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This talk has been given by Prof. Yoshua Bengio on Zoom at Princeton AI Club on Thursday, July 14th, 2022, at 4:00 PM ET.
Abstract of the talk:
Machine learning research is expanding its reach, beyond the traditional realm of the tech industry and into the activities of other scientists, opening the door to truly transformative advances in these disciplines. In this talk I will focus on two aspects, modeling and experimental design, that are intertwined in the theory-experiment-analysis active learning loop that constitutes a core element of the scientific methodology. Computers will be necessary to go beyond the currently purely manual research loop and take advantage of high-throughput experimental setups and large-scale experimental datasets. I will introduce a novel machine learning framework called GFlowNets (for “Generative Flow Networks”), related to reinforcement learning, generative modeling and variational methods and conceived as an ML-driven replacement for MCMC. GFlowNets were first used to propose a highly diverse set of molecular candidates and were then incorporated in an active learning framework for efficiently looking for molecules with desirable properties. More recently, we have been exploring how GFlowNets can generate not just molecular graphs but also causal graphs and Bayesian posterior distributions in function space. I will describe our research program to build on these bases and develop machine learning methodologies for efficiently exploring the space of causal theories as well as the space of experiments while characterizing the ambiguities left by finite datasets and non-identifiability, as well as our plans to apply these tools in areas of great societal need like the unmet challenge of antimicrobial resistance
Prof. Yoshua Bengio: [ Ссылка ]
Bio:
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.
