotel services and risk management.
The Promises and Perils of Artificial Intelligence Artificial intelligence is a powerful technology that can generate economic gains; therefore, it is critical to explore its prehistory and practical and ethical concerns.
Helpmewrite.AI Software’s Business Feasibility The report offers research on Helpmewrite.ai software, which is a product that helps writers, lawyers, and paralegals to compose distinct legal pamphlets promptly.
Business Model Canvas and Artificial Intelligence The nine blocks in the business model canvas, which include vital partners, cost structure, and others in relation to AI, can be summarized to explain their role in business.
Artificial Intelligence in Business Management The use of artificial intelligence in business management is a sound practice due to many benefits that this technology offers and an opportunity to secure operational controls.
Implementation of Artificial Intelligence in Accounting Practice Introducing artificial intelligence (AI) into accounting is viewed by many researchers as a promising practice, which has been the subject of numerous studies.
The Fundamental Role of Artificial Intelligence in the IT Industry Artificial intelligence is aimed at machine learning and providing software to address the problems in a way similar to human intelligence.
Artificial Intelligence as an Enhancer of Human Abilities The paper states that using Artificial Intelligence to enhance human capabilities is a trending factor that is growing and receiving attention.
Artificial Intelligence in Supply Chain Management This paper establishes the benefits, opportunities, and challenges of adopting machine learning technologies in logistics to help SMEs boost their performance.
Game Playing in Artificial Intelligence The 9th of March, 2016, was a watershed moment in the development of artificial intelligence when the Go champion Lee Sedol was beaten by AlphaGo.
Artificial Intelligence in Economics Currently, the amount of data available to businesses continues to grow at an exceptional rate due to the developments in artificial intelligence and big data.
Artificial Intelligence in Accounting Artificial Intelligence is already replacing some accounting operations, and research indicates that automating these processes is a cost-effective option.
E-Commerce: The Role of Artificial Intelligence Though the implementation of artificial intelligence within e-commerce continues to develop rapidly, there are several areas in which it affects the experiences of customers.
The Dawn of Artificial Intelligence: Robots Robots were created by people to satisfy their large insatiable appetites. Such a sacrilegious act against the miracle of creation may cost a lot.
Artificial Intelligence in Healthcare: Pros & Cons Rapidly advancing artificial intelligence technologies are gradually changing health care practices and bring a paradigm shift to the medical system.
AI System in Smart Energy Consumption The primary aim of the paper is to expose the significant impacts of AI integration in intelligent energy consumption methods.
Artificial Intelligence: Use and Potential Risks Automation and intelligent algorithms can significantly benefit business owners by inducing substantial savings.
Artificial Intelligence in Aviation and Human-Machine Interfaces The following study analyzes the research results to determine the impact of artificial intelligence (AI) on aviation.
Artificial Intelligence as a Part of Imperialism: Challenges and Solutions Artificial intelligence is part of the process of imperialism, its offshoot, which is commonly called information imperialism.
Algorithmic Bias of Artificial Intelligence Artificial intelligence is a rapidly developing technology which is already extensively utilized in different spheres, yet it has many considerable issues.
Artificial Intelligence: Pros and Cons Artificial intelligence is definitely a huge step in global technological progress, and like any other technology, it can be both a weapon and a lifesaver.
Technologies & Artificial Intelligence Challenges For innovative organizations, new technologies introduce not only benefits but also new challenges as the use of artificial intelligence (AI) changes the way organizations work.
Artificial Intelligence in the Workplace: Pros and Cons Artificial intelligence is requested in the modern business world because it can lead to many positive outcomes when applied to employee monitoring.
Artificial Intelligence and Its Usage in Modern Warfare and Healthcare This paper discusses the question of AI usage in modern warfare, and the usage of Artificial Intelligence used in healthcare in the current situation with the ongoing pandemic.
Artificial Intelligence in Self-Driving Cars The paper states that artificial intelligence in self-driving vehicles cannot conclude several favorable outcomes – or, the “least bad” effects.
Fire Scene Investigation: Artificial Intelligence Each container should be labeled uniquely, including the investigator’s name, date and time, sample number, case number, and location of recovery.
How AI and Machine Learning Influence Marketing in the Fashion Industry The study aims to determine if the perception of AI in fashion is a novel concept and whether it holds enough appeal to impact the purchasing decisions of fashion consumers.
Artificial Intelligence in the Oil and Gas Industry Construction, mining, and oil and gas companies are the latecomers in this digitalization, increasingly depending on AI and machine learning (ML) based solutions frenzy.
Artificial Intelligence and Global Societal Issues Examination of the latest trends in the sphere of development of manufacturing processes indicates significant growth in the interest in artificial intelligence.
AI, Human Control and Safety The given evaluative analysis will primarily focus on the topic of artificial intelligence, human control, and safety.
Artificial Intelligence and Singularity Technological development will inevitably shift humanity’s future in a highly radical way. It is especially true in the case of artificial intelligence (AI).
Artificial Intelligence in Enterprise Processes AI affects ERP systems even though AI-driven solutions are not implemented by the majority of businesses. AI is integrated into ERP systems to increase customer satisfaction
Artificial Intelligence: Ethical, Social, Legal Issues The field of artificial intelligence indeed brings numerous ethical, social, professional and legal issues; but are those so disturbing as some people claim?
Artificial Intelligence and Human Intelligence Comparison AI performs many tasks that are impossible for humans to perform and can be equal to human tasks in interpreting CT scans, recognizing faces and voices, and playing games.
AI Development, Unemployment, and Universal Basic Income The theme of AI-human relationships takes an important place in science fiction literature, movies, and video games, but it is not limited by them.
Artificial Intelligence: Integrated in Healthcare This paper aims to talk about AI as an innovative idea that can be integrated into healthcare. It will detail the strategies used in executing AI.
Medical Innovations: 3D Bioprinting Artificial Intelligence This paper will discuss two medical technological innovations that are significant for the future of a medical organization and how different stakeholders could benefit from them.
Usage of AI and Robotics in Project Management Technological progress has allowed humanity to use the technologies they could not implement in the past centuries.
Blockchain and Other Artificial Intelligence Systems The project describes the basic features of blockchain and AI technologies, along with the possibilities for their future use in different spheres of human activity.
Customer’s Brand Engagement: The Use of Artificial Intelligence Marketers are currently using artificial intelligence in marketing to automate procedures and provide clients with a distinctive brand experience.
Using Information Technology and Artificial Intelligence in Critical Care Medicine Artificial Intelligence in critical care is helping to care for patients faster, supervise more patients, calculate the exact dosage for patients, and collect more detailed data.
Artificial Intelligence Threat for Employees The article “U.S. Lost Over 60 Million Jobs” published focuses on the ongoing unrest around the future of the job market and AI presence within.
Artificial Intelligence and Big Data Impacts on Citizens The concept of artificial intelligence is complex and broad. However, researchers, theorists, and writers contribute to the creation of a clear and factual definition of this term in different ways.
Artificial Intelligence in Strategic Business Management Artificial intelligence basically refers to the intelligence that is created in the software or machines by mankind.
Artificial Intelligence Projects Failed The research paper provides a detailed worldwide timeline of artificial intelligence projects that were attempted and failed and the threats they have caused.
Risks of Artificial Intelligence Data-Mining by Tech Corporations With the exponential advancement of Artificial Intelligence, the notion of data being valuable regarding marketability has permeated the cultural zeitgeist.
Artificial Intelligence and the Future of Business AI’s modern field came into being in the 1950s; still, decades were spent on making serious progress in the development of an AI system and turning it from a dream into a reality.
What Will Happen When AI Picks Up Social Biases About Gender? Social biases on gender will not have room when Artificial Intelligence takes over and the systems are put into everyday life.
Artificial Intelligence in Machinery This essay explores an operation case, discussing the tools in AI, particularly TensorFlow and Theano, and their implementation issues.
Artificial Intelligence Implementation in Accounting Processes Artificial intelligence seems to be a prospective technology, and its implementation in accounting processes is inevitable.
Artificial Intelligence Bias and Ethical Algorithms The paper argues in order to solve the problem of lack of diversity and assessing human needs correctly, there is a need to implement better guidelines for Artificial Intelligence.
New Technology in the Air Cargo Industry: Artificial Intelligence The article “Transport logistic: Artificial Intelligence at Air cargo” discusses how artificial intelligence will revolutionize the air cargo industry.
Artificial Intelligence: Potential Problems and Threats Artificial intelligence can be used for unsuitable purposes, but this is not a scientific problem but rather a moral and ethical one.
Artificial Intelligence and How It Affects Hospitality The main challenge in regards to Artificial Intelligence is its current state, which still requires extensive development in order for it to become practical and useful.
Artificial Intelligence and the Future of Nursing The benefits of AI technologies include time and cost efficiency, as well as a high level of care consistency and comprehensiveness.
Artificial Intelligence: Article Review Review of an article by Vinyals, Gaffney & Ewalds (2017) discussing the use of the StarCraft II video game as a platform for AI development and testing.
Artificial Intelligence (AI) in Health Care The use of AI has increased over the past decades, making it easier for researchers to investigate the most complicated issues.
Artificial Intelligence Through Human Inquiry Much about the possible uses of A.I. and its potential capacities and abilities remains uncertain, which raises many questions as to what the future of A.I. will hold for humans.
Artificial Intelligence in Medical Field The medical field constantly innovates and develops new technologies to improve patient care. Societies, in general, are significantly impacted by technological innovations.
Artificial Intelligence: Emergence of Employment Issues Artificial intelligence has become particularly widespread in the modern world, but there are significant controversies about the benefits of this technology in people’s lives.
Comparing Artificial Intelligence to Human Intelligence Intelligence is essential for humanity, as it can isolate important information from the environment and systematize it into knowledge used to solve specific problems.
Integrated Apple Home-Based Artificial Intelligence System Integrated Apple Home-based Artificial Intelligence system is an artificial intelligence system that has been tailored to meet the end-users’ home needs.
The Turing Test and Development of Artificial Intelligence The Turing test is conducted with two people and a program, in which the program and one person communicate with a judge.
Artificial Intelligence, Insurtech, and Virtual Reality from a Market Perspective AI, Insurtech, and Virtual reality will be presented and discussed in relation to their impact on the market, as well as the disruptions and benefits they may cause.
Artificial Intelligence: Impact on Labor Workforce The development of artificial intelligence often affects drivers and retail workers, healthcare workers, lawyers, accountants, and financial professionals.
The Issue of Artificial Intelligence Integration in Private Health Sector It is possible to develop a particular insight into the perspectives of Artificial Intelligence integration in the private health sector.
Enabling Successful AI Implementation in the Department of Defense This paper seeks to provide a summary and discuss three main points of the article “Enabling Successful AI Implementation in the Department of Defense.”
Marketing Artificial Intelligence Problems The alignment problem when applying artificial intelligence in marketing occurs when managers ask a question that does not align with the set objectives.
Can the World Have a Fair Artificial Intelligence? It is important to consider issues to do with AI because the matter has adverse effects on the depreciation of human labor, information protection, and manipulation of people.
AI-Improved Management Information System This paper evaluates a current management information system and directs on ways to improve it using artificial intelligence and machine learning.
Artificial Intelligence and the Labor Market This essay will argue that although the use of AI is a controversial issue, AI could be implemented positively, allowing the effective cooperation of people and robots.
Artificial Intelligence: Human Trust in Healthcare In the modern epoch of digitalization, artificial intelligence (AI) is widely utilized in education, transportation, media, banking, navigation, and healthcare.
Artificial Intelligence: The Monstrous Entity The conversation around the artificial intelligence as a monstrous entity can provide new perspectives for all discourse communities revolving around this topic.
Implementing Artificial Intelligence and Managing Change in Nursing This paper is going to talk about a planned change, namely the implementation of Artificial Intelligence (AI) in perception, thinking, planning, learning, etc.
AI and Transitional Management The article presented the two sides of artificial intelligence from an objective perspective since the general implementation of AI is almost inevitable.
The Portrayal of Artificial Intelligence Artificial intelligence seems to be Frankenstein’s monster of the new age. Different sources provide significant insight into the portrayal of AI as monstrosity.
Artificial Intelligence and Related Ethical Concerns Technological progress allows people to use AI capabilities increasingly, but this concept is also related to many ethical issues about human rights.
Thinking Processes of Artificial Intelligence This essay will discuss the topic of artificial intelligence in whether artificial intelligence can be capable of thinking processes.
The Finance Portfolio Management: Impact of Artificial Intelligence Despite the existing limitations, various artificial intelligence applications can make portfolio management much more accessible.
The Future of Artificial Intelligence in Fiction and Science Although there are numerous technological advancements, not many of them have caused such a tremendous controversy as artificial intelligence.
The Limits of Global Inclusion in AI (Artificial Intelligence) Development This article is devoted to the theme of the development and implementation of elements of artificial intelligence (AI) in the context of various countries.
Artificial Intelligence: The Articles Review This paper presents the annotated bibliography dedicated the artificial intelligence technologies, their safety or harm to society.
Impact of Artificial Intelligence on the Labor Market The document presents annotated article in question considers the impact the spread of artificial intelligence technology may have on the labor market.
How to Create a Fair Artificial Intelligence The current research aims to find possible ways to create a fair AI: exploring power concentration, mass manipulation, depreciation of human labor, and information protection.
Artificial Intelligence in Scientific and Fiction Works I decided to research what possible benefits can come from cooperation between scientists and science fiction writers regarding the negative image of artificial intelligence.
Artificial Intelligence: Advantages and Applications The advantages mentioned above introduce multiple opportunities for applying AI to acquire improved outcomes. Discussion of such applications.
Artificial Intelligence (AI) and Universal Basic Income Articles included in the annotated bibliography describe problems of Automation and the spread of Artificial Intelligence (AI)-based technologies.
AI in Customer Service: Argument Flaws Analyzing AI’s comprehensive functionality can provide sufficient arguments for a variety of options to implement to attract and retain customers.
Artificial Intelligence and Emerging Ethical Risks Technological progress went far beyond our imagination, and Artificial Intelligence became an indispensable companion in everyday life.
AI and Hardware Integration in Business Work Processes AI-driven hardware within businesses has little competition as it is the leading tool for time-saving, cost-reduced, and efficient method processes.
Implementation of AI in Law Practice There are many benefits of AI application to large firms that have a lot of unprocessed data or smaller firms that do not have the staff to cover all the tasks.
Companies’ Reputation and Artificial Intelligence This paper discusses companies’ reputations and whether artificial intelligence (AI) has the capacity to predict customer and competitor behavior.
AI: Agent Human Interactions In this case study a system that detects the status of the baby, that is, if the baby is awake, and it has an interface implemented with agent human interaction is considered.
Artificial Intelligence in Business Administration Changes The current state of AI technology does not allow launching ambitious projects that will completely change the way businesses operate.
Artificial Intelligence in the Working Process The purpose of this paper is to describe the impact of artificial intelligence (AI) on the job and its results. AI can do the job that was done by the employee for decades.
Artificial Intelligence. Unmanned Mission Communications Communication networks are essential in facilitating the operations of autonomous systems as they are used in monitoring, collecting data, and exploring hard-to-reach areas.
Could Artificial Intelligence ‘End Mankind’ or Is It All Alarmist Nonsense? The idea of AI ending humankind and leading to a global catastrophe does not represent modern reality accurately.
Artificial Intelligence: Its Potential and Use Artificial intelligence has been presented as a technology that will not replace human beings, but help them perform tasks better.
Artificial Intelligence: Science Fiction Novels Many writers created stories and novels in the science fiction genre in an attempt to predict how the life where robots are not just machines but equal members of society would be.
“Artificial Intelligence: A Competitive Advantage for the US Army” Review The document offers a substantial review of how the implementation of artificial intelligence (AI) may become a crucial competitive advantage for the US military.
The Use of Starcraft II Video Games for AI Research The article is devoted to the rules for writing effective thesises, for each rule there are examples of good and bad writing.
AI and Machine Self-Learning Machine self-learning has become a perfect solution for complex business problems that cannot be solved by software engineering or human judgment.
Explainable Artificial Intelligence in Accounting The broad implementation of AI in such fields as accounting lays the ground for the drastic changes in management and methods that are utilized by specialists.
Artificial Intelligence, Internet of Things, and the Impact on Facilities’ Environments The use of AI and IoT is unlikely to replace facilities’ teams because the decision-making process still requires human input.
Artificial Intelligence and Ethical Implications If we create artificial intelligence based on human intelligence, some of the less needed qualities will be omitted during the process of abstraction.
The Artificial Intelligence Machine AlphaGo Zero The selected technology is an artificial intelligence (AI) machine by the name of AlphaGo Zero. It is an evolution of previous well-known machines from the company Deep Mind.
Regional Employment and Artificial Intelligence in Japan
Artificial Intelligence and the Human Race
Medicine and Artificial Intelligence
Artificial Intelligence and Machine Learning Applied at the Point of Care
Difference Between Artificial Intelligence and Human
The Four Debatable Viewpoints One May Have About Artificial Intelligence
Artificial Intelligence and Its Impact on Accounting
Rational Choice and Artificial Intelligence
The Ethics and Its Relation To Artificial Intelligence
Artificial Intelligence and Medicine
Privacy, Algorithms, and Artificial Intelligence
Artificial Intelligence: Can Computers Think
Cognitive Science and Its Link to Artificial Intelligence
Artificial Intelligence Replacing the Art of Traditional Selling
The Beauty and Danger of Artificial Intelligence
Digital Devices for Artificial Intelligence Applications
Artificial Intelligence and the Field of Robotics
Could Artificial Intelligence Replace Teachers
Artificial Intelligence and Neuromorphic Engineering
Artificial Intelligence Based Improvised Explosive Devices
Big Data Technologies and Artificial Intelligence
Artificial Intelligence and Its Effects on Business
Modern Technology and Artificial Intelligence
Multilayered Perceptron and Artificial Intelligence
Distributed, Decentralized, and Democratized Artificial Intelligence
Artificial Intelligence and Video Games
Some Considerations About Artificial Intelligence and Its Implications
Comparing Human Intelligence With Artificial Intelligence
Artificial Intelligence During the World Today
Artificial Intelligence and the Future of Human Rights
Economic Policy for Artificial Intelligence
Artificial Intelligence for Human Intelligence and Industrial
The Morality and Utility of Artificial Intelligence
Artificial Intelligence and Behavioral Economics
Blockchain and Artificial Intelligence Technologies
The Effects Artificial Intelligence Has Had on Society and Business
Marketing and Artificial Intelligence
Artificial Intelligence and Machines Automation
People Copy the Actions of Artificial Intelligence
Artificial Intelligence for Healthcare in Africa
Healthcare System Using Artificial Intelligence
Artificial Intelligence for the Future Radiology Diagnostic Service
Artificial Intelligence and Marketing
Copyright Protection for Artificial Intelligence
The Potential and Future of Artificial Intelligence
Artificial Intelligence and the Human Mind
Expert Systems and Its Relationship With Artificial Intelligence
Artificial Intelligence and Its Effect on Mankind
The Nexus Between Artificial Intelligence and Economics
Artificial Intelligence, Based Training and Placement Management
Artificial Intelligence and Its Implications for Income Distribution and Unemployment
Machine Learning and Artificial Intelligence in Finance
The Pros and Cons of Artificial Intelligence
Artificial Intelligence and the Legal Profession
Continual Learning: The Next Generation of Artificial Intelligence
Artificial Intelligence and Its Uses
Regulation Within the Development of Artificial Intelligence
Artificial Intelligence and Computer Science
Mysteries, Epistemological Modesty, and Artificial Intelligence in Surgery
Artificial Intelligence and Cognitive Reasoning
Can Artificial Intelligence Become Smarter Than Humans?
Should Humanity Fear Advances in Artificial Intelligence?
How Does Artificial Intelligence Affect the Retail Industry?
What Are Some of the Ethical Challenges Posed by the Use of Artificial Intelligence for Hiring?
Does Artificial Intelligence Impact the Creative Industries?
Can Artificial Intelligence Change the Way in Which Companies Recruit, Train, Develop, and Manage Human Resources in Workplace?
Will Artificial Intelligence Defeat Human Intelligence?
How Can Artificial Intelligence Help Modern Society?
Can Artificial Intelligence Lead to a More Sustainable Society?
What Role Will Artificial Intelligence Play in Human Affairs in the Next Few Decades?
How Can Artificial Intelligence Help Us Understand Human Creativity?
Will Artificial Intelligence Devices Become Human Best Friend?
Why Must Artificial Intelligence Be Regulated?
Should Artificial Intelligence Have Human Rights?
Why Artificial Intelligence Won’t Dominate the Future?
How Does Artificial Intelligence Impact Today’s Society?
Will Artificial Intelligence Overpower Human Beings?
Should Artificial Intelligence Take Over the Jobs of the Tertiary Sector?
How Will Artificial Intelligence Impact the World?
Should People Develop Artificial Intelligence?
How Does Mary Shelley’s Depiction Show the Threats of Artificial Intelligence?
What Can Artificial Intelligence Offer Coral Reef Managers?
How Will Artificial Intelligence Affect the Job Industry in the Future?
Should the Innovative Evolution of Artificial Intelligence be Regulated?
Will Artificial Intelligence Have a Progressive or Retrogressive Impact on Our Society?

