Eiso Kant

is the data set that represents being given the task, all of your intermediate reasoning and thinking, the steps that you do, the code that you write and try to run, and then it fills and you learn from those interactions, and all the way to kind of getting that final product. And that intermediate data set, that's what Poolside exists on creating.

is the data set that represents being given the task, all of your intermediate reasoning and thinking, the steps that you do, the code that you write and try to run, and then it fills and you learn from those interactions, and all the way to kind of getting that final product. And that intermediate data set, that's what Poolside exists on creating.

This is the right question. The way I think about the world is that there are problems that we cannot simulate. The real world is impossible to perfectly simulate. It's messy. It's multivariable. How do we deal with the real world when we're trying to close the gap between human capabilities and AI? We have to gather data. The best example of this is Elon and Tesla.

This is the right question. The way I think about the world is that there are problems that we cannot simulate. The real world is impossible to perfectly simulate. It's messy. It's multivariable. How do we deal with the real world when we're trying to close the gap between human capabilities and AI? We have to gather data. The best example of this is Elon and Tesla.

This is the right question. The way I think about the world is that there are problems that we cannot simulate. The real world is impossible to perfectly simulate. It's messy. It's multivariable. How do we deal with the real world when we're trying to close the gap between human capabilities and AI? We have to gather data. The best example of this is Elon and Tesla.

Elon has put millions of cars on the road that are actually capturing every single engagement and disengagement with autopilot and every single scenario and extending that back to Tesla to train increasingly more capable AI.

Elon has put millions of cars on the road that are actually capturing every single engagement and disengagement with autopilot and every single scenario and extending that back to Tesla to train increasingly more capable AI.

Elon has put millions of cars on the road that are actually capturing every single engagement and disengagement with autopilot and every single scenario and extending that back to Tesla to train increasingly more capable AI.

And if you look at how full self-driving got more capable over the years, it's directly relational to it becoming more and more learning from the data instead of rule-based and more and more cars on the road. And so to me, Elon has won full self-driving. It's inevitable that

And if you look at how full self-driving got more capable over the years, it's directly relational to it becoming more and more learning from the data instead of rule-based and more and more cars on the road. And so to me, Elon has won full self-driving. It's inevitable that

And if you look at how full self-driving got more capable over the years, it's directly relational to it becoming more and more learning from the data instead of rule-based and more and more cars on the road. And so to me, Elon has won full self-driving. It's inevitable that

The most capable AI for full self-driving is coming out of Tesla because they've been gathering and building up this data set. And he needs to gather data because it's non-simulatable. Now, this is the head fake behind Poolside. You think about AlphaGo being deterministic. You think about the other end, the real world being non-deterministic. Where does code sit?

The most capable AI for full self-driving is coming out of Tesla because they've been gathering and building up this data set. And he needs to gather data because it's non-simulatable. Now, this is the head fake behind Poolside. You think about AlphaGo being deterministic. You think about the other end, the real world being non-deterministic. Where does code sit?

The most capable AI for full self-driving is coming out of Tesla because they've been gathering and building up this data set. And he needs to gather data because it's non-simulatable. Now, this is the head fake behind Poolside. You think about AlphaGo being deterministic. You think about the other end, the real world being non-deterministic. Where does code sit?

Code sits a lot closer to being deterministic. Follows a set of rules. Every time it runs, it runs in exactly the same way. And so this is what we call execution feedback. What we're really known for is our work in reinforcement learning from code execution feedback.

Code sits a lot closer to being deterministic. Follows a set of rules. Every time it runs, it runs in exactly the same way. And so this is what we call execution feedback. What we're really known for is our work in reinforcement learning from code execution feedback.

Code sits a lot closer to being deterministic. Follows a set of rules. Every time it runs, it runs in exactly the same way. And so this is what we call execution feedback. What we're really known for is our work in reinforcement learning from code execution feedback.

It's the way where we then take a model that we've trained from the ground up, we put it in an environment, say it's an environment with 130,000 real world code bases, several orders of magnitude, the largest environment in the world, And we sent the model off to explore different solutions to sets of tasks and learn from when it passes the tests versus when it doesn't.

It's the way where we then take a model that we've trained from the ground up, we put it in an environment, say it's an environment with 130,000 real world code bases, several orders of magnitude, the largest environment in the world, And we sent the model off to explore different solutions to sets of tasks and learn from when it passes the tests versus when it doesn't.

It's the way where we then take a model that we've trained from the ground up, we put it in an environment, say it's an environment with 130,000 real world code bases, several orders of magnitude, the largest environment in the world, And we sent the model off to explore different solutions to sets of tasks and learn from when it passes the tests versus when it doesn't.