Бесплатные электронные книги по искусственному интеллекту для чтения в 2023 году

The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power

Shoshana Zuboff

There exists a serious concern that AI might deliver a dystopian future, but there is also a good argument that current computer technology is already a serious threat and could produce a global digital environment that is deeply damaging for humanity.

This book is an excellent exposition of that threat, showing how inequality and injustice are supported, even promoted, by modern computing technologies. This matters to all citizens, and all digital scientists must take on the responsibilities involved.

See more reading lists of science books:

  • Five best physics books, according to Jim Al-Khalili
  • 8 really, really big books about space
  • Linguistics: 7 language science books to help you finally understand what comes out of your mouth

HAL’s legacy: 2001’s computer as dream and reality Davis G. Stork

Applied Artificial Intelligence: A Handbook For Business Leaders by Mariya Yao, Adelyn Zhou and Marlene Jia

Despite not being a lengthy book (around 250 pages), Applied Artificial Intelligence includes more content than some of the bigger publications. Example after example, the three authors describe how companies from a variety of business verticals apply AI in their business.

Whatever industry you work in, you’ll surely find here something for yourself. Finance, accounting, HR, marketing, sales — the book by Yao, Zhou, and Jia covers nearly every department of a typical modern company. Each issue comes with an accompanying example of an actual way some business implemented machine learning in their daily work.

Automation isn’t the future, the authors of the book seem to say. Instead, they prove that automation is our present, a thing that is already used by every company that wants to stay on the market for the years to come.If you want to make sure your business keeps afloat as well, start reading Applied Artificial Intelligence right away.

Artificial Intelligence for Marketing: Practical Applications by Jim Sterne

Some people don’t think much about implementing AI in marketing, as they consider this part of the business a work that requires the creative mind of a human. In his book, Jim Sterne, an experienced marketing consultant, proves that this idea is wrong.

Sterne shows that there are multiple parts of the marketer’s job that can be improved and sped up using machine learning. An AI filled with tons of data can, for example, build buyers personas and map customer journeys with just a few clicks. Data is the power, Sterne says, and AI is simply better and faster than humans in reviewing it.

What’s also important, is that the author focuses highly on providing a proper context for marketing team leads and managers. It’s their job to onboard the AI and to make sure their teams work closely with IT to create scripts in an efficient way. Sterne’s book, therefore, serves as an especially important publication for these managers and leaders. It feels simply as a package of consultation sessions for executives packed in a book.You can get Artificial Intelligence for Marketing in hardcover or e-book on the publisher’s site. These versions, alongside with audiobook, are also available on Amazon.

HAL’s legacy: 2001’s computer as dream and reality

Davis G. Stork

I find a bit of history is always enlightening, and this book reviews the AI abilities of the HAL 9000 computer in Stanley Kubrick’s classic 1968 film, 2001: A Space Odyssey.

Although the film was science fiction, the depiction of future AI was remarkably plausible and the book compares HAL’s skills with the state of AI twenty years later.

The key failure of HAL was exactly what is still missing in AI; HAL was unable to contemplate the consequences of its own actions. This self-diagnosis, the element of self-doubt, is a key human characteristic.

How to Grow a Robot:Developing Human-Friendly, Social AI by Mark H. Lee is out now (£25, MIT Press).

