YouSaid · the spoken record

Peter Chen

lines on the record: 48
first: 2024-01-25
most recent: 2024-01-25
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“For people that study reinforcement learning, we call it behavior cloning, which means you're just asking the AI to clone the behavior of another agent. And that is like one of the most primitive way possible to train this type of systems. Because if you're just mimicking something, like there's a natural ceiling on how good you can get on that. And then there's just so many other proven toolboxes that we have not deployed yet that I would say like progress is guaranteed in everything that we have seen so far. And I'm so excited about that. And I'm also super excited about the open source movement continuing in the AI world, like where a lot of these events make available to a broad set of communities that can continue to build on and experiment with it. And so I think it will continue to be a very exciting year of AI progress.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“I think the same kind of events that we have seen in last year, like we would see at least the same order of magnitude of them in the coming year. If you look really behind all these events in large language models, image generations, they are still using relatively primitive technology. So if you're, especially large language models, they are mostly still trained just on next token prediction, like which.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“But that is very different from when you say, well, what if we hook up an arbitrarily expressive agent into a home robot that also has, like, how do you limit that to be safe is much harder, like just similar to if you just hook up a language agent to give it arbitrary Python co-execution capability and arbitrary ability to access the internet, it just becomes very difficult to say, well, how can you make sure it doesn't do anything dangerous? And that's where the alignment problem comes in, then that's where there's a lot of this good safety research comes in. But we have a simpler carve out, like at least for the near term in this kind of industrial applications.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“We have a simple carve out to this question because we focus on industrial applications. And well, all industrial robots have a set of safety rules that they need to conform to because it's not just AI can be dangerous. Like manual programming can be dangerous. You could program a robot to do dangerous things already. And so there's a really robust sets of rules around you have to put safety cages around robots. And if you don't have safety cages, you need to have certain kinds of certified controller that makes sure robot doesn't do anything that's dangerous to the surrounding equipment people. And so from that sense, like because we're just following the same rules, like any kinds of robots that we build and deploy.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“If I have to guess, it probably would be a home robot that don't involve much manipulation. So think of it as like a home robot that might be like a roombait. You can follow you around, like you can talk to it. So it has that navigation of movement aspects of it, but not necessarily the manipulation aspects of it, like not actually manipulating the physical world around it. I think that would be the most technologically feasible version. So think of it as similar to Amazon's astral robot, like this kilt robot that has two wheels that can follow you around and someone calls it, it can go there. And so I think that type of form factor would probably be like when we will see it earlier.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“I don't think it would be fully Lightshouse and no human, at least in the near future. But I think of it as would be very robotics augmented. So think of one person would be able to oversee 10, 20, if 30 robots. So instead of one person have to manually do all those work, you actually work with a fleet of robots. So think of kind of a. Physical co pilot type of setup, like you just get this large amplification of 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 expression of AI would probably be true not just for robotics, but many other fields of AI as well.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“is being used twenty four seven as opposed to. Like a home robot that might only be used two hours a week. That's a very different ROI from the hardware piece that you need to put in it.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“And this is where one of this concept that we talked about earlier comes in, like you really need large amount of high quality data to densely cover this robotic fields that you want. And so that would be what I think about as the model side of the chatGPD moment for robotics. And then you also need to think about the hardware portion of it, right? Like even if you have a robot AI that is very smart, unless you are just interacting with this robot AI in some metaverse digital 3D world, you still need some hardware body for robots. And before humanoids are fully widespread, I think we will see that the chat GPT of Moment for Robotics being articulated in the industrial settings earlier than in the commercial settings, like because those are the places that can actually justify the hardware investments because the hardware”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“The chatGPT moment for robots, you want AI that is as general as ChatGPT, like so you would be able to throw a robot into any arbitrary new scenarios and it would be cool to learn how to deal with it very quickly. But in addition to that, which is kind of like what ChatGPT allow people to experience is you can ask arbitrary problems and then they can solve to some degree to you. So you want the same kind of generality. But in addition to that, what you also need is really high reliability because you really don't want robots that only succeeds in the tasks that you ask it to do 70% of the time. And then there's like, there might be 30% really catastrophic outcomes that come out. Come with it. So I would say the bar for the chatGPT moment for robotics is higher. Like you need to solve the generality, which is the same kind of problem, but you need to solve it with high level reliability.