WHAT AI BOOKS TO READ, VOl 1
This is Volume 1 of what is sure to be many reading recommendations. We are living in an era of quick information and constant change, and in those moments, it can be essential to slow down and go deep in a subject. This first list comes from Erin, the founder of orient(ai)tion, but future editions will reflect suggestions from colleagues, attendees, and other thought leaders. Some of these are quite long, some are much shorter, but all are compelling and insightful looks at artificial intelligence.
AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference
by Arvind Narayanan and Sayash Kapoor is a well-rounded primer on history, hope, and fabulist claims that helps you start to make sense of fact/fiction in the AI information environment. It's a solid place to start, and their newsletter is also a fantastic resource. Great read.
Atlas of AI: Power, Politics, and the Planetary Cost of Artificial Intelligence
by Kate Crawford to understand the material, environmental, and human costs of AI, along with its relationship to past eras of extraction and exploitation and what it means for us today. Must read.
Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI
by Karen Hao to understand the financial incentives, megalomania behind the kinds of AI we have today (and the catastrophic flattening of academic inquiry on the subject), the ongoing push-pull between Doomers (those worried about AI threats) and Boomers (AI accelerationists), and what it means for us all. Must read.
Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell to understand the underlying technology, its evolution, and how it is/n't human. Great read but a little denser on the computer science side than some of the others on this list.
CoIntelligence: Living and Working with AI
by Ethan Mollick for a somewhat measured view of how AI can/will could help us, practical ideas on how to engage with it in various settings (including academia), and of what to be wary (because even among optimists there are areas of caution/concern). Easy read.
StolenFocus: Why You Can’t Pay Attention and How to Think Deeply Again
by Johann Hari on the intentionally-addictive products and features designed to extract our data and time while manipulating our choices, and how it affects younger people. (Tbh I was all in on the first few chapters of this and then as the purview expanded to pollution and diet, it waned a bit). This is a little AI-adjacent, but the themes and practices of persuasive technologies are very relevant for the AI space. Easy read.
by Eric Siegel to understand the mechanics of deploying an AI model from a business perspective (i.e. business objective is still more important than the tech itself). Most useful if you want to know more about actually planning to deploy.
Taming Silicon Valley: How We Can Ensure that AI Works For Us
by Gary Marcus. Fantastic read on the real societal threats of AI (from the guy who built and sold his AI system to Uber and testified before Congress about needed AI guardrails), and importantly, what we can do to demand better outcomes than the ones we’re headed for. Quick and easy read.
Enshittification: Why Everything Suddenly Got Worse and What to Do About It
by Cory Doctorow. Witty, fun, and sometimes infuriating tales of how we have the platforms and monopolies we have today — but most importantly how all of them are the product of decisions made by individual people. Important reminder that we live in a world of our making, and we’re the ones with the power to change it.
The through line of all these recommendations is there is a lot to know and understand about AI that is not reflected in the headlines and LinkedIn posts. One of the founding beliefs of orient(ai)tion is that we desperately need a massive surge in AI literacy for us to push for practices and principles that will make AI have more positive impacts on society.