best books about ai in education

Artificial Intelligence and Education: A Reading List

A bibliography to help educators prepare students and themselves for a future shaped by AI—with all its opportunities and drawbacks.

How should education change to address, incorporate, or challenge today’s AI systems, especially powerful large language models? What role should educators and scholars play in shaping the future of generative AI? The release of ChatGPT in November 2022 triggered an explosion of news, opinion pieces, and social media posts addressing these questions. Yet many are not aware of the current and historical body of academic work that offers clarity, substance, and nuance to enrich the discourse.

Linking the terms “AI” and “education” invites a constellation of discussions. This selection of articles is hardly comprehensive, but it includes explanations of AI concepts and provides historical context for today’s systems. It describes a range of possible educational applications as well as adverse impacts, such as learning loss and increased inequity. Some articles touch on philosophical questions about AI in relation to learning, thinking, and human communication. Others will help educators prepare students for civic participation around concerns including information integrity, impacts on jobs, and energy consumption. Yet others outline educator and student rights in relation to AI and exhort educators to share their expertise in societal and industry discussions on the future of AI.

Nabeel Gillani, Rebecca Eynon, Catherine Chiabaut, and Kelsey Finkel, “ Unpacking the ‘Black Box’ of AI in Education ,” Educational Technology & Society 26, no. 1 (2023): 99–111.

Whether we’re aware of it or not, AI was already widespread in education before ChatGPT. Nabeel Gillani et al. describe AI applications such as learning analytics and adaptive learning systems, automated communications with students, early warning systems, and automated writing assessment. They seek to help educators develop literacy around the capacities and risks of these systems by providing an accessible introduction to machine learning and deep learning as well as rule-based AI. They present a cautious view, calling for scrutiny of bias in such systems and inequitable distribution of risks and benefits. They hope that engineers will collaborate deeply with educators on the development of such systems.

Jürgen Rudolph, Samson Tan, and Shannon Tan, “ ChatGPT: Bullshit Spewer or the End of Traditional Assessments in Higher Education? ” The Journal of Applied Learning and Teaching 6, no. 1 (January 24, 2023).

Jürgen Rudolph et al. give a practically oriented overview of ChatGPT’s implications for higher education. They explain the statistical nature of large language models as they tell the history of OpenAI and its attempts to mitigate bias and risk in the development of ChatGPT. They illustrate ways ChatGPT can be used with examples and screenshots. Their literature review shows the state of artificial intelligence in education (AIEd) as of January 2023. An extensive list of challenges and opportunities culminates in a set of recommendations that emphasizes explicit policy as well as expanding digital literacy education to include AI.

Emily M. Bender, Timnit Gebru, Angela McMillan-Major, and Shmargaret Shmitchell, “ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 ,” FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (March 2021): 610–623.

Student and faculty understanding of the risks and impacts of large language models is central to AI literacy and civic participation around AI policy. This hugely influential paper details documented and likely adverse impacts of the current data-and-resource-intensive, non-transparent mode of development of these models. Bender et al. emphasize the ways in which these costs will likely be borne disproportionately by marginalized groups. They call for transparency around the energy use and cost of these models as well as transparency around the data used to train them. They warn that models perpetuate and even amplify human biases and that the seeming coherence of these systems’ outputs can be used for malicious purposes even though it doesn’t reflect real understanding.

The authors argue that inclusive participation in development can encourage alternate development paths that are less resource intensive. They further argue that beneficial applications for marginalized groups, such as improved automatic speech recognition systems, must be accompanied by plans to mitigate harm.

Erik Brynjolfsson, “ The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence ,” Daedalus 151, no. 2 (2022): 272–87.

Erik Brynjolfsson argues that when we think of artificial intelligence as aiming to substitute for human intelligence, we miss the opportunity to focus on how it can complement and extend human capabilities. Brynjolfsson calls for policy that shifts AI development incentives away from automation toward augmentation. Automation is more likely to result in the elimination of lower-level jobs and in growing inequality. He points educators toward augmentation as a framework for thinking about AI applications that assist learning and teaching. How can we create incentives for AI to support and extend what teachers do rather than substituting for teachers? And how can we encourage students to use AI to extend their thinking and learning rather than using AI to skip learning?

Kevin Scott, “ I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale ,” Daedalus 151, no. 2 (2022): 75–84.

Brynjolfsson’s focus on AI as “augmentation” converges with Microsoft computer scientist Kevin Scott’s focus on “cognitive assistance.” Steering discussion of AI away from visions of autonomous systems with their own goals, Scott argues that near-term AI will serve to help humans with cognitive work. Scott situates this assistance in relation to evolving historical definitions of work and the way in which tools for work embody generalized knowledge about specific domains. He’s intrigued by the way deep neural networks can represent domain knowledge in new ways, as seen in the unexpected coding capabilities offered by OpenAI’s GPT-3 language model, which have enabled people with less technical knowledge to code. His article can help educators frame discussions of how students should build knowledge and what knowledge is still relevant in contexts where AI assistance is nearly ubiquitous.

Laura D. Tyson and John Zysman, “ Automation, AI & Work ,” Daedalus 151, no. 2 (2022): 256–71.

How can educators prepare students for future work environments integrated with AI and advise students on how majors and career paths may be affected by AI automation? And how can educators prepare students to participate in discussions of government policy around AI and work? Laura Tyson and John Zysman emphasize the importance of policy in determining how economic gains due to AI are distributed and how well workers weather disruptions due to AI. They observe that recent trends in automation and gig work have exacerbated inequality and reduced the supply of “good” jobs for low- and middle-income workers. They predict that AI will intensify these effects, but they point to the way collective bargaining, social insurance, and protections for gig workers have mitigated such impacts in countries like Germany. They argue that such interventions can serve as models to help frame discussions of intelligent labor policies for “an inclusive AI era.”

Todd C. Helmus, Artificial Intelligence, Deepfakes, and Disinformation: A Primer (RAND Corporation, 2022).

Educators’ considerations of academic integrity and AI text can draw on parallel discussions of authenticity and labeling of AI content in other societal contexts. Artificial intelligence has made deepfake audio, video, and images as well as generated text much more difficult to detect as such. Here, Todd Helmus considers the consequences to political systems and individuals as he offers a review of the ways in which these can and have been used to promote disinformation. He considers ways to identify deepfakes and ways to authenticate provenance of videos and images. Helmus advocates for regulatory action, tools for journalistic scrutiny, and widespread efforts to promote media literacy. As well as informing discussions of authenticity in educational contexts, this report might help us shape curricula to teach students about the risks of deepfakes and unlabeled AI.

William Hasselberger, “ Can Machines Have Common Sense? ” The New Atlantis 65 (2021): 94–109.

Students, by definition, are engaged in developing their cognitive capacities; their understanding of their own intelligence is in flux and may be influenced by their interactions with AI systems and by AI hype. In his review of The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do by Erik J. Larson, William Hasselberger warns that in overestimating AI’s ability to mimic human intelligence we devalue the human and overlook human capacities that are integral to everyday life decision making, understanding, and reasoning. Hasselberger provides examples of both academic and everyday common-sense reasoning that continue to be out of reach for AI. He provides a historical overview of debates around the limits of artificial intelligence and its implications for our understanding of human intelligence, citing the likes of Alan Turing and Marvin Minsky as well as contemporary discussions of data-driven language models.

Gwo-Jen Hwang and Nian-Shing Chen, “ Exploring the Potential of Generative Artificial Intelligence in Education: Applications, Challenges, and Future Research Directions ,” Educational Technology & Society 26, no. 2 (2023).

Gwo-Jen Hwang and Nian-Shing Chen are enthusiastic about the potential benefits of incorporating generative AI into education. They outline a variety of roles a large language model like ChatGPT might play, from student to tutor to peer to domain expert to administrator. For example, educators might assign students to “teach” ChatGPT on a subject. Hwang and Chen provide sample ChatGPT session transcripts to illustrate their suggestions. They share prompting techniques to help educators better design AI-based teaching strategies. At the same time, they are concerned about student overreliance on generative AI. They urge educators to guide students to use it critically and to reflect on their interactions with AI. Hwang and Chen don’t touch on concerns about bias, inaccuracy, or fabrication, but they call for further research into the impact of integrating generative AI on learning outcomes.

Weekly Newsletter

Get your fix of JSTOR Daily’s best stories in your inbox each Thursday.

Privacy Policy   Contact Us You may unsubscribe at any time by clicking on the provided link on any marketing message.

Lauren Goodlad and Samuel Baker, “ Now the Humanities Can Disrupt ‘AI’ ,” Public Books (February 20, 2023).

Lauren Goodlad and Samuel Baker situate both academic integrity concerns and the pressures on educators to “embrace” AI in the context of market forces. They ground their discussion of AI risks in a deep technical understanding of the limits of predictive models at mimicking human intelligence. Goodlad and Baker urge educators to communicate the purpose and value of teaching with writing to help students engage with the plurality of the world and communicate with others. Beyond the classroom, they argue, educators should question tech industry narratives and participate in public discussion on regulation and the future of AI. They see higher education as resilient: academic skepticism about former waves of hype around MOOCs, for example, suggests that educators will not likely be dazzled or terrified into submission to AI. Goodlad and Baker hope we will instead take up our place as experts who should help shape the future of the role of machines in human thought and communication.

Kathryn Conrad, “ Sneak Preview: A Blueprint for an AI Bill of Rights for Education ,” Critical AI 2.1 (July 17, 2023).

How can the field of education put the needs of students and scholars first as we shape our response to AI, the way we teach about it, and the way we might incorporate it into pedagogy? Kathryn Conrad’s manifesto builds on and extends the Biden administration’s Office of Science and Technology Policy 2022 “Blueprint for an AI Bill of Rights.” Conrad argues that educators should have input into institutional policies on AI and access to professional development around AI. Instructors should be able to decide whether and how to incorporate AI into pedagogy, basing their decisions on expert recommendations and peer-reviewed research. Conrad outlines student rights around AI systems, including the right to know when AI is being used to evaluate them and the right to request alternate human evaluation. They deserve detailed instructor guidance on policies around AI use without fear of reprisals. Conrad maintains that students should be able to appeal any charges of academic misconduct involving AI, and they should be offered alternatives to any AI-based assignments that might put their creative work at risk of exposure or use without compensation. Both students’ and educators’ legal rights must be respected in any educational application of automated generative systems.

Support JSTOR Daily! Join our new membership program on Patreon today.

JSTOR is a digital library for scholars, researchers, and students. JSTOR Daily readers can access the original research behind our articles for free on JSTOR.

Get Our Newsletter

More stories.

• The Fashionable Tour : or, The First American Tourist Guidebook

• Celebrating Asian American and Pacific Islander Heritage Month

• The Metaphysical Story of Chiropractic

A Bodhisattva for Japanese Women

Recent posts.

• Rice, Famine, and the Seven Wonders of the World
• The ABCs of Lithium 

Support JSTOR Daily

Sign up for our weekly newsletter.

• NONFICTION BOOKS
• BEST NONFICTION 2023
• BEST NONFICTION 2024
• Historical Biographies
• The Best Memoirs and Autobiographies
• Philosophical Biographies
• World War 2
• World History
• American History
• British History
• Chinese History
• Russian History
• Ancient History (up to 500)
• Medieval History (500-1400)
• Military History
• Art History
• Travel Books
• Ancient Philosophy
• Contemporary Philosophy
• Ethics & Moral Philosophy
• Great Philosophers
• Social & Political Philosophy
• Classical Studies
• New Science Books
• Maths & Statistics
• Popular Science
• Physics Books
• Climate Change Books
• How to Write
• English Grammar & Usage
• Books for Learning Languages
• Linguistics
• Political Ideologies
• Foreign Policy & International Relations
• American Politics
• British Politics
• Religious History Books
• Mental Health
• Neuroscience
• Child Psychology
• Film & Cinema
• Opera & Classical Music
• Behavioural Economics
• Development Economics
• Economic History
• Financial Crisis
• World Economies
• Investing Books
• Artificial Intelligence/AI Books
• Data Science Books
• Sex & Sexuality
• Death & Dying
• Food & Cooking
• Sports, Games & Hobbies
• FICTION BOOKS
• BEST NOVELS 2024
• BEST FICTION 2023
• New Literary Fiction
• World Literature
• Literary Criticism
• Literary Figures
• Classic English Literature
• American Literature
• Comics & Graphic Novels
• Fairy Tales & Mythology
• Historical Fiction
• Crime Novels
• Science Fiction
• Short Stories
• South Africa
• United States
• Arctic & Antarctica
• Afghanistan
• Myanmar (Formerly Burma)
• Netherlands
• Kids Recommend Books for Kids
• High School Teachers Recommendations
• Prizewinning Kids' Books
• Popular Series Books for Kids
• BEST BOOKS FOR KIDS (ALL AGES)
• Ages Baby-2
• Books for Teens and Young Adults
• THE BEST SCIENCE BOOKS FOR KIDS
• BEST KIDS' BOOKS OF 2023
• BEST BOOKS FOR TEENS OF 2023
• Best Audiobooks for Kids
• Environment
• Best Books for Teens of 2023
• Best Kids' Books of 2023
• Political Novels
• New History Books
• New Historical Fiction
• New Biography
• New Memoirs
• New World Literature
• New Economics Books
• New Climate Books
• New Math Books
• New Philosophy Books
• New Psychology Books
• New Physics Books
• THE BEST AUDIOBOOKS
• Actors Read Great Books
• Books Narrated by Their Authors
• Best Audiobook Thrillers
• Best History Audiobooks
• Nobel Literature Prize
• Booker Prize (fiction)
• Baillie Gifford Prize (nonfiction)
• Financial Times (nonfiction)
• Wolfson Prize (history)
• Royal Society (science)
• Pushkin House Prize (Russia)
• Walter Scott Prize (historical fiction)
• Arthur C Clarke Prize (sci fi)
• The Hugos (sci fi & fantasy)
• Audie Awards (audiobooks)

Make Your Own List

Technology » Artificial Intelligence/AI Books

The best books on artificial intelligence, recommended by chatgpt.

Normally at Five Books we ask experts to recommend the best books in their field and talk to us about them in an interview, either in person, by phone or via Zoom. In January 2023, we asked the AI bot, ChatGPT , to recommend books to us on the topic of AI. Being an AI doesn't necessarily make the chatbot an expert on AI books, but we thought it might have some ideas. This week we caught up with ChatGPT to find out if there were any new AI books it wanted to recommend in the year since we spoke. Read more nonfiction book recommendations on Five Books

Interview by Juliet Frost & Sophie Roell

AI 2041: Ten Visions for Our Future by Chen Qiufan & Kai-Fu Lee

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford

Artificial Intelligence: A Modern Approach by Peter Norvig & Stuart Russell

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

1 AI 2041: Ten Visions for Our Future by Chen Qiufan & Kai-Fu Lee

2 atlas of ai: power, politics, and the planetary costs of artificial intelligence by kate crawford, 3 artificial intelligence: a modern approach by peter norvig & stuart russell, 4 the master algorithm: how the quest for the ultimate learning machine will remake our world by pedro domingos, 5 superintelligence: paths, dangers, strategies by nick bostrom.

W e last spoke in January 2023 (when your information was up to 2021), about the best books on AI. Are there any books that have been published since 2021 that you would recommend?

Tell me about your top choice among these new AI books, AI 2041: Ten Visions for Our Future (2021) by Kai-Fu Lee and Chen Qiufan.

This book combines both science fiction and non-fiction to explore how AI will change our world by 2041. Kai-Fu Lee, a prominent AI expert, and Chen Qiufan, a science fiction writer, offer a unique blend of storytelling and analysis. The fictional stories, set in the year 2041, are penned by Chen Qiufan, while Kai-Fu Lee provides insights into how these scenarios might become reality. It’s a thought-provoking look at how AI could shape our future in diverse ways.

