Ramin Hasani: recommendation

Transcript context

…So just for calibration, when you talk about the liquid neural networks that have gone up to hundreds of thousands or pushing a million neurons, what kind of hardware does that run on? Is that like a CPU supercomputer? You can do that on a CPU, like on even simple CPUs or simple GPUs, you know, because they don't have that much of, probably like one of these liquid neural networks would fit on, I don't know, 1 to 25 megabytes of like file size, basically, you can literally put them there, and then, a CPU would be enough, like a Raspberry Pi would be enough, like to perform computations. We have shown that on a lot of predictive, specialized applications of AI, that these systems can be like very, very powerful and do stuff, and they can be as powerful as the systems, like these class form variants could be as powerful as the open differential equation version of the neural networks in speech synthesis, you know, like that's one of the applications. in, as I said, predictive sequence, like imagine you have complex formats of sequences coming in, multivariate kind of sequences that are coming in and you want to perform some sort of a prediction on top of them, these neural networks are actually pretty good. And then if you're talking about like more, let's say out of distribution generalization to the extent that you are like within a bounded period, you kind of have like an open-ended continual learning. These are not, liquid norm levels are not continual learning systems. They are more adaptive formats of computations because of the many gatings, many feedbacks, and many, let's say input dependent parameters that they have in their mathematical kind of arguation. What is the definition of continual learning that you're using there that they don't satisfy?…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.