GFlowNets and System 2 Deep Learning

GFlowNets are instances of a larger family of approaches at the intersection of generative modeling and RL that can be used to train probabilistic inference functions in a way that is related to variational inference and opens a lot of new doors, especially for brain-inspired AI. Instead of maximizing some objective (like expected return), these approaches seek to sample latent random variables from a distribution defined by an energy function, for example a posterior distribution (given past data, current observations, etc). Recent work showed how GFlowNets can be used to sample a diversity of solutions in an active learning context. We will also discuss ongoing work to explore how to train such inference machinery for learning energy-based models, to approximately marginalize over infinitely many variables, perform efficient posterior Bayesian inference and incorporate inductive biases associated with conscious processing and reasoning in humans. These inductive biases include modular knowledge representation favoring systematic generalization, the causal nature of human thoughts, concepts, explanations and plans and the sparsity of dependencies captured by reusable relational or causal knowledge. Many open questions remain to develop these ideas, which will require many collaborating minds!

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Speaker Bios

Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun. He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO. In 2019, he was awarded the prestigious Killam Prize and in 2021, became the second most cited computer scientist in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. Concerned about the social impact of AI and the objective that AI benefits all, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.