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“And that being said, we believe in role model. We believe in foundation models that can learn from the real world and you can simulate new scenarios of what would happen if you do things differently. But I think of that as like different from the classical simulation that I referred to earlier, which is program-based and you are just hard coding the rules of reality and then building agents that learn from the mechanical interpretations of the rule of realities that you encoded in your simulator.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Like manipulation. Like, if you never contact something, that's also a big problem because then you actually don't do any work. And whenever you involve contact, simulation of those things become very, very difficult, like items that can deform, like the contact dynamics is incredibly challenging. And so those are wear 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 I mentioned earlier that a typical customers that we serve may have 100,000 distinct objects in a warehouse. So if you want to fully recreate that in your simulation, that is actually more work than just learning a system that can deal with the real world. So the vacation problem. In order to specify the real world in your simulation, that actually might require more data or more work or whatnot.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“So when we think about simulation, it's actually somewhat different for different kinds of autonomy domain. So when you think about simulation in self-driving car, 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 Multi agent behaviors like the avoidance of contact. But if you think about”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, it's like the architectural has changed maybe five times already. It has gone through significant transformation every year. I don't think you can be married to any single specific architecture in a field that is moving so quickly. But there is one unique bet that we are placing. That one unique bet is We believe the future of robotics would be built by whoever that has most robotics data. And essentially the whole company is built around that thesis. And you can say what is an alternative belief? Like an alternative belief would be, can we just solely rely on simulation? Like we actually don't need money.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Like it's just like whether you can get data of that domain and if you can get it like then you can for sure that you can fit it.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Kind of like need to rely on this strong form of scaling law. But you kind of don't need that when you are in a more restricted domain like robotics because like you actually could have so much data coverage that your test scenarios are just part of your training scenario so to some degree like we actually don't need to rely on this strong form of scaling law to hold for us to build really valuable technology out of it and so I expect like something similar like that would happen like would follow the similar trend that you see in the language world but at the same time like we don't we don't require it like we know that like as you get more customers as you get more data like these systems would get better and especially if you have targeted data coverage for specific domains for specific customers like they would be guaranteed to get better like so to some degree like we whether you believe like robotics can scale or not it's it's a simple”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“So I would say we see some element of it, but it is something that we rely less on. And here's like where I think there was a really interesting, crucial distinction between a full general model that is designed to solve everything in the world to what I think of as a domain specific foundation model, like in our case, like solving robotic manipulations. So in a full general model, like for example like GPT 5 that you wanted to solve everything in the world, then you have this problem of essentially out of domain generalization. Like when we say as you scale it up, like do you get something that is much smarter out of it? Like we are not saying whether GPT-5 would fit the training data better. Like we are saying like if your gif is an area that is completely outside of training data, like how well does it work? And that is where like.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Question that you're asking is probably the more, 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 is modeled as orders of magnitude smarter in some loose definition of it? Which is kind of the thing that we see from the large language model world, like when you go from GPT three to Jupiter when you go from claw one to claw two like you kind of like see this step change improvement in reliability in generalization that you get from it so I assume that's like probably what you're what you're asking”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“So I would say the most technical definition of scaling law does apply, and we have seen it apply in this domain. And it's somewhat not surprising because if you think about 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 flow at it, you get lower loss function, like training loss function out of it. And we have seen this play out across so many different domains, like more than just language model, that this not surprising. I think”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Action and outcome pairs that exist in the world. Like the closest thing to that is probably on the YouTube, you have human doing those things. But then there's a research question of like, well, can you have a robot that learns from just watching a human does it and you don't actually fully know like how hard does a human press on a tomato or like how you precisely style something? So you're still lacking a good amount of the data that completes this feedback loop.