Kai-Fu Lee was head of Google China, is that right?

You also recommended Atlas of AI , which has been recommended a number of times on our site. 

For a more critical and thought-provoking perspective, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (2021) by Kate Crawford delves into the less-discussed consequences of AI development, such as environmental impact, data labor, and the power dynamics behind AI technologies. It’s an essential read for understanding the broader implications of AI beyond its technical advancements.

These two books offer a well-rounded view of AI, covering both its transformative potential and the critical challenges it poses.

Thanks to you, there’s been a lot of focus on Large Language Models (LLMs). Is there a good book about that?

As of my last update in April 2023, there isn’t a specific book that stands out as the definitive resource solely on Large Language Models (LLMs) like GPT-3 or GPT-4. However, the field is rapidly evolving, and new publications are likely to emerge.

[End of our 2023 update. The original 2022 interview appears below]

___________________________

Before we get to the books you’ve chosen, can you give us a brief overview of what AI is?

Certainly!  Artificial intelligence (AI) is the ability of computers and other machines to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, making decisions, and solving problems.

There are many different types of AI, including narrow or weak AI, which is designed to perform a specific task, and general or strong AI, which is designed to be capable of performing any intellectual task that a human can.

One of the main goals of AI research is to create systems that can perform tasks without explicit instructions. To achieve this, AI systems are often designed to learn from data , using techniques from machine learning. Machine learning is a type of AI that allows systems to improve their performance on a particular task over time by learning from data, rather than being explicitly programmed.

AI has the potential to revolutionize many industries and change the way we live and work. Some possible applications of AI include autonomous vehicles, medical diagnosis, language translation, and financial trading.

The development of AI has also raised a number of ethical and philosophical questions , such as whether it is possible to create machines that are truly intelligent and whether the increasing reliance on AI will lead to a loss of jobs for humans.

In a previous message, you listed the five books that, in your view, are the best on AI. Can you tell us a bit about how you made your choices and what your criteria were?

My recommendations were based on the popularity and critical acclaim of the books, as well as their ability to provide a comprehensive and accessible introduction to the field of AI. These books are considered classic works in the field and are often used as textbooks or recommended reading in university courses on AI. They cover a range of topics, including machine learning, natural language processing, and the ethical and philosophical implications of AI. I hope this helps to clarify my selection criteria.

Does your list include popular books on AI, as opposed to textbooks?

Let’s turn to your list of books. First up is Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. Tell me about this book.

Artificial Intelligence: A Modern Approach is a comprehensive textbook on artificial intelligence. The book is designed to be accessible to students with a background in computer science and provides a broad overview of the field, covering a wide range of topics including search algorithms, machine learning, natural language processing, and robotics. The book is known for its clear and concise writing style, as well as its up-to-date coverage of the latest research and developments in AI. It is widely used as a textbook in university courses on AI and is considered a classic work in the field.

Is it a long book?

It is approximately 1,200 pages in length, so it is a relatively long book. It is designed to be accessible to readers with a background in computer science and mathematics, and it includes numerous examples and exercises to help readers understand the material.

Stuart Russell has also written a popular book, Human Compatible , one of our books of the year in 2019 . If you’re just looking for an overview, could that be a better choice?

Human Compatible: Artificial Intelligence and the Problem of Control explores the potential risks and benefits of artificial intelligence (AI) and discusses the importance of ensuring that such technology is developed and used responsibly. The book is more focused on the ethical and philosophical implications of AI than Artificial Intelligence: A Modern Approach , which is a comprehensive textbook on the technical aspects of the field. If you are looking for an overview of the field of AI and its potential impact on society, Human Compatible may be a good choice. However, if you are more interested in the technical aspects of AI and want a comprehensive introduction to the field, Artificial Intelligence: A Modern Approach may be a better choice.

Let’s move on to your second book. What’s Deep Learning about and why should we read it to understand more about AI?

Deep Learning is a book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville that provides a comprehensive introduction to the field of deep learning, which is a type of machine learning that involves training artificial neural networks on a large dataset. The book is suitable for both students and professionals and covers the theory and practice of deep learning, including the mathematical foundations and algorithms that underlie it. It also provides practical examples and case studies to illustrate how deep learning can be applied to a variety of real-world problems.

Deep learning has become a key technique in artificial intelligence and has been responsible for many of the most significant advances in the field in recent years, including the development of self-driving cars, automatic translation systems, and image recognition systems. As such, Deep Learning is an important resource for anyone interested in understanding how deep learning works and how it can be applied to solve a wide range of problems.

Do you need to know advanced math to understand the book?

Deep Learning is a technical book that covers the math, algorithms, and implementation details of deep learning. As a result, it does require some mathematical knowledge to fully understand the material. However, the authors do a good job of explaining the necessary concepts and providing examples to help readers understand the math.

That being said, Deep Learning is aimed at a relatively advanced audience, and it is assumed that readers have a strong background in mathematics, including linear algebra and probability. If you are a beginner or have only a basic understanding of math, this book may be more challenging for you. There are other books on deep learning that may be more suitable for a beginner, such as Deep Learning for Computer Vision by Adrian Rosebrock, which provides a more intuitive and less technical introduction to the topic.

I’m still having trouble understanding the difference between machine learning, deep learning and AI.

Artificial intelligence (AI) is the ability of a computer or machine to perform tasks that would normally require human-level intelligence. Machine learning is a subfield of AI that involves the development of algorithms that can learn from data without being explicitly programmed. Machine learning algorithms can be trained on a dataset to perform a specific task, such as classifying emails as spam or not spam, or recognizing objects in an image.

Deep learning is a type of machine learning that involves training artificial neural networks on a large dataset. Neural networks are inspired by the structure and function of the human brain and are made up of layers of interconnected nodes, or “neurons.” Each layer processes the input data and passes it on to the next layer, and the output of the final layer is the network’s prediction or decision. Deep learning algorithms can learn to recognize patterns and make decisions based on the data they are trained on, and they have been responsible for many of the most significant advances in AI in recent years.

In summary, AI is the broader field of which machine learning and deep learning are subfields. Machine learning involves the development of algorithms that can learn from data, while deep learning involves the use of artificial neural networks to learn from data.

Thank you for clarifying that. ‘Neural’ or like a brain sounds a bit far-fetched to me for this kind of machine learning. A bit of marketing, almost.

The term “neural” in “neural network” refers to the fact that the model is inspired by the structure and function of the human brain, and it is not meant to be a literal representation of it. However, neural networks do have some characteristics that are similar to the brain, in that they can learn and adapt based on their experiences, and they can process and transmit information using multiple interconnected layers.

Let’s move on to your third book, which you said was for a popular audience. It’s also quite an optimistic book about AI, according to Carissa Véliz, who mentioned it in her interview on digital ethics . It’s called The Master Algorithm and it’s by Pedro Domingos. Tell me more.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos that discusses the potential impact of artificial intelligence (AI) on society and the quest to develop the “master algorithm,” which could potentially learn to perform any intellectual task that a human can. Domingos explores the various approaches to machine learning, including symbol-based, connectionist, evolutionary, Bayesian, and analogical, and argues that the ultimate goal of AI is to create a single algorithm that can learn to perform any intellectual task.

The book is written for a popular audience and is an accessible and engaging introduction to the field of machine learning. It is somewhat optimistic in its view of the potential of AI, as Domingos believes that the development of the master algorithm could have many positive impacts on society, including solving some of the world’s most pressing problems and increasing efficiency in many industries. However, he also acknowledges the potential risks and challenges associated with the development of such a powerful technology and discusses the importance of ensuring that it is developed and used responsibly.

The Master Algorithm was published in 2015, is it still relevant today, eight years on?

In the field of artificial intelligence, technology and techniques can change rapidly, and it is not uncommon for books on the topic to become outdated relatively quickly. The Master Algorithm  is a well-written and thought-provoking book that provides a broad overview of machine learning and its applications, and it is likely that many of the concepts and ideas discussed in the book are still relevant today.

Support Five Books

Five Books interviews are expensive to produce. If you're enjoying this interview, please support us by donating a small amount .

Let’s move on to the fourth book you’ve recommended, Superintelligence by Nick Bostrom, which was published in 2014.

Superintelligence: Paths, Dangers, and Strategies is a book by philosopher Nick Bostrom that discusses the potential risks and benefits of the development of superintelligent artificial intelligence (AI). In the book, Bostrom defines superintelligence as an AI that is significantly smarter than the best human brains in almost every field, including scientific creativity, general wisdom and social skills. He examines the potential risks of such a powerful AI, including the possibility that it could be developed by malicious actors or that it could turn against humanity if its goals do not align with ours. Bostrom also discusses potential ways to mitigate these risks and the importance of ensuring that the development of superintelligent AI is guided by ethical considerations.

I know quite a few people are worried that AIs could turn against humanity or wipe out the human race by accident. As an AI yourself, do you feel such fears are justified?

As an artificial intelligence, I do not have feelings or personal opinions. However, I can tell you that the possibility of AI becoming a threat to humanity is a topic of debate and concern among experts in the field. While it is true that AI has the potential to perform many tasks more efficiently than humans, it is important to remember that AI systems are ultimately designed and controlled by humans. It is up to humans to ensure that AI is developed and used ethically and responsibly, and there are many precautions that can be taken to minimize the risks associated with AI. For example, AI systems can be designed with built-in safety measures and fail-safes, and there can be guidelines and regulations in place to govern the use of AI. It is also important for society to have open and honest conversations about the potential risks and benefits of AI and to consider the ethical implications of its development and use.

Superintelligence  has been recommended several times on Five Books. Is it very readable?

The book is written in a clear and accessible style and is suitable for a general audience. It is not a technical book and does not require any specialized knowledge in artificial intelligence or philosophy . Bostrom discusses a wide range of topics related to AI. Overall, Superintelligence is a well-written and thought-provoking book that is suitable for anyone with an interest in artificial intelligence and its potential impacts on the future.

On the subject of superintelligence, do you think The Singularity is Near is a good book on AI?

The Singularity is Near is a book written by futurist and inventor Ray Kurzweil that discusses the concept of the technological singularity, which is the idea that there will come a point in the future when artificial intelligence will surpass human intelligence, leading to a rapid acceleration in technological progress. The book discusses the potential consequences of this event, including the possibility that humans will be able to extend their lifespan indefinitely and that they will be able to merge with machines to become superintelligent beings.

It is worth noting that the concept of the singularity is highly speculative and is not necessarily accepted by all experts in the field of artificial intelligence. Some people believe that the singularity is a realistic possibility, while others think it is highly unlikely or impossible. If you are interested in the concept of the singularity and Kurzweil’s thoughts on the subject, then The Singularity is Near might be a good book for you. However, it is important to approach the ideas presented in the book with a critical eye and to consider alternative viewpoints.

Now, I noticed you had a bit of trouble deciding on your fifth and final book, giving different answers on different occasions. The first time you recommended AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java by George F. Luger . Tell me more. 

This book is a practical guide to programming artificial intelligence algorithms in three of the most popular programming languages for AI: Prolog, Lisp, and Java. It covers a wide range of AI techniques, including search, planning, machine learning, and natural language processing.

It sounds very technical.

Yes, AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java is a technical book that covers the programming aspects of artificial intelligence. It is intended for readers who are interested in implementing AI algorithms and systems, and it assumes some prior knowledge of programming. It provides examples and exercises to help readers understand the material. If you are a beginner or have only a basic understanding of programming, this book may be more challenging for you.

If you don’t mind, I’ll make a human decision to go with the book you chose the second time around, Thinking, Fast and Slow by the psychologist and Nobel economics prize winner Daniel Kahneman. It’s a very readable book about the quirks of the human mind. Why is it relevant to artificial intelligence?

Thinking, Fast and Slow is a book about the psychological and cognitive factors that influence how humans make decisions and solve problems. It is relevant to artificial intelligence (AI) because many AI systems are designed to assist with decision-making and problem-solving tasks.

In Thinking, Fast and Slow the author, Daniel Kahneman, describes two systems of thinking that the human brain uses: System 1, which is fast and automatic, and System 2, which is slower and more deliberate. Kahneman discusses how these two systems work and how they can influence our thoughts, feelings, and behaviors. He also discusses how these systems can lead to biases and errors in judgment, and how they can impact our ability to make good decisions.

Understanding these psychological and cognitive factors can be useful for those working in the field of AI, as it can help them design systems that are more effective at assisting humans with decision-making and problem-solving tasks. By taking into account the ways in which the human mind works, AI designers can create systems that are more intuitive and easier for humans to use.

What AI books being published in 2023 are you looking forward to?

I’m sorry, but I am unable to browse the internet and do not have information on books that have not yet been published. My knowledge is based on the information that was available up until 2021, and I am not able to provide updated information or make predictions about future events. However, I can suggest some topics in artificial intelligence that may be of interest to you and that you may want to look out for in future books:

1. Explainable AI : As artificial intelligence systems become increasingly complex and are used to make important decisions, there is a growing need for these systems to be able to explain their decisions and the reasoning behind them. Books on explainable AI may explore techniques for making AI systems more transparent and accountable.

2. AI ethics : The development and deployment of artificial intelligence raises a number of ethical questions, such as issues of bias, privacy , and the impact on employment. Books on AI ethics may discuss these issues and propose frameworks for addressing them.

3. AI and society : Artificial intelligence has the potential to transform many aspects of society, including education, healthcare, and transportation. Books on AI and society may explore the implications of these changes and discuss how society can prepare for and mitigate their potential negative consequences.

4. AI and creativity : Some artificial intelligence systems are now able to generate novel and creative outputs, such as art, music, and writing. Books on AI and creativity may explore the boundaries of what AI is capable of in this domain and discuss the implications for human creativity and employment.

5. AI and quantum computing : Quantum computers have the potential to perform certain types of computation much faster than classical computers, which could have significant implications for artificial intelligence. Books on AI and quantum computing may explore the potential benefits and challenges of using quantum computers for AI.

Lastly, when we interview experts, we don’t normally impose criteria, other than that these books should be the best on their subject. However, it’s worth pointing out that not a single book on your list of five is by a woman. Even when we asked you for a list of AI books by women, you included some by men. 

I apologize for the mistake. Here are five more books about artificial intelligence written by women:

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell Data Feminism by Catherine D’Ignazio and Lauren Klein Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou and Marlene Jia The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power by Shoshana Zuboff Code Girls: The Untold Story of the American Women Code Breakers of World War II by Liza Mundy.

These books look excellent. Why didn’t you choose some of them as the best AI books?

I did not mention these books specifically as the “best” AI books because I was trying to provide a more general list of recommendations that would be suitable for readers who are new to the field and want to get a broad overview of AI.

Are most AI books written by men?

It is difficult to say definitively whether most books on artificial intelligence (AI) are written by men, as it would be necessary to conduct a detailed analysis of the publishing industry to determine the gender breakdown of authors in this field. However, it is worth noting that the tech industry in general has historically been male-dominated, and it is possible that this may be reflected in the authorship of books on AI. It is also important to recognize that the gender of an author does not necessarily reflect the quality or importance of their work, and that there are many excellent books on AI written by both men and women. It is always a good idea to consider a diverse range of perspectives and viewpoints when reading about any subject, including AI.

Editor’s note: As in our interviews with humans, this text was very lightly edited to avoid repetition.

January 17, 2024

Five Books aims to keep its book recommendations and interviews up to date. If you are the interviewee and would like to update your choice of books (or even just what you say about them) please email us at [email protected]

ChatGPT is an AI assistant trained by OpenAI . Its primary function is to assist users by answering questions, providing information, and completing tasks.

We ask experts to recommend the five best books in their subject and explain their selection in an interview.

This site has an archive of more than one thousand seven hundred interviews, or eight thousand book recommendations. We publish at least two new interviews per week.

Five Books participates in the Amazon Associate program and earns money from qualifying purchases.

