Phil Treagus-EvansPedro Domingos is a renowned figure in the fields of machine learning and data mining, with an emphasis on enhancing computer autonomy, efficiency, and adaptability. As a professor emeritus of computer science and engineering at the University of Washington, his research addresses critical challenges, such as distinguishing genuine patterns, exploiting pre-existing knowledge, improving learning models, and mining semi-structured data sources. Domingos has significantly contributed to the development of algorithms that consider decision costs and probabilistic foundations of data mining.
Pedro is the author of “The Master Algorithm,” a groundbreaking book exploring machine learning and the quest for computer intelligence. This distinguished academic has won the SIGKDD Innovation Award and the IJCAI John McCarthy Award, the highest honors in data science and AI, and is a fellow of AAAS and AAAI. He co-founded the International Machine Learning Society and has written extensively for major publications while helping pioneer several fields in AI and machine learning.
Enjoy our interview with Pedro Domingos…
Was there any particular book or philosophical work that sparked the idea for your book, “The Master Algorithm”?
No particular one. I was dissatisfied with the lack of a good introduction to machine learning for a general audience. One incarnation of the idea of a master algorithm that predates the book is On Intelligence by Jeff Hawkins and Sandra Blakeslee. “On Intelligence” is about one version of a universal learning algorithm: the brain. “The Master Algorithm” explores this and several others, each pursued by a different machine learning subcommunity (e.g., evolution).
What are some of the books or authors that you’ve found personally influential or enjoyable, regardless of whether they’re directly related to your field?
Jorge Luis Borges, George Orwell, Olaf Stapledon, Fernando Pessoa, Anton Chekhov, Ernest Hemingway, Cormac McCarthy, John Updike. I like their writing style: very concise and dense with content. Also, the ideas in some of their works (e.g., Borges’ stories, “1984”) are of central importance to the world today.
Can you share any books that delve into the ideas of adaptation and learning, whether they’re about machines, humans, or even animals?
The Origin of Species by Charles Darwin. It’s a great and accessible book whose ideas apply well beyond biology. The idea of natural selection was inspired by economics, and the kind of evolutionary process the book describes is everywhere – notably in technology. One whole school of machine learning, as I mentioned above, is explicitly based on the idea of automating evolution.
Which book have you recently read that you found unexpectedly thought-provoking or inspiring, and why?
A Different Universe by Robert Laughlin. If you’re tired of the same old books about physics, this one will give you a very different view. It’s a condensed matter physicist’s view of the universe, as opposed to the particle physicist’s view that we usually get exposed to. And, for my money, it’s actually closer to the truth. E.g., spacetime itself is like a kind of condensed matter – the Standard Model is full of signs of this. Also, there’s lots in condensed matter physics that just can’t be explained with particle physics – and if that’s the case, imagine biology, etc.
When it comes to explaining complex concepts like machine learning to a broad audience, what are some books outside your field that you think do a good job simplifying complex ideas?
Steven Pinker, Richard Dawkins and James Gleick are masters of this.
Are there any fiction books or novels that you feel accurately depict the potential future of machine learning and AI?
Tall order. Most of them are way off. Really good ones . . . still waiting. (I’m writing one, so stay tuned!)
You work on the frontiers of knowledge, dealing with concepts like data overload. Do you have any favourite books that explore the future of society in our increasingly digital age?
Another tall order. The last chapter of my own book The Master Algorithm does this.
As an author, are there any books or authors whose writing style you admire or seek to emulate?
The novel I’m writing is a satire, and A Confederacy of Dunces by John Kennedy Toole and The Master and Margarita by Mikhail Bulgakov come to mind. It’s a satire of the tech industry and its collision with politics. The goal is to entertain while giving readers a window into how the world of tech, and AI in particular, really works. The one-sentence summary of the plot is “ChatGPT runs for president”. The comic potential is endless!
Can you share any books that you’ve read purely for fun or relaxation recently?
The first ones that come to mind are A Gentleman in Moscow by Amor Towles, Aztec by Gary Jennings, Empire of Blue Water by Stephen Talty, The Martian by Andy Weir.
Which book, if any, have you gifted or recommended most frequently to friends or family?
Probably Chaos by James Gleick. It’s both a great example of engaging, accessible science writing and a book that gives you new insights about how the world works.
Was there a book you read early in your life that you feel shaped your personal philosophy or worldview?
The first AI textbook by Elaine Rich, is what got me into the field.
If a novice in the world of AI and machine learning asked you for one book to ignite their interest, which would it be?
Apologies for the self-promotion, but I’d say my own book The Master Algorithm. A large part of why I wrote it, was for this very reason and I’m told it does it pretty well.
Do you have a favourite book that you’ve used in teaching or mentoring students, which is not a textbook?
Is there a book that you believe provides a unique or unexpected insight into the world of data mining and machine learning?
I don’t think we have any such yet, but I’ll keep looking.
Are there any books, fiction, or non-fiction, that you feel have contributed to your understanding of the philosophical implications of artificial intelligence?
Gödel, Escher, Bach.