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Understand effects of your own actions. And a large part of this is just because there were not a lot of robots that are doing interesting things in the world. And so there are not a lot of data sets that are in the format of robot does something and then you know the outcome of it. Is this a good way to pick up something? Like if I move an item too quickly, like would it damage it? If I press like, for example, a tomato, like what is the force that is appropriate that is possible? Like you don't have a lot of these kind of”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“There's no precise understanding of the physical world that's naturally occurring on the internet. So that's like one of the first thing that you find kind of the departure of robotics foundation models from other general multimodal foundation models is this idea of precision. Like you now actually need to understand things to a much higher level of precision that don't otherwise exist in this kind of data set. And so that's like one big thing. And then another really big thing is this ability to”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so there were a couple aspects of it. So, like, obviously this. naturally occurring text and image pair data they are typically about high level concepts like they're typically not about something that is very precise like so for example like when i presented an apple to you like you don't typically describe like the precise shape of the apple right like is is this like a very round shaped apple is this like a very full apple like you might use some high level concept to describe it but there's really nothing that describe it say down to submillimeter level precision, which is kind of like the level of like precise understanding that you need to interact with the real world. You don't just say, oh, there's kind of an apple there, but there might be like up to a two centimeter difference in understanding of where the boundary of that apple is and how should I do it. And so here's like the first dimension of like things that is missing, which is really no precise grounding.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Like these type of multimodal language models already have an understanding of those grounded concepts. So where does it get those grounding from? Like it gets those grounding from essentially the Image and taxpayers that happen on the internet, right? Like if you look at an Instagram image, it might have a set of captions along with it. So we can train this kind of multimodal models with a combination of those data, right? Like after you have seen enough of the Instagram image of an Apple and enough of people tag them as Apple, then after you have trained on a large amount of such data, you start to get that grounding. You start to pick up that associations. So that's like, I would say outside of robotics, like how typically grounding happens and how you typically get this kind of multimodal understanding concepts, but actually has an understanding of how it gets associated with the real physical world typically manifested through an image of the real world.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Okay, like something that is apple could be delicious. And if I ask for a delicious thing, you can say Apple is a delicious thing. But that is very symbolic. Like that has no actual grounding in our physical world. Like, what does an Apple look like if I give you an image of an Apple? Can you recognize it? And can you recognize the different other physical properties of an apple? And so the first thing that you'll want to do is grounding is to ground all of these symbolic abstract concepts into something that is real, that is physical. And there were actually a lot of advances of this, like even outside of robotics that's happening already. Like we have a lot of multimodal model that exists in the world. Like if you go to GPD4V, like you actually could give an image and then it can answer something for you intelligently about what's in the image. GPT-4v has ground.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so grounding is this interesting idea of Like, if you just read the text on the internet, you learn a lot about abstract concepts. But they could be purely symbolic. You might read Apple is delicious. Okay, I have this association that.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so covariance about 200 people company, and we are extremely international. I would say roughly half of our customers are in Europe, half of our customers in North America. And we have robots deploy across three continents at this point and more than 10 countries. And what is really remarkable, all of these customers, all of these different robots are networked together. It's one single foundation model and everything that they learn, come back and make this central model better. And our customers are typically large retailers, large e-commerce brands, and essentially anyone that runs Large distribution centers or a network of distribution centers like would likely choose covariant as their model that power the physical world.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“We really kind of wait for the human noise to be commercially and also technologically available. Because when that platform is available, that is really the best mechanism for us to deploy covariant brain this foundation models to go to more places more quickly. Fortunately, we are not relying on it even by using the existing industrial robots hardwares, we can build a scaling business, we can continue to bootstrap and build incrementally more capable models. But when it comes, that would be a really big acceleration for us.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Which is very different from a warehouse for a meal prep company. And across all of these, you actually have very different manipulation skills that you need and very different kinds of data that you can collect to train the foundation model and also very different large markets that we can tap into. But we are very intentional in how we build the models in a way that make sure it's generalizable and so you can actually extend into new domain. And one more comment on the humanoid question. I think that would be one of the most exciting events in robotics is to make human noid as a form factor possible because our world is designed around human bodies. So humanoid is the universal hardware form factor that can be dropped into any place in our world. And so we really cannot.