Exploring Alternative Bio-Inspired Neural Building Blocks for Fast RL | Sebastian Risi

ICARL Seminar Series - 2024 Spring

Exploring Alternative Bio-Inspired Neural Building Blocks for Fast Reinforcement Learning.
Seminar by Sebastian Risi

——————————————————
Abstract:

Despite all their recent advances, current AI methods are still brittle and fail when confronted with unexpected situations. Biological intelligent systems, on the other hand, can rapidly adapt and display an inherent level of resilience. While being inspired by the brain, the current artificial neural network paradigm abstracted away many of the properties that could turn out essential in our goal to create a more general artificial intelligence. In this talk, I'll present some of our work in exploring alternative neural building blocks. For example, it is possible to allow completely random networks to adapt to morphological damage in a robot in only a few trials through meta-learned local plasticity rules. Likewise, evolving different acitvation functions in random neural networks alone, enables them to master different reinforcement learning tasks, challenging our understanding of which ingredients to include in our neural networks. Finally, I will present our current work on Neural Developmental Program approach, in which we learn to grow artificial neural networks through a developmental process that mirrors key properties of embryonic development in biological organisms. The talk concludes with future research opportunities and challenges that we need to address to best capitalize on the same ideas that allowed biological intelligence to strive.

——————————————————
About the Speaker:

Sebastian Risi is a Full Professor at the IT University of Copenhagen where he directs the Creative AI Lab, and co-directs the Robotics, Evolution and Art Lab (REAL). Before joining ITU, he did a postdoc at Cornell University and before that, he obtained a Ph.D. from the University of Central Florida. As one of the pioneers in the emerging field of collective intelligence for deep learning, he investigates how we can make current AI approaches more robust and adaptive. He has won several international scientific awards, including multiple best paper awards, an ERC Consolidator Grant in 2022, the Distinguished Young Investigator in Artificial Life 2018 award, a Google Faculty Research Award in 2019, and an Amazon Research Award in 2020. His interdisciplinary work has been published in major machine learning, artificial life, and human-computer interaction conferences, including AAAI, NeurIPS, ICLR, Nature Machine Intelligence, ALIFE, GECCO, and CHI.

——————————————————
Links
Sebastian Risi
Site: sebastianrisi.com/
Twitter: x.com/risi1979

ICARL
Site: icarl.doc.ic.ac.uk
Twitter: x.com/ic_arl
YouTube: @ICARLSeminars
——————————————————