Искусственный интеллект: от архивов 1845 года до наших дней – история в 163 статьях

Weapons of Math Destruction by Cathy O’Neil

Weapons of Math Destruction, by Cathy O’Neil is an artificial intelligence book that aims to show the dark side of the algorithms and machine learning systems that have become ubiquitous in our day to day life.

These software systems, which are meant to be tools to increase our overall quality of life, turn out to be flawed with biases and can lead to massive feedback loops that do nothing but further increase the misery of those affected by them.

In theory, these machine based decision systems should lead to a greater fairness, having everybody be judged according to the same rules, however O’Neil reveals that these systems are used in an unregulated, uncontested and opaque manner, which leads to more discrimination rather than more fairness.

Welcome to the dark side of Big Data. Welcome to the era of the Weapons of Math Destruction. Read the full article!

Artificial Intelligence: A guide for thinking humans

Artificial Intelligence: A Guide for Thinking Humans comprehensively explains the history of AI, the recent amazing achievements it has reached, its future, and the fears around it.

The book is an incredible overview of Artificial Intelligence and its surrounding world, telling stories about AI with a human touch, and a captivating engaging discurse. If you want to understand AI, know where it comes from, and where it is going, then this is the book for you. Read the full review!

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Tags: AI Books, Artificial Intelligence Books, Best AI Books, AI Books for Beginners, Artificial Intelligence books 2021.

Prediction Machines: The Simple economics of AI

Fun, full of examples, and very insightful and practical Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon that Artificial Intelligence is opening up. It’s impact will be profound, so you better be ready for it.

For us, there is no better way to do this than by reading this profound yet surprisingly simple book. Enjoy it!

For business owners, entrepreneurs, and experts in Machine Learning and Data Science, this is a must read for people that want to understand how AI will impact our economies and the business world! Find the full review here!

Artificial Intelligence: A Modern Approach

Unlike the rest of the books in this category, Artificial Intelligence: A Modern approach, is a little technical, as it is oriented for under-grads in engineering or computer science. This book, is the most popular and recommend Artificial Intelligence book in academia.

Written by the superlative Stuart Russel, also author of Human Compatible: Artificial Intelligence and the Problem of Control ,in its more than 1000 pages it explores ALL of the concepts that surround the world of AI: Machine learning, Probability, the future of AI, its ethics, and awesome applications. Check out the review!

Frequently Asked Questions

1. Which book is best for learning AI?

The best book for artificial intelligence will depend on your skill level and your starting point. Additionally, it can also depend on your particular interests. On this list are some of the best artificial intelligence books currently available on the market. It may benefit you to check them out and see whether any of them suit your needs most.

2. Can I learn AI on my own?

Yes! Even without any prior knowledge or experience in engineering, it is entirely possible to learn AI on your own and from the comfort of your home. You can certainly learn about concepts and theories around AI, but that alone isn’t enough. You can create some simple machine learning projects to put your knowledge to the test and work with artificial intelligence in practice. Hands-on experience is key to being able to fully grasp a subject, after all.

3. How do I start learning about AI?

Getting started with AI doesn’t have to be difficult. There are clear and simple steps you can take, such as reading some of the best AI books on this list. However, don’t just pick up any artificial intelligence book — make sure you look for one that suits your level and interests. For example, if you’re new to AI, start with what you think is the best book for artificial intelligence for beginners.

4. Can I learn AI without coding?

It is definitely possible, but it also depends on your idea of learning artificial intelligence. There are now some tools online that can let you start experimenting with machine learning and artificial intelligence without ever needing to know how to code. For example, there are services through which you can train AI tools on your own without knowledge of coding or programming, but the use cases are limited.

5. Is AI based on a book?

Yes and no. An unrelated A.I. is indeed based on a book. Artificial Intelligence (A.I.) is a science fiction and drama movie from 2001 that was not just written but also directed by the legendary Steven Spielberg. That said, the AI that we’re referring to in this article is a term coined by Stanford computer science professor John McCarthy.

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About the authors

Georgios N. Yannakakis is an Associate Professor at the Institute of Digital Games, University of Malta (UoM). He received his Ph.D. in informatics from the University of Edinburgh in 2006. He was previously an Associate Professor at the Center for Computer Games Research at the IT University of Copenhagen. His research lies at the crossroads of artificial intelligence, computational creativity, affective computing, and human-computer interaction with an emphasis on the domain of games.  He has published over 200 journal and conference papers in the aforementioned fields, his research has been supported by numerous national and European grants, and it has been featured in Science Magazine and New Scientist among other publications. He is an associate editor of the IEEE Trans. on Computational Intelligence and AI in Games and was an associate editor of the IEEE Trans. on Affective Computing (2009-2016). He was the general chair of related key conferences such as IEEE CIG (Computational Intelligence and Games) and Foundations of Digital Games (FDG). 

Julian Togelius is an Associate Professor in the Dept. of Computer Science and Engineering of New York University, and a codirector of the NYU Game Innovation Lab. He was previously an Associate Professor at the Center for Computer Games Research, IT University of Copenhagen. He works on all aspects of computational intelligence and games and on selected topics in evolutionary computation and evolutionary reinforcement learning. His current main research directions involve search-based procedural content generation, game adaptation through player modelling, automatic game design, and fair and relevant benchmarking of game AI through competitions. He is the Editor-in-Chief of the IEEE Transactions on Games.

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