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, a couple, like starting at a very highest level, right? When we think about the covariant brain, this foundation model that we are building, we are not building it just for warehouse operations. We are not just building it for pick employees applications within warehouses. So definitely everything that you're talking about, it's very exciting to us. So both applications outside of warehouses, as well as applications to newer hardware form factors like humanoid robots. And so that definitely is the long-term path for us. I would say in the very immediate future, as a company, we are focused in the manipulation space of warehouses just because there is so much demand and there are so many different kinds of use cases that exist in the warehouse domain already. Because a warehouse for apparel company is very different from a warehouse for a cosmetics company.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Human can identify an item without even scanning the buckle because you can read the packaging, like you can infer what is in there. And that is also something that AI can help. And so while it is true that there are some steps of the problems that can be solved by more traditional mechanical and robotic systems, what we have found is that once you have a very flexible AI, you can actually rethink.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“The identification and routing is typically more considered more solve problem than grasping because if you there are other more Mechanical way to solve those problems. Like you can design a piece of conveyor that if you always put an item to the same place, then you can route it to a design location. And so that becomes mostly the mechanical problem. And anything that is a mechanical problem is typically more solved. And so that is very true. Like I would say like out of this grasping identification and routing, like definitely the grasping part involves more AI. But as we build more events AI and bring it into a more traditional field like robotics, like what we actually find is that even in the identification step, even in the routing steps, there are a lot of ways that AI can make more traditional mechanical systems smarter, right? Like, for example, like a classic way to do identification is through scanning the barcode. But where's the barco? Like, how do you scan the barco? Well, that's actually something that AI can inform it, right? And like oftentimes like”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“A new generation of iPhone, right? And then like a robot would be sitting there and picking up one iPhone and say, oh, this one should go to Sarah and this one should go to Peter. If you think about like what that robot needs to do, like the robot needs to have an incredibly great ability to grasp items without damaging it and have the accurate ability to identify what is the item and then route them to the appropriate customer, like in this case, like either you or me. And so put wall, you can think of it as a sortation mechanism. You can think of it as a physical router that exists in the world. So instead of thinking about network router that sends digital packets around, like you can think about put wall as a physical router that sends goods to different places.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so a common use case that we have for our customers is what we typically call a put wall use case. A put wall is a term that is used in e-commerce fulfillment, which is when you click a button to buy something online and then the box show up to your door. And you might wonder, like, well, how is that done? Well, there's a complex set of operations that's happening in the background. And the pit wall is one step of that. And this step is typically used to sort a mix of customer orders to different customers, right? Like, let's say both you and I have order.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Few and few people want to do this kind of warehouse jobs like drive an hour and a half to the suburb and then have to work through the midnight. Like these are not the kind of jobs that people want to do. And our customers have extremely high turnover rate, like an average warehouse that we serve have typically more than 100% year-over-year turnover rate. And so these are the type of places that We have an extreme shortage of people that want to do those kind of jobs. And yet, at the same time, there are no prior robots that can solve pick, pack, ship in warehouses, because traditional robots are just machines that do the motions that you're programmed to do repeatedly. But here you actually need systems that's actually adaptive and do it at a very high level of reliability.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Diversity of environments, changes in the environments, and being able to understand what's around it and make intelligent decisions and actions to handle a diverse set of circumstances. And we think this would enable really a whole different wave of robotics that is not how robotics is used today. And for covariance specifically, we are starting from logistics and warehouses as an industry that we focus on. So this is think of it as the explosive demand that is driven by the growth of e-commerce. There's a lot of complexities that's been injected into the logistics and supply chain. And at the same time, coupling that with demographics change, changing immigration landscape makes”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Carefully into a box that gets shipped to you. That's very different kinds of diversity that we're talking about. And so when we think about building AI for robots, when we think about building foundation models for robots, we're thinking about really lifting robotics as a category from this former category of just being able to do repeated things to this category of really being able to handle”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Of manually programming this robot, you could just have an AI that program that robot. We're not talking about that. Like we're really talking about 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. A good way to visualize this is on one hand think about a robot, for example, in the Tesla factory that is handling a car body. Okay, this is a very incredible feat of engineering that can move multi-ton object very fast, very precisely, but it's just doing the same thing again and again. And then imagine another robot in a e-commerce warehouse that has hundreds of thousands of unique items that it has to distinguish, pick up and pack.