Open-Endedness: Can AI Keep Discovering?

How can an AI system keep producing new ideas, behaviours, or solutions instead of converging on what it already knows? Join Tokyo AI for an in-person research evening on open-endedness. Three talks will explore the nature of open-endedness and work in this area, with useful examples and lessons for people interested in AI, from newcomers to established researchers. The event will be held in English. Each talk is a 25-minute presentation followed by five minutes of moderated Q&A. When: Thursday, November 12, 2026, 18:00-21:00 JST Where: Tokyo, venue to be announced Agenda 18:00 - Doors open 18:25-18:30 - Welcome 18:30-19:00 - Talk 1: Beyond Optimization: Open-Endedness Through Artificial Life (Mizuki Oka) 19:00-19:30 - Talk 2: Playing with Lenia (Bert Chan) 19:30-20:00 - Talk 3: The AI Picbreeder Experiment (Kai Arulkumaran) 20:00-21:00 - Networking 21:00 - Doors close Talk 1: Beyond Optimization: Open-Endedness Through Artificial Life Abstract: What would it take for AI to keep discovering new possibilities, rather than only getting better at tasks we already know how to specify? This talk explores open-endedness through the lens of artificial life, focusing on how agents, environments, and their interactions can create conditions for continuing novelty. Drawing on my research and ongoing work at the Artificial Life Institute, I will connect studies of novelty in online systems with approaches that generate and evolve environments for learning agents. I will discuss what these examples reveal, what they do not yet demonstrate, and how we might evaluate progress toward open-ended systems. The aim is to offer practical questions for researchers and engineers building AI that can explore, adapt, and expand its possibilities. Bio: Mizuki Oka is the Founder and Managing Director of the Artificial Life Institute and a researcher at Chiba Institute of Technology. Her research explores artificial life and open-endedness, asking how systems can continue to generate novelty