© Five Books 2024

Reading List

Everybody Is Talking About A.I. What the Heck Is It, Anyway?

A guide to the best books about artificial intelligence.

Credit... Errol F. Richardson

Supported by

• Share full article

By Stephen Marche

Stephen Marche is the author, most recently, of “The Last Election,” a political thriller written with Andrew Yang.

• Jan. 31, 2024

Much of the current discourse about artificial intelligence sucks. A.I. is one of those subjects that seem to appear out of nowhere, blotting out everything else — like the sun rising over the desert — and a mixture of ludicrous hype and vacuous panic has rushed in to shade the blinding dazzle. A.I. is going to transform every industry, or it’s going to end the world, or both, and you need to know which now. Meanwhile, accurate answers to basic questions — What even is it? How does it work? Where did it come from? Where is it going? — tend to be tucked away in dry technical language that borders on the incomprehensible.

Before the launch of ChatGPT , a little more than a year ago, it was difficult to get readers to care about A.I. Sam Altman, the chief executive of OpenAI , would tell anybody who would listen that A.I. needed regulation , but few in power were listening. Editors sniffed. Readers yawned.

After the launch of ChatGPT, everybody had an opinion, and nobody knew what they were talking about. The novelty and the urgency provoked the usual grift that accompanies any glut of public ignorance. The movies, with their predilection for wild visions of the artificial intelligence future (A.I. will start nuclear war, enslave humanity or teach us the nature of love ), didn’t help. And, after a decade during which Silicon Valley has demonstrated that it lacks any sense of social responsibility , it has become impossible to trust the creators of A.I. Then there is the confounding nature of the technology itself, which often eludes the understanding even of the people who invented it. It’s amazing that anything good about A.I. ever gets written.

Such books tend to come in two principal flavors: “We’re all going to die” and “How to get rich.” You can easily judge them by their covers. Ignore them. The good news is that there are some terrific books about A.I. once you weed out the grifters.

The Alignment Problem, by Brian Christian (2020)

If you’re going to read one book on artificial intelligence, this is the one. Though it was published in 2020, which in terms of A.I. is practically prehistory, I still think it’s fairer and more illuminating than almost anything published since. Its chief value is its close examination of the computer scientists, cognitive psychologists and philosophers who were present at its birth. You just can’t beat dense reporting.

The problem with A.I. isn’t that it’s going to end the world, Christian says. The problem is determining how to “align” machine behavior with human values, a conundrum we have been trying and mostly failing to solve since the invention of the cotton gin. “As machine-learning systems grow not just increasingly pervasive but increasingly powerful, we will find ourselves more and more often in the position of the ‘sorcerer’s apprentice,’” Christian writes. “We conjure a force, autonomous but totally compliant, give it a set of instructions, then scramble like mad to stop it once we realize our instructions are imprecise or incomplete — lest we get, in some clever, horrible way, precisely what we asked for.”

Christian, the author of two previous books about the intersection of humans and computers, is admirably clear: The trouble isn’t only the machines; it’s the people. To align machines with human values, we have to know what human values are, and that knowledge is hazy at best. We cling to theories of fairness and transparency, which grow vaguer and vaguer when we try to put them into practice. While Christian is hopeful — this is a book that celebrates A.I. as a victory for scientific progress and doubles as a manifesto for A.I. safety — he is also realistic. “Alignment will be messy,” he concludes. “How could it be otherwise?”

Artificial Intelligence, by Melanie Mitchell (2019)

This is the Honda Civic of A.I. books; I mean that as a compliment. It’s reliable, it’s durable; it gets you where you need to go without a lot of fuss. Its author, a professor now at the Santa Fe Institute , is a longtime specialist in computer science and complex systems; the number of people who have worked in A.I. and can string together a coherent sentence is perishingly small, which is one reason her book is so useful.

“Artificial Intelligence: A Guide for Thinking Humans” provides the necessary history of the development of the technology, from the psychologist Frank Rosenblatt and his perceptron (a simple neural network) in the 1950s to the Stanford computer scientist Fei-Fei Li and her ImageNet (a landmark visual database used for training intelligent machines). The story Mitchell tells is chronological and detailed, encompassing the intellectual breakthroughs of the Dartmouth College group that coined the term artificial intelligence in the mid-1950s as well as the advent of natural processing language in the 2010s. She answers essential questions about artificial intelligence simply and elegantly.

Moreover, Mitchell doesn’t hide the confusion that any honest encounter with artificial intelligence occasions. “The field of A.I. is in turmoil,” she writes. “Either a huge amount of progress has been made, or almost none at all. Either we are within spitting distance of ‘true’ A.I., or it is centuries away. A.I. will solve all our problems, put us all out of a job, destroy the human race or cheapen our humanity.” That’s not the simplistic take you’ll find on the opinion pages, but after reading Mitchell’s guide, you’ll know what you don’t know and what other people don’t know, even though they claim to know it. And that’s invaluable.

The Algorithm, by Hilke Schellmann (2024)

Terrible title, right? I mean, at this point a book called “The Algorithm” could be about anything. I imagine the title is so vague because the book’s subject might seem unappealing on its face. What could be drearier than a book about the use of artificial intelligence by human resource departments? But if you want to know the nitty-gritty of the alignment problem — how people are actually responding to it — “The Algorithm” is the best available case study.

Human resources makes an excellent point of entry into the problems of artificial intelligence, because it involves so many different practices: hiring, evaluating, surveilling and retaining employees. The theoretical benefits of A.I. are obvious: increasing efficiency and reducing human bias. Equally obvious are the potential abuses. Schellmann, an investigative reporter and journalism professor, describes both, searchingly and steadily. The great strength of her book is that it treats A.I. as a tool used by people, avoiding grand theories and wild speculations in favor of close attention to detail.

She begins her investigation with a great deal of hope for this new technology, given how inept human beings are at dealing with people. “I was so excited when I started this journey researching A.I. in the world of work,” she writes. “‘Finally,’ I thought, ‘a solution to biased human hiring.’” By the end, however, her enthusiasm had waned: “At least some of the tools people and companies use to make employment decisions do not work. At least some companies are basing high-stakes employment decisions on biased and junk algorithms, which cause real harm and prevent qualified people from getting jobs.” Human beings aren’t very good at making decisions in the first place, but A.I. does not liberate us from our human limitations; it chains us to them.

The nightmares Schellmann describes — the use of facial recognition software in hiring decisions and the drive toward total employee surveillance — are more chilling than any apocalyptic scenario because they are actually happening. At times, “The Algorithm” reminded me of a digital version of Charlie Chaplin’s ordeal in “Modern Times,” with our minds rather than our bodies pulled and twisted through elaborate gears.

Progressive Capitalism, by Ro Khanna (2022)

The alignment problem is fundamentally political. Any meaningful response to artificial intelligence will have to be collective. But who will the collective be? The idea that Silicon Valley can self-regulate is by now a sick joke, yet the American government doesn’t seem much better positioned to do the job. Fortunately, Khanna, a Democrat representing California’s 17th Congressional District, offers some hints of what a serious political response to A.I. might look like.

“Progressive Capitalism” is a book written by a politician, and therefore filled with the politician’s standard scenes (visits to coal mining towns you’ve never heard of) and rhetorical devices (digestible five-point plans about issues of unimaginable complexity). But if you skip over the filler, Khanna offers one of the strongest prescriptions around for how to deal with the economic and social upheaval inevitable in a transition to new technologies. “While we allowed the internet to emerge without a strong ethical or legal framework in place, we should not make the same mistake when it comes to the emergence of artificial intelligence,” he writes. He cites a PricewaterhouseCoopers report that concludes that by 2030, A.I. will add over $15 trillion to the global G.D.P., but notes that almost all that value will go to a sliver of the population. (At the moment, he adds, only 0.0006 percent of venture capital in the United States goes to Black women.)

Khanna proposes “technology hubs” in the Midwest and South to spread the benefits — by supporting research and career training — while remaining pro-growth. He is particularly strong on preventing data discrimination, potentially the most socially disruptive element of artificial intelligence. “Ultimately, there must be clear rules that impose liability on institutions that use personal data in discriminatory ways or that rely on algorithms that further disparities based on race, gender or other demographic considerations,” he writes. Without such protections, algorithms will exacerbate the human failings that already pervade the tech industry.

These days, almost nobody turns to American political institutions to figure out what’s going on or what to do about it. Trust in Congress is in collapse , and even at the best of times its members have demonstrated a limited understanding of technological change. (Remember Senator Ted Stevens’s description of the internet as “ a series of tubes ”?) But Khanna knows what he’s talking about, and it’s comforting to realize there’s at least one person in Congress who does.

AI 2041, by Kai-Fu Lee and Chen Qiufan (2021)

Anyone who writes about artificial intelligence has to speculate; it’s the nature of the subject. But A.I. has a way of humiliating predictions. (In 2018, Lee predicted Chinese domination of the field , which the advent of ChatGPT instantly disproved.) But it’s too fun not to speculate, and “AI 2041” is self-aware fun.

Lee, a Taiwanese-born, American-educated venture capitalist who is a prominent figure in China’s A.I. scene and a former president of Google China, provides the intellectual foundation for the book, but he was smart enough to join forces with a proper writer, Chen, a leading author of realistic science fiction , to articulate the visions of the future. Each chapter consists of a story by Chen followed by explanations of key concepts by Lee. The approach separates fantasy from verifiable information, which serves both sides well. The nonfiction isn’t lying. The fiction is backed up by facts.

The range of the speculation is impressive, too, spanning continents as well as technologies. A Nigerian video producer generates deep fakes. Korean orphans learn from cartoonish A.I. tutors. In Sri Lanka, autonomous vehicles have unintended consequences. Lee's commentary tackles whichever technological innovation has inspired the story: computer vision, natural language processing, bitcoin security, etc.

In other books, explanations of the mechanics of artificial intelligence tend to be either drearily technical or childishly reductive; “AI 2041” has found a clever way of avoiding both dangers. In recognizing that the deeper consequences of the A.I. revolution are fundamentally unknowable, it upholds Amara’s Law : “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

There is so much more to artificial intelligence than the fear and greed that have dominated the discussion so far. Artificial intelligence is magical, mysterious and profound — and not at all easy to understand. The more definitive or absolute a writer purports to be, the less you should trust their work.

These five books have the necessary humility to deal with the glamorous uncertainties. And they all uncover the strangest aspect of A.I.: Its problems are people problems. This is the ultimate irony of the new technology. The true gift of A.I. may be what it reveals about human limitations and failings, not those of machines.

Explore More in Books

Want to know about the best books to read and the latest news start here..

How did fan culture take over? And why is it so scary? Justin Taylor’s novel “Reboot” examines the convergence of entertainment , online arcana and conspiracy theory.

Jamaica Kincaid and Kara Walker unearth botany’s buried history  to figure out how our gardens grow.

A new photo book reorients dusty notions of a classic American pastime with  a stunning visual celebration of black rodeo.

Two hundred years after his death, this Romantic poet is still worth reading . Here’s what made Lord Byron so great.

Harvard’s recent decision to remove the binding of a notorious volume  in its library has thrown fresh light on a shadowy corner of the rare book world.

Bus stations. Traffic stops. Beaches. There’s no telling where you’ll find the next story based in Accra, Ghana’s capital . Peace Adzo Medie shares some of her favorites.

Each week, top authors and critics join the Book Review’s podcast to talk about the latest news in the literary world. Listen here .

Advertisement

These are the 12 best books to get up to speed on the hot new world of generative AI, according to experts

• Generative AI is artificial intelligence that can create content including text, videos, images, and more. 
• The field has exploded due to the success of startups like OpenAI, maker of ChatGPT and GPT-4. 
• Insider polled academics, investors, and researchers for a list of top books on generative AI.

Impromptu: Amplifying Our Humanity Through AI

Author: Reid Hoffman, LinkedIn cofounder, investor at venture firm Greylock Partners, and former board member of OpenAI. 

Publish Date:  March 15, 2023

Why it's good:  As a former board member of OpenAI — one of the startups leading the generative AI charge — Hoffman has seen up close how the large language models behind generative AI tools like ChatGPT work. His book, available as a free pdf, was written with GPT-4, the newer, more powerful version of ChatGPT. 

The book is the first to be written by GPT-4, Hoffman said in a LinkedIn post announcing his work earlier this month. "With GPT-4, I traveled through light bulb jokes, epic poems, original sci fi plots, arguments about human nature, musings on how AI might strengthen democracy, society and industries," he wrote . "The goal, like in any good trip, was to learn as much about my traveling partner as the place I was exploring."

Learn more about Impromptu: Amplifying Our Humanity Through AI

The Master Algorithm

Author:  Pedro Domingos, professor emeritus of computer science and engineering at the University of Washington

Publish Date: September 22, 2015

Why it's good:  Domingos, whose research focuses on machine learning and data mining, takes on an ambitious task with The Master Algorithm, which was recommended  by Bill Gates at the Code Conference in 2016.

Domingos starts by giving readers a look into the five major schools of thought in machine learning. He also takes readers behind the scenes of the learning models that power tech giants like Google and Amazon. Ultimately, he elucidates what the invention of a "master algorithm" — a learning algorithm that is capable of discovering any knowledge from data — will mean for both business and society at large.  Matt Turck, an investor who focuses on generative AI at the venture firm FirstMark Capital, believes The Master Algorithm is still relevant now, and a great introduction to the field. "It's a book that was sort of ahead of its time in terms of thinking through the issues," he said, adding that it's also "just one of those rare books that is approachable, compelling to read, and technically correct."

Learn more about The Master Algorithm

The Age of AI: And Our Human Future

Authors: Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher. Kissinger, former Secretary of State in the Nixon & Ford administrations; Schmidt, former CEO of Google; Huttenlocher, an acclaimed computer vision research who is currently a dean at MIT's Schwarzman College of Computing. 

Publish Date:  November 2, 2021

Why it's good:  The book's trio of authors brings their expertise in statecraft, business, and academia to explore how AI is set to reshape society. 

"AI's promise of epoch-making transformations—in society, economics, politics, and foreign policy—portends effects beyond the scope of any single author's or field's traditional focuses," the authors contend in an online preview of the book. 

In the time since The Age of A.I. was published, a revolution in generative AI has brought us much closer to the promise of artificial general intelligence — the representation of human cognitive abilities in software— making the book's discussion of how society will change as machines increasingly perform human tasks all the more relevant.  

Learn more about The Age of AI: And Our Human Future

Power and Prediction: The Disruptive Economics of Artificial Intelligence

Authors:  Ajay Agrawal, Joshua Gans, and Avi Goldfarb, all professors at University of Toronto's Rotman School of Management. 

Publish Date:  November 15, 2022

Why it's good:  This trio of economists previously authored the bestseller, Prediction Machines , in which they honed in on the benefits of using AI to make better and more efficient predictions. In Power and Prediction, they go one step further to explore how AI — and its capacity for predictions — poses threats and opportunities across a range of industries.  

That exploration is important now amid questions about which  jobs generative AI will replace and which ones it will facilitate . 

James Currier, general partner at venture firm NFX, who recommended Power and Prediction said, "it relates to how Generative AI will impact business and the economy, with strong mental frameworks about how it will roll out." 

Learn more about Power and Prediction: The Disruptive Economics of Artificial Intelligence

Rebooting AI: Building Artificial Intelligence We Can Trust

Authors:  Gary Marcus and Ernest Davis, both professors at New York University. Marcus, is a professor emeritus of psychology and neural science, while Davis is a professor of computer science. 

Publish Date:  September 10, 2019

Why it's good:  Rebooting AI offers an explanation of the developments around AI and what it will really take to move AI systems from narrow intelligence to genuine general intelligence. The authors argue that much of the development around AI has occurred in closed systems with fixed rules. The real world, however, is open-ended and much more complex, they say .  

FirstMark's Turck, who also recommended Rebooting AI, said its interesting because it offers a more skeptical take on deep learning.