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Robots are extremely common nowadays. So, what we typically work on are robotic arms. So think of these as six axes, seven axes, robotic arms that can do very flexible movements. They are super precise. They are super fast and super doable and very cheap. Lots of factories around the world have robots. 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 are not trying to make the existing dumb robot use cases better, right? Like we're not trying to say, oh, instead.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Roadmapping exercise that you have to do in autonomy. You cannot just go straight to the full general physical AGI at the beginning. Like you have to build something that represents justifiable INB spend as well as timeline that you can justify. But that allows you to build something that is valuable, that you can ship to customers. And from that process, you get more data, you get more learning that then in turn allow you to build the next generation model. So we think of it as very much. Just be in a philosophical debate of how we build this super, super general thing that is very far in the future.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, like this definitely needs to be an incremental approach. Like you have to just find the right sequence of what is the technology events that I want to build now that enable enough of a product that I can deliver, which then in turn allow you to build more capable models that then in turn like a larger service of area. And this is like, I mean, we have seen this play out in the non robotics world as well, right? Like if we think about open AI entropic cohere, a lot of these big language models players, like the models that they have are not fully general language models yet, right? But they are good enough that can solve a large section of problems that is worth productionizing them, getting commercial value out of it, which then in turn allow you to build the next incremental better system. And I think of it as the same.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Creating value for the customers, like customer use their products and those data that they collect can allow them to build much more capable models and AI. And so why we left OpenAI and academia to start covariant is very much this belief that in order to build foundation models for robots, you have to have a lot of data and in order to have a lot of data, you have to build autonomously working systems for customers. And the only way to do that is to build a company to serve those customers.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“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. And so in order to build this foundation models for robotics, you really have to build a company that can collect data to do it. And the only way to collect enough data is to build fleets of robots that are actually creating value for customers so that you can collect those data in production. Because even if you try to scale up data collection in a lab environment, there's a limit on how much you can do that. In that perspective, we strongly believe in the Tesla approach, like where they have the most self-driving car data, because they ship a grape cars that people want to drive and a good enough entry-level autopilot that people are willing to use it.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“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.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“It's a really good question. I mean, there were a lot of companies that are founded by private 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 a level of maturity that we should start to commercialize it in the real world. That was kind of knocked the journey of covariant. When we started covariant, there was not AI that was good enough to make robots do useful things commercially. 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 2017 to start covariant was the future of AI is going to be the future of foundation models. These models that are truly multitask learn from large amount of data and as such be more generalizable, they can solve new tasks more.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Utilize the advances in AI, but also we think of it as a way to also propel AI forward. Like this is where you get the grounded data. This is where you get that embodied data of not just AI that is trained on browsing the internet, but AI that is trained with physical interactions with the world. And so we also believe robotics would be a key way to advance AI.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“To make decisions by having them make trials and errors and learn from the better decisions and do less of the worst decisions. And robotics is just such a great combination of these fields in order to be really capable robots. They need to really understand the world in a very, very robust way. And they are not just passive agents that just understand text or what's in an image. They actually need to take actions in the real world and the consequences do matter. And so we found robotics to be such a great way to”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, when I was working on research at both UC Berkeley as part of my PhD and at OpenAI, there were two topics that were particularly exciting to me. One topic is, as you have introduced, unsupervised learning, like how can we build models that learn from vast amount of data. And we're now more colloquially known this as generative AI because like we train these large models on large amount of text, images, videos, and you learn from them in an unsupervised manner. That topic has always been very interesting to me because if you want to train very capable AIs, you want to have a lot of data and where you can get a lot of data is through this kind of unsupervised data set. And then the second topic that was really interesting to me was reinforcement learning. Like it's not just building models that understand, but building models that can make decisions. And reinforcement learning teach these models.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT
“Thanks, Sarah. It's great to be here. There are many exciting reasons to be here. One is I have been a frequent listener of the podcast, and the second one is just because of the name, like I just have to be on this show. So it's great to be here.”
2024-01-25 · No Priors · Building the factories of the future with Covariant CEO Peter Chen · IDENTIFIED FROM THE TRANSCRIPT