Raquel Urtasun, scientistfounder and CEO of autonomous vehicle technology company Waabi launched her company in June 2021, a time when the AV industry seemed to be consolidating.
Urtasun and her team of 40 in Toronto and California came out of the gate with an $83.5 million raise from a range of high-profile investors including Uber, Aurora and Khosla Ventures.
Waabi uses an AI-first approach to commercialize autonomous freight faster and more efficiently than its competitors, Urtasun told Marketingwithanoy. As a professor in the Department of Computer Science at the University of Toronto, co-founder of the Vector Institute for AI, and former chief scientist at Uber ATG, the self-driving unit that Uber sold to Aurora, she has gained some insight into both the industry and the science support it. After all, despite consolidation and gains from a few big players, no one really got it yet.
So what does an AI-first approach really look like?
In February 2022, Waabi launched Waabi World, a high-fidelity closed-loop simulator that not only virtually tests Waabi’s self-driving software, but also teaches him to drive a car. Waabi World automatically builds the digital twin of the world from data, performs near-real-time sensor simulation, creates scenarios to stress-test the Waabi Driver and teaches the driver to learn from its mistakes without human intervention. This, Urtasun said, saves countless hours of human labor to train the Waabi driver both in simulation and on the road.
The whole of Waabi World is powered by AI in a way that other companies’ simulators are not, as it relies more on deep neural networks, AI algorithms that allow the computer to learn by using a series of connected networks to create patterns in data. to identify . Historically, developers haven’t been able to figure out the hows and whys behind AI’s decision-making when using deep neural networks, which is very important when putting self-driving vehicles on public roads, so they have fallen back on machine learning and rules. based algorithms to fit into a wider system.
Urtasun said she has found a way to solve the problem of the “black box” effect behind deep neural networks by combining them with probabilistic inferences and complex optimization. The result? The developer can trace back the AI system decision-making process and incorporate prior knowledge so that they don’t have to relearn the AI system from scratch.
We sat down with Urtasun to discuss the pros and cons of starting a business after working for a larger company, the surprises of a founder, and why freight will be the first AV industry to launch on a large scale. scale commercializes.
The following interview, part of an ongoing series featuring founders building transportation companies, has been edited for length and clarity.
After working for Uber and being an academic, what are your findings on what it’s like to be a founder for the first time?
When I decided to start Waabi, I didn’t necessarily know what it meant to be a founder. I’ve worked in the industry and in this field and all that, but as a founder you have to wear so many hats and so much happens. I did not expect that. And Waabi is very different now than it was to begin with, so there’s something that surprised me.
But it was an incredible ride. I have to say there’s nothing better than building what you really believe in with a team you love to work with. There is nothing that cannot be done.
You wear a lot of hats now, but what was it like working under someone at Uber and not being in control of the whole show?
I was part of the executive team at Uber, so I had a lot of impact and, you know, a lot of say in a lot of things. But it’s different when you’re building – and this isn’t just Uber, this is in general. When you’re in a big company with 1000+ people going in one direction, even if you all agree that you need to steer towards something else, it’s so hard and slow to actually do this process.
From that point of view, it’s very exciting to be in a startup, which is much more dynamic, but it’s not Uber versus not Uber. I think any big company would be similar. But I had a great time with Uber. I’ve learned so many things and really discovered what it means to really be a part of a big problem, and I’ve prepared really really well for what I’m doing today.