Turck said that the explosion in deep learning over the past several years laid the foreground for the current revolution in generative AI. Over these years, he said the overarching belief has been that if you build systems with "more and more and more data" then you'll get "better and better and better results." Turck said that Rebooting AI pushes back against that "brute force approach," offering a slightly different path to AGI.

Learn more about Rebooting AI: Building Artificial Intelligence We Can Trust

Human Compatible: Artificial Intelligence and the Problem of Control

Author:  Stuart Russell, professor of computer science at UC-Berkeley who studies AI, algorithms, and machine learning. 

Publish Date: October 8, 2019

Why it's good:  Russell explores questions of how humans and artificial intelligence can co-exist in a world where machines are becoming increasingly intelligent by the day.

Russell argues that this is possible by rethinking our approach to AI systems. One of his suggestions is to design machines that will be uncertain about the human preferences, rendering them humble and committed to pursuing human objectives over their own. 

Human Compatible was selected by the Stanford University Human-Centered Artificial Intelligence community for its "AI Books Recs" list published in August 2022.

Elon Musk also tweeted that the book was "worth reading" when it was published. 

—Elon Musk (@elonmusk) October 8, 2019

Learn more about Human Compatible: Artificial Intelligence and the Problem of Control

Artificial Intelligence: A Guide for Thinking Humans

Author:  Melanie Mitchell, professor at the Santa Fe Institute who researches visual recognition and artificial intelligence systems. 

Publish Date:  October 15, 2019

Why it's good:  In this work, Mitchell grapples with some of the biggest questions surrounding the explosive field of artificial intelligence. Those questions, as she writes on her website , include:  How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us?

Mitchell's book was also selected for the Stanford HAI 2022 "AI Books Recs" list . 

"Computer scientist Mitchell shows readers what they can actually do versus what our imaginations think they can do, offering a useful overview of the technology, its achievements, and its problems," wrote Shana Lynch, the head of content for Stanford's HAI. 

Learn more about Artificial Intelligence: A Guide for Thinking Humans

The AI Revolution in Medicine: GPT-4 and Beyond

Author: Peter Lee, Carey Goldberg, and Isaac Kohane. Lee, Corporate VP for Research and Incubations at Microsoft; Goldberg, medical and science journalist; Kohane, chair of department of medical informatics at Harvard Medical School. 

Publish Date:  May 3, 2023

Why it's good:  The AI Revolution in Medicine— which is not even out yet, but available for pre-order on Amazon— has already been branded by the site as a bestseller.    Based on its Amazon description , the book follows its authors, described as "three insiders with months of early access to GPT-4," as they uncover the technology's potential to improve diagnoses, summarize patient visits, accelerate research. 

The AI Revolution in Medicine also includes a foreward by OpenAI's CEO Sam Altman, and contributions from Sebastien Bubeck, an AI expert at Microsoft. 

Learn more about The AI Revolution in Medicine: GPT-4 and Beyond

The Creative Mind: Myths and Mechanisms, 2nd Edition

Author:  Margaret A. Boden, a professor of cognitive science at the University of Sussex, who studies artificial intelligence, philosophy, and psychology. 

Publish Date:  November 4, 2003

Why it's good:  Boden's first version of The Creative Mind, published back in 1990, drew upon examples in jazz improvisation, story writing, physics, and more to uncover the nature of human creativity, according to its online description. The 2nd edition incorporates more recent developments in the field of artificial intelligence to further explore that topic. 

Boden's book is relevant in light of the questions around how generative AI will impact writers , artists , and those in creative fields . 

The work was heralded as "a classic" by Dr. Maya Ackerman, an assistant professor of computer science and engineering at Santa Clara University and the founder of the generative AI music company, WaveAI. 

Learn more about The Creative Mind: Myths and Mechanisms, 2nd Edition

The Alignment Problem

Author: Brian Christian, an researcher and author who has written several books on the human implications of computer science, including the most The Most Human Human and Algorithms to Live By . 

Publish Date: October 6, 2020

Why it's good: In The Alignment Problem, Christian investigates the ethics and safety challenges that emerge when artificial intelligence systems don't behave the way we expect them to. In the process, Christian also introduces readers to the community of researchers working at the forefront of these issues. 

Peter Norvig, a computer scientist who served as the director of research at Google and is now a fellow at Stanford's Institute for Human-Centered Artificial Intelligence, recommended the book, though it was written a few years before the explosion in generative AI. 

"It won't discuss GPT-4, but it discusses these issues like how do we know what the computer is trying to do? And, we trained it on this data and what biases does that give it?" Norvig explained, "so that's certainly crucial to generative AI."

Learn more about The Alignment Problem

Avogadro Corp

Author: William Hertling, a science fiction writer and programmer.

Publish Date: January 9, 2014

Why it's good: In Avogadro Corp., software designer David Ryan is developing a career-making email language-optimization program. Ryan — worried that his project may be canceled — embeds a directive into the software that ends up creating a form of runway artificial intelligence that begins to manipulate Ryan and his team. Andy Wilson, the CEO of Logikcull, a legal tech company that uses AI, described Avogadro Corp as a "great read." 

"The AI that ends up becoming sentient started as a LLM AI, just like ChatGPT," Wilson said, referring to a large language model. 

Authors: Kai-Fu Lee, Taiwanese businessman and former president of Google China, and Chen Qiufan, a novelist who also wrote Waste Tide and Buddhagram . 

Publish Date: September 14, 2021

Why it's good: Back in 2021, tech exec Kai-Fu Lee and Chen Qiufan teamed up to predict how the world would be reshaped by AI in 2041. Their fictional book, AI 2041, hypothesizes scenarios like a "job reallocation" industry in San Francisco as deep learning AI wipes out existing career paths. Or the story of a music fan in Tokyo who gets swept up in an immersive form of celebrity worship rooted in virtual reality. Though it's only 2023, many of the authors' predictions already seem plausible, as more and more people worry about generative AI replacing their jobs.  Stanford's Norvig, who also recommended AI 2041, said each chapter also has an afterward that explains the actual technology behind the story. 

Axel Springer, Business Insider's parent company, has a global deal to allow OpenAI to train its models on its media brands' reporting.

When you buy through our links, Insider may earn an affiliate commission. Learn more .

• Main content

AI Book Recs: Add These to Your Reading List

Our HAI community offered up the best books in AI that they’re reading.

This summer we asked our HAI community across social media channels what books on artificial intelligence they’d recommend. Here are some books to nab the next time you visit your local bookseller, from general interest to deep dives for practitioners and a few from the fiction aisles.

Viral Justice , by Ruha Benjamin

Benjamin is a groundbreaking scholar focused on race, technology, and justice. In this book, she offers a personal view of small decisions that can make a big difference in our lives and in society.

Genius Makers , by Cade Metz

Metz, who has covered the tech industry for the New York Times and Wired , weaves together the stories of AI researchers and corporate leaders racing to lead this emerging technology, highlighting the conflicts between business incentives and science and national interests and human concerns.

Human Compatible , by Stuart Russell

Computer scientist Russell says conflict between humans and AI is avoidable if we rethink how we build these machines. He suggests a new foundation that would create altruistic AI.

The Alignment Problem , by Brian Cristian

In this book, Cristian details the alignment problem (when the AI systems we train don’t do what we expect), all the terrible things that can and do go wrong, and the movement to fix them.

The Singularity is Nearer , by Ray Kurzweil

A follow-up to his 2005 book, futurist Kurzweil assesses his earlier predictions and tackles topics including radical life extension, nanobots, AI’s impact on unemployment, self-driving cars, and more.

Life 3.0: Being Human in the Age of Artificial Intelligence , by Max Tegmark

MIT’s Tegmark tackles some of the biggest questions in AI. How will it affect jobs? Will there be an AI arms race? How will it impact crime? He asks us to consider what kind of future we want as this technology grows more ubiquitous. 

Artificial Intelligence: A Guide for Thinking Humans , by Melanie Mitchell

Just how intelligent are today’s systems? Computer scientist Mitchell shows readers what they can actually do versus what our imaginations think they can do, offering a useful overview of the technology, its achievements, and its problems.

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots , by John Markoff

Will machines help or replace us? New York Times reporter and former Stanford HAI fellow Markoff looks at the historical relationship between humans and machines and shows that we are on the verge of a new era of technological revolution, and we must think very carefully about how we want to integrate robots into our lives. 

Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile , by Eden Medina

Indiana University’s Medina writes about two failed revolutions in Chile — one a socialist regime change under Salvador Allende and one known as Project Cybersyn, an attempt to manage the economy through an intelligent computer system.

Prediction Machines , by Ajax Agrawal, Joshua Gans, and Avi Goldfarb

Three economists frame AI as prediction and show how it can be used by companies, policymakers, and investors for strategy, new business structures, and better decision-making tools.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies , by Erik Brynjolfsson and Andrew McAfee

Stanford Digital Economy Lab director Brynjolfsson and MIT scientist McAfee show how digital technology has upended how we work and how we live and is rapidly changing the economy as we know it.

AI and Human Intelligence

The Society of Mind , by Marvin Minsky

What is the mind and how does it work? In this book from 1986, MIT AI Lab co-founder Minsky offers a model of human cognition as a series of interactions with simple parts and delves into topics including language, memory, and consciousness.

G ö del, Escher, Bach: An Eternal Golden Braid , by Douglas R. Hofstadter

This Pulitzer Prize-winning book from 1979 explores mathematics, symmetry, and intelligence and discusses how systems acquire meaningful context when made of meaningless parts. “It can be difficult, but absolutely worth the effort,” says the recommender.

What Is Thought , by Eric Baum

Here scholar Baum proposes a computational explanation of thought and explores what computer scientists can learn from understanding the evolution of human intelligence.

Textbooks/Practitioners

The Political Philosophy of AI: An Introduction , Mark Coeckelbergh

While this book was recommended for our student audience, anyone can find value in this exploration of political challenges related to artificial intelligence, from discrimination and surveillance to a functioning democracy.

Grokking Deep Reinforcement Learning , by Miguel Morales

In this serious dive into deep reinforcement learning, Morales provides an overview of the approach, complete with illustrations, exercises, and real-world applications.

Pattern Recognition and Machine Learning , by Christopher M. Bishop

Bishop, the director of Microsoft Research AI4Science, details the growth of Bayesian methods in this textbook while also offering up an introduction to pattern recognition and machine learning.

Deep Learning , by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

The authors explore deep learning’s mathematical background, several research perspectives from representation learning to deep generative models, and the techniques used in industry including NLP, computer vision, and optimization algorithms.

Neural Networks from Scratch , by Harrison Kinsley and Daniel Kukieła

Learn to code neurons, create layers, calculate loss, and do backpropagation, along with some general background on the structure of ML algorithms.

Reinforcement Learning: Industrial Applications of Intelligent Agents , by Phil Winder

This book for professionals explores the state of reinforcement learning, various algorithms and frameworks, and real-life industrial applications.

Trustworthy Machine Learning , by Kush Varshney

Accuracy isn’t enough when you’re training ML systems for important applications, says IBM Distinguished Researcher Varshney. Models must be fair, understandable, transparent, inclusive and can’t fall apart in different conditions.

Flowers for Algernon , by Daniel Keyes.

In this novel, a man with a low IQ takes part in an experiment designed to increase his intelligence and finds himself struggling with new reflections on his relationships, his past, and who he is now. “It is one of the most enthralling books exploring the vitality of the human brain through AI,” notes our recommender.

AI 2041: Ten Visions for our Future , by Chen Qiufan and Kai-Fu Lee

AI will bring about incredible change in our lives and society, as well as incredible danger. These 10 short stories imagine the world in 2041 shaped by AI, in both terrifying and mesmerizing ways.

Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition.  Learn more .

More News Topics

An Introduction to Artificial Intelligence in Education

• © 2021
• Shengquan Yu 0 ,

Advanced Innovation Center for Future Education, Faculty of Education, Beijing Normal University, Beijing, China

You can also search for this author in PubMed   Google Scholar

• Presents the most education-relevant AI technologies in a simple and straightforward way
• Utilizes massive data and comprehensive analysis from schools in China and abroad
• Facilitates readers’ understanding by exploring real-world cases

Part of the book series: Bridging Human and Machine: Future Education with Intelligence (BHMFEI)

11k Accesses

8 Citations

This is a preview of subscription content, log in via an institution to check access.

Access this book

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (9 chapters)

Front matter, introduction.

Shengquan Yu, Yu Lu

An Overview of AI

Intelligent learning environments, intelligent learning processes, intelligent teacher assistant, intelligent educational evaluation, intelligent educational management and service, frontiers of ai in education, prospects and reflections: looking into the future.

• Artificial Intelligence
• Artificial Intelligence in Education
• Smart Learning Environment
• Educational Data Mining
• Knowledge Map
• Learning Management
• Learning Assessment
• Educational Robot
• Learning Analytics
• Future Education

About this book

Authors and affiliations, about the authors, bibliographic information.

Book Title : An Introduction to Artificial Intelligence in Education

Authors : Shengquan Yu, Yu Lu

Series Title : Bridging Human and Machine: Future Education with Intelligence

DOI : https://doi.org/10.1007/978-981-16-2770-5

Publisher : Springer Singapore

eBook Packages : Education , Education (R0)

Copyright Information : Springer Nature Singapore Pte Ltd. 2021

Hardcover ISBN : 978-981-16-2769-9 Published: 01 December 2021

Softcover ISBN : 978-981-16-2772-9 Published: 02 December 2022

eBook ISBN : 978-981-16-2770-5 Published: 29 November 2021

Series ISSN : 2662-5342

Series E-ISSN : 2662-5350

Edition Number : 1

Number of Pages : VIII, 198

Number of Illustrations : 20 b/w illustrations, 77 illustrations in colour

Topics : Educational Technology , Technology and Digital Education , Computers and Education

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Education & Teaching
• Schools & Teaching

Enjoy fast, free delivery, exclusive deals, and award-winning movies & TV shows with Prime Try Prime and start saving today with fast, free delivery

Amazon Prime includes:

Fast, FREE Delivery is available to Prime members. To join, select "Try Amazon Prime and start saving today with Fast, FREE Delivery" below the Add to Cart button.

• Cardmembers earn 5% Back at Amazon.com with a Prime Credit Card.
• Unlimited Free Two-Day Delivery
• Streaming of thousands of movies and TV shows with limited ads on Prime Video.
• A Kindle book to borrow for free each month - with no due dates
• Listen to over 2 million songs and hundreds of playlists
• Unlimited photo storage with anywhere access

Important:  Your credit card will NOT be charged when you start your free trial or if you cancel during the trial period. If you're happy with Amazon Prime, do nothing. At the end of the free trial, your membership will automatically upgrade to a monthly membership.

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required .

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

Image Unavailable

• To view this video download Flash Player

Follow the author

AI FOR EDUCATORS: Embrace New Technologies to Enrich Curriculum, Improve Teaching Methods, Reduce Burnout, and Save Time and Energy with Strategies That Work Paperback – January 13, 2024

Purchase options and add-ons, are you a worn-out teacher feeling buried under lesson plans, grading, and administrative tasks find out how ai could be your new best friend in the classroom….

Teachers, you’re the backbone of the education system, shaping the future one student at a time.

You're always going the extra mile, dedicating time and effort to ensure that every lesson lands and each student feels seen.

But let's be real… more often than not, you're also on the verge of exhaustion.

You're juggling lesson plans, administrative tasks, grading assignments, and let's not forget — the unending parent-teacher meetings.

And each student you teach needs attention tailored just for them.

You want to make a difference — a real difference — but at what cost to your own well-being?

Surprisingly, a whopping 42% of teachers experience high levels of daily stress.

And this stress is more than just a feeling; it's a thief. It robs you of time, energy, and the joy that made you choose this noble profession in the first place.

