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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.

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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 

Top Artificial Intelligence Topics for Research   

This section of the blog will expand on some of the best Artificial Intelligence Topics for research.

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.  

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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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.  

Table of Contents

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  

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Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996. Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!

1. Machine Learning

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate. However, generally speaking, Machine Learning Algorithms are divided into 3 types i.e. Supervised Machine Learning Algorithms, Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms.

2. Deep Learning

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!). This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

3. Reinforcement Learning

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

4. Robotics

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments. An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.

5. Natural Language Processing

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

6. Computer Vision

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in. Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

7. Recommender Systems

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering. Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

8. Internet of Things

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other. Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

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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 )

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• 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.

Chui KT, Lytras MD, Visvizi A (2018) Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 11(11):2869

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Chui KT, Fung DCL, Lytras MD, Lam TM (2020) Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Comput Human Behav 107:105584

Lytras MD, Visvizi A, Daniela L, Sarirete A, De Pablos PO (2018) Social networks research for sustainable smart education. Sustainability 10(9):2974

Lytras MD, Visvizi A, Sarirete A (2019) Clustering smart city services: perceptions, expectations, responses. Sustainability 11(6):1669

Lytras MD, Visvizi A, Chopdar PK, Sarirete A, Alhalabi W (2021) Information management in smart cities: turning end users’ views into multi-item scale development, validation, and policy-making recommendations. Int J Inf Manag 56:102146

Visvizi A, Jussila J, Lytras MD, Ijäs M (2020) Tweeting and mining OECD-related microcontent in the post-truth era: A cloud-based app. Comput Human Behav 107:105958

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Effat College of Engineering, Effat Energy and Technology Research Center, Effat University, P.O. Box 34689, Jeddah, Saudi Arabia

Akila Sarirete, Zain Balfagih, Tayeb Brahimi & Miltiadis D. Lytras

King Abdulaziz University, Jeddah, 21589, Saudi Arabia

Miltiadis D. Lytras

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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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Artificial Intelligence

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Artificial intelligence describes an application or machine that mimics human intelligence.

A system in which machines execute repeated tasks based on a fixed set of human-supplied instructions.

A system in which a machine makes independent, real-time decisions based on human-supplied rules and goals.

The massive amounts of data that are coming in quickly and from a variety of sources, such as internet-connected devices, sensors, and social platforms. In some cases, using or learning from big data requires AI methods. Big data also can enhance the ability to create new AI applications.

An AI system that mimics human conversation. While some simple chatbots rely on pre-programmed text, more sophisticated systems, trained on large data sets, are able to convincingly replicate human interaction.

A subset of machine learning . Deep learning uses machine learning algorithms but structures the algorithms in layers to create "artificial neural networks." These networks are modeled after the human brain and are most likely to provide the experience of interacting with a real human.

An approach that includes human feedback and oversight in machine learning systems. Including humans in the loop may improve accuracy and guard against bias and unintended outcomes of AI.

A computer-generated simplification of something that exists in the real world, such as climate change , disease spread, or earthquakes . Machine learning systems develop models by analyzing patterns in large data sets. Models can be used to simulate natural processes and make predictions.

Interconnected sets of processing units, or nodes, modeled on the human brain, that are used in deep learning to identify patterns in data and, on the basis of those patterns, make predictions in response to new data. Neural networks are used in facial recognition systems, digital marketing, and other applications.

A hypothetical scenario in which an AI system develops agency and grows beyond human ability to control it.

The data used to " teach " a machine learning system to recognize patterns and features. Typically, continual training results in more accurate machine learning systems. Likewise, biased or incomplete datasets can lead to imprecise or unintended outcomes.

An interview-based method proposed by computer pioneer Alan Turing to assess whether a machine can think.

