Humble beginnings with a sinister future?
‘Genius Makers’ looks at how AI is creating dilemmas
Several years ago, I met a woman for coffee at Battery, a private club in San Francisco’s North Beach neighborhood where technology swells used to hang out pre-COVID-19.
The woman ran communications for Andreessen Horowitz, the famed venture capital firm whose official tagline is: “Software is eating the world.”
Our talk turned to artificial intelligence. I marveled at the wonderful things AI promised us, but I did worry about people’s jobs. “What’s an accountant displaced by AI going to do?” I asked.
“Oh, people will be able to pursue their creative passions,” she said.
For instance?
“I don’t know. Braid hair? She could set up a shop and braid hair, if that’s her passion.”
OK, then.
As with any powerful technology, there are downsides, too. Serious downsides.
I thought back to this conversation while reading Cade Metz’s excellent new book, “Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World” (384 pages, Dutton, $28).
“Genius Makers” is not really a history of AI, as such. Artificial intelligence goes back at least to the 1950s.
The key thing the field accomplished over most of those years was to explore a number of dead-end ideas that proved worthless or not ready for prime time. In other words, basic scientific research doing its thing.
Neural networking
While Metz, a reporter for the New York Times, does sketch out the early history, his focus is on the last 10 years or so, when a once-belittled AI approach known as neural networking began to insinuate itself, for good or ill, into the daily lives of humans around the world. Alexa, Google Home, Siri — all made possible with AI neural networks. Facebook’s ability to read faces in photos and identify them by name? Neural nets.
It’s not just the sinister stuff. Neural net software is helping doctors evaluate cancerous tumors and beginning to turn cars into robots that can drive themselves. Earlier this month, Sonoma County said it would start using neural net technology to help spot the earliest flames of quick-building wildfires. The possibilities are endless. But as with any powerful technology, there are downsides, too. Serious downsides.
Unlike many of the books written about AI, you don’t need a science or engineering degree to learn from and enjoy this one. Anyone with an enthusiastic curiosity about science, technology and the future of human culture will find this clear-eyed, snappily written book both entertaining and valuable. You could even call it essential for any policymakers, politicians, police, lawyers, judges and decision-makers who will be contending with the social forces unleashed by artificial intelligence. Which, soon, will mean all of them.
Future complications
The same technology that lets your daughter call up Cardi B’s “WAP” with a voice command is also being used for government surveillance, racial profiling and the creation of “deep fake” YouTube videos that can mimic a real person so closely it’s becoming nearly impossible to tell the difference — from fake Tom Cruise to fake Hillary Clinton to your fake brotherin-law.
Adding to the array of ethical tangles you can already see proliferating, these programs in some ways write themselves, making it difficult to look inside and figure out where an errant machine went wrong — a conundrum known as the black box problem.
Don’t worry: Metz addresses these AI species and subspecies quickly and clearly, explaining just enough of the technology to make sense of the larger human dilemmas. (Lay readers looking for more detail should also read the recently published “Evil Robots, Killer Computers, and Other Myths” by Steven Shwartz, another clearly written book that goes into a bit more depth on the underlying principles.)
The men behind the nets
Metz starts his story with the man who might be considered the father of modern neural nets, Geoff Hinton, a Canadian researcher who eventually sold his startup company to Google for $43 million. Hinton and another key figure, Yann LeCun (who soon went to work for Facebook), issued a research paper in 2012 that showed how a deep learning system, fed enough pictures of various