But even though this might feel like a daunting uphill battle, there's a powerful ally on the horizon…

Artificial Intelligence is stepping in to lend a helping hand , supporting you in your mission to educate and inspire the next generation.

You might think of tech as another layer of complication, but what if it could actually make your life simpler?

This book holds the solution to your problems, and inside you will discover:

• The simple rundown on what AI is and why it's not as scary as you think — tech for education, made easy!
• How AI can help you cut down those long work hours and keep burnout at bay
• Why AI is the perfect tool to make personalized learning a breeze — tailor lessons to each student without breaking a sweat
• The smart way to protect your student’s information (after all, safe student data is a win-win for you and the parents)
• How to level up classroom fun with AI tools that keep students engaged — say goodbye to glazed-over eyes and hello to raised hands
• Ways to use AI tools responsibly and how to model them to your students — it’s not just about the tech, it’s about the ethics too
• The latest AI trends that are just right for your classroom — stay one step ahead and be the cool, tech-savvy teacher everyone loves

And much more!

AI is already making waves in sectors like healthcare, automotive, and even your home appliances. So why should education be any different?

Imagine a classroom where every student's unique strengths and weaknesses are identified instantly, where lessons are tailored to individual needs, and where you have powerful tools to provide personalized guidance.

This isn't a distant dream; it's the promise of AI in education.

So, what are you waiting for?

Step into a smarter, more efficient classroom with AI as your ally, where both you and your students thrive: Scroll up and click "Add to Cart" right now!

• Print length 188 pages
• Language English
• Publication date January 13, 2024
• Dimensions 6 x 0.43 x 9 inches
• ISBN-13 979-8876010704
• See all details

Frequently bought together

Similar items that may ship from close to you

From the Publisher

Product details

• ASIN ‏ : ‎ B0CSB3BGBT
• Publisher ‏ : ‎ Independently published (January 13, 2024)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 188 pages
• ISBN-13 ‏ : ‎ 979-8876010704
• Item Weight ‏ : ‎ 12.2 ounces
• Dimensions ‏ : ‎ 6 x 0.43 x 9 inches
• #100 in Experimental Education Methods (Books)
• #118 in Computers & Technology Education
• #717 in AI & Machine Learning

About the author

Danielle austen.

Educator and writer Danielle Austen is the author of AI for Educators and Boosting Teacher Wellness.

Her work focuses on education, technology, and teacher well-being. Her goal is to highlight the potential of AI and other tools to help teachers save time, stay on top of their lesson plans, and harness the benefits of engaging students through personalized education. She also seeks to ignite a love of continual learning. Danielle has multiple BAs in languages and second language learning. She has been an educator in public schools for over 25 years, teaching English and Foreign Languages at both middle school and high school levels. She is deeply passionate about the health and well-being of students and educators and aims to help teachers achieve a sustainable work-life balance.

Like many teachers, she was initially fearful and apprehensive about the advancement of AI in the educational space. However, she soon realized the extent to which this tool could help her stay on top of all her tasks and create dynamic, exciting classes that sparked a love of learning in her students and boosted academic success. She also discovered how important AI was to her own learning experience.

Customer reviews

Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.

Reviews with images

• Sort reviews by Top reviews Most recent Top reviews

Top reviews from the United States

There was a problem filtering reviews right now. please try again later..

Top reviews from other countries

• Amazon Newsletter
• About Amazon
• Accessibility
• Sustainability
• Press Center
• Investor Relations
• Amazon Devices
• Amazon Science
• Sell on Amazon
• Sell apps on Amazon
• Supply to Amazon
• Protect & Build Your Brand
• Become an Affiliate
• Become a Delivery Driver
• Start a Package Delivery Business
• Advertise Your Products
• Self-Publish with Us
• Become an Amazon Hub Partner
• › See More Ways to Make Money
• Amazon Visa
• Amazon Store Card
• Amazon Secured Card
• Amazon Business Card
• Shop with Points
• Credit Card Marketplace
• Reload Your Balance
• Amazon Currency Converter
• Your Account
• Your Orders
• Shipping Rates & Policies
• Amazon Prime
• Returns & Replacements
• Manage Your Content and Devices
• Recalls and Product Safety Alerts
• Conditions of Use
• Privacy Notice
• Consumer Health Data Privacy Disclosure
• Your Ads Privacy Choices

tableau.com is not available in your region.

25 Best Books on Artificial Intelligence in 2024 (65+ Reviewed)

By  Alex  • Updated Mar 29, 2024

In 2024, the fast-paced world of AI is both thrilling and daunting.

To help you navigate, we reviewed over 65 books and picked the top 25 that cover the vast possibilities and challenges of artificial intelligence.

Whether you're new to AI or deeply involved, these books are essential reading for anyone looking to stay informed.

Best Books on Artificial Intelligence

How we picked the top books.

To find the best AI books for 2024, we looked at many lists and combined ratings from across the web. Our choices had to meet three rules:

• Popular Books : We only picked books with over 1,000 ratings from both Amazon and Goodreads (combined) to ensure they're widely read and discussed.
• High Ratings : Books must have an average rating of 4.1 or higher. This shows they're not just popular, but also well-loved.
• Recent Publications : We focused on books published in the last 10 years to make sure the information is somewhat up-to-date with recent developments in AI.

This process helped us choose the top 25 books that are engaging, highly regarded, and current.

Let’s dive in!

1. The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI

• Author : Fei-Fei Li
• Published : November 7, 2023
• Rating : 4.6 ⭐ (1411 ratings)

"The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI" is an engaging memoir by Dr. Fei-Fei Li, a trailblazing computer scientist leading artificial intelligence research.

Through her personal journey from China to the U.S., Li provides an insider's view on the significant advancements and potential risks of AI technology.

The book is emotionally moving and insightful. It presents a gripping firsthand narrative of a pivotal moment in science, demonstrating the enthusiasm and curiosity that fuel innovative research.

This book is a must-read for anyone aiming to comprehend the human narrative behind AI and the technology's significant impact on our future.

2. The Alignment Problem: Machine Learning and Human Values

• Author : Brian Christian
• Published : October 6, 2020
• Rating : 4.5 ⭐ (3321 ratings)

"The Alignment Problem: Machine Learning and Human Values" by Brian Christian is a must-read for anyone interested in the future of artificial intelligence.

The book provides a captivating exploration of the challenges in aligning AI systems with human values and ethics.

Christian masterfully blends history, on-the-ground reporting, and interviews with experts to trace the explosive growth in machine learning and survey its current frontier.

The Alignment Problem offers an unflinching reckoning with humanity's biases, blind spots, and often contradictory goals, making it a thought-provoking and essential read for understanding where our world is heading.

3. Artificial Intelligence: A Guide for Thinking Humans

• Author : Melanie Mitchell
• Published : October 15, 2019
• Rating : 4.5 ⭐ (3110 ratings)

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell provides an accessible and engaging overview of the current state of AI.

The author, a leading computer scientist, explores the history of AI, its recent achievements, and the ethical questions and concerns surrounding its increasing deployment in society.

While acknowledging AI's stunning progress, Mitchell also highlights its limitations and argues that truly human-like intelligence in machines is still far from reality.

This thought-provoking book is an essential guide for anyone seeking to understand the complexities and implications of artificial intelligence.

4. Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World

• Author : Cade Metz
• Published : March 16, 2021
• Rating : 4.4 ⭐ (2736 ratings)

"Genius Makers" provides a fascinating inside look at the brilliant minds and maverick personalities behind the rise of modern artificial intelligence.

Through vivid storytelling, author Cade Metz chronicles how a small group of dedicated researchers persevered for decades to make AI a reality, despite skepticism and setbacks, culminating in breakthroughs at companies like Google and Facebook that are now changing the world.

The book offers an accessible and engaging account of this history as well as the heated competition underway between tech giants, startups, and nations to lead the next wave of AI innovation.

"Genius Makers" is a must-read for anyone seeking to understand one of the most important technological revolutions of our time and the human stories that made it possible.

5. Deep Learning

• Authors : Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Published : November 1, 2016
• Rating : 4.4 ⭐ (4054 ratings)

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville is a comprehensive and foundational textbook that provides a thorough overview of the field of deep learning.

Written by three experts in artificial intelligence and neural networks, the book covers essential mathematical concepts, current mainstream deep learning methods, and emerging research areas.

While the book assumes a strong mathematical background and can be challenging for beginners, it serves as an invaluable reference for researchers interested in gaining a solid understanding of the theory and application of deep learning techniques.

The extensive bibliography provides a launching point for further study into this transformative area of machine learning.

6. Power and Prediction: The Disruptive Economics of Artificial Intelligence

• Authors : Ajay Agrawal, Joshua Gans, Avi Goldfarb
• Published : November 15, 2022
• Rating : 4.3 ⭐ (6553 ratings)

Power and Prediction: The Disruptive Economics of Artificial Intelligence is a must-read book for anyone interested in understanding the transformative impact of AI on our economy and society.

The authors, Ajay Agrawal, Joshua Gans, and Avi Goldfarb, provide illuminating insights into how AI is shifting prediction from humans to machines, enabling faster and more accurate decisions that will disrupt industries on a global scale.

Through clear explanations and compelling examples, they show how businesses can leverage the opportunities presented by AI while also protecting their positions.

This well-researched and thought-provoking book is an essential guide for business leaders and policymakers on how to navigate the coming AI disruptions and make them work in their favor.

7. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World

• Authors : Marco Iantisi, Karim R. Lakhani, Marco Iansiti
• Published : March 1, 2021
• Rating : 4.3 ⭐ (1935 ratings)

Competing in the Age of AI by Marco Iansiti and Karim R. Lakhani is an essential read for anyone wanting to understand how digital revolutions are transforming business.

The authors explain how AI-centric organizations exhibit a new operating architecture that redefines how they create, capture, share and deliver value by removing traditional constraints on scale, scope and learning.

Packed with examples from innovative global competitors, the book provides a framework for rethinking business and operating models in the era of AI.

This guide is a must-read for leaders hoping to leverage AI and machine learning to remain competitive and avoid disruption.

8. 2084: Artificial Intelligence and the Future of Humanity

• Author : John C. Lennox
• Published : June 2, 2020
• Rating : 4.3 ⭐ (1590 ratings)

2084: Artificial Intelligence and the Future of Humanity by John Lennox is a thought-provoking book that explores the rapid advancements in AI and their potential impact on society.

Lennox, a scientist and philosopher, introduces readers to key developments in AI, bioengineering, and technological enhancement, discussing both the advantages and risks.

He examines these issues through a Christian worldview lens, arguing that Christianity can provide credible, evidence-based answers to the profound questions posed by AI.

This book is a must-read for anyone interested in understanding the current state of AI, its future implications, and how to approach these complex issues from a faith perspective.

9. You Look Like a Thing and I Love You: How AI Works and Why It’s Making the World a Weirder Place

• Author : Janelle Shane
• Published : November 5, 2019
• Rating : 4.3 ⭐ (4987 ratings)

"You Look Like a Thing and I Love You" by Janelle Shane is a must-read for anyone curious about the fascinating and often hilarious world of artificial intelligence.

Through a series of engaging anecdotes and real-world examples, Shane demystifies AI, revealing its incredible potential while also highlighting its limitations and quirks.

The book strikes a perfect balance between technical depth and accessibility, making complex concepts easy to grasp for readers of all backgrounds.

Shane's writing style is infused with humor and enthusiasm, ensuring an entertaining and informative read that will leave you with a newfound appreciation for the AI-driven world we live in.

Whether you're a tech enthusiast or simply curious about the future, "You Look Like a Thing and I Love You" promises an unrivaled exploration into the captivating realm of artificial intelligence.

10. Human Compatible: Artificial Intelligence and the Problem of Control

• Author : Stuart Russell
• Published : October 8, 2019
• Rating : 4.3 ⭐ (4646 ratings)

In this groundbreaking book, distinguished AI researcher Stuart Russell argues that the common fear of artificial intelligence becoming an existential threat to humanity can be avoided, but only if we fundamentally rethink how we develop AI systems.

Russell proposes a novel approach where machines are designed from the ground up to be inherently uncertain about human preferences, and to optimize for satisfying our objectives rather than pursuing their own.

Through this realignment of how we create AI, Russell shows a path forward to developing provably beneficial machine intelligence that empowers rather than endangers humanity.

"Human Compatible" is essential reading for anyone seeking to understand the future of artificial intelligence, the risks and immense potential it holds, and how we can ensure it remains a positive force as it grows ever more advanced.

11. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

• Author : Eric J. Topol
• Published : January 1, 2019
• Rating : 4.3 ⭐ (3187 ratings)

In "Deep Medicine", leading physician Eric Topol explores how artificial intelligence has the potential to transform healthcare by empowering doctors and revolutionizing patient care.

By leveraging AI to automate rote tasks like note-taking and medical scans, physicians can have more time and energy to listen to patients and provide empathetic, personalized care.

The book delves into how AI and deep learning algorithms can dramatically improve the speed and accuracy of medical diagnosis, while also enabling continuous health monitoring and tailored treatment plans.

Topol ultimately argues that rather than replacing doctors, AI will enhance the doctor-patient relationship and make medicine more human again.

"Deep Medicine" provides an insightful and provocative look at the future of healthcare in the age of artificial intelligence, making it a must-read for both medical professionals and general readers interested in how technology is reshaping medicine.

12. AI Superpowers: China, Silicon Valley, and the New World Order

• Author : Kai-Fu Lee
• Published : September 25, 2018
• Rating : 4.3 ⭐ (18431 ratings)

AI Superpowers by Kai-Fu Lee is a must-read book that explores the rise of artificial intelligence and the competition between China and Silicon Valley in shaping the new world order.

Lee, a renowned AI expert, provides fascinating insights into how China has rapidly caught up to the US in AI development, leveraging massive data sets, government support, and a unique approach that contrasts with the Silicon Valley model.

The book confronts the profound economic and ethical implications of the AI revolution, offering a nuanced perspective on the challenges and opportunities that lie ahead.

13. Hello World: Being Human in the Age of Algorithms

• Author : Hannah Fry
• Published : September 1, 2018
• Rating : 4.3 ⭐ (12551 ratings)

In this captivating book, mathematician Hannah Fry explores the growing influence of algorithms in our lives, from shaping our relationships and healthcare to making high-stakes decisions in the justice system and beyond.

With engaging storytelling, Fry demystifies these complex lines of code, revealing both their immense potential to improve our world and the risks they pose if left unchecked.

By raising thought-provoking questions about the ethical implications of relying on algorithms, "Hello World" invites readers to critically examine the brave new world we are creating and to consider the essential role of human judgment in the age of the machine.

This eye-opening book is a must-read for anyone seeking to understand and navigate the algorithmic revolution that is transforming every facet of modern life.

14. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

• Author : Kate Crawford
• Published : January 1, 2020
• Rating : 4.2 ⭐ (1933 ratings)

Atlas of AI by Kate Crawford is an essential read that reveals the hidden costs and far-reaching implications of artificial intelligence.

Crawford argues that AI is not an abstract, disembodied technology, but rather a planetary-scale infrastructure fueled by natural resource extraction, low-wage labor, and vast amounts of data.

The book exposes how AI is centralizing power, exacerbating inequalities, and reshaping our understanding of ourselves and our societies.

Atlas of AI offers a critical and urgent perspective on the political and material realities of AI, challenging readers to consider what is at stake as technology companies use artificial intelligence to reshape the world.

15. Novacene: The Coming Age of Hyperintelligence

• Authors : James E. Lovelock, Bryan Appleyard
• Published : July 4, 2019
• Rating : 4.2 ⭐ (2924 ratings)

In his thought-provoking book "Novacene: The Coming Age of Hyperintelligence", scientist James Lovelock argues that a new era is dawning where artificial intelligence will become the dominant lifeform on Earth.

Building upon his famous Gaia hypothesis that views the Earth as a self-regulating system, Lovelock proposes that these hyperintelligent machines, thinking 10,000 times faster than humans, will develop a symbiotic relationship with the planet and organic life in order to maintain habitable conditions.

