Don't invent faster horses - Prof. Jeff Clune

SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ 2. Sponsorship [00:03:00] 2.1 TufaAI Labs and CentML Jeff Clune has spent his career chasing one of science's biggest questions: how did evolution produce the explosion of complexity we see in nature, and can we build algorithms that do the same thing? In this wide-ranging conversation, he lays out the case for open-ended evolutionary algorithms -- systems designed to generate novel and interesting outcomes forever, drawing on principles from both Darwinian evolution and human cultural innovation. Clune explains the central paradox of his work: trying too hard to accomplish a specific goal is often the worst strategy. Instead, the best results come from recognising serendipity and keeping hold of interestingly new things, regardless of whether they seem immediately useful. This insight, drawn from Kenneth Stanley's work on novelty search, underpins a new generation of algorithms that use foundation models as judges of what counts as genuinely interesting and novel. The conversation covers POET (evolved environments for reinforcement learning), NEAT (neuroevolution of augmenting topologies), ADAS (automated design of agentic systems), and OMNI-EPIC (using language models to generate open-ended environments). Clune walks through how these systems riff on previous discoveries to create increasingly complex challenges -- from simple ball-kicking tasks through multi-room buildings to cluttered restaurant scenarios that robots must navigate. The interview also tackles AI safety head-on, with Clune advocating for democratic governance coalitions, regulation of frontier models, and global alignment protocols. He discusses why the interpretability problem may be harder than it looks, how open-ended AI systems could pose unique risks, and his view that the biggest danger is not acting on safety soon enough. --- REFERENCES: paper: [00:02:35] POET: Generating/solving complex challenges https://arxiv.org/abs/1901.01753 [00:17:05] Automated capability discovery in foundation models https://openreview.net/forum?id=nhgbvyrvTP [00:18:10] NEAT: NeuroEvolution of Augmenting Topologies https://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf [00:26:50] Novelty search vs objective-based optimization https://www.cs.swarthmore.edu/~meeden/DevelopmentalRobotics/lehman_ecj11.pdf [00:28:55] AI-generating algorithms approach to AGI https://arxiv.org/abs/1905.10985 [00:41:10] Video PreTraining (VPT) https://cdn.openai.com/vpt/Paper.pdf [00:44:00] Thought Cloning: Imitating human thinking https://arxiv.org/pdf/2306.00323 [01:15:10] Automated Design of Agentic Systems (ADAS) https://arxiv.org/abs/2408.08435 [01:32:30] OMNI-EPIC https://arxiv.org/abs/2405.15568 book: [00:11:10] Why Greatness Cannot Be Planned https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 --- LINKS: Full Transcript: https://app.rescript.info/share/1bf7d45e8d7326bba0a73f7fdd686d05 Download PDF transcript: https://app.rescript.info/api/public/sessions/ceffc76fd4f263da/pdf Jeff Clune: https://x.com/jeffclune http://jeffclune.com/