Invest Like the Best with Patrick O'Shaughnessy
00:33:12 · Neil Movva
[00:33:12] S2: What it did, it allowed us to learn unsupervised data really effectively. Because transformers, what they're all about at the end of the day is taking any sequence, any arbitrary sequence of data and trying to find patterns in that data. And they critically, the attention operation, which is the headline component of transformers, it allows the model to dynamically adapt to what it thinks is the most relevant component of the sequence. Every step you take through a transformer, you are essentially like reweighting the input that you looked at before and figuring out which is most relevant for your next prediction. It's extremely amenable to learning arbitrary sequence data. And the most interesting sequences of data that we produce on a regular basis is language. And that's how we got to dominance in the language regime. To zoom out even further, think what transformers really did well is that they scaled. Transformers make no such human prior. Transformers just say, well, there's gonna be a pattern in the sequence of data and if there is a pattern, I'm gonna find I'm going to throw more and more parameters at this problem until it works. Transformers benefit from a lot of the computer vision work too. For example, one of the challenges in computer vision was we had a hard time going from hundreds of thousands of parameters, which you get for linear models like support vector machines or other legacy machine learning models. Those had thousands of parameters. Then we got to deep learning and got to tens of millions of parameters with computer vision. The biggest models were a 150,000,000 parameters was a huge model for computer vision. And now we routinely talk about trillions of parameters. And transformers are the link to go from millions to trillions of parameters.
00:34:41 · Patrick O'Shaughnessy
[00:34:41] S0: So if I think about the important units of scaling being data and compute, does it stand to reason that you think transformers will just stick around because that's the thing that we're good at getting more of, those two things?
00:34:52 · Neil Movva
[00:34:52] S2: Transformers are such great sponges. You increase the compute available to transformer by 10 x and you'll get some log improvement somewhere. And so far, the scaling laws really work. They're really quite beautiful. And to the point about what do transformers do really well, they extend to almost any data set you can throw at them. They're extremely powerful general learners. And I think what's especially useful about transformers over other techniques that we've tried to replace attention is transformers represent any pairwise relationship that you want. Any token in the sequence can attend to any other token in the sequence. So if there's any relationship that's in the sequence at all, you're gonna find it with transformer. Now, may be the case that you don't need all to all modeling. You don't need every token to look at every other token. But if you need to, transformers give you that option. And until we know a better way to prune that space down, a better way to kind of have information modeling be more selective, Attention is a very, very good operation. This is, another trick that we learned in the computer vision days. One of the old Karpathy sayings is that if you have a new dataset that you want to train a model for, your first goal should be to overparameterize the model and try to overfit the data that you have to prove that there is a relationship that you can model or memorize, that your learning algorithm works, that you can instill knowledge into the model. Once you can overfit, then you can compress. And the compression is how you get generalization. You don't want to actually memorize the data that you have in front of you, you want to generalize and therefore, once you overfit the dataset, then you can kind of work backwards and try to find the general patterns that fit into the smallest parameter count possible.
00:36:22 · Patrick O'Shaughnessy
[00:36:22] S0: What's your prediction for the future of data and riff on the importance of data in this whole story?
00:36:26 · Neil Movva
[00:36:26] S2: I like the phrase that Internet was a one time subsidy on data. We got it for free. It's extremely high quality. There are about 30,000,000,000,000 tokens of high quality text. 300,000,000,000,000 tokens if you could take a wider view on what qualifies as good text. And we've basically looked at it all already. Models have seen the entire Internet many times over at this point. And there is not a whole lot more to be done on human data from the internet. The next phase of data in my mind is model self improvement through RL environment, GIMs, basically. Now, in fact, we don't even benefit from getting more random user interactions with AI. It used to be that the new type of data that we cared about a lot was the interaction data from people using ChatGPT and giving ChatGPT signals on what they liked and didn't like. I like the argument now that the median model that we serve is so much more advanced than like a random human giving feedback that the signal you get from random human preference, unconditioned human preference is not actually worth anything anymore. You want expert human preference at this point. The model has outgrown Everyday Joe. Generic. Yeah, Everyday Joe, exactly. So the feature of data to me is giving the model a hard verifiable task and letting it run-in this gym where it's kind of isolated and it just has a problem and it can make progress on it, get measurement of whether it made progress on that problem or not. You can imagine coding problems are in this category, math problems are also in this category. More and more, you can just give the agent a computer essentially and have it act like it's a human worker and give it feedback on whether it's making progress towards the target outcome. That environment becomes the data. I think this is not a super differentiated take, but it's been really, really productive from what I've seen so far.
00:37:59 · Patrick O'Shaughnessy, Neil Movva
[00:37:59] S0: And you think that just goes on for a really long period of time? Or is that another, like, if I think about the Internet as this one big block, like, is another big block that will have its day in the sun and will kind of get it all Yeah. And then we'll have to move on to something else? [00:38:12] S2: I think it's actually more profound than that. Basically, the idea is that if you want artificial general intelligence, the best way to get there is to just keep stacking specialized intelligences until you have no more gaps to fill. The only thing you need to make sure you do to make this work is you must make sure that your task is verifiable. You need to give the model a self grading system. If you have that, you have the recipe for self improvement on any task you like. I think you've seen this held up by the way frontier labs spend. They used to spend that much on data. Now they spend a lot more on RL environments. These environments absolutely capture that relationship of recursive self improvement on a verifiable task. [00:38:48] S0: Coming back to your initial task of making existing hardware more efficient by being more in control of what's going on at the hardware level through software. Keep going on what you've done so far and what you wanna do and then we're gonna jump to hardware and then jump to energy finally.