HAI Weekly Seminar with Subutai Ahmad - Sparsity in the neocortex, and its implications for machine learning
Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.
Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.
Sign Up For Latest News
Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.
AI coding agents now complete multi-hour coding benchmarks with roughly 50% reliability, yet a randomized trial found experienced open-source developers took about 19% longer when allowed frontier AI tools than when tools were disallowed...
.png&w=1920&q=100)
AI coding agents now complete multi-hour coding benchmarks with roughly 50% reliability, yet a randomized trial found experienced open-source developers took about 19% longer when allowed frontier AI tools than when tools were disallowed...
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.

Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
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.