Chris Summerfield's guest appearances

Chris Summerfield

Brain Inspired

Machine Learning Street Talk (MLST)

James FitzGerald

Jessica Thompson

David Rosen

Ryan Poplin

Blake Richards

Terrence Sejnowski

David Ferrucci

Raia Hadsell

Melanie Mitchell

Matt Smith

Alison Gopnik

Brian Christian

Robert Wilson

Anil Seth

Jeff Hawkins

OwlTail

Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5  Christopher Summerfield, Department of Experimental Psychology, University of Oxford is a Professor of Cognitive Neuroscience at the University of Oxford and a Research Scientist at Deepmind UK. His work focusses on the neural and computational mechanisms by which humans make decisions. Chris has just released an incredible new book on AI called "Natural General Intelligence". It's my favourite book on AI I have read so so far.  The book explores the algorithms and architectures that are driving progress in AI research, and discusses intelligence in the language of psychology and biology, using examples and analogies to be comprehensible to a wide audience. It also tackles longstanding theoretical questions about the nature of thought and knowledge. With Chris' permission, I read out a summarised version of Chapter 2 from his book on which was on Intelligence during the 30 minute MLST introduction.   Buy his book here: https://global.oup.com/academic/product/natural-general-intelligence-9780192843883?cc=gb&amp;lang=en&amp;  YT version: https://youtu.be/31VRbxAl3t0 Interviewer: Dr. Tim Scarfe  TOC: [00:00:00] Walk and talk with Chris on Knowledge and Abstractions [00:04:08] Intro to Chris and his book [00:05:55] (Intro) Tim reads Chapter 2: Intelligence  [00:09:28] Intro continued: Goodhart's law [00:15:37] Intro continued: The "swiss cheese" situation   [00:20:23] Intro continued: On Human Knowledge [00:23:37] Intro continued: Neats and Scruffies [00:30:22] Interview kick off  [00:31:59] What does it mean to understand? [00:36:18] Aligning our language models [00:40:17] Creativity  [00:41:40] "Meta" AI and basins of attraction  [00:51:23] What can Neuroscience impart to AI [00:54:43] Sutton, neats and scruffies and human alignment [01:02:05] Reward is enough [01:19:46] Jon Von Neumann and Intelligence [01:23:56] Compositionality  References:  The Language Game (Morten H. Christiansen, Nick Chater https://www.penguin.co.uk/books/441689/the-language-game-by-morten-h-christiansen-and--nick-chater/9781787633483 Theory of general factor (Spearman) https://www.proquest.com/openview/7c2c7dd23910c89e1fc401e8bb37c3d0/1?pq-origsite=gscholar&amp;cbl=1818401 Intelligence Reframed (Howard Gardner) https://books.google.co.uk/books?hl=en&amp;lr=&amp;id=Qkw4DgAAQBAJ&amp;oi=fnd&amp;pg=PT6&amp;dq=howard+gardner+multiple+intelligences&amp;ots=ERUU0u5Usq&amp;sig=XqiDgNUIkb3K9XBq0vNbFmXWKFs#v=onepage&amp;q=howard%20gardner%20multiple%20intelligences&amp;f=false The master algorithm (Pedro Domingos) https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543 A Thousand Brains: A New Theory of Intelligence (Jeff Hawkins) https://www.amazon.co.uk/Thousand-Brains-New-Theory-Intelligence/dp/1541675819 The bitter lesson (Rich Sutton) http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Support us! <a href="https://www.patreon.com/mlst" rel="nofollow" target="_blank">https://www.patreon.com/mlst</a> MLST Discord: <a href="https://discord.gg/aNPkGUQtc5" rel="nofollow" target="_blank">https://discord.gg/aNPkGUQtc5</a> Christopher Summerfield, Department of Experimental Psychology, University of Oxford is a Professor of Cognitive Neuroscience at the University of Oxford and a Research Scientist at Deepmind UK. His work focusses on the neural and computational mechanisms by which humans make decisions. Chris has just released an incredible new book on AI called "Natural General Intelligence". It's my favourite book on AI I have read so so far.  