Training ground

  • OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari, Box2d, MuJoCo etc…)
  • malmo: Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft.
  • DeepMind Pysc2: StarCraft II Learning Environment.
  • Procgen: Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments.
  • TorchCraftAI: A bot platform for machine learning research on StarCraft: Brood War
  • Valve Dota2: Dota2 game acessing api. (CN doc)
  • Mario AI Framework: A Mario AI framework for using AI methods.
  • Google Dopamine: Dopamine is a research framework for fast prototyping of reinforcement learning algorithms
  • TextWorld: Microsoft – A learning environment sandbox for training and testing reinforcement learning (RL) agents on text-based games.
  • Mini Grid: Minimalistic gridworld environment for OpenAI Gym
  • MAgent: A Platform for Many-agent Reinforcement Learning
  • XWorld: A C++/Python simulator package for reinforcement learning
  • Neural MMO: A Massively Multiagent Game Environment
  • MinAtar: MinAtar is a testbed for AI agents which implements miniaturized version of several Atari 2600 games.
  • craft-env: CraftEnv is a 2D crafting environment
  • gym-sokoban: Sokoban is Japanese for warehouse keeper and a traditional video game
  • Pommerman Playground hosts Pommerman, a clone of Bomberman built for AI research.
  • MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research
  • vizdoomgym OpenAI Gym wrapper for ViZDoom (A Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information) enviroments.
  • ddz-ai 以孤立语假设和宽度优先搜索为基础,构建了一种多通道堆叠注意力Transformer结构的斗地主ai

Automate the Boring Stuff with Python. Practical Programming for Total Beginners by Al Sweigart

Even if you are a non-developer entrepreneur, it’s good to understand at least to some extent what the coders at your company are doing. When it comes to machine learning/automation, one of the best books on the topic is Automate the Boring Stuff with Python. Practical Programming for Total Beginners by Al Sweigart.

As the title suggests, Sweigart’s book introduces readers to a way to easily automatize some of the tedious tasks one might need to do during their daily work. This includes web scraping (getting specific data from multiple sites with use of scripts), cleaning up your mailbox, getting through tons of data in Excel sheets (we know you need this one especially!), and much more. Sweigart himself has written numerous books for amateurs and non-developers, trying to prove that truly anyone can code. One of the basic principles he is trying to share is that you don’t really need to be a math expert to understand how to write software. Moreover, Automate the Boring Stuff… is available under a Creative Commons license, which means you can learn how machine learning works for free!

Advanced Level 2

Deep Learning – Ian Goodfellow, Yoshua Bengio, Aaron Courville

Description: Let’s stick with the subject of Deep Learning. The authors created this resource to help beginners enter the field of machine learning, with a focus on deep understanding. Interestingly, one of the authors – Yoshua Bengio, won the 2018 Turing Award (the Nobel Prize for computing) for his work in deep learning.

Deep Learning with PyTorch – Eli Stevens, Luca Antiga, Thomas Viehmann

Description: If you plan to build neural networks with PyTorch, you’ll want to begin your journey with this popular, open-source machine-learning framework. The eBook provides a great introduction to the subject, sharing practical knowledge related to pre-trained networks, how to use a neural network and convolutions, deploy a model to production, and much more.

Affective Computing – Jimmy Or

Description: An overview of state-of-the-art research in Affective Computing. It presents new ideas, original results, and practical experiences in this increasingly important research field, consisting of 23 chapters categorised into four sections.

10 Machine Learning Frameworks to Try – DLabs.AI Team

Description: This eBook provides deeper insight into ML frameworks that can increase efficiency and decrease work time. It recommends ten ML frameworks to check and valuable tips on why to use a particular framework or when to avoid one.

#4. Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

What happens if we succeed in building an an intelligent agent, something that perceives, that acts, and that is more intelligent than its creators? How will we convince the machines to achieve our objectives instead of their own objectives?

The above is what leads to one of the most important concepts of the book “Human Compatible: Artificial Intelligence and the Problem of Control” is that we must avoid “putting a purpose into the machine,” as Norbert Wiener once said. An intelligent machine that is too certain of its fixed objectives is the ultimate type of dangerous AI. In other words if the AI becomes unwilling to consider the possibility that it is wrong in performing its pre-programmed purpose and function, then it may be impossible to have the AI system shut itself down.

The difficulty as outlined by Stuart Russell is in instructing the AI/robot that no instructed command is intended to be achieved at any cost. It is not okay to sacrifice human life to fetch a coffee, or to grill the cat to supply lunch. It must be understood that “take me to the airport as fast as possible”, does not imply that speeding laws may be broken, even if this instruction is not explicit. Should the AI get the above wrong, then the fail safe is a certain pre-programmed level of uncertainty. With some uncertainty, the AI can challenge itself before completing a task, to perhaps seek verbal confirmation.

