Why Tesla Quietly Acquired DeepScale, a Machine Learning Startup That’s ‘Squeezing’ A.I.

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Tesla has quietly acquired an artificial intelligence company to build out its Autopilot autonomous driving system.

Tesla this week closed the acquisition of DeepScale, a company that uses sophisticated “deep neural networks” and other aspects of artificial intelligence to help a vehicle’s in-car autonomous driving technology more effectively “see” what’s around it.

The news was first reported by CNBC, who cited sources claiming Tesla had acquired DeepScale. Tesla did not respond to Fortune’s request for comment, and has so far remained quiet on the acquisition’s details, but DeepScale CEO Forrest Iandola updated his LinkedIn account on Tuesday, saying that he has joined Tesla’s Autopilot team to work on deep learning and autonomous driving.

Tesla makes small acquisitions from time to time, but DeepScale appears to be its most significant acquisition since February, when the company announced that it would acquire Maxwell Technologies to improve its battery technology.

But unlike Maxwell, the purchase of DeepScale is firmly rooted in the in-car driving (or perhaps, riding) experience.

What is DeepScale?

Now part of the Tesla Autopilot team, DeepScale was previously a Silicon Valley startup with $18.5 million in venture funding that was attempting to develop artificial intelligence technology for fully autonomous self-driving cars.

In order to achieve that, DeepScale relied on deep neural networks, or multi-layered networks that use mathematics and other sophisticated technology to crunch data and deliver real-world information, to create what it called “squeezing A.I..” The “squeezing” means that its technology would use fewer resources to identify obstacles around a vehicle and inform the car’s on-board computer to keep the vehicle and its passengers safe.

Artificial intelligence and building fully autonomous driving systems can be expensive. By reducing resource-load, DeepScale’s technology could have ultimately reduced costs and allowed more car makers at all levels—from high-line to budget—to implement self-driving technology.

How will Tesla use DeepScale in its Autonomous Driving tech?

When you think of autonomous driving, it’s important to view it as a collection of components to make a car move and avoid obstacles on the road.

The vehicle itself is equipped with sensors, cameras, and sometimes, lasers, to help it identify what’s around, speeds of other vehicles, and much more. It also comes with processors that can quickly and effectively process that information and adjust the vehicle’s movement and speed accordingly.

DeepScale’s software takes information it collects from the sensors and other on-board hardware, processes it through its A.I. platform, and returns valuable information to the car about what’s around it, what to avoid, and more.

Think of DeepScale’s technology as an aid in helping the car “see” what’s around it and avoid those obstacles.

What is Carver21 and how does it work?

After years of work and research, DeepScale unveiled its Carver21 software for car makers in January.

In a blog post at the time, DeepScale’s product chief Ben Landen said that Carver21’s goal was to deliver “efficiency and modularity.”

Efficiency and modularity are both critical to carmakers. Efficient software will not hog processor resources and otherwise detract from the fully autonomous driving experience. And a modular approach means DeepScale’s Carver21 could be made to work with a long list of autonomous driving technologies, regardless of the carmaker, the chip it uses, and more.

Carver21 was designed solely for fully autonomous self-driving cars that utilize forward-facing cameras. Any technology with lasers or other forward-facing technologies, wouldn’t work with Carver21.

Interestingly, Tesla has been one of the more biggest supporters of using camera technology for autonomous driving. As a result, it’s no surprise that the company acquired DeepScale before other carmakers could take advantage of similar technology.

What’s next for Tesla’s self-driving Autopilot technology?

Since it’s still early days and Tesla hasn’t discussed its plans for DeepScale, so difficult to know for sure how DeepScale will find its way into the electric automaker’s technology. But there are clues based on what DeepScale was working on and what Iandola posted to his LinkedIn profile.

For instance, it’s apparent that DeepScale’s technology will be integrated into Tesla’s Autopilot, the self-driving technology the company is currently working on. For now, Autopilot is not considered fully autonomous and still requires a small amount of human interaction. DeepScale’s technology, however, is designed for fully autonomous, non-human driving scenarios. It bears to reason that DeepScale should play a role in driving Tesla to full autonomy.

In terms of the riding experience, drivers shouldn’t expect to be aware of the role DeepScale ultimately plays. The technology is purely software that improves autonomous driving, and if it’s working well, cars will move around roads without incident.

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