The book explores the algorithms and architectures that are driving progress in AI research, and discusses intelligence in the language of psychology and biology, using examples and analogies to be comprehensible to a wide audience. It also tackles longstanding theoretical questions about the nature of thought and knowledge. With Chris' permission, I read out a summarised version of Chapter 2 from his book on which was on Intelligence during the 30 minute MLST introduction.   Buy his book here: <a href="https://global.oup.com/academic/product/natural-general-intelligence-9780192843883?cc=gb&amp;lang=en&amp;" rel="nofollow" target="_blank">https://global.oup.com/academic/product/natural-general-intelligence-9780192843883?cc=gb&amp;lang=en&amp;</a> YT version: <a href="https://youtu.be/31VRbxAl3t0" rel="nofollow" target="_blank">https://youtu.be/31VRbxAl3t0</a> Interviewer: Dr. Tim Scarfe TOC: [00:00:00] Walk and talk with Chris on Knowledge and Abstractions [00:04:08] Intro to Chris and his book [00:05:55] (Intro) Tim reads Chapter 2: Intelligence  [00:09:28] Intro continued: Goodhart's law [00:15:37] Intro continued: The "swiss cheese" situation   [00:20:23] Intro continued: On Human Knowledge [00:23:37] Intro continued: Neats and Scruffies [00:30:22] Interview kick off  [00:31:59] What does it mean to understand? [00:36:18] Aligning our language models [00:40:17] Creativity  [00:41:40] "Meta" AI and basins of attraction  [00:51:23] What can Neuroscience impart to AI [00:54:43] Sutton, neats and scruffies and human alignment [01:02:05] Reward is enough [01:19:46] Jon Von Neumann and Intelligence [01:23:56] Compositionality References: The Language Game (Morten H. Christiansen, Nick Chater <a href="https://www.penguin.co.uk/books/441689/the-language-game-by-morten-h-christiansen-and--nick-chater/9781787633483" rel="nofollow" target="_blank">https://www.penguin.co.uk/books/441689/the-language-game-by-morten-h-christiansen-and--nick-chater/9781787633483</a> Theory of general factor (Spearman) <a href="https://www.proquest.com/openview/7c2c7dd23910c89e1fc401e8bb37c3d0/1?pq-origsite=gscholar&amp;cbl=1818401" rel="nofollow" target="_blank">https://www.proquest.com/openview/7c2c7dd23910c89e1fc401e8bb37c3d0/1?pq-origsite=gscholar&amp;cbl=1818401</a> Intelligence Reframed (Howard Gardner) <a href="https://books.google.co.uk/books?hl=en&amp;lr=&amp;id=Qkw4DgAAQBAJ&amp;oi=fnd&amp;pg=PT6&amp;dq=howard+gardner+multiple+intelligences&amp;ots=ERUU0u5Usq&amp;sig=XqiDgNUIkb3K9XBq0vNbFmXWKFs#v=onepage&amp;q=howard%20gardner%20multiple%20intelligences&amp;f=false" rel="nofollow" target="_blank">https://books.google.co.uk/books?hl=en&amp;lr=&amp;id=Qkw4DgAAQBAJ&amp;oi=fnd&amp;pg=PT6&amp;dq=howard+gardner+multiple+intelligences&amp;ots=ERUU0u5Usq&amp;sig=XqiDgNUIkb3K9XBq0vNbFmXWKFs#v=onepage&amp;q=howard%20gardner%20multiple%20intelligences&amp;f=false</a> The master algorithm (Pedro Domingos) <a href="https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543" rel="nofollow" target="_blank">https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543</a> A Thousand Brains: A New Theory of Intelligence (Jeff Hawkins) <a href="https://www.amazon.co.uk/Thousand-Brains-New-Theory-Intelligence/dp/1541675819" rel="nofollow" target="_blank">https://www.amazon.co.uk/Thousand-Brains-New-Theory-Intelligence/dp/1541675819</a> The bitter lesson (Rich Sutton) <a href="http://www.incompleteideas.net/IncIdeas/BitterLesson.html" rel="nofollow" target="_blank">http://www.incompleteideas.net/IncIdeas/BitterLesson.html</a>