In a 1965 paper titled “Speculations Concerning the First Ultraintelligence Machine“, I.J Good a brilliant mathematician who worked alongside Alan Turing stated, “The survival of man depends on the early construction of an ultraintelligent machine”. It is entirely possible that to save ourselves from ecological, biological, and humanitarian disaster that we must build the most advanced AI that we can.

This seminal paper explains the intelligence explosion, this theory being that an ultraintelligent machine can design even better and superior machines with each iteration, and this inevitably leads to the creation of an AGI. While the AGI may initially be of equal intelligence to a human, it would rapidly surpass humans within a short time span. Due to this foregone conclusion, it is important for AI developers to actualize the core principles that are shared in this book and to learn how to safely apply them to designing AI systems that are capable not only of serving humans, but of saving humans from themselves.

As outlined by Stuart Russell retreating from AI research is not an option, we must press forward. This book is a roadmap to guide us towards designing safe, responsible, and provably beneficial AI systems.

#6. How AI Work: From Sorcery to Science by Ronald T. Kneusel

“How AI Works” is a succinct and clear-cut book designed to delineate the core fundamentals of machine learning. This book facilitates learning about the rich history of machine learning, journeying from the inception of legacy AI systems to the advent of contemporary methodologies.

The history is layered, starting with the well-founded AI systems such as support vector machines, decision trees, and random forests. These earlier systems paved the way for groundbreaking advancements, leading to the development of more sophisticated approaches like neural networks and convolutional neural networks. The book doesn’t just stop here; it delves into the mesmerizing capabilities offered by Large Language Models (LLMs), which are the powerhouse behind today’s state-of-the-art Generative AI.

Understanding the basics, such as how noise-to-image technology can replicate existing imagery and even create new, unprecedented images from seemingly random prompts, is critical in grasping the forces propelling today’s image generators. This book beautifully explicates these fundamental aspects, allowing readers to comprehend the intricacies and underlying mechanics of image generation technologies.

Ron Kneusel, the author, demonstrates a commendable effort in elucidating his perspectives on why OpenAI’s ChatGPT and its LLM model signify the beginning of true AI. He meticulously presents how distinct LLMs exhibit emergent properties capable of intuitively understanding the theory of mind. These emergent properties appear to become more pronounced and influential based on the size of the training model. Kneusel discusses how a larger quantity of parameters typically results in the most proficient and successful LLM models, providing deeper insights into the scaling dynamics and efficacy of these models.

This book is a beacon for those wanting to delve into the mesmerizing world of AI, offering a detailed yet comprehensible overview of the evolutionary trajectory of machine learning technologies, from their rudimentary forms to the pioneering entities of today. Whether you are a novice or someone with a substantial grasp of the subject, “How AI Works” is designed to provide you with a refined understanding of the transformative technologies that continue to shape our world.

#1. A Thousand Brains by Jeff Hawkins

“A Thousand Brains” builds on the concepts that are discussed in the previous book by Jeff Hawkins titled “On Intelligence”. “On Intelligence” explored the framework for understanding how human intelligence works, and how these concepts can then be applied towards building the ultimate AI and AGI systems. It fundamentally analyzes how our brains predict what we will experience before we experience it.

While “A Thousand Brains” is a great standalone book, it will be best enjoyed and appreciated if “On Intelligence” is read first.

“A Thousand Brains” builds on the latest research by Jeff Hawkins and the company he founded called Numenta. Numenta has a primary goal of developing a theory on how the neocortex works, the secondary objective is how this theory of the brain can be applied to machine learning and machine intelligence.

Numenta’s first major discovery in 2010 entails how neurons make predictions, and the second discovery in 2016 involved maplike reference frames in the neocortex. The book details first and foremost what the “Thousand Brains theory” is, what reference frames are, and how the theory works in the real world. One of the most fundamental components behind this theory is understanding how the neocortex evolved to its current size.