Dive Deeper

More Caltech Computer and Information Sciences Research Coverage

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

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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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• URL: https://national.libguides.com/artificial_intelligence

The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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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
• Artificial Intelligence Versus Human Intelligence
• Impact of Artificial Intelligence on the Education System The paper analyzes how the education system can maximize the advantages of Artificial Intelligence. It compares the traditional education system.
• 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: 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.
• 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 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 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 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 and Music The paper discusses use of Artificial Intelligence is rapidly expanding, with several innovative companies adopting it to create music.
• 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.
• Artificial Intelligence Economy This annotated bibliography aims to discuss seven articles devoted to the topic of the artificial intelligence economy.
• 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.
• 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.
• 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.
• 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?
• 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.
• Artificial Intelligence in Accounting ​Artificial Intelligence is already replacing some accounting operations, and research indicates that automating these processes is a cost-effective option.
• 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.
• Artificial Intelligence: Use and Potential Risks Automation and intelligent algorithms can significantly benefit business owners by inducing substantial savings.
• 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.
• 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 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.
• 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.
• 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 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.
• 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.
• 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: 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 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 in Self-Driving Cars The paper states that artificial intelligence in self-driving vehicles cannot conclude several favorable outcomes – or, the “least bad” effects.
• 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.
• 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.
• 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 Implementation in Accounting Processes Artificial intelligence seems to be a prospective technology, and its implementation in accounting processes is inevitable.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• AI, Human Control and Safety The given evaluative analysis will primarily focus on the topic of artificial intelligence, human control, and safety.
• 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.
• 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.
• 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).
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 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 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 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 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.
• 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.
• 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 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.
• 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.
• 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 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.
• 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.
• 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.
• 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.
• 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.
• 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: 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 (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, 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
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• 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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These essay examples and topics on Artificial Intelligence were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on March 8, 2024 .

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.

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163 Unique Artificial Intelligence Topics For Your Dissertation

The artificial intelligence industry is an industry of the future, but it’s also a course many students find difficult to write about. According to some students, the main reason is that there are many research topics on artificial intelligence. Several topics are already covered, and they claim not to know what to write about.

However, one of the interesting things about writing a dissertation or thesis is that you don’t need to be the number one author of an idea. It would be best if you write about the idea from a unique perspective instead. Writing from a unique perspective also means coupling your ideas with original research, giving your long essay quality and value to your professors and other students who may want to cover the same topic in the future.

This blog post will cover basic advanced AI topics and interesting ones for your next research paper or debate. This will help prepare you for your next long essay or presentation.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the concept that enables humans to perform their tasks more smartly and faster through automated systems. AI is human intelligence packed in machines.

AI facilitates several computer systems such as voice recognition, machine vision, natural language processing, robotics engineering, and many others. All these systems revolutionize how work is done in today’s world.

Now that you know what artificial intelligence is, here are some advanced AI topics for your college research.

Writing Tips to Create a Good Thesis or Dissertation

Every student wants to create the best thesis and dissertation in their class. The first step to creating or researching the perfect dissertation is to write a great thesis. What are the things to be on the lookout for?

• Create a Strong Thesis Statement You need this to have a concise approach to your research. Your thesis statement should, therefore, be specific, precise, factual, debatable, and logical enough to be an assertive point. Afterwards, the only way to create a competitive dissertation is to draw from existing research in journals and other sources.
• Strong Arguments You can create a good dissertation if you have strong arguments. Your arguments must be backed by reputed sources such as academics, government, reputed media organizations, or statistic-oriented websites. All these make your arguments recognizable and accepted.
• Well Organized and Logically Structured Your dissertation has different subsections, including an abstract, thesis statement, background to the study, chapters (where your body is), and concluding arguments. If you’ve embarked on quantitative data analysis, you must report the data you got and what it means for your discourse. You can even add recommendations for future research. The information you want to convey must be well structured to improve its reception by your university professors.
• Concise and Free of Errors Your essay must also be straightforward. Your ideas must not be complex to understand, and you must always explain ambiguous industry terms. Revising your draft to check for grammatical errors several times is also important. Editing can be difficult, but it’s integral to determining whether your professors will love your dissertation or otherwise.

