![]() ![]() ![]() Fellow Chelsea Finn (Stanford University) published a paper on how progress in machine learning stems from a combination of data availability, computational resources and an appropriate encoding of inductive biases.ĭeveloping a neuroscientific data collection modelįellows Blake Richards (Mila, McGill University) and Joel Zylberberg (York University) engaged with a number of leaders in the neurotech industry and the Canadian neuroscience community to explore opportunities for large-scale neuroscientific data collection that will help build foundational models for neural computation.Įxploring the ethics of artificial intelligenceīuilding on two previous roundtables on ethical AI, program members and Canada CIFAR AI Chairs participated in a virtual meeting in February 2022, organized by CIFAR in conjunction with the Ada Lovelace Institute (UK) and the Partnership on AI. The study frames exclusion as a data-driven estimation problem and applies flexible machine learning methods to estimate the probability of a unit complying with the instrument. Program Co-Director Konrad Kording (University of Pennsylvania) and colleagues presented a paper at the first Causal Learning and Reasoning Conference, partially funded by the Sloan Foundation, on a study that used instrumental variables for causal inference. ![]() Fellow Bernhard Schölkopf (ETH Zurich, Max Planck Institute for Intelligent Systems) published a paper exploring cross-pollination between the fields of machine learning and graphical causality, discussing the implications and intersections for both communities. ![]() Program members published important findings this year. Advancing key research on machine learning ![]()
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