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Research Papers on Artificial Intelligence

Research papers on artificial intelligence (AI) are crucial for advancing our understanding and development of intelligent systems. These papers cover a wide range of topics, including machine learning, natural language processing, computer vision, and robotics. They provide valuable insights, innovative approaches, and breakthroughs in the field. Research papers on artificial intelligence foster collaboration, drive progress and serve as a reference for students, professionals, and enthusiasts. By exploring algorithms, methodologies, and applications, these papers contribute to the ever-evolving landscape of AI research. They play a significant role in shaping the future of AI technology and its impact on various industries.

Introduction

Artificial intelligence, as a field of study, emerged from the desire to create intelligent systems capable of performing tasks that typically require human intelligence. Research papers on artificial intelligence have been instrumental in advancing the field and have contributed to the rapid growth and widespread adoption of AI technologies in various domains.

The early research papers on AI focused on foundational concepts and theories, such as symbolic reasoning and expert systems. However, with the advent of machine learning, particularly deep learning, the field witnessed a paradigm shift. Machine learning algorithms that can automatically learn patterns and make predictions from data revolutionized AI research and application.

Research papers on artificial intelligence serve as the foundation for further advancements and applications in the field. They provide a means for researchers to communicate their findings, share methodologies, and collaborate with peers, ultimately driving the progress and innovation in AI research and development.

Discussion of AI Technologies

Research papers on artificial intelligence encompass a wide array of AI technologies, each explored and analyzed through rigorous investigation and experimentation. These papers serve as a conduit for sharing novel advancements, methodologies, and empirical results in the field. By delving into various AI technologies, researchers contribute to the continuous evolution and refinement of AI systems. Below are some key AI technologies commonly discussed in research papers:

Machine Learning

Machine learning algorithms form the backbone of many AI applications, and research papers extensively explore their development and optimization. These papers investigate different types of machine learning approaches, such as supervised learning, unsupervised learning, and reinforcement learning. They propose innovative algorithms, architectures, and optimization techniques to enhance the performance and efficiency of machine-learning models. Furthermore, research papers on machine learning often address specific challenges, such as dealing with high-dimensional data, handling imbalanced datasets, and improving interpretability.

Natural Language Processing (NLP)

Research papers on artificial intelligence also focus on NLP, a subfield that deals with enabling machines to understand, interpret, and generate human language. These papers explore techniques for tasks such as text classification, sentiment analysis, information retrieval, and language translation. NLP research papers often present state-of-the-art models and methodologies, including deep learning architectures like recurrent neural networks (RNNs) and transformer models. They delve into language representation, word embeddings, syntactic and semantic parsing, and discourse analysis, among other topics.

Computer Vision

Computer vision research papers tackle the challenges of enabling machines to interpret and understand visual information. These papers delve into various computer vision tasks, including object detection, image recognition, image segmentation, and image generation. They propose innovative convolutional neural network (CNN) architectures, feature extraction techniques, and image processing algorithms. Computer vision research papers also explore areas such as video analysis, 3D reconstruction, visual tracking, and scene understanding, contributing to advancements in autonomous vehicles, surveillance systems, and augmented reality.

Research papers on artificial intelligence and robotics focus on developing intelligent robots capable of autonomous decision-making, perception, and interaction with the physical world. These papers cover topics like motion planning, sensor fusion, robot learning, and human-robot interaction. They investigate algorithms for robot localization and mapping, object manipulation, grasping, and navigation in complex environments. Robotics research papers often include experimental evaluations using real robots or simulations, showcasing the practical applicability and performance of the proposed approaches.

Ethical and Societal Implications

As AI technologies become more pervasive, research papers also explore the ethical and societal implications associated with their development and deployment. These papers discuss topics such as bias and fairness in AI algorithms, transparency and interpretability of AI systems, privacy concerns in data collection and usage, and the impact of AI on employment and social structures. Ethical and societal implications research papers aim to provide guidelines, regulations, and frameworks for responsible AI development and usage, ensuring that AI technologies align with societal values and benefit humanity as a whole.

Reinforcement Learning

Reinforcement learning is a crucial subfield of machine learning, and research papers dedicated to this topic focus on teaching agents to make optimal decisions based on trial-and-error interactions with an environment. These papers delve into algorithms such as Q-learning, policy gradients, and deep reinforcement learning. They explore various applications, including game playing, robotics, autonomous control, and recommendation systems. Reinforcement learning research papers also investigate topics like exploration-exploitation trade-offs, reward shaping, and multi-agent reinforcement learning.

Privacy-Preserving Machine Learning

Privacy-preserving machine learning is an emerging domain that addresses the challenge of leveraging sensitive data while preserving individuals' privacy. Research papers in this area propose innovative techniques such as federated learning, secure multi-party computation, and differential privacy. These papers explore methods to train machine learning models on distributed data without sharing the raw data itself, ensuring data privacy and security. Privacy-preserving machine learning research papers also analyze the trade-offs between privacy guarantees and model performance.

Top Research papers

Research papers play a crucial role in shaping the field of artificial intelligence (AI) by presenting groundbreaking ideas, innovative approaches, and significant advancements. Here are some of the top research papers that have made a substantial impact in the realm of AI:

"Deep Residual Learning for Image Recognition" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun (2016)

This research paper on artificial intelligence introduced the concept of residual networks (ResNets), which revolutionized image recognition tasks. ResNets allowed for the training of extremely deep neural networks by introducing skip connections that facilitated the flow of information across layers. This paper demonstrated that deeper networks could achieve higher accuracy, challenging the previous belief that increasing network depth leads to diminishing performance gains.

Research Paper

"Generative Adversarial Networks" by Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, et al. (2014)

This seminal paper introduced the concept of Generative Adversarial Networks (GANs). GANs consist of two neural networks, a generator, and a discriminator, competing against each other. The generator aims to produce synthetic data that resembles the real data distribution, while the discriminator's task is to differentiate between real and synthetic data. GANs have since become a cornerstone of generative modeling, enabling the creation of realistic images, videos, and other types of data.

"Attention Is All You Need" by Vaswani et al. (2017)

This influential research paper on artificial intelligence introduced the Transformer model, which revolutionized the field of natural language processing (NLP). Transformers employ self-attention mechanisms to capture contextual relationships between words in a sequence, eliminating the need for recurrent neural networks (RNNs) or convolutional layers. The Transformer model achieved state-of-the-art performance in various NLP tasks, including machine translation, text summarization, and language understanding.

"ImageNet Classification with Deep Convolutional Neural Networks" by Krizhevsky, Sutskever, and Hinton (2012)

This groundbreaking paper introduced the AlexNet model, a deep convolutional neural network (CNN), which significantly advanced image classification performance. AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, demonstrating the power of deep learning. The paper's success paved the way for the widespread adoption of deep CNN architectures in computer vision tasks.

"Reinforcement Learning" by Richard S. Sutton and Andrew G. Barto (1998)

This influential book presents a comprehensive overview of reinforcement learning, a subfield of machine learning concerned with learning optimal behaviors through interactions with an environment. The book provides a theoretical foundation, algorithms, and practical insights into reinforcement learning, serving as a go-to resource for researchers and practitioners in the field. Reinforcement learning has been instrumental in solving complex AI problems, including game playing, robotics, and autonomous control.

"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" by Alec Radford, Luke Metz, and Soumith Chintala (2016)

This research paper on artificial intelligence introduced Deep Convolutional Generative Adversarial Networks (DCGANs), which extended the GAN framework specifically for image synthesis. DCGANs demonstrated the ability to generate high-quality, diverse images from random noise vectors. This work contributed to the advancement of unsupervised learning and paved the way for subsequent research in image generation, style transfer, and image editing.

"Neural Machine Translation by Jointly Learning to Align and Translate" by Bahdanau, Cho, and Bengio (2014)

This influential paper introduced the attention mechanism in sequence-to-sequence models, greatly improving the performance of neural machine translation. The attention mechanism allows the model to focus on different parts of the input sequence during the translation process, enabling better alignment and understanding. This work significantly advanced the state-of-the-art in machine translation and inspired further research on attention-based models in various other sequence-to-sequence tasks.

"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova (2018)

This paper introduced the BERT model, which achieved state-of-the-art results in various natural language processing tasks by pre-training a transformer-based neural network on a large corpus of unlabeled text data.

"DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf (2014)

This research paper on artificial intelligence presented the DeepFace model, which utilized deep convolutional neural networks to achieve remarkable performance in face verification tasks, narrowing the performance gap between machines and humans.

Research papers in AI contribute to the exchange of knowledge, methodologies, and breakthroughs among researchers, fostering collaboration and innovation.
Research papers cover a wide range of AI technologies, including machine learning, natural language processing, computer vision, and robotics.
Several influential research papers have significantly shaped the field of AI, including those on deep learning, generative adversarial networks, attention mechanisms, and reinforcement learning.
Research papers also explore the ethical and societal implications of AI, addressing issues such as fairness, transparency, and privacy.
Prominent research papers in AI inspire and guide future research directions, pushing the boundaries of the field and unlocking its potential.

Additional Resources

Common AI Applications

Artificial Intelligence (AI)

Getting Started
What is artificial intelligence?
What are hallucinations?
NAU policy on using Artificial Intelligence tools for course work

Generate Topics for a Paper

Brainstorm, Paraphrase, Summarize, Outline, and Revise with AI
How to Write a Prompt for ChatGPT and other Generative AI Large Language Models
Citing Chat GPT and other Generative AI Large Language Models
AI-based Literature Review Mapping Tools
AI Tools for Job Seekers
Using Artificial Intelligence (AI) in the Practice of Law

Generate topics for your research paper with ChatGPT (and other AI models)

ChatGPT can be a useful tool when it comes to deciding what your topic should be for a research paper.

It’s not good for doing the actual searching because it makes up sources. See I can’t find the citations that ChatGPT gave me. What should I do?

But you can use it to help you:

Narrow down your topic ideas.
Come up with keywords for searching in library databases.
Construct a search strategy for those databases.
Recommend possible library databases to use for your topic.

Here’s how.

1. Sign up for a free account on ChatGPT (if you haven’t already). Or go to https://chat.openai.com and log in to your account.

2. To prompt ChatGPT effectively, use this example:

Act as an expert academic librarian. I’m writing a research paper for [course] and I need help coming up with a topic. I’m interested in topics related to [subject] . Please give me a list of 10 topic ideas related to that.

Here’s an example: Act as an expert academic librarian. I’m writing a research paper for Sociology and I need help coming up with a topic. I’m interested in topics related to climate change . Please give me a list of 10 topic ideas related to that.

3. Now go to ChatGPT and paste in your prompt.

4. Look over the list it gives you and find one topic that you’re interested in. If there isn’t one, ask ChatGPT to give you more topics. Keep going until you find a topic you like.

5. Now tell ChatGPT which of those topics you want to use.

Example: I like the topic, Climate Change Denial and its Societal Influence.

Then it will give you some sub-topics or research questions. If it doesn’t, ask for some.

6. Choose your specific research question from the list. If you don’t like any of them, ask for more. Keep going until you find one you want to use.

7. Now you can tell ChatGPT which research question you’re going to use. Ask it for some keywords to use when searching library databases.

Example : My research question is going to be this: Investigate the role of social media and online communities in propagating climate change denial and misinformation. Please list some keywords I can use when searching library databases.

8. You can use the keywords and phrases it gives you in OneSearch , the library databases , Google Scholar, or Google.

9. For more specific research, you can ask ChatGPT to give you some Boolean search strings to use in OneSearch or the library databases .

Example: Please construct a few Boolean search strings I can use when researching this topic in OneSearch or the library databases. NOTE: ChatGPT will save all of this information for you, so next time you visit, you’ll see this conversation in your chat history on the left side.

10. Now that you have some search strings, keywords, and phrases, you can ask ChatGTP for advice on which library databases will work best for your topic. Example: Thank you! Now I'd like you to recommend 2 or 3 library databases that would be good to search for this topic.

11. Go to our list of library databases and check to see if our library has the databases it recommends.

If we don’t have some of them, ask ChatGPT for more databases that would work for your topic.

12. Now you can use your search strings in those databases. If you need help with search strategies, email [email protected]

Attribution : The information above, with some language and link modifications, was provided by the University of Arizona Libraries, licensed under a Creative Commons Attribution 4.0 International License.

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Last Updated: Jan 4, 2024 10:47 AM
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Innovation (Camb)
v.2(4); 2021 Nov 28

Artificial intelligence: A powerful paradigm for scientific research

1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

35 University of Chinese Academy of Sciences, Beijing 100049, China

5 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

10 Zhongshan Hospital Institute of Clinical Science, Fudan University, Shanghai 200032, China

Changping Huang

18 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

11 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China

37 Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China

26 Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China

Xingchen Liu

28 Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China

2 Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Fengliang Dong

3 National Center for Nanoscience and Technology, Beijing 100190, China

Cheng-Wei Qiu

4 Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore

6 Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China

36 Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China

7 School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China

41 Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310058, China

8 Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China

9 Zhejiang Provincial People’s Hospital, Hangzhou 310014, China

Chenguang Fu

12 School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China

Zhigang Yin

13 Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China

Ronald Roepman

14 Medical Center, Radboud University, 6500 Nijmegen, the Netherlands

Sabine Dietmann

15 Institute for Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA

Marko Virta

16 Department of Microbiology, University of Helsinki, 00014 Helsinki, Finland

Fredrick Kengara

17 School of Pure and Applied Sciences, Bomet University College, Bomet 20400, Kenya

19 Agriculture College of Shihezi University, Xinjiang 832000, China

Taolan Zhao

20 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China

21 The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

38 Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China

Jialiang Yang

22 Geneis (Beijing) Co., Ltd, Beijing 100102, China

23 Department of Communication Studies, Hong Kong Baptist University, Hong Kong, China

24 South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China

39 Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou 510650, China

Zhaofeng Liu

27 Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China

29 Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China

Xiaohong Liu

30 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China

James P. Lewis

James m. tiedje.

34 Center for Microbial Ecology, Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA

40 Zhejiang Lab, Hangzhou 311121, China

25 Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China

31 Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3FL, UK

Zhipeng Cai

32 Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

33 Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China

Jiabao Zhang

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Graphical abstract

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Public summary

• “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on.
• “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc.
• “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.

Introduction

“Can machines think?” Alan Turing posed this question in his famous paper “Computing Machinery and Intelligence.” 1 He believes that to answer this question, we need to define what thinking is. However, it is difficult to define thinking clearly, because thinking is a subjective behavior. Turing then introduced an indirect method to verify whether a machine can think, the Turing test, which examines a machine's ability to show intelligence indistinguishable from that of human beings. A machine that succeeds in the test is qualified to be labeled as artificial intelligence (AI).

AI refers to the simulation of human intelligence by a system or a machine. The goal of AI is to develop a machine that can think like humans and mimic human behaviors, including perceiving, reasoning, learning, planning, predicting, and so on. Intelligence is one of the main characteristics that distinguishes human beings from animals. With the interminable occurrence of industrial revolutions, an increasing number of types of machine types continuously replace human labor from all walks of life, and the imminent replacement of human resources by machine intelligence is the next big challenge to be overcome. Numerous scientists are focusing on the field of AI, and this makes the research in the field of AI rich and diverse. AI research fields include search algorithms, knowledge graphs, natural languages processing, expert systems, evolution algorithms, machine learning (ML), deep learning (DL), and so on.

The general framework of AI is illustrated in Figure 1 . The development process of AI includes perceptual intelligence, cognitive intelligence, and decision-making intelligence. Perceptual intelligence means that a machine has the basic abilities of vision, hearing, touch, etc., which are familiar to humans. Cognitive intelligence is a higher-level ability of induction, reasoning and acquisition of knowledge. It is inspired by cognitive science, brain science, and brain-like intelligence to endow machines with thinking logic and cognitive ability similar to human beings. Once a machine has the abilities of perception and cognition, it is often expected to make optimal decisions as human beings, to improve the lives of people, industrial manufacturing, etc. Decision intelligence requires the use of applied data science, social science, decision theory, and managerial science to expand data science, so as to make optimal decisions. To achieve the goal of perceptual intelligence, cognitive intelligence, and decision-making intelligence, the infrastructure layer of AI, supported by data, storage and computing power, ML algorithms, and AI frameworks is required. Then by training models, it is able to learn the internal laws of data for supporting and realizing AI applications. The application layer of AI is becoming more and more extensive, and deeply integrated with fundamental sciences, industrial manufacturing, human life, social governance, and cyberspace, which has a profound impact on our work and lifestyle.

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The general framework of AI

History of AI

The beginning of modern AI research can be traced back to John McCarthy, who coined the term “artificial intelligence (AI),” during at a conference at Dartmouth College in 1956. This symbolized the birth of the AI scientific field. Progress in the following years was astonishing. Many scientists and researchers focused on automated reasoning and applied AI for proving of mathematical theorems and solving of algebraic problems. One of the famous examples is Logic Theorist, a computer program written by Allen Newell, Herbert A. Simon, and Cliff Shaw, which proves 38 of the first 52 theorems in “Principia Mathematica” and provides more elegant proofs for some. 2 These successes made many AI pioneers wildly optimistic, and underpinned the belief that fully intelligent machines would be built in the near future. However, they soon realized that there was still a long way to go before the end goals of human-equivalent intelligence in machines could come true. Many nontrivial problems could not be handled by the logic-based programs. Another challenge was the lack of computational resources to compute more and more complicated problems. As a result, organizations and funders stopped supporting these under-delivering AI projects.

AI came back to popularity in the 1980s, as several research institutions and universities invented a type of AI systems that summarizes a series of basic rules from expert knowledge to help non-experts make specific decisions. These systems are “expert systems.” Examples are the XCON designed by Carnegie Mellon University and the MYCIN designed by Stanford University. The expert system derived logic rules from expert knowledge to solve problems in the real world for the first time. The core of AI research during this period is the knowledge that made machines “smarter.” However, the expert system gradually revealed several disadvantages, such as privacy technologies, lack of flexibility, poor versatility, expensive maintenance cost, and so on. At the same time, the Fifth Generation Computer Project, heavily funded by the Japanese government, failed to meet most of its original goals. Once again, the funding for AI research ceased, and AI was at the second lowest point of its life.

In 2006, Geoffrey Hinton and coworkers 3 , 4 made a breakthrough in AI by proposing an approach of building deeper neural networks, as well as a way to avoid gradient vanishing during training. This reignited AI research, and DL algorithms have become one of the most active fields of AI research. DL is a subset of ML based on multiple layers of neural networks with representation learning, 5 while ML is a part of AI that a computer or a program can use to learn and acquire intelligence without human intervention. Thus, “learn” is the keyword of this era of AI research. Big data technologies, and the improvement of computing power have made deriving features and information from massive data samples more efficient. An increasing number of new neural network structures and training methods have been proposed to improve the representative learning ability of DL, and to further expand it into general applications. Current DL algorithms match and exceed human capabilities on specific datasets in the areas of computer vision (CV) and natural language processing (NLP). AI technologies have achieved remarkable successes in all walks of life, and continued to show their value as backbones in scientific research and real-world applications.

