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Lecture | Sashank Varma | "Mathematical Concepts in Humans and Machine Learning Models"

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Sashank Varma | Psychology and Interactive Computing, Georgia Institute of Technology
"Mathematical Concepts in Humans and Machine Learning Models"
The nature of mathematical concepts has long been a topic of philosophical debate. Recent theorizing in mathematical cognition has tended towards nativist accounts and postulations of built-in neural circuitry. In this talk, I consider whether this status quo is being challenged by the emergence of machine learning models capable of near-human levels of performance at predicting text and classifying images. Ongoing research in my lab is finding that these models induce latent representations of numerical and geometric concepts that are similar to those found in humans, for example, the mental number line. I will review several of these projects. I will also preview our future work, where we are moving beyond the cognitive alignment of machine learning models to evaluate their developmental alignment by training language models on developmentally calibrated corpora. The goal of this new work is first to model typical numerical development and then to perturb these typical models to shed light on developmental dyscalculia.

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292 episodes

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Manage episode 377736952 series 2538953
Content provided by Center for Mind, Brain, and Culture, Emory College, Emory Center for Mind, and Culture (CMBC). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Center for Mind, Brain, and Culture, Emory College, Emory Center for Mind, and Culture (CMBC) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Sashank Varma | Psychology and Interactive Computing, Georgia Institute of Technology
"Mathematical Concepts in Humans and Machine Learning Models"
The nature of mathematical concepts has long been a topic of philosophical debate. Recent theorizing in mathematical cognition has tended towards nativist accounts and postulations of built-in neural circuitry. In this talk, I consider whether this status quo is being challenged by the emergence of machine learning models capable of near-human levels of performance at predicting text and classifying images. Ongoing research in my lab is finding that these models induce latent representations of numerical and geometric concepts that are similar to those found in humans, for example, the mental number line. I will review several of these projects. I will also preview our future work, where we are moving beyond the cognitive alignment of machine learning models to evaluate their developmental alignment by training language models on developmentally calibrated corpora. The goal of this new work is first to model typical numerical development and then to perturb these typical models to shed light on developmental dyscalculia.

  continue reading

292 episodes

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