A Thinker’s Guide to Artificial Intelligence

I just finished reading the book about Artificial Intelligence (AI) that I’ve been craving for years: Artificial Intelligence: A Guide for Thinking Humans, by Melanie Mitchell. More than any other book on this hot but largely misunderstood topic, this book describes AI in clear and accessible terms. It cuts through the hype to present a sane assessment with no agenda apart from a desire to inform. Reading this book, you’ll likely discover that AI is quite different from what you imagined.

Melanie Mitchell qualifies as a second-generation pioneer in the field of AI. Beginning in the mid-1980s she earned her Ph.D. in the field under the supervision of Douglas Hofstadter, the previous generation pioneer whose book Gödel, Escher, Bach: an Eternal Golden Braid inspired many to pursue AI. She continues to do research and development in AI as a professor at Portland State University and at the Santa Fe Institute. I’d wager that few people, if any, understand AI in general better than she does. In this book she explains what AI is, covers its history from inception through today, describes the approaches that have been pursued (symbolic AI, neural networks, machine learning, etc., including explanations for how these approaches work), and presents the strengths and limitations of AI in unvarnished terms. She does all of this with a practical eloquence that is rare among technology writers.

Should we be concerned about AI? You bet, but probably not for the reasons that you imagine. AI has never exhibited anything that qualifies as general intelligence (i.e., thinking as humans do), despite years of diligent effort. Will it ever? Nobody knows. In the meantime, however, we do know that computers can perform “narrow AI” tasks that are quite helpful. We should make sure that AI is only applied in ways that are truly useful and understood. If we can’t understand how AI’s results are produced, we can’t trust those results. We must also make sure that AI applications are designed in ways that are both effective and ethical. Current applications exhibit worrisome flaws. As AI researcher Pedro Domingos has said: “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” I agree. We can and should produce better, more useful AI technologies. Knowing that people like Melanie Mitchell are involved in the effort gives me hope—a glimmer, at least—that we just might head in that direction.

4 Comments on “A Thinker’s Guide to Artificial Intelligence”

By Pepe Vera. March 7th, 2020 at 2:33 am


knowing that you are keen to recognize complexity as a customary topic of study for nowadays sensemakers, I would like to recommend you the earlier book of Melanie Mitchell, named Complexity – A guided Tour, in case you much enjoyed Melanie’s style in writing.

It is not about AI, but about how complexity and information management came to be as we know them today.

I am a long-time follower of your work and I can imagine you would enjoy that book as well.

By Stephen Few. March 7th, 2020 at 8:21 am

Hi Pepe,

Thanks for the recommendation. Mitchell’s book titled “Complexity: A Guided Tour” is definitely on my list. It deals with systems thinking, which interests me a great deal.

By Bob. April 7th, 2020 at 6:24 am


Are you familiar with Martin Armstrong who created Socrates (an AI system)? His Socrates system has predicted, sometimes to the exact day, major turning point events & done so years in advance.

I do appreciate your vision & clarity in your work.


By Stephen Few. April 7th, 2020 at 9:28 am


Whenever someone claims to predict events to the exact day years in advance, it’s always worthwhile to ask a few questions. The primary question that I would ask is, “Are the predictions published years in advance of the event date?” If not, then the person responsible could be claiming a prediction that he didn’t actually make years in advance or he could be selectively sharing only those predictions that came true. Claiming to predict events that will occur on a precise date years in advance is not something that any person or system, AI or otherwise, can do based on statistics unless the event is part of a system that can be modeled mathematically with precision. For example, one can predict the exact date on which Haley’s Comet will reappear in the sky, but not the exact date on which the stock market will do something specific. I take any claims that a commercial software vendor makes about its products with a grain of salt.

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