For context, Go is a board game previously thought to require too much human intuition for a computer to succeed in, and as a result, it was a North Star for AI. Centuries ago, scientists and doctors operated largely in the dark when attempting to cure diseases and had to rely solely on their intuition. Many current and past approaches in the field relied on a single researcher or academic group's intuition for prioritizing which genes to test edit. Recently, with advances in high-throughput single-cell CRISPR sequencing methods, we are nearing the possibility of simply testing all genes simultaneously on equal footing and in various experimental scenarios. In fact, we predict that in the next 10 years, we will have an equivalent of a Move 37 against cancer: a therapy that at first may seem counterintuitive (and at which human intuition alone would not arrive) but that in the end, shocks us all and wins the game for patients.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. For context, Go is a board game previously thought to require too much human intuition for a computer to succeed in, and as a result, it was a North Star for AI. For years, researchers tried and failed to create an AI system that could beat humans in the game. In 2016, AlphaGo, an AI system created by Google's DeepMind, not only beat its champion human counterpart (Lee Sedol); it demonstrated that machines could find playing strategies that no human would come up with. AlphaGo shocked the world when it performed its unimaginable move #37.

Thankfully, in many cases, we live up to it. But our present digital reality is quite different, even sobering. Fighting terrorists for nearly 20 years after 9/11, we remained a flip-phone military in what is now a smartphone world. Infrastructure to support a robust digital force remains painfully absent. Consequently, service members lead personal lives digitally connected to almost everything and military lives connected to almost nothing.

As Marcus du Sautoy greets me at the entrance to New College, Oxford, his appearance is a quiet riot of colour. His clothes rather suggest someone who ran into White Stuff or Fat Face and frantically grabbed anything he could find – in this case, a salmon zip-up top, multihued check trousers and shoes that are a headache-inducing shade of turquoise. When we settle down to talk in a nearby meeting room, he repeatedly glances at a notepad – whose pages, just to add to all the garishness, are a bold shade of yellow. They are full of what look like scrawled equations, mixed with odd-looking shapes: the raw material, he explains, of a project involving very complicated geometry. "There's an infinite symmetrical structure that I'm looking at," he says, "and I think the top bit of it will tell me everything that's going on inside it. It's almost like an infinite lake, and I should be able to know everything that's happening in it by looking at the first centimetre."

About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent. On move 37, AlphaGo chose to put a black stone in what seemed, at first, like a ridiculous position. It looked certain to give up substantial territory--a rookie mistake in a game that is all about controlling the space on the board. Two television commentators wondered if they had misread the move or if the machine had malfunctioned somehow. In fact, contrary to any conventional wisdom, move 37 would enable AlphaGo to build a formidable foundation in the center of the board. The Google program had effectively won the game using a move that no human would've come up with. AlphaGo's victory is particularly impressive because the ancient game of Go is often looked at as a test of intuitive intelligence. The rules are quite simple. Two players take turns putting black or white stones at the intersection of horizontal and vertical lines on a board, trying to surround their opponent's pieces and remove them from play.

Why was AlphaGo's Move 37 against Lee Sedol so significant? Why was it so important that I named my 10 week course on deep reinforcement learning on it? In this final video of my course, I'll explain what move 37 symbolized for humanity and detail 3 examples of how it will affect healthcare, design, and decision-making. We'll go through a code example of a Generative Adversarial Network and even discuss China ambitious 2030 AI initiative. Theres a lot that I cover in this video, I hope that it helps connect the dots.

Peering through wire-rim glasses, he places the black stone on the board, in a mostly empty zone, just below and to the left of a single white stone. In Go parlance it is a "shoulder hit," in from the side, far away from most of the game's other action. Across the table, Lee Sedol, the best Go player of the past decade, freezes. He looks at the 37 stones fanned out across the board, then stands up and leaves. In the commentary room, about 50 feet away, Michael Redmond is watching the game via closed-circuit.

Join me as I teach this free 10-week reinforcement learning course I've called Move 37. I'll take you on a journey through the basics up to modern day techniques. Every week, we'll build apps together that will cover both toy and industry problems. You'll be able to measure your progress along the way by chatting with your peers both online and offline at the School of AI chapters globally, taking quizzes, coding challenges, and 2 graded projects. I'll have weekly coding live streams to help answer any questions, and my assistant instructors will be available to help in our community slack channel. Students who successfully complete the course will receive their own certificate signed by Siraj Raval.

Artificial intelligence is set to change everything. Over the next few years, it will transform every aspect of the legal business. But many lawyers still underestimate its impact, and many law firms haven't grasped the huge opportunity it presents. Right now, legal AI lags behind artificial intelligence in other sectors, such as retail and finance. But this won't last because the industry needs AI to cope with today's new demands.

If you have a child in your life who is headed in the "IT" / "Computers" (or honestly any job that will come in five years), please consider this valuable guidance.