Artificial Intelligence Research Topics

Artificial intelligence is here to stay in several industries and sectors worldwide. It is the technology of the present and the future, and here are some AI topics to write about:

• How will artificial intelligence contribute to the flight to Mars?
• Machine learning and the challenges it poses to scientists
• How can retail stores maximize machine learning?
• Expatiate on what is meant by deep learning
• General AI and Narrow AI: what does it mean?
• AI changes the world: a case study of the gambling industry
• AI improved business: a case study of SaaS industries
• AI in homes: how smart homes change how humans live
• The critical challenges scientists have not yet solved with AI
• How students can contribute to both research and development of AI systems
• Is automation the way forward for the interconnected world: an overview of the ethical issues in AI
• How does cybernetics connect with AI?
• How do artificial intelligence systems manifest in healthcare?
• A case for artificial intelligence in how it facilitates the use of data in the criminal department
• What are the innovations in the vision system applications
• The inductive logic program: meaning and origin
• Brain simulation and AI: right or wrong
• How to maximize AI in Big data
• How AI can increase cybersecurity threat
• AI in companies: a case study of Telegram

Hot Topics in Artificial Intelligence

If you’d love to be one of the few who will cover hot topics in AI, researching some sub-sectors could be a way to go. There are several subsections of AI, some of which are hot AI topics causing several arguments among scholars and moralists today. Some of these are:

• How natural language is generated and how AI maximizes it
• Speech recognition: a case study of Alexa and how it works
• How AI makes its decisions
• What are known as virtual agents?
• Key deep learning platforms for governments
• Text analytics and the future of text-to-speech systems
• How marketing automation works
• Do robots operate based on rules?
• AI and emotion recognition
• AI and the future of biometrics
• AI in content creation
• AI and how data is used to create social media addiction
• What can be considered core problems with AI?
• What do five pieces of literature say about AI taking over the world?
• How does AI help with predictive sales?
• Motion planning and how AI is used in video editing
• Distinguish between data science vs. artificial intelligence
• Account for five failed AI experiments in the past decade
• The world from the machine’s view
• Project management systems from the machine’s view

Artificial Intelligence Topics for Presentation

Students are sometimes fond of presentations to show knowledge or win debates. If you’re in a debate club and would love to add a presentation to your AI topics, here are topics in artificial intelligence for you.

You can even expand these for your artificial intelligence research paper topics:

• How AI has penetrated all industries
• The future of cloud technologies
• The future of AI in military equipment
• The evolution of AI in a security application
• Industrial robots: an account of Tesla’s factory
• Industrial robots: an account of Amazon’s factories
• An overview of deep generative models and what they mean
• What are the space travel ideas fueling the innovation of AI?
• What is amortized inference?
• Examine the Monte Carlo methods in AI
• How technology has improved maps
• Comment on how AI is used to find fresh craters on the moon
• Comment on two previous papers from your professor about AI

AI Research Topics

If you’d like to take a general perspective on AI, here are some topics in AI to discuss amongst your friends or for your next essay:

• Are robots a threat to human jobs?
• How automation has changed the world since 2020
• Would you say Tesla produces robot cars?
• What are the basic violations of artificial intelligence?
• Account for the evolution of AI models
• Weapon systems and the future of weaponry
• Account for the interaction between machines and humans
• Basic principles of AI risk management
• How AI protects people against spam
• Can AI predict election results?
• What are the limits of AI?
• Detailed reports on image recognition algorithms in two companies of your choice
• How is AI used in customer service?
• Telehealth and its significance
• Can AI help predict the future?
• How to measure water quality and cleanness through AI
• Analyze the technology used for the Breathometer products
• Key trends in AI and robotics research and development
• How AI helps with fraud detection in a bank of your choice
• How AI helps the academic industry.

Argument Debate Topics in AI

You’d expect controversial topics in AI, and here are some of them. These are topics for friendly debates in class or topics to start a conversation with industry leaders:

• Will humans end all work when AI replaces them?
• Who is liable for AI’s misdoing?
• AI is smarter than humans: can it be controlled?
• Machines will affect human interactions: discuss
• AI bias exists and is here to stay
• Artificial Intelligence cannot be humanized even if it understands emotions
• New wealth and AI: how will it be distributed?
• Can humans prevent AI bias?
• Can AI be protected from hackers?
• What will happen with the unintended consequences of using AI?

