Anima Anandkumar
person on 1 show · 8 statements across 2 episodes
On the record as a speaker too: Anima Anandkumar across every show, appearances and statements → this page counts the times other people say the name.
8 statements about Anima Anandkumar, every show
Anandkumar: Dense physics feedback enables better AI self-improvement than sparse LLMs
“And the difference there is compared to language where self-improvement needs something like human feedback or other reward signals that are very sparse. They just tell you yes or no, thumbs up or down. We have dense feedback because the physics laws, there's …”
Anandkumar: Multi-physics models outperform single-physics models of equivalent parameter size
“And in fact, I was going to add that it turns out that having the model of the same size with multiple areas of physics does better than giving all of those parameters to each single physics. So if you had separate models and made them big enough as the origin…”
Anandkumar: Neural models capture shared mathematical features across disparate physics domains
“So there's implicitly a lot of common features, even across physics that are gone by different equations. So that's how you see across different domains, across different mathematical models. There's a lot of Shared features that these neural models can pick u…”
Anandkumar: Purely data-driven AI fails at physical simulation without physics laws
“And so this reliance on just purely data-driven AI is not going to be enough. And that's where, you know, adding the loss of physics is really critical.”
Simulation-in-the-loop AI outperforms human intuition at highly nonlinear inverse design
“And humans are usually not good at this, right? We are not good at like looking at highly nonlinear phenomena and say, oh, somehow maybe this combination of all these gates coming together helps pull the electrons together in a quantum gate. And so our collabo…”
Curriculum learning enables multi-physics AI fine-tuning with significantly fewer samples
“And now there's coupling, like because of heat, there's also stretching or kind of the joint phenomena. You could like now hope to fine tune with much fewer samples because it kind of individually knows this phenomena. Then combining them together, maybe it ca…”
Neural operators simulate underground carbon sequestration faster than traditional methods
“So this was like, you know, being able to ask, can we sequester carbon dioxide underground and model how carbon dioxide Expands or, you know, what is the pressure buildup in these reservoirs? And, you know, can we kind of model how they migrate over several de…”
AI models predict fusion reactor plasma disruption one million times faster
“You know, I talk about plasma and fusion reactor. You know, we barely have a few thousand samples, but we are able to accurately predict events like disruption very well. And we are able to do that a million times faster than what traditional simulations were …”