Brain Inspired Paul Middlebrooks

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











Ellie Pavlick runs her Language Understanding and Representation Lab at Brown University, where she studies lots of topics related to language. In AI, large language models, sometimes called foundation models, are all the rage these days, with their ability to generate convincing language, although they still make plenty of mistakes. One of the things Ellie is interested in is how these models work, what kinds of representations are being generated in them to produce the language they produce. So we discuss how she's going about studying these models. For example, probing them to see whether something symbolic-like might be implemented in the models, even though they are the deep learning neural network type, which aren't suppose to be able to work in a symbol-like manner. We also discuss whether grounding is required for language understanding - that is, whether a model that produces language well needs to connect with the real world to actually understand the text it generates. We talk about what language is for, the current limitations of large language models, how the models compare to humans, and a lot more.




Language Understanding and Representation Lab



Twitter: @Brown_NLP



Related papers

Semantic Structure in Deep Learning.



Pretraining on Interactions for Learning Grounded Affordance Representations.



Mapping Language Models to Grounded Conceptual Spaces.






0:00 - Intro
2:34 - Will LLMs make us dumb?
9:01 - Evolution of language
17:10 - Changing views on language
22:39 - Semantics, grounding, meaning
37:40 - LLMs, humans, and prediction
41:19 - How to evaluate LLMs
51:08 - Structure, semantics, and symbols in models
1:00:08 - Dimensionality
1:02:08 - Limitations of LLMs
1:07:47 - What do linguists think?
1:14:23 - What is language for?

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











Earl Miller runs the Miller Lab at MIT, where he studies how our brains carry out our executive functions, like working memory, attention, and decision-making. In particular he is interested in the role of the prefrontal cortex and how it coordinates with other brain areas to carry out these functions. During this episode, we talk broadly about how neuroscience has changed during Earl's career, and how his own thoughts have changed. One thing we focus on is the increasing appreciation of brain oscillations for our cognition.



Recently on BI we've discussed oscillations quite a bit. In episode 153, Carolyn Dicey-Jennings discussed her philosophical ideas relating attention to the notion of the self, and she leans a lot on Earl's research to make that argument.  In episode 160, Ole Jensen discussed his work in humans showing that  low frequency oscillations exert a top-down control on incoming sensory stimuli, and this is directly in agreement with Earl's work over many years in nonhuman primates. So we continue that discussion relating low-frequency oscillations to executive control. We also discuss a new concept Earl has developed called spatial computing, which is an account of how brain oscillations can dictate where in various brain areas neural activity be on or off, and hence contribute or not to ongoing mental function. We also discuss working memory in particular, and a host of related topics.




Miller lab.



Twitter: @MillerLabMIT.



Related papers:

An integrative theory of prefrontal cortex function. Annual Review of Neuroscience.



Working Memory Is Complex and Dynamic, Like Your Thoughts.



Traveling waves in the prefrontal cortex during working memory.






0:00 - Intro
6:22 - Evolution of Earl's thinking
14:58 - Role of the prefrontal cortex
25:21 - Spatial computing
32:51 - Homunculus problem
35:34 - Self
37:40 - Dimensionality and thought
46:13 - Reductionism
47:38 - Working memory and capacity
1:01:45 - Capacity as a principle
1:05:44 - Silent synapses
1:10:16 - Subspaces in dynamics

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









Hugo Spiers runs the Spiers Lab at University College London. In general Hugo is interested in understanding spatial cognition, like navigation, in relation to other processes like planning and goal-related behavior, and how brain areas like the hippocampus and prefrontal cortex coordinate these cognitive functions. So, in this episode, we discuss a range of his research and thoughts around those topics. You may have heard about the studies he's been involved with for years, regarding London taxi drivers and how their hippocampus changes as a result of their grueling efforts to memorize how to best navigate London. We talk about that, we discuss the concept of a schema, which is roughly an abstracted form of knowledge that helps you know how to behave in different environments. Probably the most common example is that we all have a schema for eating at a restaurant, independent of which restaurant we visit, we know about servers, and menus, and so on. Hugo is interested in spatial schemas, for things like navigating a new city you haven't visited. Hugo describes his work using reinforcement learning methods to compare how humans and animals solve navigation tasks. And finally we talk about the video game Hugo has been using to collect vast amount of data related to navigation, to answer questions like how our navigation ability changes over our lifetimes, the different factors that seem to matter more for our navigation skills, and so on.




Spiers Lab.