#104 - Prof. CHRIS SUMMERFIELD - Natural General Intelligence [SPECIAL EDITION]

Support the show to get full episodes and join the Discord community.  Check out my free video series about what's missing in AI and Neuroscience  Chris Summerfield runs the Human Information Processing Lab at University of Oxford, and he's a research scientist at Deepmind. You may remember him from episode 95 with Sam Gershman, when we discussed ideas around the usefulness of neuroscience and psychology for AI. Since then, Chris has released his book, Natural General Intelligence: How understanding the brain can help us build AI. In the book, Chris makes the case that inspiration and communication between the cognitive sciences and AI is hindered by the different languages each field speaks. But in reality, there has always been and still is a lot of overlap and convergence about ideas of computation and intelligence, and he illustrates this using tons of historical and modern examples.  Human Information Processing Lab. Twitter: @summerfieldlab. Book: Natural General Intelligence: How understanding the brain can help us build AI. Other books mentioned: Are We Smart Enough to Know How Smart Animals Are? by Frans de Waal The Mind is Flat by Nick Chater.    0:00 - Intro2:20 - Natural General Intelligence8:05 - AI and Neuro interaction21:42 - How to build AI25:54 - Umwelts and affordances32:07 - Different kind of intelligence39:16 - Ecological validity and AI48:30 - Is reward enough?1:05:14 - Beyond brains1:15:10 - Large language models and brains

<a href="https://www.patreon.com/braininspired" rel="nofollow" target="_blank">Support the show</a> to get full episodes and join the Discord community. <a href="https://www.patreon.com/braininspired" rel="nofollow" target="_blank"></a> <a href="https://braininspired.co/open/" rel="noreferrer noopener nofollow" target="_blank">Check out my free video series about what's missing in AI and Neuroscience</a> <a href="https://braininspired.co/open/" rel="noreferrer noopener nofollow" target="_blank"></a> Chris Summerfield runs the Human Information Processing Lab at University of Oxford, and he's a research scientist at Deepmind. You may remember him from <a href="https://braininspired.co/podcast/95/" rel="nofollow" target="_blank">episode 95 with Sam Gershman</a>, when we discussed ideas around the usefulness of neuroscience and psychology for AI. Since then, Chris has released his book, <a href="https://amzn.to/3GIY9tO" rel="nofollow" target="_blank">Natural General Intelligence: How understanding the brain can help us build AI</a>. In the book, Chris makes the case that inspiration and communication between the cognitive sciences and AI is hindered by the different languages each field speaks. But in reality, there has always been and still is a lot of overlap and convergence about ideas of computation and intelligence, and he illustrates this using tons of historical and modern examples. <ul> <li><a href="https://humaninformationprocessing.com/" rel="nofollow" target="_blank">Human Information Processing Lab</a>.</li> <li>Twitter: <a href="https://twitter.com/summerfieldlab" rel="nofollow" target="_blank">@summerfieldlab</a>.</li> <li>Book: <a href="https://amzn.to/3GIY9tO" rel="nofollow" target="_blank">Natural General Intelligence: How understanding the brain can help us build AI</a>.</li> <li>Other books mentioned: <ul> <li><a href="https://amzn.to/3kC1FPu" rel="nofollow" target="_blank">Are We Smart Enough to Know How Smart Animals Are?</a> by Frans de Waal</li> <li><a href="https://amzn.to/3ZQi96x" rel="nofollow" target="_blank">The Mind is Flat</a> by Nick Chater.</li> </ul> </li> </ul> 0:00 - Intro 2:20 - Natural General Intelligence 8:05 - AI and Neuro interaction 21:42 - How to build AI 25:54 - Umwelts and affordances 32:07 - Different kind of intelligence 39:16 - Ecological validity and AI 48:30 - Is reward enough? 1:05:14 - Beyond brains 1:15:10 - Large language models and brains