Within AI, ML is having a substantial broad effect across many aspects of technology and science: from computer science to geoscience to materials science, from life science to medical science to chemistry to mathematics and to physics, from management science to economics to psychology, and other data-intensive empirical sciences, as ML methods have been developed to analyze high-throughput data to obtain useful insights, categorize, predict, and make evidence-based decisions in novel ways. To train a system by presenting it with examples of desired input-output behavior, could be far easier than to program it manually by predicting the desired response for all potential inputs. The following sections survey eight fundamental sciences, including information science (informatics), mathematics, medical science, materials science, geoscience, life science, physics, and chemistry, which develop or exploit AI techniques to promote the development of sciences and accelerate their applications to benefit human beings, society, and the world.

AI in information science

AI aims to provide the abilities of perception, cognition, and decision-making for machines. At present, new research and applications in information science are emerging at an unprecedented rate, which is inseparable from the support by the AI infrastructure. As shown in Figure 2 , the AI infrastructure layer includes data, storage and computing power, ML algorithms, and the AI framework. The perception layer enables machines have the basic ability of vision, hearing, etc. For instance, CV enables machines to “see” and identify objects, while speech recognition and synthesis helps machines to “hear” and recognize speech elements. The cognitive layer provides higher ability levels of induction, reasoning, and acquiring knowledge with the help of NLP, 6 knowledge graphs, 7 and continual learning. 8 In the decision-making layer, AI is capable of making optimal decisions, such as automatic planning, expert systems, and decision-supporting systems. Numerous applications of AI have had a profound impact on fundamental sciences, industrial manufacturing, human life, social governance, and cyberspace. The following subsections provide an overview of the AI framework, automatic machine learning (AutoML) technology, and several state-of-the-art AI/ML applications in the information field.

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The knowledge graph of the AI framework

The AI framework provides basic tools for AI algorithm implementation

In the past 10 years, applications based on AI algorithms have played a significant role in various fields and subjects, on the basis of which the prosperity of the DL framework and platform has been founded. AI frameworks and platforms reduce the requirement of accessing AI technology by integrating the overall process of algorithm development, which enables researchers from different areas to use it across other fields, allowing them to focus on designing the structure of neural networks, thus providing better solutions to problems in their fields. At the beginning of the 21st century, only a few tools, such as MATLAB, OpenNN, and Torch, were capable of describing and developing neural networks. However, these tools were not originally designed for AI models, and thus faced problems, such as complicated user API and lacking GPU support. During this period, using these frameworks demanded professional computer science knowledge and tedious work on model construction. As a solution, early frameworks of DL, such as Caffe, Chainer, and Theano, emerged, allowing users to conveniently construct complex deep neural networks (DNNs), such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and LSTM conveniently, and this significantly reduced the cost of applying AI models. Tech giants then joined the march in researching AI frameworks. 9 Google developed the famous open-source framework, TensorFlow, while Facebook's AI research team released another popular platform, PyTorch, which is based on Torch; Microsoft Research published CNTK, and Amazon announced MXNet. Among them, TensorFlow, also the most representative framework, referred to Theano's declarative programming style, offering a larger space for graph-based optimization, while PyTorch inherited the imperative programming style of Torch, which is intuitive, user friendly, more flexible, and easier to be traced. As modern AI frameworks and platforms are being widely applied, practitioners can now assemble models swiftly and conveniently by adopting various building block sets and languages specifically suitable for given fields. Polished over time, these platforms gradually developed a clearly defined user API, the ability for multi-GPU training and distributed training, as well as a variety of model zoos and tool kits for specific tasks. 10 Looking forward, there are a few trends that may become the mainstream of next-generation framework development. (1) Capability of super-scale model training. With the emergence of models derived from Transformer, such as BERT and GPT-3, the ability of training large models has become an ideal feature of the DL framework. It requires AI frameworks to train effectively under the scale of hundreds or even thousands of devices. (2) Unified API standard. The APIs of many frameworks are generally similar but slightly different at certain points. This leads to some difficulties and unnecessary learning efforts, when the user attempts to shift from one framework to another. The API of some frameworks, such as JAX, has already become compatible with Numpy standard, which is familiar to most practitioners. Therefore, a unified API standard for AI frameworks may gradually come into being in the future. (3) Universal operator optimization. At present, kernels of DL operator are implemented either manually or based on third-party libraries. Most third-party libraries are developed to suit certain hardware platforms, causing large unnecessary spending when models are trained or deployed on different hardware platforms. The development speed of new DL algorithms is usually much faster than the update rate of libraries, which often makes new algorithms to be beyond the range of libraries' support. 11

To improve the implementation speed of AI algorithms, much research focuses on how to use hardware for acceleration. The DianNao family is one of the earliest research innovations on AI hardware accelerators. 12 It includes DianNao, DaDianNao, ShiDianNao, and PuDianNao, which can be used to accelerate the inference speed of neural networks and other ML algorithms. Of these, the best performance of a 64-chip DaDianNao system can achieve a speed up of 450.65× over a GPU, and reduce the energy by 150.31×. Prof. Chen and his team in the Institute of Computing Technology also designed an Instruction Set Architecture for a broad range of neural network accelerators, called Cambricon, which developed into a serial DL accelerator. After Cambricon, many AI-related companies, such as Apple, Google, HUAWEI, etc., developed their own DL accelerators, and AI accelerators became an important research field of AI.

AI for AI—AutoML

AutoML aims to study how to use evolutionary computing, reinforcement learning (RL), and other AI algorithms, to automatically generate specified AI algorithms. Research on the automatic generation of neural networks has existed before the emergence of DL, e.g., neural evolution. 13 The main purpose of neural evolution is to allow neural networks to evolve according to the principle of survival of the fittest in the biological world. Through selection, crossover, mutation, and other evolutionary operators, the individual quality in a population is continuously improved and, finally, the individual with the greatest fitness represents the best neural network. The biological inspiration in this field lies in the evolutionary process of human brain neurons. The human brain has such developed learning and memory functions that it cannot do without the complex neural network system in the brain. The whole neural network system of the human brain benefits from a long evolutionary process rather than gradient descent and back propagation. In the era of DL, the application of AI algorithms to automatically generate DNN has attracted more attention and, gradually, developed into an important direction of AutoML research: neural architecture search. The implementation methods of neural architecture search are usually divided into the RL-based method and the evolutionary algorithm-based method. In the RL-based method, an RNN is used as a controller to generate a neural network structure layer by layer, and then the network is trained, and the accuracy of the verification set is used as the reward signal of the RNN to calculate the strategy gradient. During the iteration, the controller will give the neural network, with higher accuracy, a higher probability value, so as to ensure that the strategy function can output the optimal network structure. 14 The method of neural architecture search through evolution is similar to the neural evolution method, which is based on a population and iterates continuously according to the principle of survival of the fittest, so as to obtain a high-quality neural network. 15 Through the application of neural architecture search technology, the design of neural networks is more efficient and automated, and the accuracy of the network gradually outperforms that of the networks designed by AI experts. For example, Google's SOTA network EfficientNet was realized through the baseline network based on neural architecture search. 16

AI enabling networking design adaptive to complex network conditions

The application of DL in the networking field has received strong interest. Network design often relies on initial network conditions and/or theoretical assumptions to characterize real network environments. However, traditional network modeling and design, regulated by mathematical models, are unlikely to deal with complex scenarios with many imperfect and high dynamic network environments. Integrating DL into network research allows for a better representation of complex network environments. Furthermore, DL could be combined with the Markov decision process and evolve into the deep reinforcement learning (DRL) model, which finds an optimal policy based on the reward function and the states of the system. Taken together, these techniques could be used to make better decisions to guide proper network design, thereby improving the network quality of service and quality of experience. With regard to the aspect of different layers of the network protocol stack, DL/DRL can be adopted for network feature extraction, decision-making, etc. In the physical layer, DL can be used for interference alignment. It can also be used to classify the modulation modes, design efficient network coding 17 and error correction codes, etc. In the data link layer, DL can be used for resource (such as channels) allocation, medium access control, traffic prediction, 18 link quality evaluation, and so on. In the network (routing) layer, routing establishment and routing optimization 19 can help to obtain an optimal routing path. In higher layers (such as the application layer), enhanced data compression and task allocation is used. Besides the above protocol stack, one critical area of using DL is network security. DL can be used to classify the packets into benign/malicious types, and how it can be integrated with other ML schemes, such as unsupervised clustering, to achieve a better anomaly detection effect.

AI enabling more powerful and intelligent nanophotonics

Nanophotonic components have recently revolutionized the field of optics via metamaterials/metasurfaces by enabling the arbitrary manipulation of light-matter interactions with subwavelength meta-atoms or meta-molecules. 20 , 21 , 22 The conventional design of such components involves generally forward modeling, i.e., solving Maxwell's equations based on empirical and intuitive nanostructures to find corresponding optical properties, as well as the inverse design of nanophotonic devices given an on-demand optical response. The trans-dimensional feature of macro-optical components consisting of complex nano-antennas makes the design process very time consuming, computationally expensive, and even numerically prohibitive, such as device size and complexity increase. DL is an efficient and automatic platform, enabling novel efficient approaches to designing nanophotonic devices with high-performance and versatile functions. Here, we present briefly the recent progress of DL-based nanophotonics and its wide-ranging applications. DL was exploited for forward modeling at first using a DNN. 23 The transmission or reflection coefficients can be well predicted after training on huge datasets. To improve the prediction accuracy of DNN in case of small datasets, transfer learning was introduced to migrate knowledge between different physical scenarios, which greatly reduced the relative error. Furthermore, a CNN and an RNN were developed for the prediction of optical properties from arbitrary structures using images. 24 The CNN-RNN combination successfully predicted the absorption spectra from the given input structural images. In inverse design of nanophotonic devices, there are three different paradigms of DL methods, i.e., supervised, unsupervised, and RL. 25 Supervised learning has been utilized to design structural parameters for the pre-defined geometries, such as tandem DNN and bidirectional DNNs. Unsupervised learning methods learn by themselves without a specific target, and thus are more accessible to discovering new and arbitrary patterns 26 in completely new data than supervised learning. A generative adversarial network (GAN)-based approach, combining conditional GANs and Wasserstein GANs, was proposed to design freeform all-dielectric multifunctional metasurfaces. RL, especially double-deep Q-learning, powers up the inverse design of high-performance nanophotonic devices. 27 DL has endowed nanophotonic devices with better performance and more emerging applications. 28 , 29 For instance, an intelligent microwave cloak driven by DL exhibits millisecond and self-adaptive response to an ever-changing incident wave and background. 28 Another example is that a DL-augmented infrared nanoplasmonic metasurface is developed for monitoring dynamics between four major classes of bio-molecules, which could impact the fields of biology, bioanalytics, and pharmacology from fundamental research, to disease diagnostics, to drug development. 29 The potential of DL in the wide arena of nanophotonics has been unfolding. Even end-users without optics and photonics background could exploit the DL as a black box toolkit to design powerful optical devices. Nevertheless, how to interpret/mediate the intermediate DL process and determine the most dominant factors in the search for optimal solutions, are worthy of being investigated in depth. We optimistically envisage that the advancements in DL algorithms and computation/optimization infrastructures would enable us to realize more efficient and reliable training approaches, more complex nanostructures with unprecedented shapes and sizes, and more intelligent and reconfigurable optic/optoelectronic systems.

AI in other fields of information science

We believe that AI has great potential in the following directions:

• AI-based risk control and management in utilities can prevent costly or hazardous equipment failures by using sensors that detect and send information regarding the machine's health to the manufacturer, predicting possible issues that could occur so as to ensure timely maintenance or automated shutdown.
• AI could be used to produce simulations of real-world objects, called digital twins. When applied to the field of engineering, digital twins allow engineers and technicians to analyze the performance of an equipment virtually, thus avoiding safety and budget issues associated with traditional testing methods.
• Combined with AI, intelligent robots are playing an important role in industry and human life. Different from traditional robots working according to the procedures specified by humans, intelligent robots have the ability of perception, recognition, and even automatic planning and decision-making, based on changes in environmental conditions.
• AI of things (AIoT) or AI-empowered IoT applications. 30 have become a promising development trend. AI can empower the connected IoT devices, embedded in various physical infrastructures, to perceive, recognize, learn, and act. For instance, smart cities constantly collect data regarding quality-of-life factors, such as the status of power supply, public transportation, air pollution, and water use, to manage and optimize systems in cities. Due to these data, especially personal data being collected from informed or uninformed participants, data security, and privacy 31 require protection.

AI in mathematics

Mathematics always plays a crucial and indispensable role in AI. Decades ago, quite a few classical AI-related approaches, such as k-nearest neighbor, 32 support vector machine, 33 and AdaBoost, 34 were proposed and developed after their rigorous mathematical formulations had been established. In recent years, with the rapid development of DL, 35 AI has been gaining more and more attention in the mathematical community. Equipped with the Markov process, minimax optimization, and Bayesian statistics, RL, 36 GANs, 37 and Bayesian learning 38 became the most favorable tools in many AI applications. Nevertheless, there still exist plenty of open problems in mathematics for ML, including the interpretability of neural networks, the optimization problems of parameter estimation, and the generalization ability of learning models. In the rest of this section, we discuss these three questions in turn.

The interpretability of neural networks

From a mathematical perspective, ML usually constructs nonlinear models, with neural networks as a typical case, to approximate certain functions. The well-known Universal Approximation Theorem suggests that, under very mild conditions, any continuous function can be uniformly approximated on compact domains by neural networks, 39 which serves a vital function in the interpretability of neural networks. However, in real applications, ML models seem to admit accurate approximations of many extremely complicated functions, sometimes even black boxes, which are far beyond the scope of continuous functions. To understand the effectiveness of ML models, many researchers have investigated the function spaces that can be well approximated by them, and the corresponding quantitative measures. This issue is closely related to the classical approximation theory, but the approximation scheme is distinct. For example, Bach 40 finds that the random feature model is naturally associated with the corresponding reproducing kernel Hilbert space. In the same way, the Barron space is identified as the natural function space associated with two-layer neural networks, and the approximation error is measured using the Barron norm. 41 The corresponding quantities of residual networks (ResNets) are defined for the flow-induced spaces. For multi-layer networks, the natural function spaces for the purposes of approximation theory are the tree-like function spaces introduced in Wojtowytsch. 42 There are several works revealing the relationship between neural networks and numerical algorithms for solving partial differential equations. For example, He and Xu 43 discovered that CNNs for image classification have a strong connection with multi-grid (MG) methods. In fact, the pooling operation and feature extraction in CNNs correspond directly to restriction operation and iterative smoothers in MG, respectively. Hence, various convolution and pooling operations used in CNNs can be better understood.

The optimization problems of parameter estimation

In general, the optimization problem of estimating parameters of certain DNNs is in practice highly nonconvex and often nonsmooth. Can the global minimizers be expected? What is the landscape of local minimizers? How does one handle the nonsmoothness? All these questions are nontrivial from an optimization perspective. Indeed, numerous works and experiments demonstrate that the optimization for parameter estimation in DL is itself a much nicer problem than once thought; see, e.g., Goodfellow et al. 44 As a consequence, the study on the solution landscape ( Figure 3 ), also known as loss surface of neural networks, is no longer supposed to be inaccessible and can even in turn provide guidance for global optimization. Interested readers can refer to the survey paper (Sun et al. 45 ) for recent progress in this aspect.

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Recent studies indicate that nonsmooth activation functions, e.g., rectified linear units, are better than smooth ones in finding sparse solutions. However, the chain rule does not work in the case that the activation functions are nonsmooth, which then makes the widely used stochastic gradient (SG)-based approaches not feasible in theory. Taking approximated gradients at nonsmooth iterates as a remedy ensures that SG-type methods are still in extensive use, but that the numerical evidence has also exposed their limitations. Also, the penalty-based approaches proposed by Cui et al. 46 and Liu et al. 47 provide a new direction to solve the nonsmooth optimization problems efficiently.

The generalization ability of learning models

A small training error does not always lead to a small test error. This gap is caused by the generalization ability of learning models. A key finding in statistical learning theory states that the generalization error is bounded by a quantity that grows with the increase of the model capacity, but shrinks as the number of training examples increases. 48 A common conjecture relating generalization to solution landscape is that flat and wide minima generalize better than sharp ones. Thus, regularization techniques, including the dropout approach, 49 have emerged to force the algorithms to bypass the sharp minima. However, the mechanism behind this has not been fully explored. Recently, some researchers have focused on the ResNet-type architecture, with dropout being inserted after the last convolutional layer of each modular building. They thus managed to explain the stochastic dropout training process and the ensuing dropout regularization effect from the perspective of optimal control. 50

AI in medical science

There is a great trend for AI technology to grow more and more significant in daily operations, including medical fields. With the growing needs of healthcare for patients, hospital needs are evolving from informationization networking to the Internet Hospital and eventually to the Smart Hospital. At the same time, AI tools and hardware performance are also growing rapidly with each passing day. Eventually, common AI algorithms, such as CV, NLP, and data mining, will begin to be embedded in the medical equipment market ( Figure 4 ).

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AI doctor based on electronic medical records

For medical history data, it is inevitable to mention Doctor Watson, developed by the Watson platform of IBM, and Modernizing Medicine, which aims to solve oncology, and is now adopted by CVS & Walgreens in the US and various medical organizations in China as well. Doctor Watson takes advantage of the NLP performance of the IBM Watson platform, which already collected vast data of medical history, as well as prior knowledge in the literature for reference. After inputting the patients' case, Doctor Watson searches the medical history reserve and forms an elementary treatment proposal, which will be further ranked by prior knowledge reserves. With the multiple models stored, Doctor Watson gives the final proposal as well as the confidence of the proposal. However, there are still problems for such AI doctors because, 51 as they rely on prior experience from US hospitals, the proposal may not be suitable for other regions with different medical insurance policies. Besides, the knowledge updating of the Watson platform also relies highly on the updating of the knowledge reserve, which still needs manual work.

AI for public health: Outbreak detection and health QR code for COVID-19

AI can be used for public health purposes in many ways. One classical usage is to detect disease outbreaks using search engine query data or social media data, as Google did for prediction of influenza epidemics 52 and the Chinese Academy of Sciences did for modeling the COVID-19 outbreak through multi-source information fusion. 53 After the COVID-19 outbreak, a digital health Quick Response (QR) code system has been developed by China, first to detect potential contact with confirmed COVID-19 cases and, secondly, to indicate the person's health status using mobile big data. 54 Different colors indicate different health status: green means healthy and is OK for daily life, orange means risky and requires quarantine, and red means confirmed COVID-19 patient. It is easy to use for the general public, and has been adopted by many other countries. The health QR code has made great contributions to the worldwide prevention and control of the COVID-19 pandemic.

Biomarker discovery with AI

High-dimensional data, including multi-omics data, patient characteristics, medical laboratory test data, etc., are often used for generating various predictive or prognostic models through DL or statistical modeling methods. For instance, the COVID-19 severity evaluation model was built through ML using proteomic and metabolomic profiling data of sera 55 ; using integrated genetic, clinical, and demographic data, Taliaz et al. built an ML model to predict patient response to antidepressant medications 56 ; prognostic models for multiple cancer types (such as liver cancer, lung cancer, breast cancer, gastric cancer, colorectal cancer, pancreatic cancer, prostate cancer, ovarian cancer, lymphoma, leukemia, sarcoma, melanoma, bladder cancer, renal cancer, thyroid cancer, head and neck cancer, etc.) were constructed through DL or statistical methods, such as least absolute shrinkage and selection operator （LASSO）, combined with Cox proportional hazards regression model using genomic data. 57

Image-based medical AI

Medical image AI is one of the most developed mature areas as there are numerous models for classification, detection, and segmentation tasks in CV. For the clinical area, CV algorithms can also be used for computer-aided diagnosis and treatment with ECG, CT, eye fundus imaging, etc. As human doctors may be tired and prone to make mistakes after viewing hundreds and hundreds of images for diagnosis, AI doctors can outperform a human medical image viewer due to their specialty at repeated work without fatigue. The first medical AI product approved by FDA is IDx-DR, which uses an AI model to make predictions of diabetic retinopathy. The smartphone app SkinVision can accurately detect melanomas. 58 It uses “fractal analysis” to identify moles and their surrounding skin, based on size, diameter, and many other parameters, and to detect abnormal growth trends. AI-ECG of LEPU Medical can automatically detect heart disease with ECG images. Lianying Medical takes advantage of their hardware equipment to produce real-time high-definition image-guided all-round radiotherapy technology, which successfully achieves precise treatment.

