Bring your own data, and why data scarcity limits models

When you said robots, it just reminded me of actually, do you sometimes plan to leverage from robotics field in order to make sure you have enough of data, because when you go into robotics, that your data samples are richer, right? And also the learning is much more effective in terms of they react, they need to understand. I have a feeling that models are really getting more interesting when you see something that's interactive or trying to make a decision and then combine it with the mechanics. Or would that be like literally a little bit far away from to even get benefit for thesis.

I think if you have, sorry, go ahead Luigi.

You're welcome. I was just going to add quickly, I think you've got a lot better content on this, but yeah, people can use thesis to build better robotics models. Like if you're, for example, one of our investors is like trying to build a robotic arm and this is like itself, of course, an ML model. So getting it into, ⁓ if you're building, for example, a robot and ML models around there, you can absolutely use thesis to make your ML models better. And then I'm sure they're just going to tell you about the data side when it comes to robotics, which I think is the key limiting factor, at least right now.

Yeah, data scarcity is very much a limiting factor in all model performance. we, I believe the way that The way that it's structured right now, thesis is bring your own data. So you have some data that you've been exploring and you want to make powerful models. You want to analyze it, maybe it's highly unstructured messy information that is difficult for a computer to parse and you need a model to develop it. That is the current version of thesis, but I, what I think in the future that we have, is the ability to also retrieve data from, you know, whether it is internal sources that we have accumulated over time, or whether it's from the internet or various other places, like there's a potential to even like broker data, for instance. But yes, fundamentally, the performance of the model ⁓ does depend on

Mm.

the quality of the data, the sort of feature engineering that one does in order to get good model performance. So all of these are critical components. But the way that, like in the best universe possible, you come to thesis with your messy data and it can determine whether, you know, builds a model and it realizes, hey, I am at like the information theoretic limit of what is possible to squeeze out of the current data.