BI 159 Chris Summerfield: Natural General Intelligence

Sam Gershman

It's generally agreed machine learning and AI provide neuroscience with tools for analysis and theoretical principles to test in brains, but there is less agreement about what neuroscience can provide AI. Should computer scientists and engineers care about how brains compute, or will it just slow them down, for example? Chris, Sam, and I discuss how neuroscience might contribute to AI moving forward, considering the past and present. This discussion also leads into related topics, like the role of prediction versus understanding, AGI, explainable AI, value alignment, the fundamental conundrum that humans specify the ultimate values of the tasks AI will solve, and more. Plus, a question from previous guest Andrew Saxe. Also, check out Sam's previous appearance on the podcast. Chris's lab: Human Information Processing lab.Sam's lab: Computational Cognitive Neuroscience Lab.Twitter: @gershbrain; @summerfieldlabPapers we discuss or mention or are related:If deep learning is the answer, then what is the question?Neuroscience-Inspired Artificial Intelligence.Building Machines that Learn and Think Like People. 0:00 - Intro 5:00 - Good ol' days 13:50 - AI for neuro, neuro for AI 24:25 - Intellectual diversity in AI 28:40 - Role of philosophy 30:20 - Operationalization and benchmarks 36:07 - Prediction vs. understanding 42:48 - Role of humans in the loop 46:20 - Value alignment 51:08 - Andrew Saxe question 53:16 - Explainable AI 58:55 - Generalization 1:01:09 - What has AI revealed about us? 1:09:38 - Neuro for AI 1:20:30 - Concluding remarks

It's generally agreed machine learning and AI provide neuroscience with tools for analysis and theoretical principles to test in brains, but there is less agreement about what neuroscience can provide AI. Should computer scientists and engineers care about how brains compute, or will it just slow them down, for example? Chris, Sam, and I discuss how neuroscience might contribute to AI moving forward, considering the past and present. This discussion also leads into related topics, like the role of prediction versus understanding, AGI, explainable AI, value alignment, the fundamental conundrum that humans specify the ultimate values of the tasks AI will solve, and more. Plus, a question from previous guest <a href="https://braininspired.co/podcast/52/" rel="noreferrer noopener nofollow" target="_blank">Andrew Saxe</a>. Also, check out <a href="https://braininspired.co/podcast/28/" rel="noreferrer noopener nofollow" target="_blank">Sam's previous appearance on the podcast</a>. <ul><li>Chris's lab: <a href="http://decisions.psy.ox.ac.uk/#!/home" rel="noreferrer noopener nofollow" target="_blank">Human Information Processing lab</a>.</li><li>Sam's lab: <a href="https://gershmanlab.com/index.html" rel="noreferrer noopener nofollow" target="_blank">Computational Cognitive Neuroscience Lab</a>.</li><li>Twitter: <a href="https://twitter.com/gershbrain" rel="noreferrer noopener nofollow" target="_blank">@gershbrain</a>; <a href="https://twitter.com/summerfieldlab" rel="noreferrer noopener nofollow" target="_blank">@summerfieldlab</a></li><li>Papers we discuss or mention or are related:<ul><li><a href="https://arxiv.org/abs/2004.07580" rel="nofollow" target="_blank">If deep learning is the answer, then what is the question?</a></li><li><a href="https://www.cell.com/neuron/pdf/S0896-6273(17)30509-3.pdf" rel="nofollow" target="_blank">Neuroscience-Inspired Artificial Intelligence</a>.</li><li><a href="https://www.youtube.com/watch?v=O0MF-r9PsvE" rel="nofollow" target="_blank">Building Machines that Learn and Think Like People</a>.</li></ul></li></ul> 0:00 - Intro 5:00 - Good ol' days 13:50 - AI for neuro, neuro for AI 24:25 - Intellectual diversity in AI 28:40 - Role of philosophy 30:20 - Operationalization and benchmarks 36:07 - Prediction vs. understanding 42:48 - Role of humans in the loop 46:20 - Value alignment 51:08 - Andrew Saxe question 53:16 - Explainable AI 58:55 - Generalization 1:01:09 - What has AI revealed about us? 1:09:38 - Neuro for AI 1:20:30 - Concluding remarks

BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?