I'm, I'm very excited to welcome our next guest. This is Raquel artisan. She is the founder and CEO of a brand new company that just came out of stealth yesterday called Wahby. That's taking an AI first approach to solving self driving vehicle technology. quick background on Raquel, she is a full professor is kind of considered an AI pioneer, but also was leading the r&d efforts as a chief scientist over at Uber ATG. So and there's there's a lot of other things that she's been doing. But um, rock. Hello, thanks so much for joining us today. Thanks, good to be here today with you. Well, let's dig right into to this company. Um, and actually, to get the timeline down, you Uber ATG was acquired by Aurora. And that happened in December was announced in December, and now we are here like, you know, six months later, and you have this new startup? Did you start working on it? When did you start working on this company? And tell us a little bit about what you're actually trying to do here?
Yeah, so I guess, you know, I left, you know, we're never going to be there were three months ago. And, you know, to start this new company, where we, with the idea of having, you know, different way of solving inventors or songwriting. And, you know, the, basically, this is a combination of, you know, my 20 years career in AI as well as more than 10 years in self driving. And, you know, thinking about using a new company, something that was always in my head. And the more that, you know, I was in the industry, the more that I started thinking that, you know, going away from the traditional approach and trying to have a diverse different view of sources, it was actually the way to go. So that's why I decided to do this company.
Well, so you've been working on AI for a very long time, and then working to apply it towards self driving vehicles for some time as well. Why do you think not as Uber, but the rest of the industry hasn't white? unlocked it, we see really small commercial scale, way, Mo is one example. But it's pretty limited on it is growing. But why haven't we seen sort of bigger commercial scale deployments of, you know, Robo taxis, for example? What's the limiting factor there?
Yeah, so we, we've seen that you're seeing meaningful progress right over, you know, the last 17 years, right, since the DARPA challenge. But as you, as you mentioned, there's the commercial deployment is, you know, very simple, and it's manipulation domains. And, you know, one of the reasons for this, and I think it's the utilization of what they call a traditional approach, that has not really utilize the full power of AI, in order to, you know, to server such a difficult task, such as driving, and, you know, realizing complex, you know, manual tuning. So this makes, you know, scaling of this technology, and particularly finding the long tails of the scenarios and things that might happen when you drive particularly difficult.
I want to talk a little bit about the approach we're using, because it's a bit of a combination, and one of the one of what you use is deep nets, which you can, it's a little bit complicated, but maybe you can explain that briefly. But for me, my understanding is that deep nets, typically are used in a limited way. And then rules based AI is is generally how the approaches for self driving vehicle development. Um, and the problem is, is because using deep neural nets, there's the black box effect, which is basically not being able to verify or validate why the system did what it did. So, how are you solving for that?
Yeah, and it gives you actually, you know, define it really well. So, on one side, you have this more traditional approach, where AI is used to solve small problems within this more complex system. And on the other hand, you have these AI black box approach. So wealthy is doing is doing something that really takes advantage of these two approaches, but without inherently The disadvantages. So we have a new generation of power grids, that in particular, combine deep learning with probabilistic inference and complex optimization and provide us three particular characteristics that are important. So is into entry level so the system can actually learn the entire software stack from data it produces interpretable representations so we can explain why the system decides to do a specific maneuver. And at the same time, the AI system is able to do very complex reasoning for this probabilistic inference on complex optimization. So you can think of it as reading the best of the best of both worlds.
And then there's a simulator piece to this as well. But to be clear, you're still doing on road testing? Is that still an essential piece, which is kind of become a cornerstone for a lot of startups to be able to develop and scale up? self driving vehicles?
Yeah, yeah. Great question. So we have a breakthrough simulator, which is a closed loop simulator that enable us to test the entire software stack. So as a consequence, we can do, you know, we can simulate all the Generate scenarios as well as you know, all the edge cases, and we can test at the scale, as well as train the AI system in simulation. However, it's always important that you also test the system in the real world is in the simulator to understand whether there is any gaps between, you know, how it performs on simulation versus the real world. Now, these new simulator has super, super high fidelity is real time, and enabled us really to really bridge that gap. And, you know, when we say that works well in the simulation, we can mathematically say that he will actually work similarly, in the real world, which is, you know, Game Changer that really enabled us to reduce the amount of miles that we need to drive on the road, and therefore, you know, develop this technology in a safer manner.
So your initial focus is going to be, I believe, law, logistics, but specifically long haul trucking. And it's interesting because one of your investors is Aurora. Also, separately, Uber in Aurora is also developing a self driving stack, and they're going after long haul trucking. So how are you going to be working together with them at all? Is there any sort of potential competition or conflict there?
Yeah, so Oded, I said, financial minority investor in body. So we don't have any other partnerships to announce at this point video NaVi on this, you know, with the with the, you know, we all want our solar cells live in and lawful tracking makes a lot of sense, from an application perspective. Why because of the driver shortage that, you know, we have as well as the need for safety. And at the same time, from a technological perspective, perspective, technical perspective, driving in highways is simpler than driving, you know, in our cities, are there still a pretty complex problem to solve?
So are your is your plan, though, then to work with multiple other companies, not just maybe potentially trucking companies, but even other self driving full stack companies like Aurora or who I guess who what type of partner Are you going to have?
Yeah, so we plan to have a very open partnership friendly approach. And in particular, in working with, for example, OEMs, hardware providers, sensor providers, compute providers, because there has been amazing developments in the r&d space on those fronts. And we want to capitalize on that. On those vectors.
So we were talking earlier, actually, before, before we went live, and we're talking about consolidation, which we've seen a ton of, um, and there's been this sort of idea that, you know, there's only gonna be a few players left. And that just the whole new wave of startups that that's over so did that cross your mind at all, when you were about to launch this? And do you think that there's still room for even more startups that are trying to solve this problem and really, scale commercially?
Yeah, so that has been, you know, a lot of written recently about consolidation. And, you know, when you have a very capital intensive approach, indexes, consolidation, but not everything is good. And with consolidation, I one of the things is that with the cross pollination of people and ideas, you know, the industry sort of going in one approach, there was always a dragon. And, you know, we need a diversity of approaches to solve something so difficult as what we're trying to do here. So I think is, you know, the perfect time actually for new companies to come to life and in particular, you know, with the luxury of starting with an angle or a view on the technology front 2021 with no baggage of anything that looking before. So I think it's you know, an excellent time.
So, thank you so much, Raquel. We're all out of time, but I really appreciate you introducing us to Bobby and can't wait to See what you do next. My pleasure. Thanks again for having me here.