Computer Science AI Topics

Every computer science student also needs AI topics for research papers, presentations or scientific thesis . Whatever it is, here are some helpful ideas:

• AI and machine learning: how does it help healthcare systems?
• What does hierarchical deep learning neural network mean
• AI in architecture and engineering: explain
• Can robots safely perform surgery?
• Can robots help with teaching?
• Recent trends in machine learning
• Recent trends in big data that will affect the future of the internet of things
• How does AI contribute to the excavation management Industry?
• Can AI help spot drug distribution?
• AI and imaging system: Trends since 1990
• Explain five pieces of literature on how AI can be contained
• Discuss how AI reduced the escalation of COVID-19
• How can natural language processing help interpret sign languages?
• Review a recent book about AI and cybersecurity
• Discuss the key discoveries from a recent popular seminar on AI and cybercrime
• What does Stephen Hawking think about AI?
• How did AI make Tesla a possibility?
• How recommender systems work in the retail industry
• What is the artificial Internet of Things (A-IoT)?
• Explain the intricacies of enhanced AI in the pharmaceutical industry

AI Ethics Topics

There are always argumentative debate topics on AI, especially on the ethical and moral components. Here are a few ethical topics in artificial intelligence to discuss:

• Is AI the end of all jobs?
• Is artificial intelligence in concert with patent law?
• Do humans understand machines?
• What happens when robots gain self-control?
• Can machines make catastrophic mistakes?
• What happens when AI reads minds and executes actions even if they’re violent?
• What can be done about racist robots?
• Comments on how science can mediate human-machine interactions
• What does Google CEO mean when he said AI would be the world’s saviour?
• What are robots’ rights?
• How does power balance shift with a rise in AI development?
• How can human privacy be assured when robots are used as police?
• What is morality for AI?
• Can AI affect the environment?
• Discuss ways to keep robots safe from enemies.

AI Essay Topics Technology

Technology is already intertwined with AI, but you may need hot AI topics that focus on the tech side of the innovation. Here are 20 custom topics for you:

• How can we understand autonomous driving?
• Pros and cons of artificial intelligence to the world?
• How does modern science interact with AI?
• Account for the scandalous innovations in AI in the 21st century
• Account for the most destructive robots ever built
• Review a documentary on AI
• Review three books or journals that express AI as a threat to humans and draw conclusions based on your thoughts
• What do non-experts think about AI?
• Discuss the most ingenious robots developed in the past decade
• Can the robotic population replace human significance?
• Is it possible to be ruled by robots?
• What would world domination look like: from the machine perspective
• He who controls AI controls the world: discuss
• Key areas in AI engineering that man must control
• How Apple is using AI for its products
• Would you say AI is a positive or negative invention?
• AI and video gaming: how it changed the arcade Industry
• Would you say eSports is toxic?
• How AI helps in the hospitality industry
• AI and its use in sustainable energy.

Interesting Topics in AI

There are interesting ways to look at the subject of AI in today’s world. Here are some good research topics for AI to answer some questions:

• AI can be toxic: Should a high school student pursue a career in artificial intelligence?
• Prediction vs. judgment: experimenting with AI
• What makes AI know what’s right or wrong?
• Human judgment in AI: explain
• Effects of AI on businesses
• Will AI play critical roles in human future affairs?
• Tech devices and AI
• Search application and AI: account for how AI maximizes programming languages
• The history of artificial intelligence
• How AI impacts market design
• Data management and AI: discuss
• How can AI influence the future of computing
• How AI has changed the video viewing industry
• How can AI contribute to the global economy?
• How smart would you say artificial intelligence is?

Graduate AI NLP Research Topics

NLP (Natural Language Processing) is the aspect of artificial intelligence or computer science that deals with the ability of machines to understand spoken words and simplify them as humans can. It’s as simple as saying NLP is how computers understand human language.

If you’d like to focus your research topics on artificial intelligence on NLP, here are some topics for you:

• How did natural language processing help with Twitter Space discussions?
• How language is essential for regulatory and legal texts
• NLP in the eCommerce industry: top trends
• How NLP is used in language modelling and occlusion
• How does AI manoeuvre semantic analysis in natural language processing?
• History and top trends in NLP conference video call apps
• Text mining techniques and the role of NLP
• How physicians detected stroke since 2020 through NLP of radiology results
• How does big data contribute to understanding medical acronyms in the NLP section of AI?
• What does applied natural language processing mean in the mental health world?

Get Thesis Help Today

These 163 custom artificial intelligence topics are carefully selected and written by dissertation writers to help you prepare a quality dissertation. However, there are instances where you may be unable to come up with a quality dissertation by yourself.