Wearable devices for surveillance and early warning

For wearable devices, AliveCor has developed an algorithm to automatically predict the presence of atrial fibrillation, which is an early warning sign of stroke and heart failure. The 23andMe company can also test saliva samples at a small cost, and a customer can be provided with information based on their genes, including who their ancestors were or potential diseases they may be prone to later in life. It provides accurate health management solutions based on individual and family genetic data. In the 20–30 years of the near feature, we believe there are several directions for further research: (1) causal inference for real-time in-hospital risk prediction. Clinical doctors usually acquire reasonable explanations for certain medical decisions, but the current AI models nowadays are usually black box models. The casual inference will help doctors to explain certain AI decisions and even discover novel ground truths. (2) Devices, including wearable instruments for multi-dimensional health monitoring. The multi-modality model is now a trend for AI research. With various devices to collect multi-modality data and a central processor to fuse all these data, the model can monitor the user's overall real-time health condition and give precautions more precisely. (3) Automatic discovery of clinical markers for diseases that are difficult to diagnose. Diseases, such as ALS, are still difficult for clinical doctors to diagnose because they lack any effective general marker. It may be possible for AI to discover common phenomena for these patients and find an effective marker for early diagnosis.

AI-aided drug discovery

Today we have come into the precision medicine era, and the new targeted drugs are the cornerstones for precision therapy. However, over the past decades, it takes an average of over one billion dollars and 10 years to bring a new drug into the market. How to accelerate the drug discovery process, and avoid late-stage failure, are key concerns for all the big and fiercely competitive pharmaceutical companies. The highlighted emerging role of AI, including ML, DL, expert systems, and artificial neural networks (ANNs), has brought new insights and high efficiency into the new drug discovery processes. AI has been adopted in many aspects of drug discovery, including de novo molecule design, structure-based modeling for proteins and ligands, quantitative structure-activity relationship research, and druggable property judgments. DL-based AI appliances demonstrate superior merits in addressing some challenging problems in drug discovery. Of course, prediction of chemical synthesis routes and chemical process optimization are also valuable in accelerating new drug discovery, as well as lowering production costs.

There has been notable progress in the AI-aided new drug discovery in recent years, for both new chemical entity discovery and the relating business area. Based on DNNs, DeepMind built the AlphaFold platform to predict 3D protein structures that outperformed other algorithms. As an illustration of great achievement, AlphaFold successfully and accurately predicted 25 scratch protein structures from a 43 protein panel without using previously built proteins models. Accordingly, AlphaFold won the CASP13 protein-folding competition in December 2018. 59 Based on the GANs and other ML methods, Insilico constructed a modular drug design platform GENTRL system. In September 2019, they reported the discovery of the first de novo active DDR1 kinase inhibitor developed by the GENTRL system. It took the team only 46 days from target selection to get an active drug candidate using in vivo data. 60 Exscientia and Sumitomo Dainippon Pharma developed a new drug candidate, DSP-1181, for the treatment of obsessive-compulsive disorder on the Centaur Chemist AI platform. In January 2020, DSP-1181 started its phase I clinical trials, which means that, from program initiation to phase I study, the comprehensive exploration took less than 12 months. In contrast, comparable drug discovery using traditional methods usually needs 4–5 years with traditional methods.

How AI transforms medical practice: A case study of cervical cancer

As the most common malignant tumor in women, cervical cancer is a disease that has a clear cause and can be prevented, and even treated, if detected early. Conventionally, the screening strategy for cervical cancer mainly adopts the “three-step” model of “cervical cytology-colposcopy-histopathology.” 61 However, limited by the level of testing methods, the efficiency of cervical cancer screening is not high. In addition, owing to the lack of knowledge by doctors in some primary hospitals, patients cannot be provided with the best diagnosis and treatment decisions. In recent years, with the advent of the era of computer science and big data, AI has gradually begun to extend and blend into various fields. In particular, AI has been widely used in a variety of cancers as a new tool for data mining. For cervical cancer, a clinical database with millions of medical records and pathological data has been built, and an AI medical tool set has been developed. 62 Such an AI analysis algorithm supports doctors to access the ability of rapid iterative AI model training. In addition, a prognostic prediction model established by ML and a web-based prognostic result calculator have been developed, which can accurately predict the risk of postoperative recurrence and death in cervical cancer patients, and thereby better guide decision-making in postoperative adjuvant treatment. 63

AI in materials science

As the cornerstone of modern industry, materials have played a crucial role in the design of revolutionary forms of matter, with targeted properties for broad applications in energy, information, biomedicine, construction, transportation, national security, spaceflight, and so forth. Traditional strategies rely on the empirical trial and error experimental approaches as well as the theoretical simulation methods, e.g., density functional theory, thermodynamics, or molecular dynamics, to discover novel materials. 64 These methods often face the challenges of long research cycles, high costs, and low success rates, and thus cannot meet the increasingly growing demands of current materials science. Accelerating the speed of discovery and deployment of advanced materials will therefore be essential in the coming era.

With the rapid development of data processing and powerful algorithms, AI-based methods, such as ML and DL, are emerging with good potentials in the search for and design of new materials prior to actually manufacturing them. 65 , 66 By integrating material property data, such as the constituent element, lattice symmetry, atomic radius, valence, binding energy, electronegativity, magnetism, polarization, energy band, structure-property relation, and functionalities, the machine can be trained to “think” about how to improve material design and even predict the properties of new materials in a cost-effective manner ( Figure 5 ).

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AI is expected to power the development of materials science

AI in discovery and design of new materials

Recently, AI techniques have made significant advances in rational design and accelerated discovery of various materials, such as piezoelectric materials with large electrostrains, 67 organic-inorganic perovskites for photovoltaics, 68 molecular emitters for efficient light-emitting diodes, 69 inorganic solid materials for thermoelectrics, 70 and organic electronic materials for renewable-energy applications. 66 , 71 The power of data-driven computing and algorithmic optimization can promote comprehensive applications of simulation and ML (i.e., high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning, etc.), in material discovery and property prediction in various fields. 72 For instance, using a DL Bayesian framework, the attribute-driven inverse materials design has been demonstrated for efficient and accurate prediction of functional molecular materials, with desired semiconducting properties or redox stability for applications in organic thin-film transistors, organic solar cells, or lithium-ion batteries. 73 It is meaningful to adopt automation tools for quick experimental testing of potential materials and utilize high-performance computing to calculate their bulk, interface, and defect-related properties. 74 The effective convergence of automation, computing, and ML can greatly speed up the discovery of materials. In the future, with the aid of AI techniques, it will be possible to accomplish the design of superconductors, metallic glasses, solder alloys, high-entropy alloys, high-temperature superalloys, thermoelectric materials, two-dimensional materials, magnetocaloric materials, polymeric bio-inspired materials, sensitive composite materials, and topological (electronic and phonon) materials, and so on. In the past decade, topological materials have ignited the research enthusiasm of condensed matter physicists, materials scientists, and chemists, as they exhibit exotic physical properties with potential applications in electronics, thermoelectrics, optics, catalysis, and energy-related fields. From the most recent predictions, more than a quarter of all inorganic materials in nature are topologically nontrivial. The establishment of topological electronic materials databases 75 , 76 , 77 and topological phononic materials databases 78 using high-throughput methods will help to accelerate the screening and experimental discovery of new topological materials for functional applications. It is recognized that large-scale high-quality datasets are required to practice AI. Great efforts have also been expended in building high-quality materials science databases. As one of the top-ranking databases of its kind, the “atomly.net” materials data infrastructure, 79 has calculated the properties of more than 180,000 inorganic compounds, including their equilibrium structures, electron energy bands, dielectric properties, simulated diffraction patterns, elasticity tensors, etc. As such, the atomly.net database has set a solid foundation for extending AI into the area of materials science research. The X-ray diffraction (XRD)-matcher model of atomly.net uses ML to match and classify the experimental XRD to the simulated patterns. Very recently, by using the dataset from atomly.net, an accurate AI model was built to rapidly predict the formation energy of almost any given compound to yield a fairly good predictive ability. 80

AI-powered Materials Genome Initiative

The Materials Genome Initiative (MGI) is a great plan for rational realization of new materials and related functions, and it aims to discover, manufacture, and deploy advanced materials efficiently, cost-effectively, and intelligently. The initiative creates policy, resources, and infrastructure for accelerating materials development at a high level. This is a new paradigm for the discovery and design of next-generation materials, and runs from a view point of fundamental building blocks toward general materials developments, and accelerates materials development through efforts in theory, computation, and experiment, in a highly integrated high-throughput manner. MGI raises an ultimately high goal and high level for materials development and materials science for humans in the future. The spirit of MGI is to design novel materials by using data pools and powerful computation once the requirements or aspirations of functional usages appear. The theory, computation, and algorithm are the primary and substantial factors in the establishment and implementation of MGI. Advances in theories, computations, and experiments in materials science and engineering provide the footstone to not only accelerate the speed at which new materials are realized but to also shorten the time needed to push new products into the market. These AI techniques bring a great promise to the developing MGI. The applications of new technologies, such as ML and DL, directly accelerate materials research and the establishment of MGI. The model construction and application to science and engineering, as well as the data infrastructure, are of central importance. When the AI-powered MGI approaches are coupled with the ongoing autonomy of manufacturing methods, the potential impact to society and the economy in the future is profound. We are now beginning to see that the AI-aided MGI, among other things, integrates experiments, computation, and theory, and facilitates access to materials data, equips the next generation of the materials workforce, and enables a paradigm shift in materials development. Furthermore, the AI-powdered MGI could also design operational procedures and control the equipment to execute experiments, and to further realize autonomous experimentation in future material research.

Advanced functional materials for generation upgrade of AI

The realization and application of AI techniques depend on the computational capability and computer hardware, and this bases physical functionality on the performance of computers or supercomputers. For our current technology, the electric currents or electric carriers for driving electric chips and devices consist of electrons with ordinary characteristics, such as heavy mass and low mobility. All chips and devices emit relatively remarkable heat levels, consuming too much energy and lowering the efficiency of information transmission. Benefiting from the rapid development of modern physics, a series of advanced materials with exotic functional effects have been discovered or designed, including superconductors, quantum anomalous Hall insulators, and topological fermions. In particular, the superconducting state or topologically nontrivial electrons will promote the next-generation AI techniques once the (near) room temperature applications of these states are realized and implanted in integrated circuits. 81 In this case, the central processing units, signal circuits, and power channels will be driven based on the electronic carriers that show massless, energy-diffusionless, ultra-high mobility, or chiral-protection characteristics. The ordinary electrons will be removed from the physical circuits of future-generation chips and devices, leaving superconducting and topological chiral electrons running in future AI chips and supercomputers. The efficiency of transmission, for information and logic computing will be improved on a vast scale and at a very low cost.

AI for materials and materials for AI

The coming decade will continue to witness the development of advanced ML algorithms, newly emerging data-driven AI methodologies, and integrated technologies for facilitating structure design and property prediction, as well as to accelerate the discovery, design, development, and deployment of advanced materials into existing and emerging industrial sectors. At this moment, we are facing challenges in achieving accelerated materials research through the integration of experiment, computation, and theory. The great MGI, proposed for high-level materials research, helps to promote this process, especially when it is assisted by AI techniques. Still, there is a long way to go for the usage of these advanced functional materials in future-generation electric chips and devices to be realized. More materials and functional effects need to be discovered or improved by the developing AI techniques. Meanwhile, it is worth noting that materials are the core components of devices and chips that are used for construction of computers or machines for advanced AI systems. The rapid development of new materials, especially the emergence of flexible, sensitive, and smart materials, is of great importance for a broad range of attractive technologies, such as flexible circuits, stretchable tactile sensors, multifunctional actuators, transistor-based artificial synapses, integrated networks of semiconductor/quantum devices, intelligent robotics, human-machine interactions, simulated muscles, biomimetic prostheses, etc. These promising materials, devices, and integrated technologies will greatly promote the advancement of AI systems toward wide applications in human life. Once the physical circuits are upgraded by advanced functional or smart materials, AI techniques will largely promote the developments and applications of all disciplines.

AI in geoscience

Ai technologies involved in a large range of geoscience fields.

Momentous challenges threatening current society require solutions to problems that belong to geoscience, such as evaluating the effects of climate change, assessing air quality, forecasting the effects of disaster incidences on infrastructure, by calculating the incoming consumption and availability of food, water, and soil resources, and identifying factors that are indicators for potential volcanic eruptions, tsunamis, floods, and earthquakes. 82 , 83 It has become possible, with the emergence of advanced technology products (e.g., deep sea drilling vessels and remote sensing satellites), for enhancements in computational infrastructure that allow for processing large-scale, wide-range simulations of multiple models in geoscience, and internet-based data analysis that facilitates collection, processing, and storage of data in distributed and crowd-sourced environments. 84 The growing availability of massive geoscience data provides unlimited possibilities for AI—which has popularized all aspects of our daily life (e.g., entertainment, transportation, and commerce)—to significantly contribute to geoscience problems of great societal relevance. As geoscience enters the era of massive data, AI, which has been extensively successful in different fields, offers immense opportunities for settling a series of problems in Earth systems. 85 , 86 Accompanied by diversified data, AI-enabled technologies, such as smart sensors, image visualization, and intelligent inversion, are being actively examined in a large range of geoscience fields, such as marine geoscience, rock physics, geology, ecology, seismicity, environment, hydrology, remote sensing, Arc GIS, and planetary science. 87

Multiple challenges in the development of geoscience

There are some traits of geoscience development that restrict the applicability of fundamental algorithms for knowledge discovery: (1) inherent challenges of geoscience processes, (2) limitation of geoscience data collection, and (3) uncertainty in samples and ground truth. 88 , 89 , 90 Amorphous boundaries generally exist in geoscience objects between space and time that are not as well defined as objects in other fields. Geoscience phenomena are also significantly multivariate, obey nonlinear relationships, and exhibit spatiotemporal structure and non-stationary characteristics. Except for the inherent challenges of geoscience observations, the massive data at multiple dimensions of time and space, with different levels of incompleteness, noise, and uncertainties, disturb processes in geoscience. For supervised learning approaches, there are other difficulties owing to the lack of gold standard ground truth and the “small size” of samples (e.g., a small amount of historical data with sufficient observations) in geoscience applications.

Usage of AI technologies as efficient approaches to promote the geoscience processes

Geoscientists continually make every effort to develop better techniques for simulating the present status of the Earth system (e.g., how much greenhouse gases are released into the atmosphere), and the connections between and within its subsystems (e.g., how does the elevated temperature influence the ocean ecosystem). Viewed from the perspective of geoscience, newly emerging approaches, with the aid of AI, are a perfect combination for these issues in the application of geoscience: (1) characterizing objects and events 91 ; (2) estimating geoscience variables from observations 92 ; (3) forecasting geoscience variables according to long-term observations 85 ; (4) exploring geoscience data relationships 93 ; and (5) causal discovery and causal attribution. 94 While characterizing geoscience objects and events using traditional methods are primarily rooted in hand-coded features, algorithms can automatically detect the data by improving the performance with pattern-mining techniques. However, due to spatiotemporal targets with vague boundaries and the related uncertainties, it can be necessary to advance pattern-mining methods that can explain the temporal and spatial characteristics of geoscience data when characterizing different events and objects. To address the non-stationary issue of geoscience data, AI-aided algorithms have been expanded to integrate the holistic results of professional predictors and engender robust estimations of climate variables (e.g., humidity and temperature). Furthermore, forecasting long-term trends of the current situation in the Earth system using AI-enabled technologies can simulate future scenarios and formulate early resource planning and adaptation policies. Mining geoscience data relationships can help us seize vital signs of the Earth system and promote our understanding of geoscience developments. Of great interest is the advancement of AI-decision methodology with uncertain prediction probabilities, engendering vague risks with poorly resolved tails, signifying the most extreme, transient, and rare events formulated by model sets, which supports various cases to improve accuracy and effectiveness.

AI technologies for optimizing the resource management in geoscience

Currently, AI can perform better than humans in some well-defined tasks. For example, AI techniques have been used in urban water resource planning, mainly due to their remarkable capacity for modeling, flexibility, reasoning, and forecasting the water demand and capacity. Design and application of an Adaptive Intelligent Dynamic Water Resource Planning system, the subset of AI for sustainable water resource management in urban regions, largely prompted the optimization of water resource allocation, will finally minimize the operation costs and improve the sustainability of environmental management 95 ( Figure 6 ). Also, meteorology requires collecting tremendous amounts of data on many different variables, such as humidity, altitude, and temperature; however, dealing with such a huge dataset is a big challenge. 96 An AI-based technique is being utilized to analyze shallow-water reef images, recognize the coral color—to track the effects of climate change, and to collect humidity, temperature, and CO 2 data—to grasp the health of our ecological environment. 97 Beyond AI's capabilities for meteorology, it can also play a critical role in decreasing greenhouse gas emissions originating from the electric-power sector. Comprised of production, transportation, allocation, and consumption of electricity, many opportunities exist in the electric-power sector for Al applications, including speeding up the development of new clean energy, enhancing system optimization and management, improving electricity-demand forecasts and distribution, and advancing system monitoring. 98 New materials may even be found, with the auxiliary of AI, for batteries to store energy or materials and absorb CO 2 from the atmosphere. 99 Although traditional fossil fuel operations have been widely used for thousands of years, AI techniques are being used to help explore the development of more potential sustainable energy sources for the development (e.g., fusion technology). 100

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Applications of AI in hydraulic resource management

In addition to the adjustment of energy structures due to climate change (a core part of geoscience systems), a second, less-obvious step could also be taken to reduce greenhouse gas emission: using AI to target inefficiencies. A related statistical report by the Lawrence Livermore National Laboratory pointed out that around 68% of energy produced in the US could be better used for purposeful activities, such as electricity generation or transportation, but is instead contributing to environmental burdens. 101 AI is primed to reduce these inefficiencies of current nuclear power plants and fossil fuel operations, as well as improve the efficiency of renewable grid resources. 102 For example, AI can be instrumental in the operation and optimization of solar and wind farms to make these utility-scale renewable-energy systems far more efficient in the production of electricity. 103 AI can also assist in reducing energy losses in electricity transportation and allocation. 104 A distribution system operator in Europe used AI to analyze load, voltage, and network distribution data, to help “operators assess available capacity on the system and plan for future needs.” 105 AI allowed the distribution system operator to employ existing and new resources to make the distribution of energy assets more readily available and flexible. The International Energy Agency has proposed that energy efficiency is core to the reform of energy systems and will play a key role in reducing the growth of global energy demand to one-third of the current level by 2040.

AI as a building block to promote development in geoscience

The Earth’s system is of significant scientific interest, and affects all aspects of life. 106 The challenges, problems, and promising directions provided by AI are definitely not exhaustive, but rather, serve to illustrate that there is great potential for future AI research in this important field. Prosperity, development, and popularization of AI approaches in the geosciences is commonly driven by a posed scientific question, and the best way to succeed is that AI researchers work closely with geoscientists at all stages of research. That is because the geoscientists can better understand which scientific question is important and novel, which sample collection process can reasonably exhibit the inherent strengths, which datasets and parameters can be used to answer that question, and which pre-processing operations are conducted, such as removing seasonal cycles or smoothing. Similarly, AI researchers are better suited to decide which data analysis approaches are appropriate and available for the data, the advantages and disadvantages of these approaches, and what the approaches actually acquire. Interpretability is also an important goal in geoscience because, if we can understand the basic reasoning behind the models, patterns, or relationships extracted from the data, they can be used as building blocks in scientific knowledge discovery. Hence, frequent communication between the researchers avoids long detours and ensures that analysis results are indeed beneficial to both geoscientists and AI researchers.

