Dr. Minqi Jiang and Dr. Marc Rigter explain an innovative new method to make the intelligence of agents more general-purpose by training them to learn many worlds before their usual goal-directed training, which we call "reinforcement learning". Their new paper is called "Reward-free curricula for training robust world models" https://arxiv.org/pdf/2306.09205.pdf https://twitter.com/MinqiJiang https://twitter.com/MarcRigter Interviewer: Dr. Tim Scarfe Please support us on Patreon, Tim is now doing MLST full-time and taking a massive financial hit. If you love MLST and want this to continue, please show your support! In return you get access to shows very early and private discord and networking. https://patreon.com/mlst We are also looking for show sponsors, please get in touch if interested mlstreettalk at gmail. MLST Discord: https://discord.gg/machine-learning-street-talk-mlst-937356144060530778 00:00:00 - Intro 00:01:05 - Model-based Setting 00:02:41 - Similar to POET Paper 00:05:27 - Minimax Regret 00:07:21 - Why Explicitly Model the World? 00:12:47 - Minimax Regret Continued 00:18:17 - Why Would It Converge 00:20:36 - Latent Dynamics Model 00:24:34 - MDPs 00:27:11 - Latent 00:29:53 - Intelligence is Specialised / Overfitting / Sim2real 00:39:39 - Openendedness 00:44:38 - Creativity 00:48:06 - Intrinsic Motivation 00:51:12 - Deception / Stanley 00:53:56 - Sutton / Rewards is Enough 01:00:43 - Are LLMs Just Model Retrievers? 01:03:14 - Do LLMs Model the World? 01:09:49 - Dreamer and Plan to Explore 01:13:14 - Synthetic Data 01:15:21 - WAKER Paper Algorithm 01:21:24 - Emergent Curriculum 01:31:16 - Even Current AI is Externalised/Mimetic 01:36:39 - Brain Drain Academia 01:40:10 - Bitter Lesson / Do We Need Computation 01:44:31 - The Need for Modelling Dynamics 01:47:48 - Need for Memetic Systems 01:50:14 - Results of the Paper and OOD Motifs 01:55:47 - Interface Between Humans and ML