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211 Brand New Artificial Intelligence Topics for 2023

In this blog post, you will find 211+ artificial intelligence topics for high school, college and university students. The topics are split into 21 categories, so you will surely be able to find the topics you’re looking for in mere minutes. All our topics are original and have been created by our veteran writers and editors.

We have the AI essay topics technology students need – guaranteed. And the best part is that you don’t have to pay anything for these topics. They are listed on this page, so you can use any one of them 100% free. Yes, you can even reword the topics as you see fit. After all, our company has been established with a clear goal in mind: to help every student get the best grades possible.

Remember that if you need more than just some topics, our experienced writers are at your disposal. You can get writing tips, editing and proofreading assistance, and even academic writing services from our team of PhD experts in artificial intelligence, machine learning and Natural Language Processing.

Why Our Artificial Intelligence Topics?

OK, but why would you choose our AI topics? While there may be several other websites that are offering artificial intelligence topics to students, we are unique. Here is why:

All our topics are original and have been created by our team of experts Our topics are relatively easy to write about. We’ve made sure there is more than enough information online. You can use any of our topics for free without giving us any kind of credit The list of topics is updated periodically, so you can probably find at least a dozen topics that nobody in your class has thought about every time you visit this blog post.

We know you’re anxious to get started on your academic paper. After all, you probably don’t have a lot of time at your disposal to write the paper. So, without further ado, here are our best artificial intelligence topics:

Fun Artificial Intelligence Research Topics

If you are looking for some fun artificial intelligence research topics, you have definitely arrived at the right place. Check out these ideas:

• A practical application of deep learning
• How do industrial robots work?
• Discuss AI-assisted investments
• A simple machine learning application
• Using artificial intelligence for detecting fraud
• Compare and contrast 3 robots
• The history of artificial intelligence
• Discuss narrow AI implementations
• Analyze social intelligence
• Define machine consciousness
• Solving complex problems using artificial intelligence
• Can artificial intelligence simulate the human brain?

Easy Topics in Artificial Intelligence

In case you don’t want to spend days working on your essay, we would strongly recommend you to pick one of our easy topics in artificial intelligence (it’s easy to find resources and information about these topics on the Internet):

• Define deep learning
• Define machine learning
• Define social intelligence
• The AGI approach (artificial general intelligence)
• Applications of artificial intelligence in banking
• Applications of AI in space exploration
• Applications of artificial intelligence in social networks
• Discuss machine consciousness
• Ethical issues with artificial intelligence
• Discuss Natural Language Processing
• Advancements in artificial intelligence in 2023
• The future of artificial intelligence
• Using artificial intelligence to catch plagiarism
• The philosophy of artificial intelligence

AI Research Topics for High School

Yes, we have an entire category dedicated to high school students. Take a look at these awesome AI research topics for high school and pick the one you like:

• The risks of narrow artificial intelligence
• The risks of general AI
• Define and discuss the concept of superintelligence
• Limitation of current artificial intelligence
• Best machine learning algorithms
• Programming robots in 2023
• Discuss the concept of ethical machines
• The impact of AI since its inception
• Machine sentience: an in-depth analysis
• Will robots take over the world?
• Will robots replace the human workforce?
• Movements against artificial intelligence
• Artificial intelligence in the military
• AI drones in the United States
• Artificial moral agents

Difficult Artificial Intelligence Research Paper Topics

Do you want to impress your professor and your classmates? The easiest way to do this is to write about one of our difficult artificial intelligence research paper topics:

• Present the most effective deep learning algorithm
• Write a machine learning algorithm
• Compare and contrast 3 AI systems
• Discuss malevolent artificial intelligence
• Top 3 breakthroughs in AI in 2023
• Computationalism versus functionalism
• Discuss the implementation of robot rights
• Analyze the technological singularity
• Discuss the concept of hyperintelligence
• How do AI systems work?
• Alexa’s use of artificial intelligence: a case study
• Siri’s use of artificial intelligence: a case study
• Netflix’s use of artificial intelligence
• Amazon’s use of artificial intelligence

Artificial Intelligence Topics for Presentation

Are you preparing to start working on your presentation? No problem; we’re here to help! Take a look at these excellent artificial intelligence topics for presentation:

• The current state of artificial intelligence
• Major breakthroughs in AI
• The basic functionality of an AI system
• What does deep learning mean?
• Machine learning algorithms
• A presentation of Natural Language Processing
• The impact of artificial intelligence
• Present the concepts and ideas behind narrow AI
• Present general artificial intelligence
• Artificial intelligence regulations in the US
• Artificial intelligence regulations in Europe
• Artificial intelligence in fiction

Controversial Topics in AI

Artificial intelligence is a relatively new field, so it has plenty of controversies surrounding it. Here are some interesting, controversial topics in AI for you to write about:

• Should robots be allowed to become sentient?
• Do the 3 laws of robotics actually exist?
• Facial recognition software concerns
• Privacy laws and artificial intelligence
• Should robots have rights?
• The role of human judgment in robotics
• Signs of bias in AI behavior
• Signs of discrimination in AI behavior
• Building a superintelligent artificial intelligence
• Can artificial intelligence development be stopped?
• Discuss AI and religion (do they get along?)
• Creative works by artificial intelligence systems
• Analyze the apparition of Deepfake videos
• Automated grading systems in our schools

Artificial Intelligence Topics for a Thesis

If you are preparing to start working on your thesis, you surely need some good ideas. Here are some of our best suggestions for artificial intelligence topics for a thesis:

• The latest advancements in AI algorithms
• Quantum computing and artificial intelligence
• AI experiments and their success rate
• Teaching your computer to create music
• AI in social media marketing campaigns
• Tesla’s use of artificial intelligence: a case study
• Artificial intelligence predicting election results
• Analyze the most prominent machine learning technology
• Discuss the simulation of the human brain by AI systems
• Image recognition using artificial intelligence
• Important applications of artificial intelligence today
• Security applications using artificial intelligence
• Analyze deep generative models

Argument Debate Topics on AI

Are you looking for an argument debate topic? We have plenty of argument debate topics on AI right here for free:

• Pros and cons of probabilistic programming
• AI and the Internet of Things
• AI development should be heavily regulated
• Giving artificial intelligence access to our weapons systems
• Robot hunter-killers on the battlefield
• Real-life artificial intelligence versus movies
• Can AI distinguish between good or bad?
• Can a computer be ethical?
• Large Scale Machine Learning: the future?
• Do robots have morals?
• Two artificial intelligence applications that revolutionized the industry
• Teaching artificial intelligence in school

AI Topics for Research Paper in College

College students should pick more difficult topics than high school students. Here are some AI topics for research paper in college that are not overly difficult:

• Tools you need to write an artificial intelligence program
• Regulating the AI field correctly
• Human judgment in artificial intelligence
• The major types of artificial intelligence
• Analyzing NLP algorithms
• Predicting the price of housing with AI
• Analyzing reinforcement learning in artificial intelligence
• Ethical problems with artificial intelligence

Computer Science AI Topics

Are you a computer science student? Do you want the most interesting computer science AI topics? Check out these ideas and pick the one you like the most:

• What is artificial intelligence? (a short history)
• Measuring water quality with help from artificial intelligence
• Email spam prevention with artificial intelligence
• Discuss automated weapons
• Is AI violating your privacy?
• Image recognition software
• Machine learning explained
• Artificial neural networks explained

AI Ethics Topics

Discussing artificial intelligence ethics issues can be a very quick way to get a top grade on your paper. Here are some of the most interesting AI ethics topics:

• Making the difference between right and wrong
• AI and discrimination problems
• Most important ethical issues with AI
• Robot assassins controlled by artificial intelligence
• Weapons system errors caused by artificial intelligence
• Is artificial intelligence biased?
• The need for tougher regulations
• Can AI become more intelligent than the human race?