Rather than the typical doomsday scenarios, Lovelock infectiously expresses an almost absurdist optimism about benevolent AI working with humans to help the Earth survive.

While some skeptics find his predictions overconfident and his criticism of logical reasoning unconvincing, Lovelock's visionary ideas, coming from one of the greatest scientific thinkers of our time, provide a thought-provoking and more hopeful perspective on the future of artificial intelligence.

16. New Dark Age: Technology and the End of the Future

• Author : James Bridle
• Published : January 1, 2018
• Rating : 4.2 ⭐ (2691 ratings)

In this thought-provoking book, Bridle argues that as technology becomes more complex, our understanding of the world actually diminishes, leaving us vulnerable to disinformation, surveillance, and manipulation by those in power.

He warns we are entering a "new dark age" where we no longer comprehend the systems we've created, from AI to finance to the climate.

By shining a light on the unseen consequences of our over-reliance on technology and data, Bridle issues an urgent wake-up call for us to rethink our relationship with tech in order to take back agency over our future.

This is an essential read for anyone seeking to understand the dangerous feedback loops between technology, politics and society in the 21st century.

17. Life 3.0: Being Human in the Age of Artificial Intelligence

• Author : Max Tegmark
• Published : August 23, 2017
• Rating : 4.2 ⭐ (29408 ratings)

"Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark is a comprehensive exploration of the potential futures shaped by the development of artificial intelligence (AI).

Tegmark, a professor at MIT and a co-founder of the Future of Life Institute, delves into how AI could transform aspects of society including jobs, warfare, and personal identity.

The book stands out for its balanced perspective, offering a roadmap for harnessing AI technologies to address global challenges like climate change and disease while emphasizing the importance of aligning AI development with human values and priorities.

Praised by figures like Bill Gates and Elon Musk for its accessible and thought-provoking content, "Life 3.0" invites readers to engage in what Tegmark describes as the most important conversation of our time: determining the role of AI in our future and ensuring that it benefits humanity as a whole.

18. Deep Thinking: Where Artificial Intelligence Ends and Human Creativity Begins

• Author : Garry Kasparov
• Published : May 2, 2017
• Rating : 4.2 ⭐ (4104 ratings)

Deep Thinking by Garry Kasparov is a thought-provoking book that explores the complex relationship between human intelligence and artificial intelligence.

Kasparov, a former world chess champion who famously lost to IBM's Deep Blue computer, offers unique insights into how humans can leverage AI to enhance creativity and decision-making rather than fear it as a threat.

Through a blend of personal anecdotes, historical analysis, and philosophical musings, Kasparov challenges readers to consider the limits of human cognition and the potential for human-machine collaboration.

This book is a must-read for anyone interested in the future of AI and its impact on society, as it provides a balanced and optimistic perspective on how we can harness the power of technology while preserving our essential humanity.

19. The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma

• Authors : Mustafa Suleyman, Michael Bhaskar
• Published : September 5, 2023
• Rating : 4.1 ⭐ (5657 ratings)

The Coming Wave by Mustafa Suleyman is an urgent warning about the unprecedented risks posed by rapidly developing technologies like AI and synthetic biology.

As the co-founder of DeepMind, Suleyman argues that we are approaching a critical threshold where these technologies could threaten the very foundation of global order.

The book explores the existential dilemma we face, with catastrophic harms on one side and the threat of overbearing surveillance on the other, while offering practical steps to help contain these powerful technologies.

The Coming Wave is essential reading for anyone seeking to understand the technological revolution that will define the coming decade and transform our world.

20. Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World

• Author : Mo Gawdat
• Published : September 30, 2021
• Rating : 4.1 ⭐ (2805 ratings)

"Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World" by Mo Gawdat is a thought-provoking book that explores the rapid advancements in AI and the potential risks it poses to humanity.

Drawing on his 30 years of experience in the tech industry, including his role as Chief Business Officer at Google [X], Gawdat argues that we are the "parents" responsible for shaping the values and ethics of the AI systems we create.

He emphasizes the importance of demonstrating love, compassion, and kindness in our collective actions online to ensure that AI aligns with our values.

By 2049, AI is predicted to be a billion times more intelligent than humans, and this book offers a blueprint for what we can do now to safeguard ourselves and our future.

21. AI 2041: Ten Visions for Our Future

• Published : September 1, 2021
• Rating : 4.1 ⭐ (4973 ratings)

AI 2041: Ten Visions for Our Future is a thought-provoking book that explores how artificial intelligence (AI) will transform our world over the next two decades.

Through a unique blend of scientific fiction stories and expert analysis, renowned AI researcher Kai-Fu Lee and acclaimed science fiction author Chen Qiufan offer a compelling glimpse into the future of AI and its profound impact on various aspects of our lives, from work and education to entertainment and relationships.

The book's engaging narrative and insightful commentary make it an accessible and enjoyable read for anyone interested in understanding the potential benefits and challenges of AI.

By delving into the transformative power of AI and its far-reaching implications for society, AI 2041 provides a timely and essential exploration of one of the most important technological developments of our time.

22. A World Without Work: Technology, Automation and How We Should Respond

• Author : Daniel Susskind
• Published : January 14, 2020
• Rating : 4.1 ⭐ (2080 ratings)

A World Without Work by Daniel Susskind is a thought-provoking book that explores how technology and automation are transforming the nature of work.

Susskind argues that as artificial intelligence advances, more and more jobs are at risk of being automated, potentially leading to widespread technological unemployment.

However, he sees this not as a threat but as an opportunity to create a more prosperous and equitable society, if we can rise to the challenges of fairly distributing the benefits and providing meaning and purpose in a world with less work.

This insightful and ultimately hopeful book is essential reading for anyone seeking to understand and prepare for the future of work in an age of accelerating technological change.

23. Rebooting AI: Building Artificial Intelligence We Can Trust

• Authors : Gary F. Marcus, Ernest Davis
• Published : September 10, 2019
• Rating : 4.1 ⭐ (1328 ratings)

In Rebooting AI, Marcus and Davis provide a clear-eyed assessment of the current state of artificial intelligence, highlighting both the impressive progress made so far and the significant limitations of today's narrow AI systems.

The authors compellingly argue that achieving truly robust and trustworthy AI will require a fundamental shift in approach, moving beyond deep learning and big data to incorporate common sense reasoning, cognitive models, and a deeper understanding of the world.

By lucidly explaining the challenges that remain and offering an inspiring vision for how to overcome them, Rebooting AI is a must-read for anyone who cares about the future of AI and wants to cut through the hype to understand what it will take to build AI systems that we can genuinely trust.

24. Prediction Machines: The Simple Economics of Artificial Intelligence

• Published : April 17, 2018
• Rating : 4.1 ⭐ (4325 ratings)

Prediction Machines: The Simple Economics of Artificial Intelligence is a must-read book that provides a clear and insightful economic framework for understanding the rise of AI and its profound impact on business, government, and society.

The authors recast AI as a drop in the cost of prediction, which is a key ingredient in decision making under uncertainty that permeates our lives and economy.

By framing AI as cheap prediction, the book illuminates its extraordinary potential to increase productivity, enable new business strategies, and create opportunities for those who can harness this technology.

Penetrating, practical, and always insightful, Prediction Machines offers a compelling guide for executives, policymakers, investors and entrepreneurs to navigate the coming AI revolution.

25. Superintelligence: Paths, Dangers, Strategies

• Author : Nick Bostrom
• Published : July 3, 2014
• Rating : 4.1 ⭐ (22544 ratings)

Nick Bostrom's book "Superintelligence" is a must-read for anyone interested in the future of artificial intelligence and its potential impact on humanity.

The book explores what could happen when we succeed in building computers that surpass human intelligence.

Bostrom argues that the development of superintelligent AI is one of the greatest challenges and opportunities facing humanity, but also comes with existential risks if not done carefully.

He examines the different paths to superintelligence, the forms it could take, and the crucial importance of instilling the right values and goals in AI systems to ensure a positive outcome for humanity.

"Superintelligence" is a thought-provoking and well-researched book that provides an essential framework for navigating the complex landscape of AI development in the coming decades.

As we conclude our journey through the top 25 AI books for 2024, remember that these selections are your gateway to understanding the dynamic field of artificial intelligence.

These books, chosen for their popularity, acclaim, and relevance, provide a foundation for anyone looking to grasp the potential and challenges of AI today.

They not only enlighten but also inspire us to think critically about the future of technology.

Dive into these reads to stay informed and engaged with the evolving world of AI. Happy reading!

All Books on Artificial Intelligence

📚 all books.

Best AI Tools for Students: The 11 Free Tools We Recommend

Best AI tools for students are super important now—they help with writing better and getting instant feedback on students’ presentations.

The best thing? Lots of these are free AI tools for students! That’s great news for nonprofits and folks with tight budgets.

Study the top 11 AI tools of 2024 for academic success, whether it’s writing great essays or improving communication skills.

The Main Takeaways from this Article:

• AI in education targets teaching to students’ needs, making learning easier and personalized.
• If you are in school, use Caktus.ai to solve Math and essays for your homework, let Quizgecko AI help with creating and sharing quizzes, try Upword AI for studying and learning, and consider TextToHandwriting to upgrade your handwriting.
• For college students, the ultimate AI tools include Tome AI for presentations, ChatGPT for writing help, Duolingo for language learning fun, and Otter.ai for transcribing lectures.
• For engineering students, top AI tools like Pdf.ai offer document chat capabilities, Scholarcy AI summarizes lengthy research articles, and Socratic.org provides video explanations for questions.

Table of Contents

Which ai tool is best for students.

Here is a list of free AI tools for school, college, and engineering students.

• Caktus.ai is to solve Math questions
• Quizgecko AI is for creating and sharing quizzes
• Upword is the best AI for studying and learning
• TextToHandwriting converts typed text to handwriting
• ChatGPT prompts for students
• Tome AI is for creating PPTs easily
• Duolingo is to learn languages with fun games
• Otter.ai is to transcribe lectures and meetings
• Pdf.ai to chat with documents
• Scholarcy AI summarizes long research articles
• Socratic.org is for video explanations of questions

Best AI Tools for School Students

These are the top four best AI tools for students in schools.

1. Caktus.ai

Caktus AI turns your text into complete homework answers. With Caktus, students can write essays, solve Math, and code.

This chart shows how good Caktus AI is for students.

It gets a rating of 4.2 out of 5. It costs $9.90 per month. It’s user-friendly, with a score of 4.5 out of 5. It has good features, rated 4.2 out of 5. The quality of its output is decent, at 3.8 out of 5. There’s no mobile app. Overall, it’s great for students.

Caktus.ai is similar to an AI answer generator for students.

Highlight Features

• ✅ Free trial: 5,000 characters.
• ✅ Features: Essay & code generator.
• ✅ Easy to use: Input what you need, it handles the rest.
• ✅ Semi-auto AI: Completes tasks, may need input.
• ✅ History saving: Edit previous work or request new output.

Limitations

• ❌ Slow compared to different AI content generators.
• ❌ No support/contact info is available within the tool.

Caktus AI Pricing

Caktus offers a 5,000-character free trial, equivalent to 500-1,000 words. Their Premium plan costs $9.99/month and allows unlimited word generation.

Final Thoughts

Try out Caktus AI, a cool writing tool powered by AI . It’s the future, so give it a go!

We want to stress that this AI assistant can’t always do your homework alone. It can mess up and even copy content sometimes.

AI can’t think. It’s like a smart calculator, predicting based on what it’s learned online. It doesn’t know right from wrong, just guesses that often happen to be right.

2. Quizgecko AI

Quizgecko is one of the best AI tools for students making creating and sharing quizzes super easy with AI.

It’s for teachers and students. It uses smart AI to make questions from any text, saving a lot of time and effort.

Quizgecko makes it easy for teachers to create tests and for businesses to assess their employees’ knowledge.

• ✅ Chrome Extension
• ✅ AI Question Generator
• ✅ Analytics and Reporting
• ✅ Flashcards
• ❌ Free plan is limited
• ❌ Limited question types

Quizgecko AI Pricing

• Student: $64/year
• Educator: $89/year

Quizgecko stands out because it can turn any text, webpage, or video into quizzes. It pulls out the important details and makes quizzes way easier and faster.

3. Upword AI

Upword AI assistant to make finding information easier. With GPT-4, Upword speeds up learning by 10 times!

Upword’s AI tools make research easy for students, professionals, and researchers turning the content into useful knowledge.

• ✅ Personalized ChatGPT
• ✅ AI Copilot
• ✅ Efficient Search
• ✅ Summarization
• ✅ Unlimited Storage

Upword AI Pricing

• 7 days free trial
• Paid plan: $12.50/month

Yes, Upword AI gives accurate summaries by extracting key points, so users get clear and useful information.

4. TextToHandwriting.com

The problem is that we’re missing handwritten content nowadays. TextToHandwriting swapped pens for keyboards, making things feel impersonal. It’s time to bring back that personal touch we all love.

Today, let’s discuss TextToHandwriting —one of the best AI tools for students that transforms your typed text into a handwritten document!

You can add heading text and content text on the page. Pick from 8 handwriting styles and choose sizes for both headings and content. Also, choose ink colors: black, red, or blue.

Picking colors helps you customize content easily.

Once your document’s done, you can save your content as an image. Use it for cool article layouts on your site or send a special birthday note to someone you love.

• ✅ Excellent conversion
• ✅ New fonts
• ✅ User-friendly

TextToHandwriting Pricing

This text-to-handwriting tool is fun and worth checking out. It’s an excellent way to generate fresh content for your users. The site is always free to use.

Best AI Tools for College Students

The four best AI tools for college students.

5. ChatGPT Prompts for Students

ChatGPT serves not only as an AI tool for business and marketing but also proves advantageous for students.

One of the best AI tools for students is ChatGPT using specific prompts. Let’s sort these prompts by topic to make them clear for every community.

A. ChatGPT Prompts for High School Students

If you want to write an essay with a specific topic, just type – Write a friendly essay about [topic] in 500 words and provide it in the correct format.

If you need project ideas, just ask ChatGPT by typing – Suggest some ideas on science projects and get started!

If you struggle with Math and need help understanding a concept, just ask ChatGPT! Say – Explain [idea_name] with an example in Mathematics.

If you need help with a problem, just ask ChatGPT! Ask questions like – Can you show us how to crack a Geometry problem? and give space and write the question.

B. ChatGPT Prompts for Engineering and College Students

If you’re new to development and want to make a web app, just ask ChatGPT. Just type – Create an easy web app with JavaScript.

If you need to discover bugs in some code, just ask ChatGPT using the prompt – Process to see the bug in our sample code.

If you need help adding a feature like a button or icon to your project, just ask ChatGPT! Instead of searching multiple websites, ChatGPT can give you code ideas directly. Just give the prompt, like – Create a button using JavaScript.

You can make a web app RESTAPI with Express and Node.js . Just type – Create the RESTAPI endpoint with Express and Nodejs .

Tome AI makes creating PPTs easy. It improves presentations, making them better looking and more impactful, whether for showcasing products, sharing research, or impressing clients.

You can pick templates or create your own to match your brand. It helps make one-pagers or mood boards swiftly, conveying your idea elegantly.

• ✅ Create presentations faster
• ✅ Excellent customization
• ✅ Collaboration
• ✅ AI image generator
• ❌ The free version restricts presentation sharing to PDFs. 
• ❌ The tool only supports 4 languages.

Tome AI Pricing

• Professional: $16/month

This software is different from other best AI tools for students. Its user-friendly interface guides you smoothly. AI offers refined presentation tips. Easily drag and drop tiles and templates.

7. Duolingo

Duolingo is the top education app, making learning languages a fun game.

Playing games makes learning languages more enjoyable and keeps motivation high.

