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Self-driving startup Waabi just managed to net $83.5M — how?

todayJune 12, 2021 1

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It is not the best of times for self-driving car startups. The past year has seen large tech companies acquire startups that were running out of cash and ride-hailing companies shutter costly self-driving car projects with no prospect of becoming production-ready anytime soon.

Yet, in the midst of this downturn, Waabi, a Toronto-based self-driving car startup, has just come out of stealth with an insane amount of $83.5 million in a Series A funding round led by Khosla Ventures, with additional participation from Uber, 8VC, Radical Ventures, OMERS Ventures, BDC, and Aurora Innovation. The company’s financial backers also include Geoffrey Hinton, Fei-Fei Li, Peter Abbeel, and Sanja Fidler, artificial intelligence scientists with great influence in the academia and applied AI community.

What makes Waabi qualified for such support? According to the company’s press release, Waabi aims to solve the “scale” challenge of self-driving car research and “bring commercially viable self-driving technology to society.” Those are two key challenges of the self-driving car industry and are mentioned numerous times in the release.

What Waabi describes as its “next generation of self-driving technology” has yet to pass the test of time. But its execution plan provides hints at what directions the self-driving car industry could be headed.

Better machine learning algorithms and simulations

According to Waabi’s press release: “The traditional approach to engineering self-driving vehicles results in a software stack that does not take full advantage of the power of AI, and that requires complex and time-consuming manual tuning. This makes scaling costly and technically challenging, especially when it comes to solving for less frequent and more unpredictable driving scenarios.”

Leading self-driving car companies have driven their cars on real roads for millions of miles to train their deep learning models. Real-road training is costly both in terms of logistics and human resources. It is also fraught with legal challenges as the laws surrounding self-driving car tests vary in different jurisdictions. Yet despite all the training, self-driving car technology struggles to handle corner cases, rare situations that are not included in the training data. These mounting challenges speak to the limits of current self-driving car technology.

Here’s how Waabi claims to solve these challenges (emphasis mine): “The company’s breakthrough, AI-first approach, developed by a team of world leading technologists, leverages deep learning, probabilistic inference and complex optimization to create software that is end-to-end trainable, interpretable and capable of very complex reasoning. This, together with a revolutionary closed loop simulator that has an unprecedented level of fidelity, enables testing at scale of both common driving scenarios and safety-critical edge cases. This approach significantly reduces the need to drive testing miles in the real world and results in a safer, more affordable, solution.”

There’s a lot of jargon in there (a lot of which is probably marketing lingo) that needs to be clarified. I reached out to Waabi for more details and will update this post if I hear back from them.

By “AI-first approach,” I suppose they mean that they will put more emphasis on creating better machine learning models and less on complementary technology such as lidars, radars, and mapping data. The benefit of having a software-heavy stack is the very low costs of updating the technology. And there will be a lot of updating in the coming years as scientists continue to find ways to circumvent the limits of self-driving AI.

The combination of “deep learning, probabilistic reasoning, and complex optimization” is interesting, albeit not a breakthrough. Most deep learning systems use non-probabilistic inference. They provide an output, say a category or a predicted value, without giving the level of uncertainty on the result. Probabilistic deep learning, on the other hand, also provides the reliability of its inferences, which can be very useful in critical applications such as driving.

“End-to-end trainable” machine learning models require no manual-engineered features. This means once you have developed the architecture and determined the loss and optimization functions, all you need to do is provide the machine learning model with training examples. Most deep learning models are end-to-end trainable. Some of the more complicated architectures require a combination of hand-engineered features and knowledge along with trainable components.

Finally, “interpretability” and “reasoning” are two of the key challenges of deep learning. Deep neural networks are composed of millions and billions of parameters. This makes it hard to troubleshoot them when something goes wrong (or find problems before something bad happens), which can be a real challenge in critical scenarios such as driving cars. On the other hand, the lack of reasoning power and causal understanding makes it very difficult for deep learning models to handle situations they haven’t seen before.

According to 98bmp’s coverage of Waabi’s launch, Raquel Urtasan, the company’s CEO, described the AI system the company uses as a “family of algorithms.”

“When combined, the developer can trace back the decision process of the AI system and incorporate prior knowledge so they don’t have to teach the AI system everything from scratch,” 98bmp wrote.

Credit: CARLA