Advancements in Language Learning with AI

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Check out Hugo’s latest episode with Fei-Fei Li, on How Human-Centered AI Actually Gets Built

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Computational cognitive science seeks to understand intelligence mathematically.
• Bayesian statistics is crucial for understanding human cognition.
• Inductive biases help explain how humans learn from limited data.
• Eliciting prior distributions can reveal implicit beliefs.
• The wisdom of individuals can provide richer insights than averaging group responses.
• Generative AI can mimic human cognitive processes.
• Human intelligence is shaped by constraints of data, computation, and communication.
• AI systems operate under different constraints than human cognition. Human intelligence differs fundamentally from machine intelligence.
• Generative AI can complement and enhance human learning.
• AI systems currently lack intrinsic human compatibility.
• Language training in AI helps align its understanding with human perspectives.
• Reinforcement learning from human feedback can lead to misalignment of AI goals.
• Representational alignment can improve AI's understanding of human concepts.
• AI can help humans make better decisions by providing relevant information.
• Research should focus on solving problems rather than just methods.

Chapters:

00:00 Understanding Computational Cognitive Science

13:52 Bayesian Models and Human Cognition

29:50 Eliciting Implicit Prior Distributions

38:07 The Relationship Between Human and AI Intelligence

45:15 Aligning Human and Machine Preferences

50:26 Innovations in AI and Human Interaction

55:35 Resource Rationality in Decision Making

01:00:07 Language Learning in AI Models

01:06:04 Inductive Biases in Language Learning

01:11:55 Advice for Aspiring Cognitive Scientists

01:21:19 Future Trends in Cognitive Science and AI

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık and Suyog Chandramouli.

Links from the show:

• Check out Hugo’s latest episode with Fei-Fei Li, on How Human-Centered AI Actually Gets Built: https://high-signal.delphina.ai/episode/fei-fei-on-how-human-centered-ai-actually-gets-built?utm_source=laplace&utm_medium=podcast&utm_campaign=feifei_launch
• Tom's profile at Princeton University: https://psychology.princeton.edu/people/tom-griffiths
• Computational Cognitive Science Lab: https://cocosci.princeton.edu/
• Tom’s Google Scholar: https://scholar.google.com/citations?user=UAwKvEsAAAAJ&hl=en
• Tom's latest book, Bayesian Models of Cognition: https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.