Open-Ended AI: The Key to Superhuman Intelligence?

Prof. Tim Rocktäschel, AI researcher at UCL and Google DeepMind, talks about open-ended AI systems. These systems aim to keep learning and improving on their own, like evolution does in nature.

TOC:
00:00:00 Introduction to Open-Ended AI and Key Concepts
00:01:37 Tim Rocktäschel’s Background and Research Focus
00:06:25 Defining Open-Endedness in AI Systems
00:10:39 Subjective Nature of Interestingness and Learnability
00:16:22 Open-Endedness in Practice: Examples and Limitations
00:17:50 Assessing Novelty in Open-ended AI Systems
00:20:05 Adversarial Attacks and AI Robustness
00:24:05 Rainbow Teaming and LLM Safety
00:25:48 Open-ended Research Approaches in AI
00:29:05 Balancing Long-term Vision and Exploration in AI Research
00:37:25 LLMs in Program Synthesis and Open-Ended Learning
00:37:55 Transition from Human-Based to Novel AI Strategies
00:39:00 Expanding Context Windows and Prompt Evolution
00:40:17 AI Intelligibility and Human-AI Interfaces
00:46:04 Self-Improvement and Evolution in AI Systems

Show notes (New!) https://www.dropbox.com/scl/fi/5avpsy…

REFS:
00:01:47 – UCL DARK Lab (Rocktäschel) – AI research lab focusing on RL and open-ended learning – https://ucldark.com/

00:02:31 – GENIE (Bruce) – Generative interactive environment from unlabelled videos – https://arxiv.org/abs/2402.15391

00:02:42 – Promptbreeder (Fernando) – Self-referential LLM prompt evolution – https://arxiv.org/abs/2309.16797

00:03:05 – Picbreeder (Secretan) – Collaborative online image evolution – https://dl.acm.org/doi/10.1145/135705…

00:03:14 – Why Greatness Cannot Be Planned (Stanley) – Book on open-ended exploration – https://www.amazon.com/Why-Greatness-…

00:04:36 – NetHack Learning Environment (Küttler) – RL research in procedurally generated game – https://arxiv.org/abs/2006.13760

00:07:35 – Open-ended learning (Clune) – AI systems for continual learning and adaptation – https://arxiv.org/abs/1905.10985

00:07:35 – OMNI (Zhang) – LLMs modeling human interestingness for exploration – https://arxiv.org/abs/2306.01711

00:10:42 – Observer theory (Wolfram) – Computationally bounded observers in complex systems – https://writings.stephenwolfram.com/2…

00:15:25 – Human-Timescale Adaptation (Rocktäschel) – RL agent adapting to novel 3D tasks – https://arxiv.org/abs/2301.07608

00:16:15 – Open-Endedness for AGI (Hughes) – Importance of open-ended learning for AGI – https://arxiv.org/abs/2406.04268

00:16:35 – POET algorithm (Wang) – Open-ended approach to generate and solve challenges – https://arxiv.org/abs/1901.01753

00:17:20 – AlphaGo (Silver) – AI mastering the game of Go – https://deepmind.google/technologies/…

00:20:35 – Adversarial Go attacks (Dennis) – Exploiting weaknesses in Go AI systems – https://www.ifaamas.org/Proceedings/a…

00:22:00 – Levels of AGI (Morris) – Framework for categorizing AGI progress – https://arxiv.org/abs/2311.02462

00:24:30 – Rainbow Teaming (Samvelyan) – LLM-based adversarial prompt generation – https://arxiv.org/abs/2402.16822

00:27:45 – AI Debate (Khan) – Improving LLM truthfulness through debate – https://proceedings.mlr.press/v235/kh…

00:29:40 – Gemini (Google DeepMind) – Advanced multimodal AI model – https://deepmind.google/technologies/…

00:30:15 – How to Take Smart Notes (Ahrens) – Effective note-taking methodology – https://www.amazon.com/How-Take-Smart…

00:35:05 – Voyager (Wang) – Open-ended embodied agent using GPT-4 in Minecraft – https://arxiv.org/abs/2305.16291

00:38:00 – AlphaGo Nature paper (Silver) – Deep neural networks and tree search for Go – https://www.nature.com/articles/natur…

00:38:05 – AlphaStar (Vinyals) – AI achieving grandmaster level in StarCraft II – https://www.nature.com/articles/s4158…

00:42:00 – The Beginning of Infinity (Deutsch) – Book on explanations and scientific progress – https://www.amazon.com/Beginning-Infi…

00:43:30 – AI model collapse (Shumailov) – Risks of training on AI-generated content – https://www.nature.com/articles/s4158…

00:48:35 – Chain-of-Thought Prompting (Wei) – Improving LLM reasoning through prompting – https://arxiv.org/abs/2201.11903

00:49:35 – Self-improving neural networks (Schmidhuber) – Early work on self-referential networks –

00:54:45 – UCL DARK Lab (UCL Computer Science) – RL and Deep Learning research group – https://www.ucl.ac.uk/computer-scienc…