AI in the life sciences

The developments of AI and the life sciences are intertwined. The ultimate goal of AI is to achieve human-like intelligence, as the human brain is capable of multi-tasking, learning with minimal supervision, and generalizing learned skills, all accomplished with high efficiency and low energy cost. 107

Mutual inspiration between AI and neuroscience

In the past decades, neuroscience concepts have been introduced into ML algorithms and played critical roles in triggering several important advances in AI. For example, the origins of DL methods lie directly in neuroscience, 5 which further stimulated the emergence of the field of RL. 108 The current state-of-the-art CNNs incorporate several hallmarks of neural computation, including nonlinear transduction, divisive normalization, and maximum-based pooling of inputs, 109 which were directly inspired by the unique processing of visual input in the mammalian visual cortex. 110 By introducing the brain's attentional mechanisms, a novel network has been shown to produce enhanced accuracy and computational efficiency at difficult multi-object recognition tasks than conventional CNNs. 111 Other neuroscience findings, including the mechanisms underlying working memory, episodic memory, and neural plasticity, have inspired the development of AI algorithms that address several challenges in deep networks. 108 These algorithms can be directly implemented in the design and refinement of the brain-machine interface and neuroprostheses.

On the other hand, insights from AI research have the potential to offer new perspectives on the basics of intelligence in the brains of humans and other species. Unlike traditional neuroscientists, AI researchers can formalize the concepts of neural mechanisms in a quantitative language to extract their necessity and sufficiency for intelligent behavior. An important illustration of such exchange is the development of the temporal-difference (TD) methods in RL models and the resemblance of TD-form learning in the brain. 112 Therefore, the China Brain Project covers both basic research on cognition and translational research for brain disease and brain-inspired intelligence technology. 113

AI for omics big data analysis

Currently, AI can perform better than humans in some well-defined tasks, such as omics data analysis and smart agriculture. In the big data era, 114 there are many types of data (variety), the volume of data is big, and the generation of data (velocity) is fast. The high variety, big volume, and fast velocity of data makes having it a matter of big value, but also makes it difficult to analyze the data. Unlike traditional statistics-based methods, AI can easily handle big data and reveal hidden associations.

In genetics studies, there are many successful applications of AI. 115 One of the key questions is to determine whether a single amino acid polymorphism is deleterious. 116 There have been sequence conservation-based SIFT 117 and network-based SySAP, 118 but all these methods have met bottlenecks and cannot be further improved. Sundaram et al. developed PrimateAI, which can predict the clinical outcome of mutation based on DNN. 119 Another problem is how to call copy-number variations, which play important roles in various cancers. 120 , 121 Glessner et al. proposed a DL-based tool DeepCNV, in which the area under the receiver operating characteristic (ROC) curve was 0.909, much higher than other ML methods. 122 In epigenetic studies, m6A modification is one of the most important mechanisms. 123 Zhang et al. developed an ensemble DL predictor (EDLm6APred) for mRNA m6A site prediction. 124 The area under the ROC curve of EDLm6APred was 86.6%, higher than existing m6A methylation site prediction models. There are many other DL-based omics tools, such as DeepCpG 125 for methylation, DeepPep 126 for proteomics, AtacWorks 127 for assay for transposase-accessible chromatin with high-throughput sequencing, and deepTCR 128 for T cell receptor sequencing.

Another emerging application is DL for single-cell sequencing data. Unlike bulk data, in which the sample size is usually much smaller than the number of features, the sample size of cells in single-cell data could also be big compared with the number of genes. That makes the DL algorithm applicable for most single-cell data. Since the single-cell data are sparse and have many unmeasured missing values, DeepImpute can accurately impute these missing values in the big gene × cell matrix. 129 During the quality control of single-cell data, it is important to remove the doublet solo embedded cells, using autoencoder, and then build a feedforward neural network to identify the doublet. 130 Potential energy underlying single-cell gradients used generative modeling to learn the underlying differentiation landscape from time series single-cell RNA sequencing data. 131

In protein structure prediction, the DL-based AIphaFold2 can accurately predict the 3D structures of 98.5% of human proteins, and will predict the structures of 130 million proteins of other organisms in the next few months. 132 It is even considered to be the second-largest breakthrough in life sciences after the human genome project 133 and will facilitate drug development among other things.

AI makes modern agriculture smart

Agriculture is entering a fourth revolution, termed agriculture 4.0 or smart agriculture, benefiting from the arrival of the big data era as well as the rapid progress of lots of advanced technologies, in particular ML, modern information, and communication technologies. 134 , 135 Applications of DL, information, and sensing technologies in agriculture cover the whole stages of agricultural production, including breeding, cultivation, and harvesting.

Traditional breeding usually exploits genetic variations by searching natural variation or artificial mutagenesis. However, it is hard for either method to expose the whole mutation spectrum. Using DL models trained on the existing variants, predictions can be made on multiple unidentified gene loci. 136 For example, an ML method, multi-criteria rice reproductive gene predictor, was developed and applied to predict coding and lincRNA genes associated with reproductive processes in rice. 137 Moreover, models trained in species with well-studied genomic data (such as Arabidopsis and rice) can also be applied to other species with limited genome information (such as wild strawberry and soybean). 138 In most cases, the links between genotypes and phenotypes are more complicated than we expected. One gene can usually respond to multiple phenotypes, and one trait is generally the product of the synergism between multi-genes and multi-development. For this reason, multi-traits DL models were developed and enabled genomic editing in plant breeding. 139 , 140

It is well known that dynamic and accurate monitoring of crops during the whole growth period is vitally important to precision agriculture. In the new stage of agriculture, both remote sensing and DL play indispensable roles. Specifically, remote sensing (including proximal sensing) could produce agricultural big data from ground, air-borne, to space-borne platforms, which have a unique potential to offer an economical approach for non-destructive, timely, objective, synoptic, long-term, and multi-scale information for crop monitoring and management, thereby greatly assisting in precision decisions regarding irrigation, nutrients, disease, pests, and yield. 141 , 142 DL makes it possible to simply, efficiently, and accurately discover knowledge from massive and complicated data, especially for remote sensing big data that are characterized with multiple spatial-temporal-spectral information, owing to its strong capability for feature representation and superiority in capturing the essential relation between observation data and agronomy parameters or crop traits. 135 , 143 Integration of DL and big data for agriculture has demonstrated the most disruptive force, as big as the green revolution. As shown in Figure 7 , for possible application a scenario of smart agriculture, multi-source satellite remote sensing data with various geo- and radio-metric information, as well as abundance of spectral information from UV, visible, and shortwave infrared to microwave regions, can be collected. In addition, advanced aircraft systems, such as unmanned aerial vehicles with multi/hyper-spectral cameras on board, and smartphone-based portable devices, will be used to obtain multi/hyper-spectral data in specific fields. All types of data can be integrated by DL-based fusion techniques for different purposes, and then shared for all users for cloud computing. On the cloud computing platform, different agriculture remote sensing models developed by a combination of data-driven ML methods and physical models, will be deployed and applied to acquire a range of biophysical and biochemical parameters of crops, which will be further analyzed by a decision-making and prediction system to obtain the current water/nutrient stress, growth status, and to predict future development. As a result, an automatic or interactive user service platform can be accessible to make the correct decisions for appropriate actions through an integrated irrigation and fertilization system.

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Integration of AI and remote sensing in smart agriculture

Furthermore, DL presents unique advantages in specific agricultural applications, such as for dense scenes, that increase the difficulty of artificial planting and harvesting. It is reported that CNNs and Autoencoder models, trained with image data, are being used increasingly for phenotyping and yield estimation, 144 such as counting fruits in orchards, grain recognition and classification, disease diagnosis, etc. 145 , 146 , 147 Consequently, this may greatly liberate the labor force.

The application of DL in agriculture is just beginning. There are still many problems and challenges for the future development of DL technology. We believe, with the continuous acquisition of massive data and the optimization of algorithms, DL will have a better prospect in agricultural production.

AI in physics

The scale of modern physics ranges from the size of a neutron to the size of the Universe ( Figure 8 ). According to the scale, physics can be divided into four categories: particle physics on the scale of neutrons, nuclear physics on the scale of atoms, condensed matter physics on the scale of molecules, and cosmic physics on the scale of the Universe. AI, also called ML, plays an important role in all physics in different scales, since the use of the AI algorithm will be the main trend in data analyses, such as the reconstruction and analysis of images.

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Scale of the physics

Speeding up simulations and identifications of particles with AI

There are many applications or explorations of applications of AI in particle physics. We cannot cover all of them here, but only use lattice quantum chromodynamics (LQCD) and the experiments on the Beijing spectrometer (BES) and the large hadron collider (LHC) to illustrate the power of ML in both theoretical and experimental particle physics.

LQCD studies the nonperturbative properties of QCD by using Monte Carlo simulations on supercomputers to help us understand the strong interaction that binds quarks together to form nucleons. Markov chain Monte Carlo simulations commonly used in LQCD suffer from topological freezing and critical slowing down as the simulations approach the real situation of the actual world. New algorithms with the help of DL are being proposed and tested to overcome those difficulties. 148 , 149 Physical observables are extracted from LQCD data, whose signal-to-noise ratio deteriorates exponentially. For non-Abelian gauge theories, such as QCD, complicated contour deformations can be optimized by using ML to reduce the variance of LQCD data. Proof-of-principle applications in two dimensions have been studied. 150 ML can also be used to reduce the time cost of generating LQCD data. 151

On the experimental side, particle identification (PID) plays an important role. Recently, a few PID algorithms on BES-III were developed, and the ANN 152 is one of them. Also, extreme gradient boosting has been used for multi-dimensional distribution reweighting, muon identification, and cluster reconstruction, and can improve the muon identification. U-Net is a convolutional network for pixel-level semantic segmentation, which is widely used in CV. It has been applied on BES-III to solve the problem of multi-turn curling track finding for the main drift chamber. The average efficiency and purity for the first turn's hits is about 91%, at the threshold of 0.85. Current (and future) particle physics experiments are producing a huge amount of data. Machine leaning can be used to discriminate between signal and overwhelming background events. Examples of data analyses on LHC, using supervised ML, can be found in a 2018 collaboration. 153 To take the potential advantage of quantum computers forward, quantum ML methods are also being investigated, see, for example, Wu et al., 154 and references therein, for proof-of-concept studies.

AI makes nuclear physics powerful

Cosmic ray muon tomography (Muography) 155 is an imaging graphe technology using natural cosmic ray muon radiation rather than artificial radiation to reduce the dangers. As an advantage, this technology can detect high-Z materials without destruction, as muon is sensitive to high-Z materials. The Classification Model Algorithm (CMA) algorithm is based on the classification in the supervised learning and gray system theory, and generates a binary classifier designing and decision function with the input of the muon track, and the output indicates whether the material exists at the location. The AI helps the user to improve the efficiency of the scanning time with muons.

AIso, for nuclear detection, the Cs 2 LiYCl 6 :Ce (CLYC) signal can react to both electrons and neutrons to create a pulse signal, and can therefore be applied to detect both neutrons and electrons, 156 but needs identification of the two particles by analyzing the shapes of the waves, that is n-γ ID. The traditional method has been the PSD (pulse shape discrimination) method, which is used to separate the waves of two particles by analyzing the distribution of the pulse information—such as amplitude, width, raise time, fall time, and the two particles that can be separated when the distribution has two separated Gaussian distributions. The traditional PSD can only analyze single-pulse waves, rather than multipulse waves, when two particles react with CLYC closely. But it can be solved by using an ANN method for classification of the six categories (n,γ,n + n,n + γ,γ + n,γ). Also, there are several parameters that could be used by AI to improve the reconstruction algorithm with high efficiency and less error.

AI-aided condensed matter physics

AI opens up a new avenue for physical science, especially when a trove of data is available. Recent works demonstrate that ML provides useful insights to improve the density functional theory (DFT), in which the single-electron picture of the Kohn-Sham scheme has the difficulty of taking care of the exchange and correlation effects of many-body systems. Yu et al. proposed a Bayesian optimization algorithm to fit the Hubbard U parameter, and the new method can find the optimal Hubbard U through a self-consistent process with good efficiency compared with the linear response method, 157 and boost the accuracy to the near-hybrid-functional-level. Snyder et al. developed an ML density functional for a 1D non-interacting non-spin-polarized fermion system to obtain significantly improved kinetic energy. This method enabled a direct approximation of the kinetic energy of a quantum system and can be utilized in orbital-free DFT modeling, and can even bypass the solving of the Kohn-Sham equation—while maintaining the precision to the quantum chemical level when a strong correlation term is included. Recently, FermiNet showed that the many-body quantum mechanics equations can be solved via AI. AI models also show advantages of capturing the interatom force field. In 2010, the Gaussian approximation potential (GAP) 158 was introduced as a powerful interatomic force field to describe the interactions between atoms. GAP uses kernel regression and invariant many-body representations, and performs quite well. For instance, it can simulate crystallization of amorphous crystals under high pressure fairly accurately. By employing the smooth overlap of the atomic position kernel (SOAP), 159 the accuracy of the potential can be further enhanced and, therefore, the SOAP-GAP can be viewed as a field-leading method for AI molecular dynamic simulation. There are also several other well-developed AI interatomic potentials out there, e.g., crystal graph CNNs provide a widely applicable way of vectorizing crystalline materials; SchNet embeds the continuous-filter convolutional layers into its DNNs for easing molecular dynamic as the potentials are space continuous; DimeNet constructs the directional message passing neural network by adding not only the bond length between atoms but also the bond angle, the dihedral angle, and the interactions between unconnected atoms into the model to obtain good accuracy.

AI helps explore the Universe

AI is one of the newest technologies, while astronomy is one of the oldest sciences. When the two meet, new opportunities for scientific breakthroughs are often triggered. Observations and data analysis play a central role in astronomy. The amount of data collected by modern telescopes has reached unprecedented levels, even the most basic task of constructing a catalog has become challenging with traditional source-finding tools. 160 Astronomers have developed automated and intelligent source-finding tools based on DL, which not only offer significant advantages in operational speed but also facilitate a comprehensive understanding of the Universe by identifying particular forms of objects that cannot be detected by traditional software and visual inspection. 160 , 161

More than a decade ago, a citizen science project called “Galaxy Zoo” was proposed to help label one million images of galaxies collected by the Sloan Digital Sky Survey (SDSS) by posting images online and recruiting volunteers. 162 Larger optical telescopes, in operation or under construction, produce data several orders of magnitude higher than SDSS. Even with volunteers involved, there is no way to analyze the vast amount of data received. The advantages of ML are not limited to source-finding and galaxy classification. In fact, it has a much wider range of applications. For example, CNN plays an important role in detecting and decoding gravitational wave signals in real time, reconstructing all parameters within 2 ms, while traditional algorithms take several days to accomplish the same task. 163 Such DL systems have also been used to automatically generate alerts for transients and track asteroids and other fast-moving near-Earth objects, improving detection efficiency by several orders of magnitude. In addition, astrophysicists are exploring the use of neural networks to measure galaxy clusters and study the evolution of the Universe.

In addition to the amazing speed, neural networks seem to have a deeper understanding of the data than expected and can recognize more complex patterns, indicating that the “machine” is evolving rather than just learning the characteristics of the input data.

AI in chemistry

Chemistry plays an important “central” role in other sciences 164 because it is the investigation of the structure and properties of matter, and identifies the chemical reactions that convert substances into to other substances. Accordingly, chemistry is a data-rich branch of science containing complex information resulting from centuries of experiments and, more recently, decades of computational analysis. This vast treasure trove of data is most apparent within the Chemical Abstract Services, which has collected more than 183 million unique organic and inorganic substances, including alloys, coordination compounds, minerals, mixtures, polymers, and salts, and is expanding by addition of thousands of additional new substances daily. 165 The unlimited complexity in the variety of material compounds explains why chemistry research is still a labor-intensive task. The level of complexity and vast amounts of data within chemistry provides a prime opportunity to achieve significant breakthroughs with the application of AI. First, the type of molecules that can be constructed from atoms are almost unlimited, which leads to unlimited chemical space 166 ; the interconnection of these molecules with all possible combinations of factors, such as temperature, substrates, and solvents, are overwhelmingly large, giving rise to unlimited reaction space. 167 Exploration of the unlimited chemical space and reaction space, and navigating to the optimum ones with the desired properties, is thus practically impossible solely from human efforts. Secondly, in chemistry, the huge assortment of molecules and the interplay of them with the external environments brings a new level of complexity, which cannot be simply predicted using physical laws. While many concepts, rules, and theories have been generalized from centuries of experience from studying trivial (i.e., single component) systems, nontrivial complexities are more likely as we discover that “more is different” in the words of Philip Warren Anderson, American physicist and Nobel Laureate. 168 Nontrivial complexities will occur when the scale changes, and the breaking of symmetry in larger, increasingly complex systems, and the rules will shift from quantitative to qualitative. Due to lack of systematic and analytical theory toward the structures, properties, and transformations of macroscopic substances, chemistry research is thus, incorrectly, guided by heuristics and fragmental rules accumulated over the previous centuries, yielding progress that only proceeds through trial and error. ML will recognize patterns from large amounts of data; thereby offering an unprecedented way of dealing with complexity, and reshaping chemistry research by revolutionizing the way in which data are used. Every sub-field of chemistry, currently, has utilized some form of AI, including tools for chemistry research and data generation, such as analytical chemistry and computational chemistry, as well as application to organic chemistry, catalysis, and medical chemistry, which we discuss herein.

AI breaks the limitations of manual feature selection methods

In analytical chemistry, the extraction of information has traditionally relied heavily on the feature selection techniques, which are based on prior human experiences. Unfortunately, this approach is inefficient, incomplete, and often biased. Automated data analysis based on AI will break the limitations of manual variable selection methods by learning from large amounts of data. Feature selection through DL algorithms enables information extraction from the datasets in NMR, chromatography, spectroscopy, and other analytical tools, 169 thereby improving the model prediction accuracy for analysis. These ML approaches will greatly accelerate the analysis of materials, leading to the rapid discovery of new molecules or materials. Raman scattering, for instance, since its discovery in the 1920s, has been widely employed as a powerful vibrational spectroscopy technology, capable of providing vibrational fingerprints intrinsic to analytes, thus enabling identification of molecules. 170 Recently, ML methods have been trained to recognize features in Raman (or SERS) spectra for the identity of an analyte by applying DL networks, including ANN, CNN, and fully convolutional network for feature engineering. 171 For example, Leong et al. designed a machine-learning-driven “SERS taster” to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at ppm levels. Principal-component analysis is employed for the discrimination of alcohols with varying degrees of substitution, and supported with vector machine discriminant analysis, is used to quantitatively classify all flavors with 100% accuracy. 172 Overall, AI techniques provide the first glimmer of hope for a universal method for spectral data analysis, which is fast, accurate, objective and definitive and with attractive advantages in a wide range of applications.

AI improves the accuracy and efficiency for various levels of computational theory

Complementary to analytical tools, computational chemistry has proven a powerful approach for using simulations to understand chemical properties; however, it is faced with an accuracy-versus-efficiency dilemma. This dilemma greatly limits the application of computational chemistry to real-world chemistry problems. To overcome this dilemma, ML and other AI methods are being applied to improve the accuracy and efficiency for various levels of theory used to describe the effects arising at different time and length scales, in the multi-scaling of chemical reactions. 173 Many of the open challenges in computational chemistry can be solved by ML approaches, for example, solving Schrödinger's equation, 174 developing atomistic 175 or coarse graining 176 potentials, constructing reaction coordinates, 177 developing reaction kinetics models, 178 and identifying key descriptors for computable properties. 179 In addition to analytical chemistry and computational chemistry, several disciplines of chemistry have incorporated AI technology to chemical problems. We discuss the areas of organic chemistry, catalysis, and medical chemistry as examples of where ML has made a significant impact. Many examples exist in literature for other subfields of chemistry and AI will continue to demonstrate breakthroughs in a wide range of chemical applications.