The neocortex started small, similar to other mammals, but it grew exponentially larger (only being limited by the size of the birth canal) not by creating anything new, but by copying a basic circuit repeatedly. In essence, what differentiates humans is not the organic material of the brain but the number of copies of the identical elements that form the neocortex.

The theory further evolves into how the neocortex is formed with approximately 150,000 cortical columns that are not visible under a microscope as there are no visible boundaries between them. How these cortical columns communicate amongst one another, is the implementation of a fundamental algorithm that is responsible for every aspect of perception and intelligence.

More importantly the book unveils how this theory can be applied towards building intelligent machines, and the possible future implications for society. For example, the brain learns a model of the world by observing how inputs change over time, especially when movement is applied. The cortical columns require a reference frame that is fixed to an object, these reference frames allow a cortical column to learn the locations of features that define the realities of an object. In essence reference frames can organize any type of knowledge. This leads to the most important part of this seminal book, can reference frames potentially be the vital missing link towards building a more advanced AI or even an AGI system? Jeff himself believes in an inevitable future when an AGI will learn models of the world using maplike reference frames similar to the neocortex, and he does a remarkable job illustrating why he believes this.

6 Best Books on Artificial Intelligence

Books are a great place to get started if you want to learn more about artificial intelligence. They can help you learn about the basics and where the technology is going. Plus, they may provide insights into how it could impact your finances, career, or investments in the future.

“AI 2041: 10 Visions for Our Future”

“AI 2041: Ten Visions for Our Future” was written by Chen Qiufan and Kai-Fu Lee. The book won numerous accolades, including being named one of the best books of the year by the Wall Street Journal, Washington Post and Financial Times.

According to the publisher, this book envisions how artificial intelligence could change our world within the next twenty years.

The book’s description promises a story centered around the fact that “AI will be the defining development of the twenty-first century. Within two decades, aspects of daily human life will be unrecognizable.”

It discusses everything from job reallocation to virtual companions and more via stories that highlight the impact AI could have. It warns of new risks that the technology will pose while reminding readers that humans are in charge of shaping these advances.

“A World Without Work: Technology, Automation and How We Should Respond”

Daniel Susskind published “A World Without Work: Technology, Automation and How We Should Respond in 2020.” In 2020, it was shortlisted for the Financial Times business book of the year. Fortune and Inc. also named it one of the year’s best business books.

The book focuses on how this changing technology might transform our relationship with work. It takes a close look at how AI could eliminate some jobs and warns that there is a real chance “technological unemployment” could occur.

However, it theorizes that this could also mean a shift in our world that causes work to be put on the back burner by ensuring everyone has enough resources to live on.

“The Alignment Problem: Machine Learning and Human Values”

“The Alignment Problem: Machine Learning and Human Values” was written by Brian Christian. It was a finalist for the Los Angeles Times Book Prize. Within it, Christian looks at some things that can go wrong with AI systems.

As AI is applied to more and more processes, Christian outlines some of the biases that are showing up within the systems.

The book description promises an “unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture―and finds a story by turns harrowing and hopeful.”

“2084: Artificial Intelligence and the Future of Humanity”

“2084: Artificial Intelligence and the Future of Humanity” was written by John Lennox. Although Lennox typically writes Christian-themed content, this book promises to separate the facts from fiction where AI is concerned.

According to the book description, “You will discover the current capacity of AI, its advantages and disadvantages, the facts and the fiction, as well as potential future implications. The questions posed by AI are open to all of us. And they demand answers.”

“A Brief History of Artificial Intelligence: What It Is, Where We Are and Where We Are Going”

“A Brief History of Artificial Intelligence: What It Is, Where We Are and Where We Are Going” was written by Micheal Wooldridge. Wooldridge is a leading AI researcher at Oxford and has over 25 years of experience in this sector.

You’ll enjoy this book if you are looking for a backstory on how AI has developed through the years. Its description promises “a one-stop-shop for AI’s past, present and world-changing future.”

Within the book, you’ll find optimism paired with realism about the industry’s future, giving you a balanced perspective on where artificial intelligence is headed and its capabilities.

