This week Tesla acquired DeepScale, which is a startup that focuses on developing computer vision technologies (the price of the deal was not disclosed). This appears to be a part of the company’s focus on building an Uber-like service as well building fully autonomous vehicles.
Founded in 2015, DeepScale has raised $15 million from investors like Point72, next47, Andy Bechtolsheim, Ali Partovi, and Jerry Yang. The founders include Forrest Iandola and Kurt Keutzer, who are both PhD’s. In fact, about a quarter of the engineering team has a PhD and they have more than 30,000 academic citations.
“DeepScale is a great fit for Tesla because the company specializes in compressing neural nets to work in vehicles, and hooking them into perception systems with multiple data types,” said Chris Nicholson, who is the CEO and founder of Skymind. “That’s what Tesla needs to make progress in autonomous driving.”
Tesla has the advantage of an enormous database of vehicle information. So with software expertise, the company should help accelerate the innovation. “If ‘data is the new oil’ then ‘AI models are the new Intellectual Property and barrier to entry,’” said Joel Vincent, who is the CMO of Zededa. “This is the dawn of a new age of competitive differentiation. AI models are useless without data and Telsa has an astounding amount of edge data.”
Now when it comes to autonomous driving, there are other major requirements–some which may get little attention.
Just look at the use of energy. “Large models require more powerful processors and larger memory to run them in production,” said Dr. Sumit Gupta, who is the the IBM Cognitive Systems VP of AI and HPC. “But vehicles have a limited energy budget, so the market is always trying to minimize the energy that the electronics in the car consume. This is what DeepScale is good at. The company invented an AI model called ‘SqueezeNet’ that requires a smaller memory footprint and also less CPU horsepower.”
Keep in mind that the lower energy consumption will mean there will be more capacity for sensors for vision. “This should help make autonomous vehicles safer,” said Arjan Wijnveen, who is the CEO of CVEDIA. “Tesla seems certain that they don’t need LiDAR for effective computer vision, but there are lots of other types of sensors you could see on their vehicles in the future, and sometimes just placing a second camera facing another angle can improve the AI model.”
Not using LiDAR would be a big deal, which would mean a much lower cost per vehicle. “There are concerns about the deployment of LIDAR lasers in the public sphere,” said Gavin D. J. Harper, who is a Faraday Institution Research Fellow at the University of Birmingham. “Safety measures include limiting the power and exposure of lasers. There is also the concern about the potential for causing inadvertent harm to those nearby.”
So all in all, the DeepScale deal could move the needle for Tesla and represent a shift in the industry. Although, it is still important to keep in mind that autonomous driving is still in the nascent stages (regardless of what Elon Musk boasts!) There remain many tough issues to work out, which could easily drag on because of regulatory processes.
“To get to full autonomy, you’re still going to need some major algorithmic improvements,” said Nicholson. “Some of the smartest people in the world are working on this, and it seems clear that we’ll get there, even if we don’t know when. In any case, companies like Tesla and Waymo have the right mix of talent, data, and cars on the road.”