Here are 100 books that Genius Makers fans have personally recommended if you like
Genius Makers.
Shepherd is a community of 10,000+ authors and super readers sharing their favorite books with the world.
Shepherd is reader supported. When you buy books, we may earn an affiliate commission.
I’m a mathematics professor who ended up writing the internationally bestselling novel The Death of Vishnu (along with two follow-ups) and became better known as an author. For the past decade and a half, I’ve been using my storytelling skills to make mathematics more accessible (and enjoyable!) to a broad audience. Being a novelist has helped me look at mathematics in a new light, and realize the subject is not so much about the calculations feared by so many, but rather, about ideas. We can all enjoy such ideas, and thereby learn to understand, appreciate, and even love math.
A primary reason to love math is because of its usefulness. This book shows two sides of math’s applicability, since it is so ubiquitously used in various algorithms.
On the one hand, such usage can be good, because statistical inferences can make our life easier and enrich it. On the other, when these are not properly designed or monitored, it can lead to catastrophic consequences. Mathematics is a powerful force, as powerful as wind or fire, and needs to be harnessed just as carefully.
Cathy O’Neil’s message in this book spoke deeply to me, reminding me that I need to be always vigilant about the subject I love not being deployed carelessly.
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made…
I’m a storyteller writing on business and technology. I specialize in clear views of complex systems. When Juliette showed me her research on tech companies and AI responsibility, I saw the power of a book – the book that ultimately became The AI Dilemma. The core dilemma is that in the right hands the technology is needed, and in the wrong hands it’s dangerous. When Juliette asked me to coauthor it, I jumped at the chance. As we worked, I realized that the topic brought into focus all the research and thinking I’d ever done about human, organizational, and machine behavior.
This is the book I suggest to people who worry that AI will take their jobs or control the world.
These U of Toronto economists make a case for AI as a disruptor of systems and power structures – not on their own, but because of the decision makers who control them. Those companies won’t necessarily be Alphabet, Amazon, or Meta. Their competitive advantage – access to data – will now be available cheaply.
It’s judgment that’s at a premium now. After reading this, I feel pretty trusting of AI. I’m just not so sure about people.
Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines can help you prepare.
Artificial intelligence (AI) has impacted many industries around the world-banking and finance, pharmaceuticals, automotive, medical technology, manufacturing, and retail. But it has only just begun its odyssey toward cheaper, better, and faster predictions that drive strategic business decisions. When prediction is taken to the max, industries transform, and with such transformation comes disruption.
What is at the root of this? In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet…
I’m a storyteller writing on business and technology. I specialize in clear views of complex systems. When Juliette showed me her research on tech companies and AI responsibility, I saw the power of a book – the book that ultimately became The AI Dilemma. The core dilemma is that in the right hands the technology is needed, and in the wrong hands it’s dangerous. When Juliette asked me to coauthor it, I jumped at the chance. As we worked, I realized that the topic brought into focus all the research and thinking I’d ever done about human, organizational, and machine behavior.
Another classic, which influenced my book. Ron is the founder of RiskThinking AI, which focuses on the financial effects of climate change.
His fascinating stories include one of the first fatalities from self-driving cars, when it struck a woman with a bicycle running to cross the road. Any human driver would have seen an uncertain image and slowed down, playing out a group of possible scenarios. But AI hasn’t learned that kind of humility.
Eventually, when it does, the machines and people will learn how to manage risk together, and that will lead to more courage. The best part of this book is his insight into how the use of new technology changes human behavior.
Our age of radical uncertainty requires a new way of assessing risk that pays more attention to the extreme outliers that too often become tomorrow’s reality.
Today’s models cannot cope with the frightening new unpredictable risks we face every day that frequently seem to come out of left field – the effects of climate change, a killer pandemic, a cascading wildfire, a financial crisis triggered by faceless algorithms, or a devastating cyber-attack that shuts down the electric power grid.
This accessible book advocates a new, more realistic approach to analyzing risk and strategizing—one that is less reliant on a single…
I embarked on this arduous journey of making sense of the I in AI while working as an Assistant Professor of Finance, which, however, began to look increasingly uninteresting and oppressive. With this innovative endeavor, I return home to philosophy. Apart from being passionate about AI in academia, I’m a tech entrepreneur by heart with three software start-ups in Germany, Switzerland, and Malawi under my belt. Moreover, I served as Deputy Director of and Head of AI at the Swiss Fintech Innovation Lab in Zurich, as Director of Startup Grind Geneva, and I continue to fulfill my role as start-up coach/judge and mentor in various startup programs.
AI is arguably the most disruptive technology that humankind has ever developed. AI development has come in different waves since the 1950s and, over time, the machine intelligence has increased.
Where will this ongoing trend lead us in the future? I love Kai-Fu Lee and Chen Qiufan’s book for two chief reasons: On the one hand, I found it highly rewarding as a reader to learn about the future of AI and its practical impact on our everyday life by gaining insights into today’s variety of AI methods, the challenges they pose as well as into how those challenges will be overcome in the next 10 to 20 years.
On the other hand, I have been deeply impressed by the strength of the author team with a complimentary skillset. While Kai-Fu Lee worked both in Chinese and US-American internet sector as a leading software engineer, thereby combining very interesting perspectives,…
A WALL STREET JOURNAL, WASHINGTON POST, AND FINANCIAL TIMES BEST BOOK OF THE YEAR
In this ground-breaking blend of imaginative storytelling and scientific forecasting, a pioneering AI expert and a leading writer of speculative fiction join forces to answer an imperative question: How will artificial intelligence change our world within twenty years?