Twitter: @hugospiers.



Related papers

Predictive maps in rats and humans for spatial navigation.



From cognitive maps to spatial schemas.



London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London.



Explaining World-Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest.

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











Ole Jensen is co-director of the Centre for Human Brain Health at University of Birmingham, where he runs his Neuronal Oscillations Group lab. Ole is interested in how the oscillations in our brains affect our cognition by helping to shape the spiking patterns of neurons, and by helping to allocate resources to parts of our brains that are relevant for whatever ongoing behaviors we're performing in different contexts. People have been studying oscillations for decades, finding that different frequencies of oscillations have been linked to a bunch of different cognitive functions. Some of what we discuss today is Ole's work on alpha oscillations, which are around 10 hertz, so 10 oscillations per second. The overarching story is that alpha oscillations are thought to inhibit or disrupt processing in brain areas that aren't needed during a given behavior. And therefore by disrupting everything that's not needed, resources are allocated to the brain areas that are needed. We discuss his work in the vein on attention - you may remember the episode with Carolyn Dicey-Jennings, and her ideas about how findings like Ole's are evidence we all have selves. We also talk about the role of alpha rhythms for working memory, for moving our eyes, and for previewing what we're about to look at before we move our eyes, and more broadly we discuss the role of oscillations in cognition in general, and of course what this might mean for developing better artificial intelligence.




The Neuronal Oscillations Group.







Twitter: @neuosc.



Related papers

Shaping functional architecture by oscillatory alpha activity: gating by inhibition



FEF-Controlled Alpha Delay Activity Precedes Stimulus-Induced Gamma-Band Activity in Visual Cortex



The theta-gamma neural code



A pipelining mechanism supporting previewing during visual exploration and reading.



Specific lexico-semantic predictions are associated with unique spatial and temporal patterns of neural activity.






0:00 - Intro
2:58 - Oscillations import over the years
5:51 - Oscillations big picture
17:62 - Oscillations vs. traveling waves
22:00 - Oscillations and algorithms
28:53 - Alpha oscillations and working memory
44:46 - Alpha as the controller
48:55 - Frequency tagging
52:49 - Timing of attention
57:41 - Pipelining neural processing
1:03:38 - Previewing during reading
1:15:50 - Previewing, prediction, and large language models
1:24:27 - Dyslexia

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 - 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

Check out my free video series about what's missing in AI and Neuroscience







Support the show to get full episodes and join the Discord community.









Paul Rosenbloom is Professor Emeritus of Computer Science at the University of Southern California. In the early 1980s, Paul , along with John Laird and the early AI pioneer Alan Newell, developed one the earliest and best know cognitive architectures called SOAR. A cognitive architecture, as Paul defines it, is a model of the fixed structures and processes underlying minds, and in Paul's case the human mind. And SOAR was aimed at generating general intelligence. He doesn't work on SOAR any more, although SOAR is still alive and well in the hands of his old partner John Laird. He did go on to develop another cognitive architecture, called Sigma, and in the intervening years between those projects, among other things Paul stepped back and explored how our various scientific domains are related, and how computing itself should be considered a great scientific domain. That's in his book On Computing: The Fourth Great Scientific Domain.





He also helped develop the Common Model of Cognition, which isn't a cognitive architecture itself, but instead a theoretical model meant to generate consensus regarding the minimal components for a human-like mind. The idea is roughly to create a shared language and framework among cognitive architecture researchers, so the field can , so that whatever cognitive architecture you work on, you have a basis to compare it to, and can communicate effectively among your peers.



All of what I just said, and much of what we discuss, can be found in Paul's memoir, In Search of Insight: My Life as an Architectural Explorer.




Paul's website.



Related papers

Working memoir: In Search of Insight: My Life as an Architectural Explorer.



Book: On Computing: The Fourth Great Scientific Domain.



A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics.



Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains.



Common Model of Cognition Bulletin.






0:00 - Intro
3:26 - A career of exploration
7:00 - Alan Newell
14:47 - Relational model and dichotomic maps
24:22 - Cognitive architectures
28:31 - SOAR cognitive architecture
41:14 - Sigma cognitive architecture
43:58 - SOAR vs. Sigma
53:06 - Cognitive architecture community
55:31 - Common model of cognition
1:11:13 - What's missing from the common model
1:17:48 - Brains vs. cognitive architectures
1:21:22 - Mapping the common model onto the brain
1:24:50 - Deep learning
1:30:23 - AGI