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Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
An AI Measurement Science (AIMS) Seminar Series
An AI Measurement Science (AIMS) Seminar Series
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
In this talk, Subutai will first discuss what is known about the sparsity of activations and connectivity in the neocortex. He will also summarize new experimental data around active dendrites, branch-specific plasticity, and structural plasticity, each of which has surprising implications for how we think about sparsity. In the second half of the talk, Subutai will discuss how these insights from the brain can be applied to practical machine learning applications. He will show how sparse representations can give rise to improved robustness, continuous learning, powerful unsupervised learning rules, and improved computational efficiency.