AI enables robotics capable of automating the synthesis of molecules

Organic chemistry studies the structure, property, and reaction of carbon-based molecules. The complexity of the chemical and reaction space, for a given property, presents an unlimited number of potential molecules that can be synthesized by chemists. Further complications are added when faced with the problems of how to synthesize a particular molecule, given that the process relies much on heuristics and laborious testing. Challenges have been addressed by researchers using AI. Given enough data, any properties of interest of a molecule can be predicted by mapping the molecular structure to the corresponding property using supervised learning, without resorting to physical laws. In addition to known molecules, new molecules can be designed by sampling the chemical space 180 using methods, such as autoencoders and CNNs, with the molecules coded as sequences or graphs. Retrosynthesis, the planning of synthetic routes, which was once considered an art, has now become much simpler with the help of ML algorithms. The Chemetica system, 181 for instance, is now capable of autonomous planning of synthetic routes that are subsequently proven to work in the laboratory. Once target molecules and the route of synthesis are determined, suitable reaction conditions can be predicted or optimized using ML techniques. 182

The integration of these AI-based approaches with robotics has enabled fully AI-guided robotics capable of automating the synthesis of small organic molecules without human intervention Figure 9 . 183 , 184

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A closed loop workflow to enable automatic and intelligent design, synthesis, and assay of molecules in organic chemistry by AI

AI helps to search through vast catalyst design spaces

Catalytic chemistry originates from catalyst technologies in the chemical industry for efficient and sustainable production of chemicals and fuels. Thus far, it is still a challenging endeavor to make novel heterogeneous catalysts with good performance (i.e., stable, active, and selective) because a catalyst's performance depends on many properties: composition, support, surface termination, particle size, particle morphology, atomic coordination environment, porous structure, and reactor during the reaction. The inherent complexity of catalysis makes discovering and developing catalysts with desired properties more dependent on intuition and experiment, which is costly and time consuming. AI technologies, such as ML, when combined with experimental and in silico high-throughput screening of combinatorial catalyst libraries, can aid catalyst discovery by helping to search through vast design spaces. With a well-defined structure and standardized data, including reaction results and in situ characterization results, the complex association between catalytic structure and catalytic performance will be revealed by AI. 185 , 186 An accurate descriptor of the effect of molecules, molecular aggregation states, and molecular transport, on catalysts, could also be predicted. With this approach, researchers can build virtual laboratories to develop new catalysts and catalytic processes.

AI enables screening of chemicals in toxicology with minimum ethical concerns

A more complicated sub-field of chemistry is medical chemistry, which is a challenging field due to the complex interactions between the exotic substances and the inherent chemistry within a living system. Toxicology, for instance, as a broad field, seeks to predict and eliminate substances (e.g., pharmaceuticals, natural products, food products, and environmental substances), which may cause harm to a living organism. Living organisms are already complex, nearly any known substance can cause toxicity at a high enough exposure because of the already inherent complexity within living organisms. Moreover, toxicity is dependent on an array of other factors, including organism size, species, age, sex, genetics, diet, combination with other chemicals, overall health, and/or environmental context. Given the scale and complexity of toxicity problems, AI is likely to be the only realistic approach to meet regulatory body requirements for screening, prioritization, and risk assessment of chemicals (including mixtures), therefore revolutionizing the landscape in toxicology. 187 In summary, AI is turning chemistry from a labor-intensive branch of science to a highly intelligent, standardized, and automated field, and much more can be achieved compared with the limitation of human labor. Underlying knowledge with new concepts, rules, and theories is expected to advance with the application of AI algorithms. A large portion of new chemistry knowledge leading to significant breakthroughs is expected to be generated from AI-based chemistry research in the decades to come.

Conclusions

This paper carries out a comprehensive survey on the development and application of AI across a broad range of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. Despite the fact that AI has been pervasively used in a wide range of applications, there still exist ML security risks on data and ML models as attack targets during both training and execution phases. Firstly, since the performance of an ML system is highly dependent on the data used to train it, these input data are crucial for the security of the ML system. For instance, adversarial example attacks 188 providing malicious input data often lead the ML system into making false judgments (predictions or categorizations) with small perturbations that are imperceptible to humans; data poisoning by intentionally manipulating raw, training, or testing data can result in a decrease in model accuracy or lead to other error-specific attack purposes. Secondly, ML model attacks include backdoor attacks on DL, CNN, and federated learning that manipulate the model's parameters directly, as well as model stealing attack, model inversion attack, and membership inference attack, which can steal the model parameters or leak the sensitive training data. While a number of defense techniques against these security threats have been proposed, new attack models that target ML systems are constantly emerging. Thus, it is necessary to address the problem of ML security and develop robust ML systems that remain effective under malicious attacks.

Due to the data-driven character of the ML method, features of the training and testing data must be drawn from the same distribution, which is difficult to guarantee in practice. This is because, in practical application, the data source might be different from that in the training dataset. In addition, the data feature distribution may drift over time, which leads to a decline of the performance of the model. Moreover, if the model is trained with only new data, it will lead to catastrophic “forgetting” of the model, which means the model only remembers the new features and forgets the previously learned features. To solve this problem, more and more scholars pay attention on how to make the model have the ability of lifelong learning, that is, a change in the computing paradigm from “offline learning + online reasoning” to “online continuous learning,” and thus give the model have the ability of lifelong learning, just like a human being.

Acknowledgments

This work was partially supported by the National Key R&D Program of China (2018YFA0404603, 2019YFA0704900, 2020YFC1807000, and 2020YFB1313700), the Youth Innovation Promotion Association CAS (2011225, 2012006, 2013002, 2015316, 2016275, 2017017, 2017086, 2017120, 2017204, 2017300, 2017399, 2018356, 2020111, 2020179, Y201664, Y201822, and Y201911), NSFC (nos. 11971466, 12075253, 52173241, and 61902376), the Foundation of State Key Laboratory of Particle Detection and Electronics (SKLPDE-ZZ-201902), the Program of Science & Technology Service Network of CAS (KFJ-STS-QYZX-050), the Fundamental Science Center of the National Nature Science Foundation of China (nos. 52088101 and 11971466), the Scientific Instrument Developing Project of CAS (ZDKYYQ20210003), the Strategic Priority Research Program (B) of CAS (XDB33000000), the National Science Foundation of Fujian Province for Distinguished Young Scholars (2019J06023), the Key Research Program of Frontier Sciences, CAS (nos. ZDBS-LY-7022 and ZDBS-LY-DQC012), the CAS Project for Young Scientists in Basic Research (no. YSBR-005). The study is dedicated to the 10th anniversary of the Youth Innovation Promotion Association of the Chinese Academy of Sciences.

Author contributions

Y.X., Q.W., Z.A., Fei W., C.L., Z.C., J.M.T., and J.Z. conceived and designed the research. Z.A., Q.W., Fei W., Libo.Z., Y.W., F.D., and C.W.-Q. wrote the “ AI in information science ” section. Xin.L. wrote the “ AI in mathematics ” section. J.Q., K.H., W.S., J.W., H.X., Y.H., and X.C. wrote the “ AI in medical science ” section. E.L., C.F., Z.Y., and M.L. wrote the “ AI in materials science ” section. Fang W., R.R., S.D., M.V., and F.K. wrote the “ AI in geoscience ” section. C.H., Z.Z., L.Z., T.Z., J.D., J.Y., L.L., M.L., and T.H. wrote the “ AI in life sciences ” section. Z.L., S.Q., and T.A. wrote the “ AI in physics ” section. X.L., B.Z., X.H., S.C., X.L., W.Z., and J.P.L. wrote the “ AI in chemistry ” section. Y.X., Q.W., and Z.A. wrote the “Abstract,” “ introduction ,” “ history of AI ,” and “ conclusions ” sections.

Declaration of interests

The authors declare no competing interests.

Published Online: October 28, 2021

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The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots

2020’s Top AI & Machine Learning Research Papers

November 24, 2020 by Mariya Yao

Despite the challenges of 2020, the AI research community produced a number of meaningful technical breakthroughs. GPT-3 by OpenAI may be the most famous, but there are definitely many other research papers worth your attention.

For example, teams from Google introduced a revolutionary chatbot, Meena, and EfficientDet object detectors in image recognition. Researchers from Yale introduced a novel AdaBelief optimizer that combines many benefits of existing optimization methods. OpenAI researchers demonstrated how deep reinforcement learning techniques can achieve superhuman performance in Dota 2.

To help you catch up on essential reading, we’ve summarized 10 important machine learning research papers from 2020. These papers will give you a broad overview of AI research advancements this year. Of course, there are many more breakthrough papers worth reading as well.

We have also published the top 10 lists of key research papers in natural language processing and computer vision . In addition, you can read our premium research summaries , where we feature the top 25 conversational AI research papers introduced recently.

Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries.

If you’d like to skip around, here are the papers we featured:

A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning
Efficiently Sampling Functions from Gaussian Process Posteriors
Dota 2 with Large Scale Deep Reinforcement Learning
Towards a Human-like Open-Domain Chatbot
Language Models are Few-Shot Learners
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
EfficientDet: Scalable and Efficient Object Detection
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

Best AI & ML Research Papers 2020

1. a distributed multi-sensor machine learning approach to earthquake early warning , by kévin fauvel, daniel balouek-thomert, diego melgar, pedro silva, anthony simonet, gabriel antoniu, alexandru costan, véronique masson, manish parashar, ivan rodero, and alexandre termier, original abstract .

Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to their propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data, consequently affecting the response time and the robustness of EEW systems.

In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.

Our Summary

The authors claim that traditional Earthquake Early Warning (EEW) systems that are based on seismometers, as well as recently introduced GPS systems, have their disadvantages with regards to predicting large and medium earthquakes respectively. Thus, the researchers suggest approaching an early earthquake prediction problem with machine learning by using the data from seismometers and GPS stations as input data. In particular, they introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, which is specifically tailored for efficient computation on large-scale distributed cyberinfrastructures. The evaluation demonstrates that the DMSEEW system is more accurate than other baseline approaches with regard to real-time earthquake detection.

What’s the core idea of this paper?

Seismometers have difficulty detecting large earthquakes because of their sensitivity to ground motion velocity.
GPS stations are ineffective in detecting medium earthquakes, as they are prone to producing lots of noisy data.
takes sensor-level class predictions from seismometers and GPS stations (i.e. normal activity, medium earthquake, large earthquake);
aggregates these predictions using a bag-of-words representation and defines a final prediction for the earthquake category.
Furthermore, they introduce a distributed cyberinfrastructure that can support the processing of high volumes of data in real time and allows the redirection of data to other processing data centers in case of disaster situations.

What’s the key achievement?

precision – 100% vs. 63.2%;
recall – 100% vs. 85.7%;
F1 score – 100% vs. 72.7%.
precision – 76.7% vs. 70.7%;
recall – 38.8% vs. 34.1%;
F1 score – 51.6% vs. 45.0%.

What does the AI community think?

The paper received an Outstanding Paper award at AAAI 2020 (special track on AI for Social Impact).

What are future research areas?

Evaluating DMSEEW response time and robustness via simulation of different scenarios in an existing EEW execution platform.
Evaluating the DMSEEW system on another seismic network.

2. Efficiently Sampling Functions from Gaussian Process Posteriors , by James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth

Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model’s success hinges upon its ability to faithfully represent predictive uncertainty. These problems typically exist as parts of larger frameworks, wherein quantities of interest are ultimately defined by integrating over posterior distributions. These quantities are frequently intractable, motivating the use of Monte Carlo methods. Despite substantial progress in scaling up Gaussian processes to large training sets, methods for accurately generating draws from their posterior distributions still scale cubically in the number of test locations. We identify a decomposition of Gaussian processes that naturally lends itself to scalable sampling by separating out the prior from the data. Building off of this factorization, we propose an easy-to-use and general-purpose approach for fast posterior sampling, which seamlessly pairs with sparse approximations to afford scalability both during training and at test time. In a series of experiments designed to test competing sampling schemes’ statistical properties and practical ramifications, we demonstrate how decoupled sample paths accurately represent Gaussian process posteriors at a fraction of the usual cost.

In this paper, the authors explore techniques for efficiently sampling from Gaussian process (GP) posteriors. After investigating the behaviors of naive approaches to sampling and fast approximation strategies using Fourier features, they find that many of these strategies are complementary. They, therefore, introduce an approach that incorporates the best of different sampling approaches. First, they suggest decomposing the posterior as the sum of a prior and an update. Then they combine this idea with techniques from literature on approximate GPs and obtain an easy-to-use general-purpose approach for fast posterior sampling. The experiments demonstrate that decoupled sample paths accurately represent GP posteriors at a much lower cost.

The introduced approach to sampling functions from GP posteriors centers on the observation that it is possible to implicitly condition Gaussian random variables by combining them with an explicit corrective term.
The authors translate this intuition to Gaussian processes and suggest decomposing the posterior as the sum of a prior and an update.
Building on this factorization, the researchers suggest an efficient approach for fast posterior sampling that seamlessly pairs with sparse approximations to achieve scalability both during training and at test time.
Introducing an easy-to-use and general-purpose approach to sampling from GP posteriors.
avoid many shortcomings of the alternative sampling strategies;
accurately represent GP posteriors at a much lower cost; for example, simulation of a well-known model of a biological neuron required only 20 seconds using decoupled sampling, while the iterative approach required 10 hours.
The paper received an Honorable Mention at ICML 2020.

Where can you get implementation code?

The authors released the implementation of this paper on GitHub .

3. Dota 2 with Large Scale Deep Reinforcement Learning , by Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław “Psyho” Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Susan Zhang

On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.

The OpenAI research team demonstrates that modern reinforcement learning techniques can achieve superhuman performance in such a challenging esports game as Dota 2. The challenges of this particular task for the AI system lies in the long time horizons, partial observability, and high dimensionality of observation and action spaces. To tackle this game, the researchers scaled existing RL systems to unprecedented levels with thousands of GPUs utilized for 10 months. The resulting OpenAI Five model was able to defeat the Dota 2 world champions and won 99.4% of over 7000 games played during the multi-day showcase.

The goal of the introduced OpenAI Five model is to find the policy that maximizes the probability of winning the game against professional human players, which in practice implies maximizing the reward function with some additional signals like characters dying, resources collected, etc.
While the Dota 2 engine runs at 30 frames per second, the OpenAI Five only acts on every 4th frame.
At each timestep, the model receives an observation with all the information available to human players (approximated in a set of data arrays) and returns a discrete action , which encodes the desired movement, attack, etc.
A policy is defined as a function from the history of observations to a probability distribution over actions that are parameterized as an LSTM with ~159M parameters.
The policy is trained using a variant of advantage actor critic, Proximal Policy Optimization.
The OpenAI Five model was trained for 180 days spread over 10 months of real time.

defeated the Dota 2 world champions in a best-of-three match (2–0);
won 99.4% of over 7000 games during a multi-day online showcase.
Applying introduced methods to other zero-sum two-team continuous environments.

What are possible business applications?

Tackling challenging esports games like Dota 2 can be a promising step towards solving advanced real-world problems using reinforcement learning techniques.

4. Towards a Human-like Open-Domain Chatbot , by Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, Quoc V. Le

We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of a human-like multi-turn conversation. Our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated.

In contrast to most modern conversational agents, which are highly specialized, the Google research team introduces a chatbot Meena that can chat about virtually anything. It’s built on a large neural network with 2.6B parameters trained on 341 GB of text. The researchers also propose a new human evaluation metric for open-domain chatbots, called Sensibleness and Specificity Average (SSA), which can capture important attributes for human conversation. They demonstrate that this metric correlates highly with perplexity, an automatic metric that is readily available. Thus, the Meena chatbot, which is trained to minimize perplexity, can conduct conversations that are more sensible and specific compared to other chatbots. Particularly, the experiments demonstrate that Meena outperforms existing state-of-the-art chatbots by a large margin in terms of the SSA score (79% vs. 56%) and is closing the gap with human performance (86%).

Despite recent progress, open-domain chatbots still have significant weaknesses: their responses often do not make sense or are too vague or generic.
Meena is built on a seq2seq model with Evolved Transformer (ET) that includes 1 ET encoder block and 13 ET decoder blocks.
The model is trained on multi-turn conversations with the input sequence including all turns of the context (up to 7) and the output sequence being the response.
making sense,
being specific.
The research team discovered that the SSA metric shows high negative correlation (R2 = 0.93) with perplexity, a readily available automatic metric that Meena is trained to minimize.
Proposing a simple human-evaluation metric for open-domain chatbots.
The best end-to-end trained Meena model outperforms existing state-of-the-art open-domain chatbots by a large margin, achieving an SSA score of 72% (vs. 56%).
Furthermore, the full version of Meena, with a filtering mechanism and tuned decoding, further advances the SSA score to 79%, which is not far from the 86% SSA achieved by the average human.
“Google’s “Meena” chatbot was trained on a full TPUv3 pod (2048 TPU cores) for 30 full days – that’s more than $1,400,000 of compute time to train this chatbot model.” – Elliot Turner, CEO and founder of Hyperia .
“So I was browsing the results for the new Google chatbot Meena, and they look pretty OK (if boring sometimes). However, every once in a while it enters ‘scary sociopath mode,’ which is, shall we say, sub-optimal” – Graham Neubig, Associate professor at Carnegie Mellon University .

Lowering the perplexity through improvements in algorithms, architectures, data, and compute.
Considering other aspects of conversations beyond sensibleness and specificity, such as, for example, personality and factuality.
Tackling safety and bias in the models.
further humanizing computer interactions;
improving foreign language practice;
making interactive movie and videogame characters relatable.
Considering the challenges related to safety and bias in the models, the authors haven’t released the Meena model yet. However, they are still evaluating the risks and benefits and may decide otherwise in the coming months.

5. Language Models are Few-Shot Learners , by Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei

Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions – something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10× more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3’s few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.

The OpenAI research team draws attention to the fact that the need for a labeled dataset for every new language task limits the applicability of language models. Considering that there is a wide range of possible tasks and it’s often difficult to collect a large labeled training dataset, the researchers suggest an alternative solution, which is scaling up language models to improve task-agnostic few-shot performance. They test their solution by training a 175B-parameter autoregressive language model, called GPT-3 , and evaluating its performance on over two dozen NLP tasks. The evaluation under few-shot learning, one-shot learning, and zero-shot learning demonstrates that GPT-3 achieves promising results and even occasionally outperforms the state of the art achieved by fine-tuned models.

The GPT-3 model uses the same model and architecture as GPT-2, including the modified initialization, pre-normalization, and reversible tokenization.
However, in contrast to GPT-2, it uses alternating dense and locally banded sparse attention patterns in the layers of the transformer, as in the Sparse Transformer .
Few-shot learning , when the model is given a few demonstrations of the task (typically, 10 to 100) at inference time but with no weight updates allowed.
One-shot learning , when only one demonstration is allowed, together with a natural language description of the task.
Zero-shot learning , when no demonstrations are allowed and the model has access only to a natural language description of the task.
On the CoQA benchmark, 81.5 F1 in the zero-shot setting, 84.0 F1 in the one-shot setting, and 85.0 F1 in the few-shot setting, compared to the 90.7 F1 score achieved by fine-tuned SOTA.
On the TriviaQA benchmark, 64.3% accuracy in the zero-shot setting, 68.0% in the one-shot setting, and 71.2% in the few-shot setting, surpassing the state of the art (68%) by 3.2%.
On the LAMBADA dataset, 76.2 % accuracy in the zero-shot setting, 72.5% in the one-shot setting, and 86.4% in the few-shot setting, surpassing the state of the art (68%) by 18%.
The news articles generated by the 175B-parameter GPT-3 model are hard to distinguish from real ones, according to human evaluations (with accuracy barely above the chance level at ~52%).
“The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.” – Sam Altman, CEO and co-founder of OpenAI .
“I’m shocked how hard it is to generate text about Muslims from GPT-3 that has nothing to do with violence… or being killed…” – Abubakar Abid, CEO and founder of Gradio .
“No. GPT-3 fundamentally does not understand the world that it talks about. Increasing corpus further will allow it to generate a more credible pastiche but not fix its fundamental lack of comprehension of the world. Demos of GPT-4 will still require human cherry picking.” – Gary Marcus, CEO and founder of Robust.ai .
“Extrapolating the spectacular performance of GPT3 into the future suggests that the answer to life, the universe and everything is just 4.398 trillion parameters.” – Geoffrey Hinton, Turing Award winner .
Improving pre-training sample efficiency.
Exploring how few-shot learning works.
Distillation of large models down to a manageable size for real-world applications.
The model with 175B parameters is hard to apply to real business problems due to its impractical resource requirements, but if the researchers manage to distill this model down to a workable size, it could be applied to a wide range of language tasks, including question answering, dialog agents, and ad copy generation.
The code itself is not available, but some dataset statistics together with unconditional, unfiltered 2048-token samples from GPT-3 are released on GitHub .

