Zico Colter

sort of spatio-temporal data, this is hugely important to our conception of intelligence, right? This is hugely important to the way that we interact with the world, the way that we sort of think about our own intelligence. And so I can't fathom that there is not a value to many, many more modalities of data, be it video, be it audio, be it

sort of spatio-temporal data, this is hugely important to our conception of intelligence, right? This is hugely important to the way that we interact with the world, the way that we sort of think about our own intelligence. And so I can't fathom that there is not a value to many, many more modalities of data, be it video, be it audio, be it

sort of spatio-temporal data, this is hugely important to our conception of intelligence, right? This is hugely important to the way that we interact with the world, the way that we sort of think about our own intelligence. And so I can't fathom that there is not a value to many, many more modalities of data, be it video, be it audio, be it

other time series and things like this that we sort of don't quite, that are not audio, but the sort of other sensory signals, stuff like this. There are massive amounts of data available. And I think we have not yet figured out how to properly leverage those due to either limitations of compute.

other time series and things like this that we sort of don't quite, that are not audio, but the sort of other sensory signals, stuff like this. There are massive amounts of data available. And I think we have not yet figured out how to properly leverage those due to either limitations of compute.

other time series and things like this that we sort of don't quite, that are not audio, but the sort of other sensory signals, stuff like this. There are massive amounts of data available. And I think we have not yet figured out how to properly leverage those due to either limitations of compute.

I mean, you have to process all that data and it does take, we don't have current models to do this very well, or just to do the limitations and sort of how we transfer and generalize across these modalities here. I think there has to be a use for it.

I mean, you have to process all that data and it does take, we don't have current models to do this very well, or just to do the limitations and sort of how we transfer and generalize across these modalities here. I think there has to be a use for it.

I mean, you have to process all that data and it does take, we don't have current models to do this very well, or just to do the limitations and sort of how we transfer and generalize across these modalities here. I think there has to be a use for it.

There are a few different sort of notions here. One is just the fact that we still seem to be in a world where you can increase model size and get better performance, even with the same data. So obviously, the real value of bigger models is they can suck up more data. They're able to ingest more and more data.

There are a few different sort of notions here. One is just the fact that we still seem to be in a world where you can increase model size and get better performance, even with the same data. So obviously, the real value of bigger models is they can suck up more data. They're able to ingest more and more data.

There are a few different sort of notions here. One is just the fact that we still seem to be in a world where you can increase model size and get better performance, even with the same data. So obviously, the real value of bigger models is they can suck up more data. They're able to ingest more and more data.

But it is also true that if you just take a fixed data set and run over it multiple times, if you use a bigger model, it will often work better. We have not really reached the plateau there.

But it is also true that if you just take a fixed data set and run over it multiple times, if you use a bigger model, it will often work better. We have not really reached the plateau there.

But it is also true that if you just take a fixed data set and run over it multiple times, if you use a bigger model, it will often work better. We have not really reached the plateau there.

The other thing, though, I don't think anyone would argue, or most people would not argue, that the current models in some sense extract the maximum information possible out of the data that is presented to them. And a very simple example of this is if you train a classifier, just to classify images of cats versus dogs on a bunch of images, you get a certain level of performance.

The other thing, though, I don't think anyone would argue, or most people would not argue, that the current models in some sense extract the maximum information possible out of the data that is presented to them. And a very simple example of this is if you train a classifier, just to classify images of cats versus dogs on a bunch of images, you get a certain level of performance.

The other thing, though, I don't think anyone would argue, or most people would not argue, that the current models in some sense extract the maximum information possible out of the data that is presented to them. And a very simple example of this is if you train a classifier, just to classify images of cats versus dogs on a bunch of images, you get a certain level of performance.

If you train a generative model on those exact same images, generate more synthetic data from that generative model, and then train on that more synthetic data, you don't do that much better, but you do a little bit better. And that's just wild. What that means is our current algorithms, we are not yet maximally extracting the information from data we have.

If you train a generative model on those exact same images, generate more synthetic data from that generative model, and then train on that more synthetic data, you don't do that much better, but you do a little bit better. And that's just wild. What that means is our current algorithms, we are not yet maximally extracting the information from data we have.