Tim Scarfe & Raphaël Millière , Machine Learning Street Talk (MLST)

#107 - Dr. RAPHAËL MILLIÈRE - Linguistics, Theory of Mind, Grounding

13 Mar 2023 • 103 min • EN
103 min
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Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Dr. Raphaël Millière is the 2020 Robert A. Burt Presidential Scholar in Society and Neuroscience in the Center for Science and Society, and a Lecturer in the Philosophy Department at Columbia University. His research draws from his expertise in philosophy and cognitive science to explore the implications of recent progress in deep learning for models of human cognition, as well as various issues in ethics and aesthetics. He is also investigating what underlies the capacity to represent oneself as oneself at a fundamental level, in humans and non-human animals; as well as the role that self-representation plays in perception, action, and memory. In a world where technology is rapidly advancing, Dr. Millière is striving to gain a better understanding of how artificial neural networks work, and to establish fair and meaningful comparisons between humans and machines in various domains in order to shed light on the implications of artificial intelligence for our lives. https://www.raphaelmilliere.com/ https://twitter.com/raphaelmilliere Here is a version with hesitation sounds like "um" removed if you prefer (I didn't notice them personally): https://share.descript.com/view/aGelyTl2xpN YT: https://www.youtube.com/watch?v=fhn6ZtD6XeE TOC: Intro to Raphael [00:00:00] Intro: Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière) [00:01:18] Show Kick off [00:07:10] LLMs [00:08:37] Semantic Competence/Understanding [00:18:28] Forming Analogies/JPG Compression Article [00:30:17] Compositional Generalisation [00:37:28] Systematicity [00:47:08] Language of Thought [00:51:28] Bigbench (Conceptual Combinations) [00:57:37] Symbol Grounding [01:11:13] World Models [01:26:43] Theory of Mind [01:30:57] Refs (this is truncated, full list on YT video description): Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière) https://nautil.us/moving-beyond-mimicry-in-artificial-intelligence-238504/ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (Bender et al) https://dl.acm.org/doi/10.1145/3442188.3445922 ChatGPT Is a Blurry JPEG of the Web (Ted Chiang) https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web The Debate Over Understanding in AI's Large Language Models (Melanie Mitchell) https://arxiv.org/abs/2210.13966 Talking About Large Language Models (Murray Shanahan) https://arxiv.org/abs/2212.03551 Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (Bender) https://aclanthology.org/2020.acl-main.463/ The symbol grounding problem (Stevan Harnad) https://arxiv.org/html/cs/9906002 Why the Abstraction and Reasoning Corpus is interesting and important for AI (Mitchell) https://aiguide.substack.com/p/why-the-abstraction-and-reasoning Linguistic relativity (Sapir–Whorf hypothesis) https://en.wikipedia.org/wiki/Linguistic_relativity Cooperative principle (Grice's four maxims of conversation - quantity, quality, relation, and manner) https://en.wikipedia.org/wiki/Cooperative_principle

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