The Exchanges

Q That makes sense. You were at places that are great places to do research. Why did you decide to start a commercial company?

A It's a really good question. Um, I mean, there are a lot of companies that are funded by prior PhDs that are kind of the classic journey of there's a technology that was built in a lab environment, and it got to enough at level of maturity that, oh, we should start to commercialize it in the real world. That was kind of not the journey of CoVariant. Like when we started CoVariant, there was not AI that was good enough to make robots do Useful things commercially. Uh, and so it was not a classic journey of technology developed in academia and then transition to a commercial landscape. The key insight that we had at that time when we left OpenAI in, to start CoVariant was the future of AI is going to be the future of foundation models. These models that are truly multi-task, learn from large amount of data, And as such be more generalizable. They can solve new tasks more easily, and are also more capable at every single one of the tasks because of the transfer that you get across tasks. We just had early conviction that there was the path to build AI, and that is also going to be true for the physical world, for robotics. But there's one big problem, which is you have no data set to build robotics foundation model. Like there's no data set that you can build This AI that understands the physical world and take actions in the physical world. Um, and so in order to build this found…

AI assessment note: “in order to build this foundation models for robotics, you really have to build a company”

Q Then I think the right way to start actually be to ground the conversation and kind of like the application landscape. Can you walk us through the sort of limitations of robotics in warehousing and manufacturing that are commonplace right now and how much intelligence these robots have?

A Robots are extremely common nowadays. Like, so what we typically work on are robotic arms. So think of these as six axes, seven axes, um, robotic arms that can do very flexible movements. They are super precise. They're super fast and super doable and very cheap. Lots of factories around the world have robots. Um, but the challenge is like, 99 plus percent of the robots that are deployed in the world, Are dumb robots. Like these robots are pre-programmed to do the same thing again and again, and they don't really have any kinds of intelligence that can adapt to new circumstances, communicate with people and change what they do on the flight. And so think of robotics that exist today are extremely rigid. And so really the problem that we are solving is we're not trying to make the existing Dumb robot use cases better, right? Like we're not trying to say, oh, instead of, ah, manually programming this robot, you could just have an AI that, that program that robot. We're not talking about that. Like we're really talking about like opening up a couple orders of magnitude more use cases where the robots actually need to be smart. Like they need to adapt what they do based on the scenario that is presented to them, right? So like the w a good way to visualize this is on one hand, like think about A robot, for example, in a Tesla factory that is handling a car body. Like, okay, this is…

AI assessment note: “99 plus percent of the robots that are deployed in the world, Are dumb robots”

Q There are lots of smart Tesla and ex Tesla people where Tesla has been a, I guess, big proponent of high quality simulation, including for, um, you know, training data generation, right? Where are the gaps or why do you believe that's, that's insufficient?

A So when we think about simulation, it's actually somewhat different for different kinds of autonomy domain. Like, so when you think about simulation in self-driving car, like we are really mostly thinking about systems that hopefully don't physically interact with each other, right? Like if two cars get in contact with each other, that's a really terrible thing, right? And so the simulation there is more about simulation of avoidance, multi-agent behaviors, like avoidance of contact. But if you think about like Like manipulation. Like if you never contact something, that's also a big problem. Like, because like, then you actually don't do any work. Um, and whenever you involve contact, simulation of those things become very, very difficult. Like items that can deform, like, like the contact dynamics is incredibly challenging. And so those are where simulation becomes very difficult. Like it's when it involves contact complex dynamics. And then there's the second thing that makes simulation difficult is like, I mentioned earlier that a typical customers that we serve, like, may have a 100,000 distinct objects in a warehouse. Like, so, like, if you want to fully recreate that in your simulation, like, that is actually more work than just learning a system that can deal with, um, the real world. Like, so the vacation problem, like, in order to specify the real world in your simula…

AI assessment note: “whenever you involve contact, simulation of those things become very, very difficult”

Q Robotics AI work, it triggers a lot of concern around safety and both like the short term practical sense and in sort of the AGI breaking into the real world sense. How do you think about safety at CoVariant?

A Uh, we have a simple carve out to this question, like, because we focus on industrial applications, uh, and well, all industrial robots, like, have a, uh, uh, set of safety rules that they need to conform to, like, because it's not just AI can be dangerous, like, manual programming can be dangerous, like, you could make, you could program a robot to do dangerous things already, and so there's a really robust set of rules around, you have to put Safety cages around robots. Uh, and if you have, you don't have safety cages, you would need to have certain kinds of certified controller that make sure robot doesn't do anything that's dangerous, um, to the surrounding equipments, people. And so from that sense, like, because we're just following the same, um, rules, like any kinds of robots that we build and deploy are by definition safe, um, or by construction safe. But that is very different from like when you say, well, what if we hook up Like an arbitrarily expressive agent into a home robot that also has, um, like how do you limit that to be safe? It's much harder. Like just similar to like, if you just hook up a language agent to give it arbitrary Python code execution capability and arbitrary ability to access the internet, it just becomes very difficult to say, well, how can you make sure like it doesn't do anything dangerous? And, and that's where the alignment problem comes …

AI assessment note: “because we focus on industrial applications... all industrial robots, like, have a set of safety rules”

Q What does the, uh, like, warehouse or factory or, um, logistics center of the future look like? Like lights out, no humans?

A I don't think it would be fully lights out and no human, at least in the near future, but I think of it as would be very robotics augmented. Like, so, um, think of one person would be able to oversee 10, 20, 30 robots. Like, so, like, like, Instead of like one person have to manually do all those work, like you actually work with a fleet of robots. So think of it kind of as a physical co-pilot type of setup. Like you just get this like large amplification of like what a one person can do, but most likely it wouldn't be completely lights out. Like you will still have people there. I think this form of, um, expression of AI, like would probably be true, not just for robotics, but many other fields of AI as well.

AI assessment note: “I don't think it would be fully lights out and no human”

Q Do you have some sense of like how or if scaling laws apply for you? Like, do you know how many robots you need to deploy or how much data you need to go collect to get to certain levels of improvement? Or can you try to predict it now?

A So I would say the most technical definition of scaling law, um, does apply and we have seen it apply, uh, in this domain. And it's somewhat not surprising because like, like if you think about like the scaling law in the most technical sense, which is if you scale up data and you scale up your model capacity and you scale up the compute that you throw at it, you'll get lower loss function, like training loss function, um, out of it. And we have seen this play out of course. So many different domains, like more than just language model that is not surprising. Um, I think the question that you're asking is probably the more, um, not the most technical definition of scaling law, but it's the general definition of scaling law, which is as you scale those up, would you get emerging capabilities out of it? Like, would you kind of like get something that's just like modeled as orders of magnitude smarter in some loose definition of it? Like, which is kind of the thing that we see from the large language model world, like when you go from GPT-III to GPT-IV, when you go from Cloud-I to Cloud-II, like you kind of like see this step change, improvement in reliability in generalization, um, that you get from it. So I assume that's like probably what you're, what you're asking.

AI assessment note: “the most technical definition of scaling law, um, does apply and we have seen it”