“Artificial Unintelligence: How Computers Misunderstand the World”

“Artificial Unintelligence: How Computers Misunderstand the World” was written by Meredith Broussard and published by the MIT Press. The book looks at how AI has difficulty replacing humans in many applications.

Essentially, Broussard examines the limits of how far AI can take us.

Its description says the book is “a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right.”

Intermediate Level 1

Artificial Intelligence: Foundations of Computational Agents – David Poole, Alan Mackworth

Description: This book introduces AI as the study of designing intelligent computational agents. It serves a wide variety of readers, including professionals and researchers.

 Mathematics for Machine Learning – Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Description: Published by Cambridge University Press, this book motivates people to learn mathematical concepts and provides the necessary skills to read other books on advanced machine learning techniques. It is split into two parts: mathematical foundations and example machine learning algorithms that use the mathematical foundations.

Artificial Intelligence – Agents and Environments – William John Teahan

Description: This book is the first in a series on Artificial Intelligence. It introduces the topic, emphasising the use of agent-oriented design. Topics include agents, environments, agent movement, and agent embodiment.

Artificial Intelligence – Agent Behaviour – William John Teahan

Description: This book adopts a behaviour-based approach to the design of agent-oriented systems. The topics from a behaviour-based perspective include agent communication, searching, knowledge and reasoning, and intelligence.

Autonomous Agents – Vedran Kordic

Description: The field of multi-agent systems investigates the process underlying distributed problem-solving and designs some protocols and mechanisms involved in this process. This book presents an overview of the research issues in multi-agents.

Computational Intelligence and Modern Heuristics – Al-Dahoud Ali

Description: This book takes readers on a stunning voyage of computational intelligence heuristics research and applications. It covers various computational intelligence techniques, including neural networks, fuzzy logic, genetic algorithms, etc.

Artificial Intelligence and Molecular Biology – Lawrence Hunter

Description: This book offers a current sampling of AI approaches to problems of biological significance. It covers genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and the simulation of biological systems.

Brief Introduction to Educational Implications of Artificial Intelligence – David Moursund

Description: This book is designed to help teachers learn about the educational implications of current uses of Artificial Intelligence to solve problems and accomplish tasks. It is intended for self-study or use in workshops.

Books

Introductory theory and get start

  • Artificial Intelligence-A Modern Approach (3rd Edition) – Stuart Russell & peter Norvig
  • COMMERCIAL Grokking Artificial Intelligence Algorithms – Rishal Hurbans

Mathematics

  • A First Course in ProbabilityA First Course in Probability (8th) – Sheldon M Ross
  • Convex Optimization – Stephen Boyd
  • Elements of Information Theory Elements – Thomas Cover & Jay A Thomas
  • Discrete Mathematics and Its Applications 7th – Kenneth H. Rosen
  • Introduction to Linear Algebra (5th) – Gilbert Strang
  • Linear Algebra and Its Applications (5th) – David C Lay
  • Probability Theory The Logic of Science – Edwin Thompson Jaynes
  • Probability and Statistics 4th – Morris H. DeGroot
  • Statistical Inference (2nd) – Roger Casella
  • 信息论基础 (原书Elements of Information Theory Elements第2版) – Thomas Cover & Jay A Thomas
  • 凸优化 (原书Convex Optimization) – Stephen Boyd
  • 数理统计学教程 – 陈希儒
  • 数学之美 2th – 吴军
  • 概率论基础教程 (原书A First Course in ProbabilityA First Course in Probability第9版) – Sheldon M Ross
  • 线性代数及其应用 (原书Linear Algebra and Its Applications第3版) – David C Lay
  • 统计推断 (原书Statistical Inference第二版) – Roger Casella
  • 离散数学及其应用 (原书Discrete Mathematics and Its Applications第7版) – Kenneth H.Rosen

Data mining

  • Introduction to Data Mining – Pang-Ning Tan
  • Programming Collective Intelligence – Toby Segaran
  • Feature Engineering for Machine Learning – Amanda Casari, Alice Zheng
  • 集体智慧编程 – Toby Segaran