AI will be the defining development of the twenty-first century. Within two decades, aspects of daily human life will be unrecognizable. AI will generate unprecedented wealth, revolutionize medicine and education through human-machine symbiosis, and create brand new forms of communication and entertainment. In liberating us from routine work,…
My personal passion behind ethical AI started early in my life. I was raised by someone who had a personality disorder, and grew up being gaslit and manipulated. It was hard for me personally to understand what was reality and what was made up. Being a nerdy kid, I spent most of my time studying computers and math to escape it all. And while I have made my own life writing books on machine learning, and programming for a living, I also care deeply about how what I do affects others. Being thoughtful is deep within me, and I sit with a Zen group and volunteer with the Mankind Project.
Peter Flach’s book on machine learning had a profound impact on me. The book is simple to understand, and highly visual. But beyond that Peter himself is a lovely person who obviously cares about all his students. I believe for getting started in machine learning and wanting to understand the algorithms that power many models, this is a great place to start.
But most importantly it’s a great way to understand the power and gain more intention behind what we are doing.
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role…
We’re a nonprofit that aims to help people have a positive social impact with their careers. Since you have, on average, 80,000 hours in your career, what you decide to do with that time might be your biggest opportunity to make a difference. Over the past ten years, we’ve conducted careful research into high-impact careers, and have helped thousands of people plan a career that has a high positive impact.
One example of an especially pressing threat facing humanity is the rapid development of artificial intelligence. If we want this new technology to go well, it needs to be ‘aligned’ – that is, it should have or act on the same values as us.
In this book, Brian sets out why aligning artificial intelligence is an extremely tricky issue and one which deserves more attention from talented and dedicated people.
Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.
Systems cull resumes until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess Black…
I have worked in the field of machine learning and predictive analytics for many years. Having started out as a technical specialist, I have become increasingly interested in the legal, ethical, and social aspects of these subjects. This is because it is these “soft issues” that often determine how successful these technologies are in practice and if they are viewed as a force for good or evil in wider society. This has led me to write several books focusing on the practical and cultural aspects of these subjects and how best to apply them for the benefit of business, individuals, and wider society.
As ever more powerful AI-based tools are created, Russell asks the question (and provides some answers) as to how we can ensure that we stay in the control of our creations. In particular, what safeguards are needed to protect us from something that will potentially be more intelligent than ourselves? Some might argue that this is all just science fiction and, even if it’s possible to build machines that are more intelligent than we are, it’s a problem for the distant future. However, there are many areas where AI is already making the key decisions about how we are treated. For example, whether or not to offer you a job or if you should get that loan you applied for. Consequently, I found this book to present a compelling case that controlling AI is something that we need to address as a matter of urgency, sooner rather than later.
A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines
In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable.
In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines.…
I’ve been a geeky kid all my life. (I don’t think I’ve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. I’ve since spent 25 years exploring how to make computers think, build, invent, compose… and I’ve also spent 20 years writing popular science books. I’m lucky enough to be a Professor in one of the world’s best universities for Computer Science and Machine Learning: UCL, and I guess I’ve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?
I’ve not met Harry, but he seems to have a logical and sensible head on his shoulders. His writing is considered and grounded, which is exactly what you need when discussing the hype that forever seems to surround AI. This book is another look at this topic and finds yet more ways to explain to readers the difference between human intelligence and our algorithmic attempts at intelligence – which are frequently pretty stupid.
Recent startling successes in machine intelligence using a technique called 'deep learning' seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the 'Surrender'.
By dissecting the intricacies of language use and meaning, Collins shows how far…
I became fascinated by the highest achievements of human intelligence while a graduate student in philosophy working on the discovery and justification of scientific theories. Shortly after I got my PhD, I started working with cognitive psychologists who gave me an appreciation for empirical studies of intelligent thinking. Psychology led me to computational modeling of intelligence and I learned to build my own models. Much later a graduate student got me interested in questions about intelligence in non-human animals. After teaching a course on intelligence in machines, humans, and other animals, I decided to write a book that provides a systematic comparison: Bots and Beasts.
This book provides a good introduction to the current state of machine intelligence through interviews with many leading practitioners including Geoffrey Hinton, Yann LeCun, Stuart Russell, and Demis Hassabis (DeepMind). You will get a sense of both of AI’s recent accomplishments and how far it falls short of full human intelligence.
How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances?
Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community.
Martin has wide-ranging conversations with twenty-three…
I grew up and completed the formative years of my college education in Cape Town, South Africa, while active also in anti-apartheid struggles. My Ph.D. dissertation in the 1980s focused on the elaboration of key racial ideas in the modern history of philosophy. I have published extensively on race and racism in the U.S. and globally, in books, articles, and public media. My interests have especially focused on the transforming logics and expressions of racism over time, and its updating to discipline and constrain its conventional targets anew and new targets more or less conventionally. My interest has always been to understand racism in order to face it down.
Digital technology, like technology generally, is commonly assumed to be value neutral. Wendy Chun reveals that structurally embedded in digital operating systems and data collection are values that reproduce and extend existing modes of discriminating while also originating new ones. In prompting and promoting the grouping together of people who are alike—in habits, culture, looks, and preferences—the logic of the algorithm reproduces and amplifies discriminatory trends. Chun reveals how the logics of the digital reinforce the restructuring of racism by the neoliberal turn that my own book lays out.
How big data and machine learning encode discrimination and create agitated clusters of comforting rage.
In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt…
