Jeff Clune
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2021
Jeff Clune is Associate Professor of Computer Science at the University of British Columbia, a Canada CIFAR AI Chair at the Vector Institute and a Senior Research Advisor at DeepMind. His work focuses on deep learning (also known as deep neural networks), including deep reinforcement learning. He also works in evolving neural networks (aka neuroevolution). Previous work has included exploring open questions in evolutionary biology using digital evolution, or computer simulations of evolution.
A focus area of Clune’s recent research is producing AI that improves itself, or AI-generating algorithms, as well as advancing open-ended algorithms that can innovate and learn forever. He also works on quality-diversity algorithms that learn to produce a diverse set of high-quality solutions, much as Darwinian evolution produced all species on Earth.
Awards
- SIGEVO Impact Award, 2023
- Kavli Fellow, 2022
- Presidential Early Career Award for Scientists and Engineers, USA, 2019
- Outstanding Publication of the Decade, ISAL, 2019
- Distinguished Young Investigator Award, ISAL, 2016
- Invited by the White House to its White House AI Summit, 2018
- Early Tenure, 2017, University of Wyoming
Relevant Publications
- Hu, S., & Clune, J. (2024). Thought cloning: Learning to think while acting by imitating human thinking. Advances in Neural Information Processing Systems. 36.
- Zhang, J., Lehman, J., Stanley, K., & Clune, J. (2023). OMNI: Open-endedness via Models of human Notions of Interestingness.
- Ecoffet A*, Huizinga J*, Lehman J, Stanley KO, Clune J. (2021) First return, then explore. Nature.
- Norouzzadeh M, Nguyen A, Kosmala M, Swanson A, Palmer MS, Parker C, Clune J. (2018) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences (PNAS). 115:25. (cover article)
- Cully A, Clune J, Tarapore D, Mouret JB. (2015) Robots that can adapt like natural animals. Nature. 521.7553: pp. 503-507. (cover article)
- Stanley K, Clune J, Lehman J, Miikkulainen R. (2019) Neuroevolution: Designing Neural Networks through Evolutionary Algorithms (2018) Nature Machine Intelligence. 1:1: 24-35.
- Baker B, Akkaya I, Zhokhov P, Huizinga J, Tang J, Ecoffet A, Houghton B, Sampedro R, Clune J. (2022) Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos. Neural Information Processing Systems (NeurIPS).