00:00:00 Tim Introduction
00:07:15 Main show kick off
00:09:27 On meta gradients
00:11:43 Taxonomy of meta gradient methods developed in recent years
00:13:58 Why don’t you just do one big learning run?
00:16:01 Transfer learning / life long learning
00:17:55 Does the meta algorithm also have hyperparameters?
00:19:45 Are monolithic learning architectures bad then?
00:24:44 Why not have the learning agent (self-) modify its own parameters?
00:26:29 Learning optimizers using evolutionary approaches
00:28:24 Which parameters should we leave alone in meta optimization?
00:30:42 Evolutionary methods are great in this space! Diversity preservation
00:33:25 Approaches to divergence, intrinsic control
00:35:55 How to decide on parameters to optimise and build a meta learning framework
00:39:32 Proxy models to move from discrete domain to differentiable domain
00:43:35 Multi lifetime training -- picking environments
00:46:07 2016 Minecraft paper
00:49:54 Lifelong learning
00:52:09 Corporations are real world AIs. Could we recognise non-human AGIs?
00:55:09 Tim invokes Francois Chollet, of course!
00:56:57 But David Silver says that reward is all you need?
00:59:59 Program centric generalization
01:02:10 Sara Hooker -- The hardware lottery, JAX, Bitter Lesson
01:05:15 Concerning trends in the community right now?
01:06:47 Unexplored areas in ML research?
01:08:18 Should Ph.D Students be going into Meta Gradient work?
01:10:45 Is RL too hard for the average person to embark on?
01:15:16 People back in the 80s had a pretty good idea already, concept papers were cool
01:17:36 Non-stationary data, do you have to re-train the model all the time
01:19:16 Graying the Blackbox paper and visualizing the structure of DQNs with tSNE