6. Beyond Accuracy: Behavioral Testing of NLP models with CheckList , by Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, Sameer Singh

Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.

The authors point out the shortcomings of existing approaches to evaluating performance of NLP models. A single aggregate statistic, like accuracy, makes it difficult to estimate where the model is failing and how to fix it. The alternative evaluation approaches usually focus on individual tasks or specific capabilities. To address the lack of comprehensive evaluation approaches, the researchers introduce CheckList , a new evaluation methodology for testing of NLP models. The approach is inspired by principles of behavioral testing in software engineering. Basically, CheckList is a matrix of linguistic capabilities and test types that facilitates test ideation. Multiple user studies demonstrate that CheckList is very effective at discovering actionable bugs, even in extensively tested NLP models.

The primary approach to the evaluation of models’ generalization capabilities, which is accuracy on held-out data, may lead to performance overestimation, as the held-out data often contains the same biases as the training data. Moreover, this single aggregate statistic doesn’t help much in figuring out where the NLP model is failing and how to fix these bugs.
The alternative approaches are usually designed for evaluation of specific behaviors on individual tasks and thus, lack comprehensiveness.
CheckList provides users with a list of linguistic capabilities to be tested, like vocabulary, named entity recognition, and negation.
Then, to break down potential capability failures into specific behaviors, CheckList suggests different test types , such as prediction invariance or directional expectation tests in case of certain perturbations.
Potential tests are structured as a matrix, with capabilities as rows and test types as columns.
The suggested implementation of CheckList also introduces a variety of abstractions to help users generate large numbers of test cases easily.
Evaluation of state-of-the-art models with CheckList demonstrated that even though some NLP tasks are considered “solved” based on accuracy results, the behavioral testing highlights many areas for improvement.
helps to identify and test for capabilities not previously considered;
results in more thorough and comprehensive testing for previously considered capabilities;
helps to discover many more actionable bugs.
The paper received the Best Paper Award at ACL 2020, the leading conference in natural language processing.
CheckList can be used to create more exhaustive testing for a variety of NLP tasks.
Such comprehensive testing that helps in identifying many actionable bugs is likely to lead to more robust NLP systems.
The code for testing NLP models with CheckList is available on GitHub .

7. EfficientDet: Scalable and Efficient Object Detection , by Mingxing Tan, Ruoming Pang, Quoc V. Le

Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDet-D7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4×–9× smaller and using 13×–42× fewer FLOPs than previous detectors. Code is available on https://github.com/google/automl/tree/master/efficientdet .

The large size of object detection models deters their deployment in real-world applications such as self-driving cars and robotics. To address this problem, the Google Research team introduces two optimizations, namely (1) a weighted bi-directional feature pyramid network (BiFPN) for efficient multi-scale feature fusion and (2) a novel compound scaling method. By combining these optimizations with the EfficientNet backbones, the authors develop a family of object detectors, called EfficientDet . The experiments demonstrate that these object detectors consistently achieve higher accuracy with far fewer parameters and multiply-adds (FLOPs).

A weighted bi-directional feature pyramid network (BiFPN) for easy and fast multi-scale feature fusion. It learns the importance of different input features and repeatedly applies top-down and bottom-up multi-scale feature fusion.
A new compound scaling method for simultaneous scaling of the resolution, depth, and width for all backbone, feature network, and box/class prediction networks.
These optimizations, together with the EfficientNet backbones, allow the development of a new family of object detectors, called EfficientDet .
the EfficientDet model with 52M parameters gets state-of-the-art 52.2 AP on the COCO test-dev dataset, outperforming the previous best detector with 1.5 AP while being 4× smaller and using 13× fewer FLOPs;
with simple modifications, the EfficientDet model achieves 81.74% mIOU accuracy, outperforming DeepLabV3+ by 1.7% on Pascal VOC 2012 semantic segmentation with 9.8x fewer FLOPs;
the EfficientDet models are up to 3× to 8× faster on GPU/CPU than previous detectors.
The paper was accepted to CVPR 2020, the leading conference in computer vision.
The high level of interest in the code implementations of this paper makes this research one of the highest-trending papers introduced recently.
The high accuracy and efficiency of the EfficientDet detectors may enable their application for real-world tasks, including self-driving cars and robotics.
The authors released the official TensorFlow implementation of EfficientDet.
The PyTorch implementation of this paper can be found here and here .

8. Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild , by Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences.

The research group from the University of Oxford studies the problem of learning 3D deformable object categories from single-view RGB images without additional supervision. To decompose the image into depth, albedo, illumination, and viewpoint without direct supervision for these factors, they suggest starting by assuming objects to be symmetric. Then, considering that real-world objects are never fully symmetrical, at least due to variations in pose and illumination, the researchers augment the model by explicitly modeling illumination and predicting a dense map with probabilities that any given pixel has a symmetric counterpart. The experiments demonstrate that the introduced approach achieves better reconstruction results than other unsupervised methods. Moreover, it outperforms the recent state-of-the-art method that leverages keypoint supervision.

no access to 2D or 3D ground truth information such as keypoints, segmentation, depth maps, or prior knowledge of a 3D model;
using an unconstrained collection of single-view images without having multiple views of the same instance.
leveraging symmetry as a geometric cue to constrain the decomposition;
explicitly modeling illumination and using it as an additional cue for recovering the shape;
augmenting the model to account for potential lack of symmetry – particularly, predicting a dense map that contains the probability of a given pixel having a symmetric counterpart in the image.
Qualitative evaluation of the suggested approach demonstrates that it reconstructs 3D faces of humans and cats with high fidelity, containing fine details of the nose, eyes, and mouth.
The method reconstructs higher-quality shapes compared to other state-of-the-art unsupervised methods, and even outperforms the DepthNet model, which uses 2D keypoint annotations for depth prediction.

The paper received the Best Paper Award at CVPR 2020, the leading conference in computer vision.
Reconstructing more complex objects by extending the model to use either multiple canonical views or a different 3D representation, such as a mesh or a voxel map.
Improving model performance under extreme lighting conditions and for extreme poses.
The implementation code and demo are available on GitHub .

9. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale , by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer can perform very well on image classification tasks when applied directly to sequences of image patches. When pre-trained on large amounts of data and transferred to multiple recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer attain excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

The authors of this paper show that a pure Transformer can perform very well on image classification tasks. They introduce Vision Transformer (ViT) , which is applied directly to sequences of image patches by analogy with tokens (words) in NLP. When trained on large datasets of 14M–300M images, Vision Transformer approaches or beats state-of-the-art CNN-based models on image recognition tasks. In particular, it achieves an accuracy of 88.36% on ImageNet, 90.77% on ImageNet-ReaL, 94.55% on CIFAR-100, and 77.16% on the VTAB suite of 19 tasks.

When applying Transformer architecture to images, the authors follow as closely as possible the design of the original Transformer designed for NLP.
splitting images into fixed-size patches;
linearly embedding each of them;
adding position embeddings to the resulting sequence of vectors;
feeding the patches to a standard Transformer encoder;
adding an extra learnable ‘classification token’ to the sequence.
Similarly to Transformers in NLP, Vision Transformer is typically pre-trained on large datasets and fine-tuned to downstream tasks.
88.36% on ImageNet;
90.77% on ImageNet-ReaL;
94.55% on CIFAR-100;
97.56% on Oxford-IIIT Pets;
99.74% on Oxford Flowers-102;
77.16% on the VTAB suite of 19 tasks.

The paper is trending in the AI research community, as evident from the repository stats on GitHub .
It is also under review for ICLR 2021 , one of the key conferences in deep learning.
Applying Vision Transformer to other computer vision tasks, such as detection and segmentation.
Exploring self-supervised pre-training methods.
Analyzing the few-shot properties of Vision Transformer.
Exploring contrastive pre-training.
Further scaling ViT.
Thanks to their efficient pre-training and high performance, Transformers may substitute convolutional networks in many computer vision applications, including navigation, automatic inspection, and visual surveillance.
The PyTorch implementation of Vision Transformer is available on GitHub .

10. AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients , by Juntang Zhuang, Tommy Tang, Sekhar Tatikonda, Nicha Dvornek, Yifan Ding, Xenophon Papademetris, James S. Duncan

Most popular optimizers for deep learning can be broadly categorized as adaptive methods (e.g. Adam) or accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum). For many models such as convolutional neural networks (CNNs), adaptive methods typically converge faster but generalize worse compared to SGD; for complex settings such as generative adversarial networks (GANs), adaptive methods are typically the default because of their stability. We propose AdaBelief to simultaneously achieve three goals: fast convergence as in adaptive methods, good generalization as in SGD, and training stability. The intuition for AdaBelief is to adapt the step size according to the “belief” in the current gradient direction. Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step. We validate AdaBelief in extensive experiments, showing that it outperforms other methods with fast convergence and high accuracy on image classification and language modeling. Specifically, on ImageNet, AdaBelief achieves comparable accuracy to SGD. Furthermore, in the training of a GAN on Cifar10, AdaBelief demonstrates high stability and improves the quality of generated samples compared to a well-tuned Adam optimizer. Code is available at https://github.com/juntang-zhuang/Adabelief-Optimizer .

The researchers introduce AdaBelief , a new optimizer, which combines the high convergence speed of adaptive optimization methods and good generalization capabilities of accelerated stochastic gradient descent (SGD) schemes. The core idea behind the AdaBelief optimizer is to adapt step size based on the difference between predicted gradient and observed gradient: the step is small if the observed gradient deviates significantly from the prediction, making us distrust this observation, and the step is large when the current observation is close to the prediction, making us believe in this observation. The experiments confirm that AdaBelief combines fast convergence of adaptive methods, good generalizability of the SGD family, and high stability in the training of GANs.

The idea of the AdaBelief optimizer is to combine the advantages of adaptive optimization methods (e.g., Adam) and accelerated SGD optimizers. Adaptive methods typically converge faster, while SGD optimizers demonstrate better generalization performance.
If the observed gradient deviates greatly from the prediction, we have a weak belief in this observation and take a small step.
If the observed gradient is close to the prediction, we have a strong belief in this observation and take a large step.
fast convergence, like adaptive optimization methods;
good generalization, like the SGD family;
training stability in complex settings such as GAN.
In image classification tasks on CIFAR and ImageNet, AdaBelief demonstrates as fast convergence as Adam and as good generalization as SGD.
It outperforms other methods in language modeling.
In the training of a WGAN , AdaBelief significantly improves the quality of generated images compared to Adam.
The paper was accepted to NeurIPS 2020, the top conference in artificial intelligence.
It is also trending in the AI research community, as evident from the repository stats on GitHub .
AdaBelief can boost the development and application of deep learning models as it can be applied to the training of any model that numerically estimates parameter gradient.
Both PyTorch and Tensorflow implementations are released on GitHub.

If you like these research summaries, you might be also interested in the following articles:

GPT-3 & Beyond: 10 NLP Research Papers You Should Read
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About Mariya Yao

Mariya is the co-author of Applied AI: A Handbook For Business Leaders and former CTO at Metamaven. She "translates" arcane technical concepts into actionable business advice for executives and designs lovable products people actually want to use. Follow her on Twitter at @thinkmariya to raise your AI IQ.

May 16, 2021 at 8:13 pm

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March 16, 2024 at 10:58 pm

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March 21, 2024 at 5:48 pm

2020’s Top AI & Machine Learning Research Papers mytgpczlq http://www.gabu6e0lozi87m5i503901r03g7p5ec4s.org/ [url=http://www.gabu6e0lozi87m5i503901r03g7p5ec4s.org/]umytgpczlq[/url] amytgpczlq

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change.

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

The influence of AI in scientific and academic research is an exciting development, opening the doors to more efficient, comprehensive, and rigorous exploration.

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my Youtube channel.

Best ChatGPT interface – Chat with PDFs/websites and more

I get more out of ChatGPT with HeyGPT . It can do things that ChatGPT cannot which makes it really valuable for researchers.

Use your own OpenAI API key ( h e re ). No login required. Access ChatGPT anytime, including peak periods. Faster response time. Unlock advanced functionalities with HeyGPT Ultra for a one-time lifetime subscription

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

Elicit – https://elicit.org
Supersymmetry.ai: https://www.supersymmetry.ai
Semantic Scholar: https://www.semanticscholar.org
Connected Papers – https://www.connectedpapers.com/
Research rabbit – https://www.researchrabbit.ai/
Laser AI – https://laser.ai/
Litmaps – https://www.litmaps.com
Inciteful – https://inciteful.xyz/
Scite – https://scite.ai/
System – https://www.system.com

If you like AI tools you may want to check out this article:

How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research.

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from.

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

Bit AI – https://bit.ai/
Consensus – https://consensus.app/
Exper AI – https://www.experai.com/
Hey Science (in development) – https://www.heyscience.ai/
Iris AI – https://iris.ai/
PapersGPT (currently in development) – https://jessezhang.org/llmdemo
Research Buddy – https://researchbuddy.app/
Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in.

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

Open Read – https://www.openread.academy
Chat PDF – https://www.chatpdf.com
Explain Paper – https://www.explainpaper.com
Humata – https://www.humata.ai/
Lateral AI – https://www.lateral.io/
Paper Brain – https://www.paperbrain.study/
Scholarcy – https://www.scholarcy.com/
SciSpace Copilot – https://typeset.io/
Unriddle – https://www.unriddle.ai/
Sharly.ai – https://www.sharly.ai/

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

Paper Wizard – https://paperwizard.ai/
Jenny.AI https://jenni.ai/ (20% off with code ANDY20)
Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

Paper Pal – https://paperpal.com/
Writefull – https://www.writefull.com/
Trinka – https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

Granted AI – https://grantedai.com/
Grantable – https://grantable.co/

Free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

Elicit – https://elicit.org
Connected Papers – https://www.connectedpapers.com/
Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

We are here to help you navigate Academia as painlessly as possible. We are supported by our readers and by visiting you are helping us earn a small amount through ads and affiliate revenue - Thank you!

2024 © Academia Insider

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AI has moved into its era of deployment; throughout 2022 and the beginning of 2023, new large-scale AI models have been released every month. These models, such as ChatGPT, Stable Diffusion, Whisper, and DALL-E 2, are capable of an increasingly broad range of tasks, from text manipulation and analysis, to image generation, to unprecedentedly good speech recognition. These systems demonstrate capabilities in question answering, and the generation of text, image, and code unimagined a decade ago, and they outperform the state of the art on many benchmarks, old and new. However, they are prone to hallucination, routinely biased, and can be tricked into serving nefarious aims, highlighting the complicated ethical challenges associated with their deployment.

Although 2022 was the first year in a decade where private AI investment decreased, AI is still a topic of great interest to policymakers, industry leaders, researchers, and the public. Policymakers are talking about AI more than ever before. Industry leaders that have integrated AI into their businesses are seeing tangible cost and revenue benefits. The number of AI publications and collaborations continues to increase. And the public is forming sharper opinions about AI and which elements they like or dislike.

AI will continue to improve and, as such, become a greater part of all our lives. Given the increased presence of this technology and its potential for massive disruption, we should all begin thinking more critically about how exactly we want AI to be developed and deployed. We should also ask questions about who is deploying it—as our analysis shows, AI is increasingly defined by the actions of a small set of private sector actors, rather than a broader range of societal actors. This year’s AI Index paints a picture of where we are so far with AI, in order to highlight what might await us in the future.

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New report highlights global strategies for accelerating AI in science and research

by International Science Council Regional Focal Point for Asia and the Pacific

A comprehensive analysis of the integration of artificial intelligence in science and research across various countries addresses both the advancements made and the challenges faced in this field.

While advancements in AI have huge implications for national R&D systems, very little is known about how governments plan to accelerate the uptake of AI by science and research institutions . In the report , "Preparing National Research Ecosystems for AI: Strategies and Progress in 2024," the International Science Council's Center for Science Futures addresses this knowledge gap by presenting a review of the existing literature on this topic, as well as a series of country case studies.

This working paper provides new insights and resources from countries from all regions of the world, at various stages of integrating AI into their research ecosystems:

Australia: Preparing for human-centric use of artificial intelligence
Benin: Anticipating the impacts of artificial intelligence on West Africa's aspiring digital services hub
Brazil: Reaping the benefits of artificial intelligence with some cautionary notes
Cambodia: Seeking artificial intelligence approaches to national research missions
Chile: Finding possibilities to apply artificial intelligence in an existing research financing ecosystem
China: Promoting the Artificial Intelligence for Science approach
India: Gaining insights into transformative technologies and their social integration
Malaysia: Enabling the Fourth Industrial Revolution
Mexico: Creating a national lead agency for artificial intelligence
Oman: Fostering innovation through an Executive Program
Uruguay: Following a roadmap to prepare national science systems for artificial intelligence
Uzbekistan: Building the right conditions and skills for artificial intelligence

Country-based experts who authored the case studies are at the forefront of integrating AI into their national science systems shaping the future of innovation and discovery.

"It's tempting to focus on the experiences of the usual AI powerhouses, but hardly any other country can emulate the U.S. or China when it comes to AI or the size of their research ecosystems.

"This paper, however, provides visions of the ambitions, achievements and challenges ahead worldwide. It will be useful to decision-makers from a much larger pool of countries," shared one of the co-authors Nurfadhlina Mohd Sharef, from the Academy of Sciences Malaysia.

The paper not only serves as a critical source of first-hand information—it makes an urgent call for continued discussion and collaboration between countries as they introduce AI in their research priorities.

"This is the beginning of a conversation. Our ambition with this paper is not only to document current initiatives, but also support the collective journey to better prepare for this critical technological transformation of science systems. Ultimately, this is about making sure that AI works for science," says Mathieu Denis, Senior Director of the International Science Council and Head of the ISC Center for Science Futures.

In the coming months, the ISC Center for Science Futures will continue engaging with experts from different countries around the world, seeking feedback and recommendations for additional countries to include in the follow up version of the paper, coming out in the second half of 2024.

This effort reflects the growing recognition of AI's transformative potential in the scientific community and the need for informed, collaborative strategies to harness its benefits in and for science.

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Large language models use a surprisingly simple mechanism to retrieve some stored knowledge

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Illustration of a blue robot-man absorbing and generating info. On left are research and graph icons going into his brain. On right are speech bubble icons, as if in conversation.

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Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex. Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don’t fully grasp how they work.

In an effort to better understand what is going on under the hood, researchers at MIT and elsewhere studied the mechanisms at work when these enormous machine-learning models retrieve stored knowledge.

They found a surprising result: Large language models (LLMs) often use a very simple linear function to recover and decode stored facts. Moreover, the model uses the same decoding function for similar types of facts. Linear functions, equations with only two variables and no exponents, capture the straightforward, straight-line relationship between two variables.

The researchers showed that, by identifying linear functions for different facts, they can probe the model to see what it knows about new subjects, and where within the model that knowledge is stored.

Using a technique they developed to estimate these simple functions, the researchers found that even when a model answers a prompt incorrectly, it has often stored the correct information. In the future, scientists could use such an approach to find and correct falsehoods inside the model, which could reduce a model’s tendency to sometimes give incorrect or nonsensical answers.

“Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them. This is one instance of that,” says Evan Hernandez, an electrical engineering and computer science (EECS) graduate student and co-lead author of a paper detailing these findings .

Hernandez wrote the paper with co-lead author Arnab Sharma, a computer science graduate student at Northeastern University; his advisor, Jacob Andreas, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); senior author David Bau, an assistant professor of computer science at Northeastern; and others at MIT, Harvard University, and the Israeli Institute of Technology. The research will be presented at the International Conference on Learning Representations.

Finding facts

Most large language models, also called transformer models, are neural networks . Loosely based on the human brain, neural networks contain billions of interconnected nodes, or neurons, that are grouped into many layers, and which encode and process data.

Much of the knowledge stored in a transformer can be represented as relations that connect subjects and objects. For instance, “Miles Davis plays the trumpet” is a relation that connects the subject, Miles Davis, to the object, trumpet.

As a transformer gains more knowledge, it stores additional facts about a certain subject across multiple layers. If a user asks about that subject, the model must decode the most relevant fact to respond to the query.

If someone prompts a transformer by saying “Miles Davis plays the. . .” the model should respond with “trumpet” and not “Illinois” (the state where Miles Davis was born).

