T im Metz is worried about the "Google Maps-ification" of his mind. Just as many people have come to rely on GPS apps to get around, the 44-year-old content marketer fears that he is becoming dependent on AI. He told me that he uses AI for up to eight hours each day, and he's become particularly fond of Anthropic's Claude. Sometimes, he has as many as six sessions running simultaneously. He consults AI for marriage and parenting advice, and when he goes grocery shopping, he takes photos of the fruits to ask if they are ripe.

Last Monday an eminent, elderly British scientist lobbed a grenade into the febrile anthill of researchers and corporations currently obsessed with artificial intelligence or AI (aka, for the most part, a technology called machine learning). The scientist was Geoffrey Hinton, and the bombshell was the news that he was leaving Google, where he had been doing great work on machine learning for the last 10 years, because he wanted to be free to express his fears about where the technology he had played a seminal role in founding was heading. To say that this was big news would be an epic understatement. The tech industry is a huge, excitable beast that is occasionally prone to outbreaks of "irrational exuberance", ie madness. One recent bout of it involved cryptocurrencies and a vision of the future of the internet called "Web3", which an astute young blogger and critic, Molly White, memorably describes as "an enormous grift that's pouring lighter fluid on our already smoldering planet".

What do you think about driverless cars? Would you ride in one? Do you think they are the way of the future? In "Stuck on the Streets of San Francisco in a Driverless Car," the Times technology reporter Cade Metz went for a ride in the back seat of an experimental autonomous vehicle and wrote about his experience: It was about 9 p.m. on a cool Tuesday evening in San Francisco this month when I hailed a car outside a restaurant a few blocks from Golden Gate Park. A few minutes later, as I waited at a stoplight, a white Mercedes pulled up next to me.

Over the past few decades, a bunch of smart guys built artificial intelligence systems that have had deep impact on our everyday lives. But do they -- and their billion-dollar companies -- have the human intelligence to keep artificial intelligence safe and ethical? Questions like this are part of the history and overview of artificial intelligence in Cade Metz's book "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World." On Monday, Jan. 17, Metz, a technology correspondent for The New York Times and former senior writer for Wired magazine, is the first speaker in the 2022 Nonfiction Author Series, sponsored by the nonprofit Friends of the Library of Collier County, which raises money for public library programs and resources. The lecture series includes breakfast and is being held this year at a new venue, the Kensington Country Club in Naples.

Today's voice assistants are still a far cry from the hyper-intelligent thinking machines we've been musing about for decades. And it's because that technology is actually the combination of three different skills: speech recognition, natural language processing and voice generation. Each of these skills already presents huge challenges. In order to master just the natural language processing part? You pretty much have to recreate human-level intelligence. Deep learning, the technology driving the current AI boom, can train machines to become masters at all sorts of tasks. But it can only learn one at a time. And because most AI models train their skillset on thousands or millions of existing examples, they end up replicating patterns within historical data--including the many bad decisions people have made, like marginalizing people of color and women. Still, systems like the board-game champion AlphaZero and the increasingly convincing fake-text generator GPT-3 have stoked the flames of debate regarding when humans will create an artificial general intelligence--machines that can multitask, think, and reason for themselves. In this episode, we explore how machines learn to communicate--and what it means for the humans on the other end of the conversation. This episode was produced by Jennifer Strong, Emma Cillekens, Anthony Green, Karen Hao and Charlotte Jee.

Since people invented writing, communications technology has become steadily more high-bandwidth, pervasive and persuasive, taking a commensurate toll on human attention and cognition. In that bandwidth war between machines and humans, the machines' latest weapon is a class of statistical algorithm dubbed "deep AI." This computational engine already, at a stroke, conquered both humankind's most cherished mind-game (Go) and our unconscious spending decisions (online). This month, finally, we can read how it happened, and clearly enough to do something. But I'm not just writing a book review, because the interaction of math with brains has been my career and my passion. Plus, I know the author. So, after praising the book, I append an intellectual digest, debunking the hype in favor of undisputed mathematical principles governing both machine and biological information-processing systems. That makes this article unique but long. "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World" is the first book to chronicle the rise of savant-like artificial intelligence (AI), and the last we'll ever need. Investigative journalist Cade Metz lays out the history and the math through the machines' human inventors. The title, "Genius Makers," refers both to the genius-like brilliance of the human makers of AI, as well as to the genius-like brilliance of the AI programs they create. Of all possible AIs, the particular flavor in the book is a class of data-digestion algorithms called deep learning. Metz's book is a ripping good read, paced like a page-turner prodding a reader to discover which of the many genius AI creators will outflank or outthink the others, and how. Together, in collaboration and competition, the computer scientists Metz portrays are inventing and deploying the fastest and most human-impacting revolution in technology to date, the apparently inexorable replacement of human sensation and choice by machine sensation and choice. This is the story of the people designing the bots that do so many things better than us.

"Artificial intelligence" is now a household term, whether it's powering driving directions, spotting tumors in cancer patients or driving big discussions over ethics, bias, autonomous weapons or the future of work. But despite the fact that the first neural network was created in the late 1950s, a lot of what I just described has taken place over only about 10 years. In his new book, "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World," New York Times tech correspondent Cade Metz writes about the history of AI and the corporate forces that have shaped it since the mid-2000s. He told me AI pioneer Geoffrey Hinton really rebranded neural networks as "deep learning," and that happened just as a bunch of other factors were coming together. The following is an edited transcript of our conversation.

Lee Sedol, a world champion in the Chinese strategy board game Go, faced a new kind of adversary at a 2016 match in Seoul. Developers at DeepMind, an artificial intelligence startup acquired by Google, had fed 30 million Go moves into a deep neural network. Their creation, dubbed AlphaGo, then figured out which moves worked by playing millions of games against itself, learning at a faster rate than any human ever could. The match, which AlphaGo won 4 to 1, "was the moment when the new movement in artificial intelligence exploded into the public consciousness," technology journalist Cade Metz writes in his engaging new book, "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World." Metz, who covers AI for The New York Times and previously wrote for Wired magazine, is well positioned to chart the decades-long effort to build artificially intelligent machines.

"I last sat down in 2005," Geoffrey Hinton often says, "and it was a mistake". Now 73, the British computer scientist has spent his entire career driving forward the field of artificial intelligence (AI), doing almost all of his work while standing due to back injuries beginning in his teens. By the time he reached his 50s, Mr Hinton's back problems were so severe that he decided to just stop sitting down. These days, when travelling by car he lies sprawled across the back seat - and he eats "like a monk at the altar" by kneeling on a foam cushion before the table. "If you let it completely control your life, it doesn't give you any problems," he told Cade Metz, a journalist for The New York Times, who has detailed his life in the book Genius Makers.

It was on the motorway near Phoenix, Arizona, that I realised fully driverless cars might be quite a distant dream. And that was because our Google Waymo robo-taxi seemed incapable of leaving that motorway. We were in Arizona to record a radio documentary for the BBC World Service about the progress towards creating autonomous vehicles that would make our roads safer and replace human drivers with robots. Google leads this race at the moment and for the past six months has been offering a robo-taxi service, Waymo One, to a select few early adopters in and around the Phoenix suburb of Chandler. Our first ride with Waymo took us through the quiet suburban streets, where traffic is sparse and drivers well mannered.