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.
00:02:42 — Promptbreeder (Fernando) — Self-referential LLM prompt evolution — https://arxiv.org/abs/2309.
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