Glen Berseth

Sep 19, 2024 | mila.quebec | Glen Berseth |Alissa Centivany |Ulrich Aïvodji

The AI information space is noisy, often highly technical, and full of competing claims and vested interests. Finding reliable, independent and, above all, relevant insights is challenging. This rings especially true for policymakers who are grappling with how best to approach, regulate or leverage AI for the public interest despite having limited access to technical AI expertise and no clear mechanism to engage with scientific experts.

Sep 7, 2024 | arxiv.org | Glen Berseth

Jul 25, 2024 | arxiv.org | Haochen Shi |Marc-Alexandre Côté |Glen Berseth |Xingdi Yuan

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Feb 22, 2023 | channel969.com | Coline Manon Devin |Glen Berseth |Charles Solar |BAIR Weblog

By Jędrzej Orbik, Charles Solar, Coline Devin, Glen BersethReinforcement studying supplies a conceptual framework for autonomous brokers to be taught from expertise, analogously to how one may practice a pet with treats. However sensible functions of reinforcement studying are sometimes removed from pure: as a substitute of utilizing RL to be taught by way of trial and error by truly trying the specified process, typical RL functions use a separate (often simulated) coaching section.