9: Heads-Up Limit Hold'em Poker Is Solved

In this episode we talk about the paper "Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean.

We talk about Low Rank Approximation for fine tuning Transformers. We are also on YouTube now! Check out the video here: https://youtu.be/lLzHr0VFi3Y

In this episode we discuss the paper "Training language models to follow instructions with human feedback" by Ouyang et al (2022). We discuss the RLHF paradigm and how important RL is to tuning GPT.

This week we talk about Whisper. It is a weakly supervised speech recognition model.

We talk about AlphaTensor, and how researchers were able to find a new algorithm for matrix multiplication.

In this episode we talked about "Implicit Neural Representations with Periodic Activation Functions" and the strength of periodic non-linearities.

In this episode we discuss this video: https://youtu.be/jPCV4GKX9DwHow Tesla approaches collision detection with novel methods.

We discuss Sony AI's accomplishment of creating a novel AI agent that can beat professional racers in Gran Turismo. Some topics include:- The crafting of rewards to make the agent behave nicely- What is QR-SAC?- How to deal with "rare" experiences in the replay bufferLink to paper: https://www.nature.com/articles/s41586-021-04357-7

Today we talk about recent AI advances in Poker; specifically the use of counterfactual regret minimization to solve the game of 2-player Limit Texas Hold'em.

Today we talk about GATO, a multi-modal, multi-task, multi-embodiment generalist agent.