In this engaging discussion, Melanie Mitchell, a Davis Professor at the Santa Fe Institute and author, dives into the complexities of intelligence and AI. She highlights the challenges of getting AI to make analogies, drawing parallels with social learning observed in humans. The conversation explores alternative learning paradigms and their implications for machine intelligence. Mitchell also addresses the limitations of current AI systems, emphasizing the need for responsible application and a focus on interdisciplinary research to advance the field.
32:48
forum Ask episode
web_stories AI Snips
view_agenda Chapters
menu_book Books
auto_awesome Transcript
info_circle Episode notes
insights INSIGHT
Intelligence as a Collective Phenomenon
Intelligence is a complex phenomenon studied in various disciplines, including AI and complex systems.
A key question is how intelligence manifests, not just in individuals, but in collectives like cultures or economies.
insights INSIGHT
Limitations of Supervised Learning
Supervised learning, where humans label data, is a limited form of social interaction.
It differs greatly from how humans and animals learn, which is primarily an active, unsupervised process.
insights INSIGHT
Analogy Research with Letter Strings
Melanie Mitchell's PhD research explored analogy-making in AI using letter strings as a micro-world.
This approach aimed to capture real-world analogy concepts, revealing the challenge of generalizing analogy-making in machines.
Get the Snipd Podcast app to discover more snips from this episode
This book by Douglas Hofstadter is a comprehensive and interdisciplinary work that explores the interrelated ideas of Kurt Gödel, M.C. Escher, and Johann Sebastian Bach. It delves into concepts such as self-reference, recursion, and the limits of formal systems, particularly through Gödel's Incompleteness Theorem. The book uses dialogues between fictional characters, including Achilles and the Tortoise, to intuitively present complex ideas before they are formally explained. It covers a wide range of topics including cognitive science, artificial intelligence, number theory, and the philosophy of mind, aiming to understand how consciousness and intelligence emerge from formal systems[2][4][5].
Human Compatible
Artificial Intelligence and the Problem of Control
Stuart J. Russell
In this book, Stuart Russell explores the concept of intelligence in humans and machines, outlining the near-term benefits and potential risks of AI. He discusses the misuse of AI, from lethal autonomous weapons to viral sabotage, and proposes a novel solution by rebuilding AI on a new foundation where machines are inherently uncertain about human preferences. This approach aims to create machines that are humble, altruistic, and committed to pursuing human objectives, ensuring they remain provably deferential and beneficial to humans.
Today we’re joined by Melanie Mitchell, Davis Professor at the Santa Fe Institute and author of Artificial Intelligence: A Guide for Thinking Humans.
While Melanie has had a long career with a myriad of research interests, we focus on a few, complex systems and the understanding of intelligence, complexity, and her recent work on getting AI systems to make analogies. We explore examples of social learning, and how it applies to AI contextually, and defining intelligence.
We discuss potential frameworks that would help machines understand analogies, established benchmarks for analogy, and if there is a social learning solution to help machines figure out analogy. Finally we talk through the overall state of AI systems, the progress we’ve made amid the limited concept of social learning, if we’re able to achieve intelligence with current approaches to AI, and much more!
The complete show notes for this episode can be found at twimlai.com/go/464.
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) 
