Leveraging JAX for GPU-Enhanced Reinforcement Learning

Prof. Jakob Foerster, a leading AI researcher at Oxford University and Meta, and Chris Lu, a researcher at OpenAI -- they explain how AI is moving beyond just mimicking human behaviour to creating truly intelligent agents that can learn and solve problems on their own. Foerster champions open-source AI for responsible, decentralised development. He addresses AI scaling, goal misalignment (Goodhart's Law), and the need for holistic alignment, offering a quick look at the future of AI and how to guide it.

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TRANSCRIPT/REFS:

https://www.dropbox.com/scl/fi/yqjszhntfr00bhjh6t565/JAKOB.pdf?rlkey=scvny4bnwj8th42fjv8zsfu2y&dl=0

Prof. Jakob Foerster

https://x.com/j_foerst

https://www.jakobfoerster.com/

University of Oxford Profile:

https://eng.ox.ac.uk/people/jakob-foerster/

Chris Lu:

https://chrislu.page/

TOC

1. GPU Acceleration and Training Infrastructure

[00:00:00] 1.1 ARC Challenge Criticism and FLAIR Lab Overview

[00:01:25] 1.2 GPU Acceleration and Hardware Lottery in RL

[00:05:50] 1.3 Data Wall Challenges and Simulation-Based Solutions

[00:08:40] 1.4 JAX Implementation and Technical Acceleration

2. Learning Frameworks and Policy Optimization

[00:14:18] 2.1 Evolution of RL Algorithms and Mirror Learning Framework

[00:15:25] 2.2 Meta-Learning and Policy Optimization Algorithms

[00:21:47] 2.3 Language Models and Benchmark Challenges

[00:28:15] 2.4 Creativity and Meta-Learning in AI Systems

3. Multi-Agent Systems and Decentralization

[00:31:24] 3.1 Multi-Agent Systems and Emergent Intelligence

[00:38:35] 3.2 Swarm Intelligence vs Monolithic AGI Systems

[00:42:44] 3.3 Democratic Control and Decentralization of AI Development

[00:46:14] 3.4 Open Source AI and Alignment Challenges

[00:49:31] 3.5 Collaborative Models for AI Development

REFS

[[00:00:05] ARC Benchmark, Chollet

https://github.com/fchollet/ARC-AGI

[00:03:05] DRL Doesn't Work, Irpan

https://www.alexirpan.com/2018/02/14/rl-hard.html

[00:05:55] AI Training Data, Data Provenance Initiative

https://www.nytimes.com/2024/07/19/technology/ai-data-restrictions.html

[00:06:10] JaxMARL, Foerster et al.

https://arxiv.org/html/2311.10090v5

[00:08:50] M-FOS, Lu et al.

https://arxiv.org/abs/2205.01447

[00:09:45] JAX Library, Google Research

https://github.com/jax-ml/jax

[00:12:10] Kinetix, Mike and Michael

https://arxiv.org/abs/2410.23208

[00:12:45] Genie 2, DeepMind

https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/

[00:14:42] Mirror Learning, Grudzien, Kuba et al.

https://arxiv.org/abs/2208.01682

[00:16:30] Discovered Policy Optimisation, Lu et al.

https://arxiv.org/abs/2210.05639

[00:24:10] Goodhart's Law, Goodhart

https://en.wikipedia.org/wiki/Goodhart%27s_law

[00:25:15] LLM ARChitect, Franzen et al.

https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf

[00:28:55] AlphaGo, Silver et al.

https://arxiv.org/pdf/1712.01815.pdf

[00:30:10] Meta-learning, Lu, Towers, Foerster

https://direct.mit.edu/isal/proceedings-pdf/isal2023/35/67/2354943/isal_a_00674.pdf

[00:31:30] Emergence of Pragmatics, Yuan et al.

https://arxiv.org/abs/2001.07752

[00:34:30] AI Safety, Amodei et al.

https://arxiv.org/abs/1606.06565

[00:35:45] Intentional Stance, Dennett

https://plato.stanford.edu/entries/ethics-ai/

[00:39:25] Multi-Agent RL, Zhou et al.

https://arxiv.org/pdf/2305.10091

[00:41:00] Open Source Generative AI, Foerster et al.

https://arxiv.org/abs/2405.08597

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