ICLR 2021 - Workshop on "How Can Findings About The Brain Improve AI Systems?"

Submission deadline: extended to March 5, 2021

Workshop website: https://iclrbrain2ai.github.io/

Contact: brain2ai.2021@gmail.com

We will consider the following (non-exhaustive) list of topics for contribution:

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Integration of neuroscientific data at different granularities (neural recordings, neuroimaging etc.) to train AI systems -

Brain-inspired inductive biases and ML architectures for AI systems -

Neuroscientific data as an evaluation metric for AI systems -

Mechanistic insight into cognitive functions (vision, language, decision making, attention, etc.) -

Limitations of current AI systems in tasks at which humans excel (compositionality, commonsense reasoning, continual learning, few-shot learning, causality, etc.) -

Cross-modality and cross-species comparisons of neuroscientific data with respect to benefits to AI -

Datasets that can facilitate the transfer of neuroscientific insight to AI

Submit at: https://cmt3.research.microsoft.com/BRAIN2AI2021/Submission/Index

The workshop will feature a diverse set of topics led by the following invited speakers and panelists (in alphabetical order):

1. Anima Anandkumar, California Institute of Technology 2. David Cox, Harvard University 3. Kenji Doya, Okinawa Institute of Science and Technology Graduate University 4. Allyson Ettinger, University of Chicago 5. Alona Fyshe, University of Alberta 6. Jack Gallant, University of California, Berkeley 7. Andrea Martin, Max Planck Institute for Psycholinguistics 8. Kim Stachenfeld, DeepMind 9. Josh Tenenbaum, MIT