Speaker: Tom Dean
Affiliation: Stanford, Wu Tsai Neurosciences Institute & Brown University, Department of Computer Science
Date: Tuesday, April 7, 2020
Title: Biological Blueprints for Human Inspired AI: Part I (SLIDES) (VIDEO)
Talking Points:
introduce the idea of a mesoscale model from physics that serves to bridge the gap between two levels of abstraction
reductionist theories that predominate in particle physics and in neuroscience at the molecular and cellular level
antireductionist theories that study collective behavior in complex systems characterized by their emergent properties
in moving from a molecular to a behavioral perspective some phenomena are obscured but new phenomena become apparent
quantum electrodynamics (QED) predicts the interactions between quarks and other fundamental constituents of matter
kinetic theory of gasses predicts the thermodynamic behavior of gasses which is computationally infeasible using QED
the digital abstraction in computer science allows the division of labor between electrical and software engineering
mathematical abstractions underlying artificial neural networks serve to bridge the gap between neurons and behavior
project Neuromancer at Google, completely automated neural reconstructions working with Max Planck, Janelia, Harvard
opinions on the value of complete connectomes – examples of how structural connectomics reveals computational insight
beyond structure – prospects for automated functional connectomics depend on development of new imaging technologies
an illustration of how artificial neural networks provide an ontology and methodology for testing cognitive theories
Resources: Below you'll find an assortment of course-related resources:
An introduction to the concepts, methods and technologies covered in this course, including basic neural network components and topics relating to cognitive and systems neuroscience;
The calendar listing the presentation day and topic of discussion for each of the student-guided discussion sections — five primary to start with and eight additional depending on interest;
A description of the first five discussion sections including suggestions for presentation and resources in the form of technical papers, tutorials and recorded lectures;
A short primer on how to run a student-guided discussion section, including suggestions for topic preparation, audiovisual materials and reading assignments;
A document that is part autobiography and part history of my involvement in artificial intelligence and computational neuroscience over a period spanning more than forty years;