Talk Title: Possible Impossibilities, Impossible Possibilities, and Paradoxes

Abstract: Generative AI has led to an unprecedented amount of global attention---both excitements and concerns, in part due to our relatively limited understanding about intelligence---both artificial and natural. In this talk, I will question if there can be possible impossibilities of large language models (i.e., the fundamental limits of transformers, if any) and the impossible possibilities of language models (i.e., seemingly impossible alternative paths beyond scale, if at all). I will then discuss the Generative AI Paradox hypothesis: for AI, at least in its current form, generation capability may often exceed understanding capability, in stark contrast to human intelligence where generation (of e.g., novels, paintings) can be substantially harder than understanding.

Bio: Yejin Choi is Wissner-Slivka Professor and a MacArthur Fellow at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She is also a senior director at AI2 overseeing the project Mosaic and a Distinguished Research Fellow at the Institute for Ethics in AI at the University of Oxford. Her research investigates if (and how) AI systems can learn commonsense knowledge and reasoning, if machines can (and should) learn moral reasoning, and various other problems in NLP, AI, and Vision including neuro-symbolic integration, language grounding with vision and interactions, and AI for social good. She is a co-recipient of 2 Test of Time Awards (at ACL 2021 and ICCV 2021), 7 Best/Outstanding Paper Awards (at ACL 2023, NAACL 2022, ICML 2022, NeurIPS 2021, AAAI 2019, and ICCV 2013), the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI's 10 to Watch in 2016.

Webinar Link: https://mit.zoom.us/j/96292554152

Followed by a reception with food and drink in 3^rd floor atrium