In 1989, Sir Tim revolutionized the online world. Today, in the era of misinformation, addictive algorithms, and extractive monopolies, he thinks he can do it again. Berners-Lee is building tools that aim to resist the Big Tech platforms, give users control over their own data, and prevent A.I. from hollowing out the open web. Tim Berners-Lee may have the smallest fame-to-impact ratio of anyone living. Strangers hardly ever recognize his face; on "Jeopardy!," Berners-Lee invented the World Wide Web, in 1989, but people informed of this often respond with a joke: Wasn't that Al Gore? Still, his creation keeps growing, absorbing our reality in the process. If you're reading this online, Berners-Lee wrote the hypertext markup language (HTML) that your browser is interpreting. He's the necessary condition behind everything from Amazon to Wikipedia, and if A.I. brings about what Sam Altman recently called "the gentle singularity"--or else buries us in slop--that, too, will be an outgrowth of his global collective consciousness. Somehow, the man responsible for all of this is a mild-mannered British Unitarian who loves model trains and folk music, and recently celebrated his seventieth birthday with a picnic on a Welsh mountain. An emeritus professor at Oxford and M.I.T., he divides his time between the U.K., Canada, and Concord, Massachusetts, where he and his wife, Rosemary Leith, live in a stout greige house older than the Republic. On the summer morning when I visited, geese honked and cicadas whined. Leith, an investor and a nonprofit director who co-founded a dot-com-era women's portal called Flametree, greeted me at the door. "We're basically guardians of the house," she said, showing me its antique features. I almost missed Berners-Lee in the converted-barn kitchen, standing, expectantly, in a blue plaid shirt. He shook my hand, then glanced at Leith. Minutes later, he and I were gliding across a pond behind the house. Berners-Lee is bronzed and wiry, with sharp cheekbones and faraway blue eyes, the right one underscored by an X-shaped wrinkle. A twitchier figure emerged when he spoke.

At Springwood library in the Blue Mountains, a librarian appears with a cat carrier in each hand. About 30 children gather around in a semicircle. Inside each carrier, a pair of beaming, sci-fi-like eyes peer out at the expectant crowd. "That is the funniest thing ever," one child says. The preschoolers have just finished reading The Truck Cat by Deborah Frenkel and Danny Snell for the annual National Simultaneous Storytime.

You'll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your knowledge as a web developer, you'll add a whole new field of development to your tool set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically.

My son Charlie will be 18 soon. Like all Scottish males before him, he will be dropped on a Hebridean island with nothing but a rusty knife and his own anger. If he can't make it back to the mainland, he will live the rest of his life among feral, abandoned Scottish sons, and he will only survive if he likes sport, because that's how any group of men get through enforced time together. He tried sport as a kid, but as he is on the autism spectrum, he was obsessed with rules to the point where if he felt another kid broke them, he would pick the ball up and stop the game. He was basically human VAR.

Privid's a privacy-preserving video analytics system supports aggregation queries, which process large amounts of video data. "Privid" could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people. Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. As these powerful little devices have cropped up seemingly everywhere, the use of machine learning tools has automated video content analysis at a massive scale -- but with increasing mass surveillance, there are currently no legally enforceable rules to limit privacy invasions. Security cameras can do a lot -- they've become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes "hero tool" in crime media.

Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. As these powerful little devices have cropped up seemingly everywhere, the use of machine learning tools has automated video content analysis at a massive scale -- but with increasing mass surveillance, there are currently no legally enforceable rules to limit privacy invasions. Security cameras can do a lot -- they've become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes "hero tool" in crime media. Now, video surveillance can help health officials measure the fraction of people wearing masks, enable transportation departments to monitor the density and flow of vehicles, bikes, and pedestrians, and provide businesses with a better understanding of shopping behaviors. But why has privacy remained a weak afterthought?