“Somewhere in the network’s computation, there has to be a mechanism that goes and looks for the fact that Miles Davis plays the trumpet, and then pulls that information out and helps generate the next word. We wanted to understand what that mechanism was,” Hernandez says.

The researchers set up a series of experiments to probe LLMs, and found that, even though they are extremely complex, the models decode relational information using a simple linear function. Each function is specific to the type of fact being retrieved.

For example, the transformer would use one decoding function any time it wants to output the instrument a person plays and a different function each time it wants to output the state where a person was born.

The researchers developed a method to estimate these simple functions, and then computed functions for 47 different relations, such as “capital city of a country” and “lead singer of a band.”

While there could be an infinite number of possible relations, the researchers chose to study this specific subset because they are representative of the kinds of facts that can be written in this way.

They tested each function by changing the subject to see if it could recover the correct object information. For instance, the function for “capital city of a country” should retrieve Oslo if the subject is Norway and London if the subject is England.

Functions retrieved the correct information more than 60 percent of the time, showing that some information in a transformer is encoded and retrieved in this way.

“But not everything is linearly encoded. For some facts, even though the model knows them and will predict text that is consistent with these facts, we can’t find linear functions for them. This suggests that the model is doing something more intricate to store that information,” he says.

Visualizing a model’s knowledge

They also used the functions to determine what a model believes is true about different subjects.

In one experiment, they started with the prompt “Bill Bradley was a” and used the decoding functions for “plays sports” and “attended university” to see if the model knows that Sen. Bradley was a basketball player who attended Princeton.

“We can show that, even though the model may choose to focus on different information when it produces text, it does encode all that information,” Hernandez says.

They used this probing technique to produce what they call an “attribute lens,” a grid that visualizes where specific information about a particular relation is stored within the transformer’s many layers.

Attribute lenses can be generated automatically, providing a streamlined method to help researchers understand more about a model. This visualization tool could enable scientists and engineers to correct stored knowledge and help prevent an AI chatbot from giving false information.

In the future, Hernandez and his collaborators want to better understand what happens in cases where facts are not stored linearly. They would also like to run experiments with larger models, as well as study the precision of linear decoding functions.

“This is an exciting work that reveals a missing piece in our understanding of how large language models recall factual knowledge during inference. Previous work showed that LLMs build information-rich representations of given subjects, from which specific attributes are being extracted during inference. This work shows that the complex nonlinear computation of LLMs for attribute extraction can be well-approximated with a simple linear function,” says Mor Geva Pipek, an assistant professor in the School of Computer Science at Tel Aviv University, who was not involved with this work.

This research was supported, in part, by Open Philanthropy, the Israeli Science Foundation, and an Azrieli Foundation Early Career Faculty Fellowship.

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Researchers at MIT have found that large language models mimic intelligence using linear functions, reports Kyle Wiggers for TechCrunch . “Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them,” writes Wiggers.

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AI agents help explain other AI systems

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3 Questions: Jacob Andreas on large language models

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Gen AI field experiment shows mixed results in helping small businesses grow

A woman wearing red walks past an internet cafe in the Kibera neighborhood of Nairobi, Kenya.

While generative AI may hold promise as an efficient way to help small businesses grow, a study of entrepreneurs in Kenya found real-world limitations for those businesses that need it most.

The more successful entrepreneurs in the study were able to get a 15% performance boost after consulting an AI-powered business mentor. But the low performers did worse, seeing an 8% drop in revenue.

The difference, the researchers found, was that the high performers tended to ask for help on relatively straightforward tasks while those who were struggling sought help with more challenging tasks.

“Generative AI has the potential to significantly influence business performance,” says Nicholas Otis , a doctoral candidate at the Haas School of Business and the paper’s primary researcher. “Our results suggest that whether this impact is positive or negative depends on the tasks that entrepreneurs select for AI assistance.”

The five-month randomized control trial included 640 Kenyan entrepreneurs running fast food joints, poultry farms, cybershops for computer services, and a range of other businesses. Otis ran the study with Berkeley Haas assistant professors Solène Delecourt and David Holtz , as well as Harvard Business School doctoral candidate Rowan Clarke and associate professor Rembrand Koning.

AI business mentor

The researchers spent months building a GPT-4-powered AI business “mentor” that entrepreneurs interacted with via WhatsApp, which is used by 90% of Kenyans as a low-cost messaging platform. The researchers tailored the AI to the Kenyan business environment, and programmed it to give multiple pieces of practical advice for each prompt, with details on implementation. The participating entrepreneurs were then randomly assigned to receive a standard business guide or to work with the AI.

The entrepreneurs could use the AI mentor as much as they wanted on any questions that arose, and they used it in a variety of ways. For example, a restaurant owner considering changing a menu asked for help in thinking through the possibilities and uncertainties in making the decision. In another example, a business owner selling wholesale and retail milk asked for help in expanding offerings to increase profits.

No effect on average

Over a period of five months, the researchers gathered over 4,000 data points on firm performance and thousands of interactions with the gen-AI mentor. In their first analysis, the researchers found no evidence, on average, of a positive effect on business performance from those with access to the AI mentor.

But when they split the sample between those whose business performance was above and below the median at the start of the experiment, differences emerged. As noted, the above-median performers saw profits or revenue climb by 15%, whereas the low-performers’ revenues sagged by 8%.

The researchers found no difference in the number or general quality of the questions asked by the two groups. Rather, the content of the questions differed: Low performers, already struggling with weaker revenues and profits, sought advice on difficult tasks that would be challenging for AI—or even humans— to solve. For example, a farm might face stiff competition or a drought, or a business might need capital to survive.

“…Whether by choice or necessity, low-performing entrepreneurs in our sample asked the AI mentor for assistance with more challenging tasks than high performers,” the researchers wrote.

Implications

The results contrast with recent research that found college-educated workers using gen AI were more productive on well-defined tasks—especially benefitting those with the weakest skills. Another recent paper found gen AI can increase productivity for low-skilled workers, thus reducing disparities overall.

Overall, the researchers conclude that generative AI has the potential to benefit millions of companies in emerging economies through personalized advice. But it also has the potential to widen the gap between high-performers—who could address weaknesses and surge further ahead—and those who are struggling.

“This suggests that for gen AI to really add value to entrepreneurs in more open-ended contexts, they would also need expanded access to complementary skills training and resources—including financial resources,” Holtz says.

Even so, a carefully implemented AI intervention does hold some promise for business development, Delecourt adds.

“An optimistic way to view our results is that we had a positive effect for a subset of the population, with a very low-cost intervention,” she says. “It’s just not a one-size-fits-all solution.”

Read the full paper:

“ The Uneven Impact of Generative AI on Entrepreneurial Performance ” By Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning February 2024

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Research on LLMs performing system development

Excited to share our two latest papers on the role of GPT beyond being an assistant, acting as an engineer in Autonomous Vehicles Design and Development tasks (e.g., analysis, requirements engineering) which both are available in arXiv: 1- “Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models” - Discover how safety analysis can be performed by AI in the automotive industry. (Accepted in CAIN’2024)

2- “Engineering Safety Requirements for Autonomous Driving with Large Language Models” - Learn how to leverage AI to define safety requirements for autonomous vehicles. (Accepted in RE’2024)

10 Best AI Tools for Academic Research in 2024

There is no denying that artificial intelligence is gaining popularity day by day. The AI tool is widely used in marketing, booking agents, self-driving cars, robots, intelligent assistants, and social media monitoring. Best AI Tools for academic research have entered the education system.

With this tool, you can write academic research papers efficiently. You do not have to dust endless libraries and pages to write a paper; instead, you can use the AI tool to write educational research papers. We have done this job for you if you need to know which tool to choose. Learn about the top 10 in the article list.

This post discusses 10 AI tools to boost academic research.

Top 10 AI Tools for Academic Research in 2024

Semantic scholar, google scholar, how to choose the best ai tools for academic research, what is the best ai tool for academic research, faqs – best ai tools for academic research.

The following are the ten best tools for academic research in 2024

Semantic Scholar is one of the Top AI tools for academic research widely used by students who are pursuing computer science, biomedical science, and neuroscience. It uses natural language processing to analyze academic papers to find relevant literature.

Deliver results by understanding the context of the scientific paper.
Gives you access to over 200 million documents through different data sources and web crawls.
Auto-generate one-line summaries for each article to choose the one with more in-depth information about the topic
Semantic readers will offer you comprehension of scientific literature.
Refine search results for greater efficiency and relevance.
Continuously improves the tool based on user feedback and provides a personalized user experience.
The summary has accuracy issues and AI-generated citations.
It only gives you access to the full text of some papers.

Pricing: Starts at free.

Link: https://www.semanticscholar.org/

Google Scholar is among the best academic research AI tools available to students and scholars. The simple interface has no complex navigation and generates the results by typing the topics.

Help researchers get updated articles, research papers, literature, and conference papers.
Highlight the search results with tags like PDF, HTML, and BOOK to search for the results manually.
Reliable sources to find citations and relevant publications
Search for specific topics, find relevant searches, and access full-length articles for academic studies.
Use natural language searching to find academic and literature topics.
Allow your search for gray literature for systematic reviews.
The content you find will not be reviewed thoroughly, and there are concerns about source credibility.
Missed to show significant literature topics.

Pricing: Free

Link : https://scholar.google.com/

Consensus is one of the best AI tools for academic research , and it gathers information from published material and peer-reviewed articles. The tool is helpful for those who want to understand scientific subjects thoroughly.

Scan peer-reviewed research to offer only trustworthy and accurate research articles.
Web indexing will use artificial intelligence to give relevant articles.
Give solutions for scientific queries to help researchers.
Extract conclusions from peer-reviewed research and provide access to reliable information from famous researchers.
Natural language processing is used to analyze data and verify the source.
Generate a summary of research queries and help get information for the early research stage.
Favour only for STEM and business fields, not humanities and fine arts.
Not suitable for rigorous and reproducible research works.
Premium – $8.99/month, billed annually.
Enterprise – Contact sales for a custom plan.

Link : https://consensus.app/

Trinka is one of the best academic research AI tools , and it is widely used for technical and academic writing. The tool offers 3000 grammar checks and maintains the tone and style of every article.

Help in preparing precise and quality thesis papers for different projects.
Identify the grammar, spelling, tone, and style and correct them.
It makes academic papers compliant with AMA and APA style guides.
Offer readiness for publication checks and auto-file edits to auto-edit the files.
Save time in checking grammar while doing academic writing.
Let you check grammar and correct spelling and offer context suggestions based on your writing style.
The response time of the tool could be faster, which would cause a hindrance to quick feedback needs.
It may be challenging to understand the technical jargon.
Basic : Free.
Premium : INR 626.67/month.
Premium Plus : INR 979.16/month.
Enterprise : Contact sales for a custom plan.

Link : https://www.trinka.ai/

Mendeley is the best academic research too l that can effectively reference research papers. It organizes PDFs, crafts bibliographies, and annotates scholarly documents accurately.

Offer collaboration features to allow team members to collaborate, share work, and find articles of interest.
Integrate with standard academic procedures for managing research papers.
The web importer plugin will let you import all documents in one place
Search for annotations and notes in PDF documents with ease.
Offer citation styles for journals and boost citation efficiency.
Organize and share references for collaborative research.
Do not make PDF annotations as expected.
Users commonly face server downtime and syncing errors.

Link: https://www.mendeley.com/

Scholarcy is the best AI tool for academic research to automate reading, summarizing, and extracting critical information from scholarly articles. It will recognize figures and tables and gather critical concepts of a topic.

Citation features organize and cite the sources used in the research work.
The tool will highlight critical contributions to the topics you are searching for research without miss
Create flashcards to help users access research papers with ease.
Deepen and fasten your understanding of research on the topic you are searching for.
Summarize the topics of research papers to save time and effort.
Offer links to the cited resources to access the research material.
The essay summary may need to be more precise, which may result in plagiarism.
The AI-generated summary will only cover some of the critical points of the research paper.
Scholarly plus : $4.99/month.

Link: https://www.scholarcy.com/

Knewton is an AI-driven platform and is an AI-powered research tool for students and scholars to personalize their learning experience. It will generate educational content to match your learning style and needs.

Change the educational content according to the student’s learning style.
Analyze the student’s performance and give them strengths and weaknesses in real-time.
It contains fun and interactive content with videos, games, and puzzles to make learning engaging.
Courses available in various subjects that help you learn quickly whatever you want.
Provide you with a personalized learning experience with the help of adaptive learning.
Offer student interaction to keep cheating at bay by offering different questions to each student.
The problems in the tool are generic and need to align with the curriculum.
Offer feedback and flexible assessment options that impact students’ learning.
Monthly : $10.95+taxes.
Single-term – $44.95+taxes.
Altapass – $89.95+taxes.

Link: https://support.knewton.com/s/

Elicit is one of the 10 best AI tools for academic research in 2024 , which is suitable for researchers doing qualitative studies. It can process and analyze data and discover crucial themes, emotions, and recurring patterns.

Generate summaries and visualizations to interpret the data effectively.
Automate the process of extracting data and summarizing the findings.
It contains 200 million research papers and summarizes them in a single sentence.
It is easy to extract the details of the research paper into a table and easily search for the required information.
Automate the entire literature review process and save time.
Find the appropriate papers that you might have missed to search with the keyword.
Refrain from replacing critical thinking and judgment of humans while evaluating the quality of the paper.
Complex questions would need deep analysis and synthesis.
Plus : $10/month.

Link : https://elicit.com/

IBM Watson is one of the free AI tools for academic research that will use Watson’s natural language processing and Watson’s discovery to find relevant articles. It streamlines the research process by thoroughly analyzing complicated datasets.

Use NLP and other AI technologies to produce quick insights.
They are used across different industries, such as finance, education, and healthcare, to provide relevant articles.
Perform data extraction, sentiment, and language analysis to smoothen the research process.
Find insights from unstructured data.
Offer detailed analysis of complicated data through AI capabilities.
Share data securely and customize computational power based on user needs.
The tool is challenging for beginners and needs extra training.
Difficulty in navigating through the dashboard due to its design.
Lite : free.
Plus : Starts at $140/month.

Link: https://www.ibm.com/watson

Scite is one of the best AI tools for academic research to help users research scholarly articles and check for citations. NLP and machine learning techniques assess the references’ dependency and check the research’s quality and impact.

We offer visualizations and metrics to help you learn the citation landscape and find a specific topic.
It contains articles, datasets, preprints, and citation statements for in-depth research.
The innovative citation feature lets you check how to cite a particular publication.
Create a dashboard to search for the citation statements and visualize the research.
Offer contextual information for citations indicating the claim contradiction.
Give you access to 1.2 billion citation statements, offering you access to draft academic papers.
It cannot be integrated with editors like LaTex or used by mathematics and computer science researchers.
The database only has some scientific articles you want.
Monthly plan : $20/month.
Yearly plan : $12/month and total $144/year.

Link: https://scite.ai/

There are some factors which you must consider while choosing the best AI tools for academic research :

Objective: You need to know the aim you want to achieve using this tool. You can use this for research, generating images, and creating textural content.

Purpose : You must go through the tool’s official site to understand its functionalities and choose the one that serves you better.

Learning curve: The complicated tool will take away much of your time, so choose the self-explanatory one.

Related Articles 10 Best AI Tools for Stock Market Analysis [2024] 10 Best AI Tools For Students 2024 10 Best AI Tools for Assignment Writing in 2024

Out of all the AI tools for academic research , consensus AI is the ideal choice for many educational researchers. It helps you find scientific-based search results with ease. It takes the help of AI to acquire information from different sources and accurate ones. The tool uses machine learning algorithms to analyze enormous datasets, make the search process quick, and extract results accurately.

The best AI tools for academic research are game-changers in the research world. These tools have come in handy for many researchers and scholars to find the relevant information for the topic they are researching with them and get a summary of the issues in no time.

Using the tools, you can reduce academic research stress and do the work briskly. You can improve through these academic research AI tools by writing better theses. We have discussed the top 10 that let you quickly work efficiently and access relevant information.

How is AI used in academic research?

AI will streamline the research process, help you analyze data, review academic papers, extract information from vast datasets, and gain insights.

Are there AI tools that can summarize academic papers?

Yes, many AI tools will help you summarize the data quickly and do fact-checking. It also extracts relevant information based on the given topic for you.

How will AI tools help you through the academic and literature review process?

AI tools will help you find a suitable theme, suggest relevant papers, and summarize complex texts into simple lines for easier understanding. It will be your research companion while writing research papers.

Are AI tools easy for researchers with limited technical knowledge to use?

Everyone can use the tool for academic research due to the user-friendly interface. This makes it accessible to non-tech users, too. They get used to the tool in no time.

Will AI generate research hypotheses?

Yes, the AI will thoroughly analyze the data, find the pattern, and develop the hypothesis based on the information available.

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Playboy image from 1972 gets ban from ieee computer journals, use of "lenna" image in computer image processing research stretches back to the 1970s..

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On Wednesday, the IEEE Computer Society announced to members that, after April 1, it would no longer accept papers that include a frequently used image of a 1972 Playboy model named Lena Forsén. The so-called " Lenna image ," (Forsén added an extra "n" to her name in her Playboy appearance to aid pronunciation) has been used in image processing research since 1973 and has attracted criticism for making some women feel unwelcome in the field.

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Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. ... The aim of this paper is to provide a broad research guideline on fundamental sciences with ...
Generative AI Special Topic Hub :: SSRN
Generative AI Special Topic Hub. SSRN's Generative AI Special Topic Hub provides a curated view into new early-stage research addressing generative artificial intelligence (AI), its potential applications in fields as varied as search technology, law, finance, and education, and evolving conversations around the ethical use of these technologies.
2020's Top AI & Machine Learning Research Papers
The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots. 2020's Top AI & Machine Learning Research Papers. November 24, 2020 by Mariya Yao. Despite the challenges of 2020, the AI research community produced a number of meaningful technical breakthroughs. GPT-3 by OpenAI may be the most famous, but there are ...
Machine learning
Machine learning articles from across Nature Portfolio. Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers ...
The best AI tools for research papers and academic research (Literature
AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. ... AI for scientific writing and research papers. In the ever-evolving realm of academic research, AI tools are increasingly taking center stage. Enter Paper Wizard, Jenny.AI, and Wisio - these ...
AI Index Report
The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.
Semantic Scholar
Try it for select papers. Learn More. G r een AI R o y Schwa r tz, Jesse Dodge, N. A. Smith, ... Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Learn More. About About Us Meet the Team Publishers Blog (opens in a new tab) ...
New report highlights global strategies for accelerating AI in science
While advancements in AI have huge implications for national R&D systems, very little is known about how governments plan to accelerate the uptake of AI by science and research institutions.In the report, "Preparing National Research Ecosystems for AI: Strategies and Progress in 2024," the International Science Council's Center for Science Futures addresses this knowledge gap by presenting a ...
Large language models use a surprisingly simple mechanism to retrieve
Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex. Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don't fully grasp how they work.
Gen AI field experiment shows mixed results in helping small businesses
The results contrast with recent research that found college-educated workers using gen AI were more productive on well-defined tasks—especially benefitting those with the weakest skills. Another recent paper found gen AI can increase productivity for low-skilled workers, thus reducing disparities overall.
AI Academic Writing Tool for Researchers
Input your research topic, and Jenni AI generates comprehensive prompts to kickstart your paper. Research Assistance Find credible sources, articles, and relevant data with ease through our powerful AI-driven research assistant.
Research on LLMs performing system development
Excited to share our two latest papers on the role of GPT beyond being an assistant, acting as an engineer in Autonomous Vehicles Design and Development tasks (e.g., analysis, requirements engineering) which both are available in arXiv: 1- "Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models" - Discover how safety analysis can be performed by AI in the ...
10 Best AI Tools for Academic Research in 2024
It contains 200 million research papers and summarizes them in a single sentence. It is easy to extract the details of the research paper into a table and easily search for the required information. Pros . Automate the entire literature review process and save time. Find the appropriate papers that you might have missed to search with the ...
Playboy image from 1972 gets ban from IEEE computer journals
Due to some of this criticism, which dates back to at least 1996, the journal Nature banned the use of the Lena image in paper submissions in 2018.. The comp.compression Usenet newsgroup FAQ ...

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