Melanie Mitchell

Have suggestions for future podcast guests (or other feedback)? Let us know here!In episode 53 of The Gradient Podcast, Daniel Bashir speaks to Professor Melanie Mitchell. Professor Mitchell is the Davis Professor at the Santa Fe Institute. Her research focuses on conceptual abstraction, analogy-making, and visual recognition in AI systems. She is the author or editor of six books and her work spans the fields of AI, cognitive science, and complex systems. Her latest book is Artificial Intelligence: A Guide for Thinking Humans. Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (02:20) Melanie’s intro to AI* (04:35) Melanie’s intellectual influences, AI debates over time* (10:50) We don’t have the right metrics for empirical study in AI* (15:00) Why AI is Harder than we Think: the four fallacies* (20:50) Difficulties in understanding what’s difficult for machines vs humans* (23:30) Roles for humanlike and non-humanlike intelligence* (27:25) Whether “intelligence” is a useful word* (31:55) Melanie’s thoughts on modern deep learning advances, brittleness* (35:35) Abstraction, Analogies, and their role in AI* (38:40) Concepts as analogical and what that means for cognition* (41:25) Where does analogy bottom out* (44:50) Cognitive science approaches to concepts* (45:20) Understanding how to form and use concepts is one of the key problems in AI* (46:10) Approaching abstraction and analogy, Melanie’s work / the Copycat architecture* (49:50) Probabilistic program induction as a promising approach to intelligence* (52:25) Melanie’s advice for aspiring AI researchers* (54:40) OutroLinks:* Melanie’s homepage and Twitter* Papers* Difficulties in AI, hype cycles* Why AI is Harder than we think* The Debate Over Understanding in AI’s Large Language Models* What Does It Mean for AI to Understand?* Abstraction, analogies, and reasoning* Abstraction and Analogy-Making in Artificial Intelligence* Evaluating understanding on conceptual abstraction benchmarks Get full access to The Gradient at thegradientpub.substack.com/subscribe

In a thought-provoking discussion, Melanie Mitchell, a professor of computer science, dives into the complex world of artificial intelligence and common sense. She highlights the significance of analogy-making in both human cognition and AI, explaining how simple rules can lead to complex behaviors. The conversation also touches on the challenges of achieving human-like understanding in machines and the existential risks posed by advanced AI. With insights from her book, she explores the future implications of AI technology on society.

In this engaging discussion, David Foster, co-founder of an AI consultancy and author, joins a panel of thought leaders including Max Tegmark from MIT and deep learning pioneers Yann LeCun, Melanie Mitchell, and Yoshua Bengio. They tackle daunting questions about AI's existential risks. Foster critiques speculative arguments, while Tegmark's concerns about AI's potential for catastrophic outcomes spark debate. The conversation highlights the necessity of grounding fears in evidence, examining AI's role in education, and exploring its philosophical implications.

Prof. Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, dives into the murky waters of AI understanding. She argues that current benchmarks are inadequate, as machines often replicate human tasks without true comprehension. Mitchell highlights the limitations of large language models, noting their lack of common sense despite impressive statistical capabilities. She emphasizes the need for evolving evaluation methods and suggests a deeper, context-specific look at intelligence, advocating for more rigorous testing to reflect genuine understanding.

Jim talks with Melanie Mitchell about her critique of applying standardized exams to LLMs and the debate over understanding in AI. They discuss ChatGPT and GPT-4's performance on standardized exams, questioning the underlying assumptions, OpenAI's lack of transparency, soon-to-be-released open-source LLMs, prompt engineering, making GPT its own skyhook to reduce hallucinations, the number of parameters in GPT-4, why LLMs should be probed differently than humans, how LLMs lie differently than humans, Stanford's holistic assessment for LLMs, a College Board for LLMs, why the term "understanding" is overstressed today, consciousness vs intelligence, the human drive for compression, working memory limitations as the secret to human intellectual abilities, episodic memory, embodied emotions, the idea that AIs don't care, calling for a new science of intelligence, the effects of differing evolutionary pressures, whether a model of physics could emerge from language learning, how little we understand these systems, and much more. Episode Transcript JRS Currents 036: Melanie Mitchell on Why AI is Hard Complexity: A Guided Tour, by Melanie Mitchell Artificial Intelligence: A Guide for Thinking Humans, by Melanie Mitchell AI: A Guide for Thinking Humans (Substack) "Did ChatGPT Really Pass Graduate-Level Exams?" (Part 1), by Melanie Mitchell Currents 087: Shivanshu Purohit on Open-Source Generative AI Holistic Evaluation of Language Models (HELM) - Stanford "The Debate Over Understanding in AI's Large Language Models," by Melanie Mitchell and David Krakauer Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Co-Chair of the Science Board at the Santa Fe Institute. Mitchell has also held faculty or professional positions at the University of Michigan, Los Alamos National Laboratory, and the OGI School of Science and Engineering. She is the author or editor of seven books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems, including her latest, Artificial Intelligence: A Guide for Thinking Humans.