
#49 - Meta-Gradients in RL - Dr. Tom Zahavy (DeepMind)
About this episode
The race is on, we are on a collective mission to understand and create artificial general intelligence. Dr. Tom Zahavy, a Research Scientist at DeepMind thinks that reinforcement learning is the most general learning framework that we have today, and in his opinion it could lead to artificial general intelligence. He thinks there are no tasks which could not be solved by simply maximising a reward.
Back in 2012 when Tom was an undergraduate, before the deep learning revolution he attended an online lecture on how CNNs automatically discover representations. This was an epiphany for Tom. He decided in that very moment that he was going to become an ML researcher. Tom's view is that the ability to recognise patterns and discover structure is the most important aspect of intelligence. This has been his quest ever since. He is particularly focused on using diversity preservation and metagradients to discover this structure.
In this discussion we dive deep into meta gradients in reinforcement learning.
Video version and TOC @ https://www.youtube.com/watch?v=hfaZwgk_iS0
Get every episode summarized
Each time Machine Learning Street Talk (MLST) publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from Machine Learning Street Talk (MLST)

How Replication Could Teach Machines What Good Science Looks Like — Edward Hughe...
Machine Learning Street Talk (MLST)

AI 2040: Plan A report - Daniel Kokotajlo & Thomas Larsen
Machine Learning Street Talk (MLST)

Designing How AI Grows — Tom McGrath
Machine Learning Street Talk (MLST)

Stealing Reasoning Traces from Proprietary LLM APIs — Ilia Shumailov & Alexander...
Machine Learning Street Talk (MLST)