Professionals are indeed constructing a future world replete with artificial intelligence, while most of us are still trying to figure out what AI is in the first place. This is a technology that will have an effects on many areas of our lives, including jobs, culture, and health care, but it will also raise fundamental concerns about what it means to be human. “What is the essence of creativity?” is an example of a question. “How do we describe consciousness?” It’s almost as difficult to ask, “How can I understand AI?” as it is to ask, “What is the meaning of life?” A sensation of overawing complexity, like that tough life question, doesn’t imply we shouldn’t strive. To assist you, we’ve put together an AI reading list – the best books on AI and machine learning: a concise yet broad collection of books, short stories, and blogs selected by prominent AI experts to help you better comprehend artificial intelligence.
The Book Of Why
The Book Of Why, one of the best books on AI, is written by Judea Pearl and Dana Mackenzie and recommended by Rumman Chowdhury, Responsible AI lead at Accenture.
“An AI book with no robots, no doomsday scenarios, and no grandiose predictions of the future? How refreshing. This book’s humble and engaging writing style belies a deep hypothesis: the fundamental roots of our current systems of predictive modeling are wrong. According to the authors, we lack a language of causality; that is, quantifiable proof that one thing causes another. This is a fundamental weakness embedded in the history of statistics and tarnishes how we ask questions and seek answers.
The dirty secret of the AI and machine learning methods we use for prediction is that they cannot actually tell us with certainty whether some factor caused another, instead relying on millions of repetitions to give us high-value correlations. Many of our issues of biased outcomes in AI systems stem from an incomplete or poor understanding of interrelated variables (race and zip code, or socioeconomic status and education, for example). While still considered controversial (see Pearl’s debate with statistician Andrew Gelman on Twitter), The Book of Why presents a new narrative that questions and redefines the building blocks of our AI systems.”
Profiles Of The Future
Profiles Of The Future , one of the best artificial intelligence books, is written by Arthur C. Clarke and recommended by Greg Brockman and Ilya Sutskever, co-founders of OpenAI.
“Profiles of the Future changed our beliefs about how rapidly AI might affect the world. We used to think of technological change as a gradual, slow process — the sum of many small innovations that, when zoomed out, create only the illusion of rapid technological change.
Profiles made us realize there are some highly important exceptions. While later chapters describe Arthur C. Clarke’s predictions about the future, early chapters analyze others’ predictions about technologies like airplanes, space travel, and nuclear power before their development. In each case, the technology was predicted by a small number of optimists amongst a very large, vocal set of genuinely accomplished experts who were confident that a particular dramatic technological advance would never be achieved (at least not on a practical timescale). As a result, even to most experts, massive technological change appeared to come ‘out of nowhere.’
How will long term progress in AI look? Will it follow a predictable trajectory, with the field having a clear view of the upcoming progress in the next 5-10 years, or will we stumble upon a surprising yet disproportionate advance in AI that will transform the world rapidly? The perspective in Profiles means these questions are worth pondering.”
Franchise, one of the best books on AI and machine learning, is written by Isaac Asimov and recommended by Tim Hwang, director of the Harvard-MIT Ethics and Governance of AI Initiative.
“Asimov’s Robot series is perhaps the cliche reference that gets rolled out when talking about the social impact of artificial intelligence. It’s mostly a convenient excuse to repeat well-worn tropes about The Three Laws of Robotics and point out — sagely — that the dreams of building intelligent machines are long-standing.
But, the cliche misses the mark. In the Asimov oeuvre, it is the stories featuring the massive, impersonal Multivac — rather than the Robot series — that best capture the present day reality of machine learning. In contrast to the walking, talking robots of the Robot stories, Multivac is an unwieldy server farm that requires specialized expertise to operate and frequently produces outputs uninterpretable to the technicians that run it.
One story I’ve found myself revisiting over and over again is Asimov’s ‘Franchise,’ published as a short story in the August 1955 edition of If magazine. In it, a future America (2008), decides to reduce voting to a statistical model that extrapolates the outcomes of all elections based on a set of questions answered by one, extremely representative person.
‘Franchise’ deftly captures the weirdly recursive nature of prediction, and the personal stresses of being the focus of algorithmic analysis. Importantly, the story illustrates the real and tricky balance between predictability and legitimacy. Even if we could do a perfect job predicting voting behavior, or recidivism, or employment performance, what does it mean for this to be an automated process versus a human one? Give it a read.”
The Diamond Age
The Diamond Age, one of the best AI books and machine learning, is written by Neal Stephenson and recommended by Jeremy Howard, co-founder of fast.ai.
“The ‘Primer’ in the title refers to a leather-bound book. There are three Primers in existence, each one owned by a little girl. The primer is the greatest work of its creator, the top software engineer at the world’s most successful software company. Because, you see, it’s not an ordinary book; it is truly interactive, showing the reader exactly what they need at every moment, described in a way that is designed to maximize their interest. One of the three girls that owns a Primer is the protagonist, Nell, who after finding herself homeless discovers that the Primer has been teaching her all the skills she needs to survive, and to thrive. We follow her journey, guided by the Primer, from a little girl that’s lost everything, to a young woman who may just change the world.
I first read Diamond Age 20 years ago, and this message has stayed with me: technology can be harnessed to give opportunities to those that otherwise would not have them. As with all new technologies, there is today a knee-jerk reaction against ‘screens’ for children. There is no well-designed modern research to support this reaction. If we deny the opportunity to leverage technology in education, then we limit the best education to only those privileged enough to have access to the best teachers.
Our mission at fast.ai is to help provide access to AI tools and education to all. Technology is vital to this mission. Without it, our users and students wouldn’t have access to our online lessons and community, or the cloud compute platforms we rely on. However, I haven’t yet seen AI used to create a highly customized educational experience like the Primer. The technology foundations are largely in place now; it just needs someone to put them together. When that happens, we may hear of real-world stories like Nell’s.”
Weapons Of Math Destruction
Weapons Of Math Destruction, one of the best books on AI and machine learning, is written by Cathy O’neil and recommended by Kate Darling, Research Specialist at the MIT Media Lab.
“At first, I wanted to recommend a speculative science fiction book. But sometimes our current reality is a more interesting dystopia. In January 2019, US Congresswoman Alexandria Ocasio-Cortez was ridiculed for claiming that algorithms can be biased. No matter your political affiliation, I think everyone can benefit from a basic understanding of the pitfalls in contemporary AI systems. This book, illustrated with fascinating (and terrifying) real-world examples, is a great primer on the algorithms and data that we’re using, the delegation of power to systems that can make or break people’s lives, and the completely disastrous ways that we get it all wrong.
Cathy O’Neil is a mathematician and data scientist who went from academia to the world of Wall Street quants and later joined the Occupy Wall Street movement. Her acclaimed book covers the problems with algorithms in the finance industry, but also in the areas of criminal justice, employment, education, and many more. Many of the AI systems we’re currently deploying and are likely to use in the near future run into the issues that O’Neil highlights. This book should be required basic reading for anyone interested in artificial intelligence implementation.”