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  3. Quest | CBMM Seminar Series
Quest | CBMM Seminar Series
Center for Brains, Minds and Machines (CBMM)

Quest | CBMM Seminar Series

Add to CalendarAmerica/New_YorkQuest | CBMM Seminar Series02/11/2025 4:00 pm02/11/2025 5:00 pmBuilding 46,Singleton Auditorium 46-3002
February 11, 2025
4:00 pm - 5:00 pm
Location
Building 46,Singleton Auditorium 46-3002
    Description

    Dr. Thomas Serre, Thomas J. Watson, Sr. Professor of Science at Brown University's Carney Center for Computational Brain Science, to speak as part of the Quest for Intelligence and CBMM seminar series.

     

    Title: Aligning deep networks with human vision will require novel neural architectures, data diets and training algorithms

    Abstract: Recent advances in artificial intelligence have been mainly driven by the rapid scaling of deep neural networks (DNNs), which now contain unprecedented numbers of learnable parameters and are trained on massive datasets, covering large portions of the internet. This scaling has enabled DNNs to develop visual competencies that approach human levels. However, even the most sophisticated DNNs still exhibit strange, inscrutable failures that diverge markedly from human-like behavior—a misalignment that seems to worsen as models grow in scale.

    In this talk, I will discuss recent work from our group addressing this misalignment via the development of DNNs that mimic human perception by incorporating computational, algorithmic, and representational principles fundamental to natural intelligence. First, I will review our ongoing efforts in characterizing human visual strategies in image categorization tasks and contrasting these strategies with modern deep nets. I will present initial results suggesting we must explore novel data regimens and training algorithms for deep nets to learn more human-like visual representations. Second, I will show results suggesting that neural architectures inspired by cortex-like recurrent neural circuits offer a compelling alternative to the prevailing transformers, particularly for tasks requiring visual reasoning beyond simple categorization.

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