Machine Learning

  • Information Theory, Inference and Learning Algorithms – David J C MacKay
  • Machine Learning – Tom M. Mitchell
  • Pattern Recognition and Machine Learning – Christopher Bishop
  • The Elements of Statistical Learning – Trevor Hastie
  • Machine Learning for OpenCV – Michael Beyeler (Source code here)
  • 机器学习 – 周志华
  • 机器学习 (原书Machine Learning) – Tom M. Mitchell
  • 统计学习方法 – 李航

Deep Learning

  • Online Quick learning
    • Dive into Deep Learning – (Using MXNet)An interactive deep learning book with code, math, and discussions.
    • d2l-pytorch – (Dive into Deep Learning) pytorch version.
    • 动手学深度学习 – (Dive into Deep Learning) for chinese.
  • Deep Learning – Ian Goodfellow & Yoshua Bengio & Aaron Courville
  • Deep Learning Methods and Applications – Li Deng & Dong Yu
  • Learning Deep Architectures for AI – Yoshua Bengio
  • Machine Learning An Algorithmic Perspective (2nd) – Stephen Marsland
  • Neural Network Design (2nd) – Martin Hagan
  • Neural Networks and Learning Machines (3rd) – Simon Haykin
  • Neural Networks for Applied Sciences and Engineering – Sandhya Samarasinghe
  • 深度学习 (原书Deep Learning) – Ian Goodfellow & Yoshua Bengio & Aaron Courville
  • 神经网络与机器学习 (原书Neural Networks and Learning Machines) – Simon Haykin
  • 神经网络设计 (原书Neural Network Design) – Martin Hagan
  • COMMERCIAL Interpretable AI – Ajay Thampi
  • COMMERCIAL Conversational AI – Andrew R. Freed

Philosophy

  • COMMERCIAL Human Compatible: Artificial Intelligence and the Problem of Control – Stuart Russell
  • COMMERCIAL Life 3.0: Being Human in the Age of Artificial Intelligence – Max Tegmark
  • COMMERCIAL Superintelligence: Paths, Dangers, Strategies – Nick Bostrom

The Selling Revolution: Prospering in the New World of Artificial Intelligence by DJ Sebastian

Important note to start with — the DJ in DJ Sebastian stands for Donald J. and not a disc jockey. This means that the author of The Selling Revolution is not a random musician who decided to turn his career upside down, but a top-performing sales executive who worked with companies such as IBM, SAS, and QAD.

His book has been written for a very specific niche that is often forgotten when thinking about AI implementation — the salespeople. In the book itself, DJ Sebastian tackles all the questions one might have asked themselves when thinking about artificial intelligence or machine learning, but from the perspective of the sales team.

Thinking, fast and slow

Daniel Kahneman

Psychology is one of the human sciences that can tell us so much of relevance to AI. Nobel laureate Daniel Kahneman and his intellectual collaborator, the late Amos Tversky, studied the way humans reason and make decisions.

This very readable account contains stories and reflections on their findings and will convince you that the human mind functions in very different ways from the rational, logical processes we like to think we use.

While human irrationality probably won’t be reproduced in AI, there is much to learn from psychologists, who know so much about human behaviour.

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

If you are missing the university student-like textbooks that cover all the major things on the topic from A to Z, here is one of the best machine learning books for you. Russel and Norvig cooperated to create a comprehensive overview of the AI world on over 1000 pages. Yes, this thing is big, but none of the pages written by the authors will leave you disappointed. 

Artificial Intelligence: A Modern Approach is filled with scripts, cases, scenarios and their explanations that will satisfy everyone, from a computer science student to an entrepreneur that wishes to understand how AI works from the inside out. It is not an easy read, though, so save yourself at least an hour or two every evening to really dive into this book instead of just turning pages.

As one of the reviewers on Amazon said, “If you are looking for a really good introductory textbook to AI that does not completely dumb things down, buy this book.” And we fully agree with this!You can get the book, for example, on the official site of its publisher.

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