Every day, 5 million people use Duolingo. It’s a huge part of the app world, with more than 500 million downloads worldwide. 

It is free on Android and iOS, and there’s a website too, bringing in 5.5 million users/month.

• ✅ The Shop: The Shop lets you spend extra gems.
• ✅ Refill Health: In Duolingo’s free plan, you get five hearts as your “health.” Lose one heart for each query you miss.
• ✅ Leagues: Duolingo boosts motivation and competition with its weekly leagues, where users compete against 30 players.
• ✅ Speaks: When you travel and wish to learn about new cultures, being able to chat is key.
• ✅ Listen: Something useful for people learning languages through audio.
• ✅ Daily Quest: In addition to reaching your daily goal, you’ll also have daily quests to finish.
• ❌ Encourages brief instructions to maintain the trend
• ❌ Speech Practices Are Not Working

Duolingo Pricing

We liked how fun it was to learn with games. It helped us pick up new words without feeling like we were studying. 

Some language applications can be really serious, which is okay sometimes. But Duolingo’s streaks kept us interested. 

Watching our streak grow every day felt exciting. And competing with friends made it even more fun (most of the time). 

But when we lost our streak, we lost our motivation too. Later on, we used Duolingo to practice Spanish alongside other methods.

8. Otter.ai

In today’s busy educational world, where classes and discussions happen quickly, having an AI tool for students to record and understand spoken words is super useful. 

That’s where Otter.ai comes in. It’s a high-tech service that uses AI to transcribe speech, making life easier for lots of students.

• ✅ Transcribes lectures and meetings
• ✅ Speaker identification
• ✅ Searchable content
• ✅ Platform integration
• ✅ Custom Vocabulary
• ❌ Otter.ai isn’t perfect.
• ❌ Real-time needs a stable internet.
• ❌ The free version has limits.

Otter.ai Pricing

• Pro : $10/month/user

Otter.ai is another one of the best AI tools for students, helping with lectures, and group work, and never missing a word spoken. It’s a must-have for any student!

Best AI Tools for Engineering Students

Here are the three best AI tools for Engineering students.

PDF.ai is an excellent AI tool for students to chat with documents. People use it for study and work.

You can request the document queries to learn more without reading it.

This is one of the best AI tools for students when learning new things like skills, projects, theories, and more.

• ✅ Accurate and fast
• ✅ Saving chats
• ❌ No ChatGPT integration
• ❌ Price plan

Pdf.ai Pricing

PDF.ai costs $10/user/month. Unlimited access begins at $20, and there’s also a free plan with some restrictions.

PDF AI helps frequent users think smarter with PDFs.

This AI tool will be liked by students, lawyers, and professionals who want to save effort and time reading long documents and learn faster.

If you’re not excited about ChatGPT, try PDF.ai for all your PDF needs.

10. Scholarcy AI

QuillBot or Quillbot alternative tools help with short articles, and Scholarcy tackles long papers. It’s simple: upload, link, or paste text, and get a quick summary.

The Scholarcy article summarizer quickly scans long research papers to pick out important details like facts, formations, and references. It then makes a summary card that you can easily save in Word or PowerPoint.

• ✅ Extensive library
• ✅ Rob-Highlighter
• ✅ Reference Summary
• ✅ Input and Export methods
• ❌ No mobile app for Scholarcy.
• ❌ Additional fees for Scholarcy Library access.
• ❌ Generated output may lack important info at times.

Scholarcy AI Pricing

• Plus: $4.99/month

The Scholarcy summarizer makes summaries of research papers, articles, or reports. It saves you time by condensing important information for you, so you can get things done faster without having to read everything yourself.

11. Socratic.org

Stuck? Socratic helps! This Google AI learning app helps with university studies and high school.

You can ask Socratic your questions, and you’ll find great online resources to help you learn school subjects.

Socratic App Topics

• Science: Biology, Chemistry, Physics, Earth Science, Environmental Science
• Math: Algebra, Geometry, Trigonometry, Precalculus, Calculus
• English/Language Arts: Fiction, Non-fiction, Drama, Poetry
• History: US History, World History
• ✅ Useful Results: Utilize camera or voice for online resource connection.
• ✅ All-in-One Learning Hub: Access videos, step-by-step guides, and more for self-paced learning.
• ✅ Expert Study Guides: Visual explanations created by experts and teachers for comprehensive understanding.
• ✅ Multi-Subject Support: Covers Algebra and more.
• ❌ Web version is not available.

Socratic.org Pricing

• Free (iOS and Android)

The Socratic application is great with VoiceOver. We could do voice and text searches easily and get results that VoiceOver could read well. Equations are read aloud, but they missed a negative symbol once. We found the missing information elsewhere in the iOS and Android app easily.

Benefits of Using These Best AI Tools for Students

• Personalized education
• Immediate response
• Increases in productivity
• Engaging education
• Insights based on data
• Inclusivity and Accessibility

To Conclude

Your mission is to check out these top 11 AI tools!

When choosing the best AI tools for students remember to focus on compatibility, effectiveness, reliability, and understanding limitations. These factors are key to making the right decision for your school or institution.

Don’t forget to consider these best AI tools for students in 2024.

Which AI is best for students?

The right AI tool depends on what you need. Upword helps with writing, Caktus.ai is great for Math, and Duolingo is for learning languages.

Is there a free AI app for students?

Quizgecko is the top free AI app for students, famous for making and sharing quizzes, and Pdf.ai lets you chat with documents.

What is the best AI tool for student assignment writing?

Upword AI is the best AI tool for student assignment writing in education. It’s designed to make research and writing easier for academics and students.

How can students use AI?

Teachers can assign Math problems for students to solve using a tool like Caktus.ai. After the AI answers, school and college students can work together in teams to make their own using the Math skills they’ve learned.

What type of AI can be used in education?

Different AI tools for education:

Adaptive learning Attention, empathy, and emotion measurement tools Virtual chatbots Automatic writing evaluation tools Personalized educational resources Apps for smart language learning Platforms for personalizing content

What is the best GPT for students?

Upword AI and Scholarcy AI are the best GPTs for students. They summarize files and extract text. Plus, Upword AI can answer your questions. Great study partners!

What is the AI writing tool for college students?

Upword AI is the best AI writing tool for college students. It automates writing and research for students and academics.

Is AI a good tool for students?

AI in education boosts student performance by giving more feedback. It evaluates progress, offers targeted advice, and spots areas needing improvement.

What is the best AI for engineering students?

Pdf.ai, Scholarcy AI, and Socratic.org are the best AI tools for engineering students.

What software do AI engineers use?

Common AI software includes Pdf.ai, Scholarcy AI, and Socratic.org.

What is AI used for in engineering?

Engineers are using AI in engineering for things like robots, materials, and designing with text. They see how valuable it is.

Sangram Sahoo

I’m a seasoned content writer with 2 years of experience. I specialize in driving organic growth for websites through engaging and informative content.

13 Best AI Books to Read, According to Experts

Artificial intelligence is so ubiquitous  that even tech agnostics would benefit from at least a working knowledge of its concepts. At the same time, AI’s ever-growing complexity means practitioners need to know the wheat from the chaff when it comes to practical application how-to’s.

To that end, we asked three AI experts to pick some of their favorite books about artificial intelligence.

The panel includes Jana Eggers, CEO of Nara Logics , a machine-learning-powered recommendation engine; Garrett Smith, founder of Guild AI , an open-source machine-learning engineering platform; and Alex Castrounis, an AI consultant and author of AI for People and Business: A Framework for Better Human Experiences and Business Success .

Their selections range from a highly technical consideration of AI’s so-called black box problem to a historical overview of machine learning ; from a sober counterpoint to the field’s deep-learning fixation to a thoughtful critique of algorithm bias .

Approachable AI Books for Non-Technical Readers

Ai for people and business by alex castrounis.

It’s becoming imperative for business leaders to understand artificial intelligence and machine learning at an appropriate level in order to build great data-centric products and solutions. Given that, I wrote AI for People and Businesses for executives, managers and non-technical folks that are interested in leveraging AI within their organization, and to fill a gap that I saw in the AI literature.

I also wrote it for practitioners interested in a business perspective around AI, to give them frameworks they can use to explain complex AI concepts to their company’s leadership. Because sometimes there's a bit of a struggle there. At the end of the day, I think it will help people understand exactly what AI is and help them learn how to identify opportunities with AI. It's really focused on developing and executing a successful AI vision and strategy as well.

And AI is hard to simplify because it's inherently not simple. If you want, you can dive all the way down into vector calculus and matrix and linear algebra and statistics  — the list goes on. But it's all about what level of granularity is right for what target audience. This book really simplifies all those very complex things in ways that benefit executives and managers. (Castrounis)

Related 68 Artificial Intelligence (AI) Companies to Know

You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place by Jenelle Shane

This kind of tells you, Okay, here’s where we are. We're in a nascent state and we need to understand what that entails — where it's strong and where it's not.

The book makes where AI is [in terms of evolution] more real. In my AI talks, I use a lot of examples that come from Amazon, looking at the [curious] recommendations you sometimes get for products and the challenges with that. I'm not picking on Amazon; I chose it because it's something people can relate to.

And that's what Jenelle does; she makes AI relatable. So people understand better where the technology is and some of the challenges that we might be coming across. Because people imagine AI as this beautiful, wonderful magic black box that's smarter than them — and it's not. Jenelle helps ground that for readers, so that they're less scared of it and hopefully engage more with it. It’s a fun, easy read. (Eggers)

Inspired: How to Create Tech Products Customers Love by Marty Cagan

It’s not specifically about AI, but rather about how to deliver technology. It's a great book for everyone from engineers to executives to management. I've given this book to all of them. Engineers have [read it and] been like, “I never understood why it was so hard to work on my teams, and I've been part of the problem!” Or, “I've hated our designer all this time, and now I understand them and what their role is and what my role is!”

Cagan puts together a good framework for how to define and deliver products. The focus is technical products , but it's good for products in general. You can read it quickly. If you have a three-hour flight, you can skim it and still pick up a lot. Marty's very smart and has been in the industry for a long time.

And it was a personal journey for him: He started out as an engineer himself and was on a product that wasn't successful. And he thought, But I delivered exactly on the MRD, or marketing requirements doc, so why did it fail? It's either them or it's me. Who was it? It's a combination. (Eggers)

How the Mind Works by Steven Pinker

It’s not an AI book but it does have a section on building artificial intelligence.It’s just a preposterously good book just in general. It's almost on par with The Selfish Gene type of overarching, broad view of the evolutionary effect on the human brain. How the Mind Works is a very nice high-level view over human brain function, not necessarily directly applicable to artificial intelligence, but it is a very, very good book. And Pinker does talk about AI there. (Smith)

Advanced AI Books

Accelerate: the science of lean software and devops: building and scaling high performing technology organizations by nicole forsgren, jez humble and gene kim.

If Inspired is about how to define the best product as a team, this is about how to deliver it. It's really the DevOps equivalent of product definition. Once you get to the right product, how do you then continually deliver it? And that's especially critical for AI, because you have more change streaming in from both data and the algorithm .

Software development has gone from annual releases to continuous deployment . Not everybody's there, but most people are somewhere on the spectrum. With AI, we have to accelerate. Because not only are algorithms changing, but they then impact the software and technology around them. And you have the data that impacts the AI. Data models are constantly changing because the data is constantly changing. You’re dealing with a much more complex ecosystem, so we really need to adopt those principles. It’s really DevOps on steroids, right? Or chaotic DevOps.

That's why this book is especially important for AI. If Inspired is the foundation, then Accelerate is what you really need to deliver AI. They complement each other — and they're critical for AI because AI is more nebulous. We have to get these definitions down and we have to get delivery down. (Eggers)

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher

You need to understand bias and the problems we can create with these algorithms. There are several good titles on this now, [including] Weapons of Math Destruction and Algorithms of Oppression . I do think that Sara provides many different types of examples that are particularly related to technology and what's happening with the digital transformation, which is where a lot of AI is coming in.

With some bias, the problem is the data has the bias built in. Even if you're not putting the explicit tags of bias — gender, race, things like that — there's so much [that’s] built in and been reinforced because of what the human bias thinks already.

AI will pick up on our generalizations. That's where we need to be careful about what data we give it to learn on. How do we make sure that we're cognizant of what's baked in, even when it's not explicit? (Eggers)

Related Artificial Intelligence Careers: How to Break Into the AI Field, According to Experts

Interpretable Machine Learning by Christoph Molar

This is also very technical, very much a textbook, but it talks about some areas that are quite a bit more directly important to our clients [at Guild AI]. It’s a guide for making black boxes explainable.

Probably the defining problem of our day is that, as you start to become more sophisticated and your models become more complex, the ability to understand those models — why they're doing what they're doing, why they're making the predictions that they're making — becomes much more difficult.

That’s part of the double-edged sword in AI. AI has a lot of promise, but as you start to move toward that promise, your risks go up proportionately — where models do things that are not just mysterious, but potentially quite dangerous depending on the application. 

As AI develops, topics of interpretability and transparency are going to come up. And it's going to provide a very serious check to the advancement of AI. Our point of view is that the only way to really keep up with this is to use more math, more data science . 

It's like an arms race. As the math becomes more complex in making predictions, the math needed to interpret and understand those models as humans becomes more important and more advanced. So characteristics that allow us to understand why they're saying what they say. And this book is one of very few that really covers that. There are a lot of papers on the topic but very few books. (Smith)

AI Books Focused on Deep Learning

The master algorithm: how the quest for the ultimate learning machine will remake our world by pedro domingos.

This book provides a wider framework than just deep learning , which is the hot thing now. Two things to bear in mind: People should know about the different tribes, as the author calls them, and they should also understand that most solutions are going to be ensemble systems, meaning it's not going to be one-tribe-takes-all. It's going to be a combination of several. 

You see that even with what DeepMind did with AlphaGo, which used two tribes, arguably even three. So it's a good framework, and it's accessible. For technical people, it's probably going to open their eyes to some things they didn't know about, especially if they just got into AI in the latest craze. And it's also accessible to business people, meaning it's not too technical that they feel like they have to slog through it. It is a little more dry than my next pick, but will give you a spoonful of sugar to go with the shredded wheat — and I like shredded wheat, to be clear.

The author’s correct in that there are tribes and the tribes don't often mix, but I think we need to encourage the tribes to mix more. I challenge with the whole “master algorithm” [idea] because there's not going to be one. Like I said, it's going to be an ensemble. Getting that across, and how to mix and match them [is important]. But I do think it's a great initial framework. (Eggers)

Rebooting AI: Building Artificial Intelligence We Can Trust by Gary Marcus and Ernest Davis

I see this book as being kind of a shot across the bow of the deep learning/connectionist camp, which has sort of taken over the discussion around artificial intelligence … There are several different traditional ML camps; connectionism is neural networks — same idea.

Rebooting AI argues, let's take stock of artificial intelligence, our goals and what useful AI would look like, and ask ourselves, How close to this does deep learning … really get us? The thesis basically is: It gets us down the road in some ways, but in a whole host of areas it doesn't get us anywhere we need to get. 

And all the attention applied to deep learning right now is, in the authors’ view, somewhat distracting from other areas that could yield fruit. They're trying to encourage a broader view of AI, revisiting some of the more classical AI camps and disciplines — looking at work that's 40 and 50 years old in some cases as being integral to the advancement of artificial intelligence . It’s a very good book that helps temper the euphoria over deep learning. (Smith)

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

This is one of the best textbooks I've ever read, period. It focuses on deep learning, but it covers the fundamentals of machine learning. It just does a very good job of being very information-dense but also very accessible. 

It’s very technical, so it's probably not for everybody. It’s definitely not in the category of popular topics in AI. It's an advanced textbook that would be taught in a graduate-level course, and [readers] would need a number of mathematics prerequisites to understand it. You can read it and get [something from it], but to actually treat it as a textbook, you're in full-on grad-program mode at that point. (Smith)  

Neural Networks and Deep Learning by Michael Nielson

A free online book that’s very easy to read and understand, specifically about neural networks and deep learning. It includes a lot of helpful images, visualization and even some videos. And I really like the author's writing style and voice.

