The guide to AI for the rest of us
How to AI is a brief, instructive, deeply researched, accessible book about what AI is, how it works, and what it might be useful for at your day job, whatever that might be. It is also a book about what AI is not and what we should not be using it for.
I’m a long-time tech columnist at the Wall Street Journal. I’ve been covering AI for close to a decade. And in that time, dear reader, I have ~seen some things~.
In an insidery, taking-you-behind-the-scenes kind of way, I would like to share them with you.
Hence, this book. And this newsletter.
You can pre-order How to AI here.
Or just subscribe to this newsletter.
me, recording the audiobook version of How to AI … skeptically
How to AI is a living document, and this is its continued existence
found this perched on the creaking shelves of a local used bookstore
None of us aspired to being witnesses to history, but here we are.
I don’t claim to be an expert in anything other than what I write about every week for the Wall Street Journal — that is, “technology” in the broadest possible sense of that word. But hoo boy, it sure feels like that subject, and AI in particular, is having an impact on nearly every other area of our lives.
So, if you’ll indulge me, I’d like to send you a little note every week, putting AI and related matters into context.
This newsletter is, if I’m being perfectly honest, just a curated version of what I’m already putting on social media.
So if you’re curious about what I’ve reported on in a given week for the Journal, what I’m learning on my “listening tour” for the book (more on that later) and what good stuff others have written/said/posted about AI lately, please subscribe. (It’s free forever.)
A Final Note
this creature has never met you / but she’s already a fan
My writing companion. I can’t believe her ancestors were wolves.
“My parents sent me to a British public school, that is, a private school. Same one that John Cleese went to. I got Christianity at school and Stalinism at home. I think that was a very good preparation for being a scientist, because I got used to the idea that at least half the people are completely wrong.”
—Geoffrey Hinton, winner of a Nobel prize for pioneering research in machine learning and artificial neural networks
From Talking Nets: An Oral History of Neural Network Research