Advanced AI Topics

Would you like to talk about more advanced artificial intelligence topics? We have a long list of advanced AI topics for you:

• Discuss the Bayesian inference
• Discuss amortized inference
• Analyze the most complex AI algorithm
• How does NLP work?
• How does machine learning work?
• How is Alexa using artificial intelligence?
• Siri using artificial intelligence
• An in-depth analysis of deep generative models

Artificial Intelligence in Space Ideas

As you probably already know, artificial intelligence is being used in space exploration right now. So why not write a paper about one of our artificial intelligence in space ideas:

• Artificial intelligence on the International Space Station
• AI use in telescope array systems
• Searching for alien life using artificial intelligence
• Exploring Mars using artificial intelligence
• Space exploration advancements related to AI
• Mars Rover Perseverance’s use of artificial intelligence
• Searching for Earth-like planets using AI systems
• Early detection of space bodies on a collision course with Earth

Interesting Topics in AI

If you want to write about some interesting topics, you have arrived at the right place. Check out these interesting topics in AI and choose one now:

• The artificial intelligence arms race
• Discuss robotics process automation
• What is synthetic intelligence?
• Analyze the emergent algorithm
• Discuss the concept of transhumanism
• Analyze the behavior selection algorithm
• The COMPAS program (US courts)
• Robots increasing unemployment rates in the US

Good Research Topics for AI

Looking for good topics to write about? Need a topic that won’t keep you working for an entire week? Here are some good research topics for AI that are also relatively simple:

• Japan’s artificial intelligence market
• Discuss Strong AI
• Deep learning algorithms in real life
• Making weather predictions using artificial intelligence
• Discuss Alan Turing’s Polite Convention
• How to ensure machines behave ethically?
• Discuss the Turing test
• Discuss the “AI effect”

Graduate AI NLP Research Topics

If you are a graduate and need to write an essay about Natural Language Processing, we have some very nice graduate AI NLP research topics right here:

• Cybersecurity and the use of machine learning
• Machine learning in lead generation
• Artificial intelligence in police drones
• Sending AI probes to distant planets
• Top artificial intelligence applications in robotics
• The limits of machine learning
• NLP limitations today
• AI help for terminally ill patients

Machine Learning Topics in AI

Machine learning is an integral part of artificial intelligence, so it warrants its own section. Pick one of these machine learning topics in AI and start writing your essay right away:

• Machine learning optimization
• Machine learning generalization
• Discuss supervised machine learning
• The Dimensionality Reduction approach
• Discuss training models for machine learning
• Analyze reinforcement learning
• The ethics behind machine learning
• Bias in machine learning
• Applications of machine learning in 2023

Hot AI Topics

Not all artificial intelligence topics are hot. There are some that have been trending for some time though. Here are some hot AI topics that should remain trending for a while:

• Can artificial intelligence help us prevent another world war?
• Machine learning and its contribution to the AI field
• How does reasoning work from an AI system’s perspective?
• Coding AI applications in Prolog effectively
• Seeing the world through the “eyes” of a robot
• Discuss the concept of predictive sales in today’s world
• Explain how a machine learning algorithm works

Latest Trends in Artificial Intelligence

Breakthroughs in artificial intelligence happen on almost a weekly basis nowadays. So, why not write about the latest trends in artificial intelligence:

• Greater Cloud
• Top artificial solutions for the IT field
• Structuring big data using artificial intelligence
• Discuss Automated Machine Learning tech
• Conceptual design aided by artificial intelligence
• Discuss the approach of Tiny ML
• Analyze advancements in quantum machine learning
• Discuss the concept of responsible AI

AI Risks Topics

Artificial intelligence, like any new technology, has some risks associated with it. Here are some of the best AI risks topics you can find online:

• Can AI become sentient and attack us?
• Can artificial intelligence be programmed to respect our privacy?
• Bias in data equals bias in artificial intelligence systems that analyze it
• Can a robot be trustworthy?
• The risks posed by narrow AI
• AI-controlled weapons of mass destruction
• Artificial intelligence used as a weapon in 2023
• The dangers of a superintelligent AI system

The Future of Artificial Intelligence Ideas

Are you interested in writing about the future of artificial intelligence? We have some very nice the future of artificial intelligence ideas for you. Check them out below:

• The future of humans in an AI-dominated world
• AI impact on our transportation industry
• Customer service benefitting from artificial intelligence
• Artificial intelligence replacing journalists
• Amazon’s heavy use of artificial intelligence in Fulfillment Centers
• Human rights in the era of artificial intelligence
• Artificial general intelligence and what does it mean
• War robots are not a thing of the future anymore

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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

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.

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.

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.

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 ).

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 ).

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

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.

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.

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

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