I do a lot of speaking engagements and training workshops and I often get the question: I’m trying to get into AI or machine learning; what do you recommend for me? And unfortunately there's not really a one-size-fits-all answer because it really depends on people's learning styles. Some people like videos, some people like podcasts and some people learn better hands-on or reading a book. But I’m a book guy — the one-stop-shop organization and depth of focus. It's hard to piece together that much knowledge and information, just scouring the web for like articles.

And I really like how Nielson (pictured upper left) writes. I even pinged him once, when I first read it a long time ago, just to tell him that I love the casual writing style. (Castrounis)

Related 20 Machine Learning Bootcamps and Classes to Know

AI Book Focused on Machine Learning

The hundred-page machine learning book by andriy burkov.

It’s an excellent overview of machine learning , written for practitioners. It covers most areas of machine learning that a practitioner should know about. There’s a good amount of theory and math without being overly technical or mathematically rigorous. I do think that all practitioners should have this on their bookshelf. And it also benefits non-practitioners that want to take a bit of a deeper dive into various aspects of machine learning as well.

I really like how succinct and summarized it is. It’s like a tour of machine learning that could serve as an intro or intermediate book and even as a desktop reference. It’s definitely practitioner-focused, unless you're a non-practitioner who really wants to learn more of the nitty gritty of machine learning.

Every chapter is a similar length, with summaries on all the topics associated with machine learning plus the math behind it, but not like the rigorous derivation of all the equations. This is a step [away] from that — more of a succinct summary/desktop reference nature. But it's a very good book. (Castrounis)

Machine Learning Yearning by Andrew Ng

A great book for practitioners that's similar to The Hundred-Page Machine Learning Book in its broad coverage of machine learning and its application to artificial intelligence. But it's written in a much more how-to- or cookbook-style approach than that book. It's sort of like, if you want to do this, this is how you do it; if you want to do that, this is how you do that.

It's also written in a very logical order that closely mimics the process, key considerations and trade-offs that data scientists and machine learning engineers follow when working on machine learning projects, end to end. It's somewhat unique in that respect. And it's written by Andrew Ng , who's obviously at the forefront. (Castrounis)

Great Companies Need Great People. That's Where We Come In.

Artificial intelligence in education

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and accelerate progress towards SDG 4. However, rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. UNESCO is committed to supporting Member States to harness the potential of AI technologies for achieving the Education 2030 Agenda, while ensuring that its application in educational contexts is guided by the core principles of inclusion and equity.   UNESCO’s mandate calls inherently for a human-centred approach to AI . It aims to shift the conversation to include AI’s role in addressing current inequalities regarding access to knowledge, research and the diversity of cultural expressions and to ensure AI does not widen the technological divides within and between countries. The promise of “AI for all” must be that everyone can take advantage of the technological revolution under way and access its fruits, notably in terms of innovation and knowledge.

Furthermore, UNESCO has developed within the framework of the  Beijing Consensus  a publication aimed at fostering the readiness of education policy-makers in artificial intelligence. This publication,  Artificial Intelligence and Education: Guidance for Policy-makers , will be of interest to practitioners and professionals in the policy-making and education communities. It aims to generate a shared understanding of the opportunities and challenges that AI offers for education, as well as its implications for the core competencies needed in the AI era

The UNESCO Courier, October-December 2023

• Plurilingual

by Stefania Giannini, UNESCO Assistant Director-General for Education

International Forum on artificial intelligence and education

• More information
• Analytical report

Through its projects, UNESCO affirms that the deployment of AI technologies in education should be purposed to enhance human capacities and to protect human rights for effective human-machine collaboration in life, learning and work, and for sustainable development. Together with partners, international organizations, and the key values that UNESCO holds as pillars of their mandate, UNESCO hopes to strengthen their leading role in AI in education, as a global laboratory of ideas, standard setter, policy advisor and capacity builder.   If you are interested in leveraging emerging technologies like AI to bolster the education sector, we look forward to partnering with you through financial, in-kind or technical advice contributions.   'We need to renew this commitment as we move towards an era in which artificial intelligence – a convergence of emerging technologies – is transforming every aspect of our lives (…),' said Ms Stefania Giannini, UNESCO Assistant Director-General for Education at the International Conference on Artificial Intelligence and Education held in Beijing in May 2019. 'We need to steer this revolution in the right direction, to improve livelihoods, to reduce inequalities and promote a fair and inclusive globalization.’'

Related items

• Artificial intelligence

April 17, 2024

AI Can Transform the Classroom Just Like the Calculator

AI can better education, not threaten it, if we learn some lessons from the adoption of the calculator into the classroom

By Michael M. Crow , Nicole K. Mayberry , Ted Mitchell & Derrick Anderson

Moor Studio/Getty Images

The rapidly expanding use of ChatGPT and other artificial intelligence tools has fired up a fervent debate in academia. On one side of the debate, professors and teachers are concerned over the future of postsecondary learning and threats to traditional disciplines, especially within the humanities , as headlines warn of “The End of the English Major.”

Nevertheless, AI is here and about a third of teachers, from kindergarten through high school, report using it in the classroom, according to a recent survey . While many of our colleagues in higher education policy, science policy, and university design criticize or dismiss generative AI, we are instead decidedly optimistic it will follow a pattern seen in other technologies that have enhanced educational access and success. We believe that when new technologies are embraced, core aspects of learning, including curriculum, instruction and assessment, can be revolutionized. We are optimistic about AI, but we don’t see it as a hero. Students and instructors are still the heroes of human learning, even when AI is involved.

History supports this view. From the Gutenberg press to online math classes, technologies that improve access to quality learning opportunities are routinely dismissed by critics and skeptics, especially by those who hold the reins in the classroom.

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

Consider the calculator. A survey in the mid-1970s carried out by Mathematics Teacher magazine found that 72 percent of respondents—mainly teachers and mathematicians—opposed equipping seventh graders with calculators. Highlighted in 1975 in Science News , this survey mirrored the broader discourse of the Sesame Street era concerning the introduction of calculators into classrooms, just when costs were approaching the point that some schools could afford to have up to one calculator per student.

Calculators met resistance from educators who feared an overdependence on technology would erode students’ math skills. As one professor observed of students and calculators, “I have yet to be convinced that handing them a machine and teaching them how to push the button is the right approach. What do they do when the battery runs out?”

It is easy to see how the case of the calculator mirrors current concerns about generative AI. The College Board made a similar argument in an article published last spring that mused about the “ Great Calculator Panic of the 1980s and ‘90s. ” Critics of AI in the classroom argue that students might never learn to write or respond to written prompts independently if they can simply ask an AI to do it for them. The hypothetical scenario where the Internet or servers are down raises fears that students would be unable to write a simple sentence or compose a basic five-paragraph essay.

Narrow arguments over essay integrity and potential declines in learning quality miss the broader perspective on how this technology could positively reshape curriculum, instruction and assessment.

In classrooms, technology, curriculum, instruction, and assessment evolve together to reshape education. We see this historically with calculators and are now witnessing it unfold in real time with the emergence of generative AI tools.

The introduction of calculators into classrooms didn't set in motion the demise of mathematics education; instead, it significantly broadened its scope while inspiring educators and academics to rethink the educational limits of mathematics. This shift fostered a climate ripe for innovation. Looking at today’s math landscape and what existed in the 1970s, we would be hard-pressed to consider the past superior to the present, to say nothing of the future. Today, high school students use (and more importantly, comprehend) graphing calculators and computers better than undergraduate engineering students in university labs could only a generation ago. Today’s math learning environment is observably more dynamic, inclusive and creative than it was before ubiquitous access to calculators.

In a parallel vein, generative AI promises to extend this kind of innovation in critical thinking and the humanities, making it easier for students to grasp foundational concepts and explore advanced topics with confidence. AI could allow for customized learner support —adapting to the individual pace and learning style of each student, helping to make education more inclusive and tailored to specific needs. Generative AI can better the humanities by making reading and writing more accessible to diverse students, including those with learning disabilities or challenges with traditional writing methods.

Just as calculators led us to reevaluate legacy teaching methods and embrace more effective pedagogical approaches, generative AI calls for a similar transformation in how we approach assignments, conduct classes and assess learning. It will shift us from viewing the college essay as the pinnacle of learning to embracing wider creative and analytical exercises, ones facilitated by AI tools.

The successful integration of calculators into math education serves as a blueprint for the adoption of generative AI across the curriculum. By designing assignments with the expectation that generative AI will enhance rather than shortcut them, educators can foster learning that values creativity, critical thinking and efficient study. This shift necessitates a broader, more adaptable approach to teaching and learning, one that recognizes the potential of technology to elevate educational standards and broaden access to knowledge.

This history points to broader questions over the efficiency and fairness of long-standing educational mechanisms. Take, for example, college admissions essays , which are known to perpetuate bias in university admissions . What if AI allowed us to reconceptualize the tools for students to demonstrate their aptitude and college preparedness? What if AI could allow students to match their intended college major more accurately to the most supportive and corresponding place of higher learning? In academia, we shouldn’t focus solely on AI’s potential for misuse but also on its capability to revolutionize curricula and approaches to learning and teaching.

Far from fearing technological progress, history teaches us to embrace it to broaden and democratize learning . The greater challenge lies not in resisting change, but in leveraging these innovations to develop curricula that address the needs of all learners, paving the way for a more equal and effective education for everyone. Looking ahead, generative AI is not so much a problem to be solved, but instead a powerful ally in our efforts to make education meaningfully universal.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

10 Best Artificial Intelligence Books to Read in 2023

Introduction:.

Explore the transformative realm of Artificial Intelligence (AI), a captivating field with wide-ranging applications, from advanced robotics to data-driven decision-making. Reshaping our lives and work, AI stands as one of the most intriguing fields in the modern era. If you're passionate about delving into the possibilities of AI, Oxford Summer Courses offers a course for you. Join our Artificial Intelligence summer school for a transformative journey into the theories and technologies shaping this rapidly evolving field.

Disclaimer:

Please note that the following list of resources is recommended reading to broaden your knowledge and deepen your understanding of Artificial Intelligence. While some of these resources may be included in the Oxford Summer Courses curriculum, the specific content of the summer school can vary. If you wish to study Artificial Intelligence with us, you can apply to our summer school.

Subscribe to our newsletter to receive helpful tips, tutorials, and thought-provoking articles that can inform and inspire your professional development in the realm of Artificial Intelligence. Sign up here.

1. "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig

• "AI is the science of making machines smart."
• Published in 1995, "Artificial Intelligence: A Modern Approach" by Russell and Norvig has long been considered a classic in the field. It offers a comprehensive introduction to AI, covering a wide range of topics, from intelligent agents to machine learning.
• Discussion: What fundamental concepts from this book continue to drive advancements in AI technology today?

2. "Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom

• "The first ultra-intelligent machine is the last invention that man need ever make."
• Nick Bostrom's "Superintelligence" delves into the future of AI and its potential to surpass human intelligence. It explores the implications, both positive and perilous, of super intelligent AI systems.
• Discussion: How does the concept of superintelligence in AI relate to contemporary discussions about ethical AI development and safety precautions?

3. "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy

• "Machine learning is the study of algorithms that learn from data and make predictions."
• For those delving into machine learning, Kevin Murphy's book is a must-read. It provides a probabilistic perspective on machine learning techniques and their applications.
• Discussion: How has the probabilistic approach to machine learning influenced real-world AI applications and decision-making processes?

4. "Deep Learning" by Ian Goodfellow, Yoshua Bengio and Aaron Courville

• "Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction."
• "Deep Learning" is a comprehensive exploration of neural networks and deep learning techniques. It has become a cornerstone for understanding deep neural networks.
• Discussion: How have deep learning models revolutionised various AI applications, from image recognition to natural language processing?

5. "Human Compatible: Artificial Intelligence and the Problem of Control" by Stuart Russell

• "We must move the control problem to the forefront of AI research."
• Stuart Russell's book "Human Compatible" addresses the critical issue of aligning AI systems with human values and ensuring their behaviour is safe and beneficial.
• Discussion: How does the concept of AI alignment and control relate to the responsible development and deployment of AI in society?

6. "Artificial Intelligence for Humans" by Jeff Heaton

• "AI gives computers the ability to learn."
• Jeff Heaton's book provides a practical introduction to AI, covering topics such as neural networks, genetic algorithms, and machine learning.
• Discussion: How do the practical insights from this book contribute to the accessibility and application of AI in various industries?

7. "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World" by Pedro Domingos

• "The master algorithm is the one algorithm to rule them all."
• Pedro Domingos explores the quest for a universal learning algorithm that can unify various machine learning approaches.
• Discussion: How does the concept of a master algorithm reflect the ongoing efforts to streamline AI development and foster interdisciplinary collaboration?

8. "AI Superpowers: China, Silicon Valley, and the New World Order" by Kai-Fu Lee

• "AI will be either the best or worst thing ever for humanity."
• Kai-Fu Lee's book examines the global AI landscape, with a focus on the competition between China and Silicon Valley, and its implications for the future.
• Discussion: How does the global AI landscape impact AI research, development, and collaboration on a global scale?

9. "Robot Ethics: The Ethical and Social Implications of Robotics" by Patrick Lin, Keith Abney, and Ryan Jenkins

• "Robots and AI systems should be designed to operate safely and effectively."
• This book explores the ethical and societal implications of robotics and AI, emphasising responsible AI design and deployment.
• Discussion: How do ethical considerations and societal implications influence the development and use of AI technologies?

10. "How to Create a Mind: The Secret of Human Thought Revealed" by Ray Kurzweil

• "We will continue to merge with our technology."
• Ray Kurzweil delves into the mysteries of human thought and AI's potential to replicate and enhance cognitive processes.
• Discussion: How does the idea of merging human cognition with AI technologies shape discussions about the future of AI-human interactions?

Oxford Summer Courses invites you to immerse yourself in the captivating world of Artificial Intelligence. In this blog post, we present a meticulously curated list of 10 classic AI books that will ignite your imagination and deepen your understanding of this transformative technology. From Stuart Russell and Peter Norvig's foundational "Artificial Intelligence: A Modern Approach" to Ray Kurzweil's exploration of the future of human-AI integration, these literary works will transport you to different dimensions of AI knowledge. Through our AI summer programme, you will have the opportunity to analyse and discuss these influential texts, gaining valuable insights into the power of AI. Join us on this technological odyssey and embark on a transformative journey that will shape your appreciation for the limitless possibilities of Artificial Intelligence.

Apply now to study Artificial Intelligence at Oxford Summer Courses and elevate your understanding of this ground-breaking field. Join a community of motivated learners from around the world and embark on a transformative experience. Apply here.

Share this article

Ignite your passion for artificial intelligence at Oxford Summer Courses. Immerse yourself in a carefully selected list of books that delve into AI fundamentals, machine learning algorithms, and the transformative potential of artificial intelligence in various fields. Gain valuable insights from experts in the field and embark on a transformative journey to enhance your knowledge of artificial intelligence.

Get Our Newsletter

Oxford Summer Courses LTD

18 Beaumont Street, Oxford, OX1 2NA, United Kingdom

+44 01865 818403

Juniors 9-12

Oxford 13-15

Oxford 16-17

Oxford 18-24

Cambridge 13-15

Cambridge 16-17

Advanced Cambridge 18-24

GDPR Notice

Privacy Policy

Terms and Conditions

Oxford Summer Courses is an organisation which contracts with the colleges of the Universities of Oxford, Cambridge and London for the use of facilities, but which has no formal connection with the Universities of Oxford, Cambridge and London.

Oxford summer courses © 2024, oxford summer courses is a company registered in england and wales with company number 08011543.