Combining Deep Learning and Reinforcement Learning

This is one of my favorite books over recent years. Sebastian Mallaby is the Paul A. Cocker Senior Fellow for International Economics at the Council of Foreign Relations and author of 6 bestselling books.

THE INFINITY MACHINE tells the story of AI’s progress over the past 15 years largely, but not exclusively, from Demis Hassabis as the protagonist and leader of DeepMind’, with its 2010 mission statement to achieve superintelligence by 2030. It’s a rich, informative, page turner.

What We Discussed:

—What is an Infinity Machine?

—Influence of Claude Shannon’s Information Theory and Douglas Hofstadter’s Pulitzer Prize winning book Gödel, Escher, Bach

—Origin of DeepMind in 2010. Prescient. Charter, business plan, included use of agents. How Demis Hassabis was made for the mission!

—Contrasts with Sam Altman and the other AI leaders, the Oligopoly (cover of The Economist this week). For example, Nature papers vs white papers on company websites.

—In March 2016, the same day when DeepMind’s AlphaGo beat Lee Sedol, Hassabis says it’s time to do protein folding (later known as AlphaFold).

—Symbolic AI (historic, deductive, rule-based) vs Deep Learning (Toronto tribe) and Reinforcement Learning (Alberta tribe).

—The Big Miss: DeepMind’s lack of early recognition of the importance of transformer models (leading to ChatGPT), creating a big opening for OpenAI. And why was this missed? The Comeback Story. Is this happening again with coding (not in the book)?

—The AI Arms Race and Hyperscaling

—How the complex relationship between Google and DeepMind evolved

—The Double Cross

—With the dangers anticipated (parallels to Oppenheimer, Manhattan Project, and the atomic bomb), how to promote AI safety?

—Is the major build up of data centers justified?

Thank you Bob Fleischman, Jeanie, Ruben Max, FelonBroke America, Seitzinator ❌👑, and more than 600 others for tuning into our live video with Sebastian Mallaby! Join me for my next live video in the app.

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