Videos of selected talks

  • 2023 NeurIPS invited workshop talk: Open-ended and AI-generating Algorithms in the Era of Foundation Models (video)
  • 2023 at MIT: Improving Deep Reinforcement Learning via Quality Diversity, Open-Ended and AI-Generating Algorithms (video)
  • 2021 CORL Keynote: Improving Robot and Deep Reinforcement Learning via Quality Diversity, Open-Ended, and AI-Generating Algorithms (video)
  • 2022 CORL Workshop: Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos (video)
  • 2020 ICML Workshop on Continual Learning. Learning to Continually Learn (covers ANML and AI-GAs) (video). I gave a similar talk at the 2020 ICLR Workshop on Beyond Tabula-Rosa RL. (video)
  • 2019 NeurIPS Workshop on Meta-Learning. How Meta-Learning Could Help Us Accomplish Our Grandest AI Ambitions, and Early, Exotic Steps in that Direction (video)
  • ReWork 2020. Learning to Continually Learn. (describes ANML) (video)
  • Rework 2019. Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer (POET) (video)
  • ReWork 2019. Go-Explore. A new type of algorithm for hard-exploration problems (video)
  • 2019 ICML Tutorial. Recent Advances in Population-Based Search: Quality Diversity, Open-Ended Algorithms, and Indirect Encodings (video)
  • 2019. Robots that adapt like animals. (video)
  • 2019 NeurIPS Workshop on Biolotical and Artificial RL. Materials Matter: How biologically inspired alternatives to conventional neural networks improve meta-learning and continual learning (video)
  • 2016 Deep Learning Overview & Visualizing What Deep Neural Networks Learn (AI neuroscience) (video)
  • 2015 Santa Fe Institute. Two Projects in BioInspired AI. Evolving regular, modular, hierarchical neural networks, and robot damage recovery (video)
  • 2014 UC Berkeley. Non-Adaptive Evolvability (video)
  • 2012 Cornell University. Evolving Regular, Modular Neural Networks (video)

Short paper summary videos: