2020/11/30 by Anirudh Goyal, Yoshua Bengio, Goyal, Anirudh +1 · 2 voices · 19 citations
Computer Science · Mathematics · Neuroscience · #Artificial Intelligence (cs.AI) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2011.15091
openalex publication_date 2020/11/30 · arxiv published 2020/11/30 · arxiv updated 2022/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.