Argmax

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.

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.

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

In this episode we discuss this video: https://youtu.be/jPCV4GKX9Dw How Tesla approaches collision detection with novel methods.

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.

We start talking about diffusion models as a technique for generative deep learning.

We talk about QMIX https://arxiv.org/abs/1803.11485 as an example of Deep Multi-agent RL.

Todays paper: VICReg (https://arxiv.org/abs/2105.04906) Summary of the paper VICReg prevents representation collapse using a mixture of variance, invariance and covariance when calculating the loss. It does not require negative samples and achieves great performance on downstream tasks. Highlights of discussion The VICReg architecture (Figure 1)Sensitivity to hyperparameters (Table 7)Top 5 metric usefulness

This is the first episode of Argmax! We talk about our motivations for doing a podcast, and what we hope listeners will get out of it. Todays paper: Reward is Enough Summary of the paper The authors present the Reward is Enough hypothesis: Intelligence, and its associated abilities, can be understood as subserving the maximisation of reward by an agent acting in its environment. Highlights of discussion High level overview of Reinforcement LearningHow evolution can be encoded as a reward m...