Tesla sues former employee for allegedly stealing trade secrets then trying to cover up

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Tesla has sued a former employee it accuses of stealing trade secrets related to its supercomputer project, Bloomberg reported Friday. According to a filing in the U.S. District Court in San Jose, thermal engineer Alexander Yatskov resigned on May 2 after joining the company a few months earlier, in January. According to Tesla, Yatskov admitted to transferring confidential information to his personal devices and later handing over a “dummy” laptop after company officials confronted him on suspicion of theft.

In addition to violating a nondisclosure agreement intended to protect trade secrets, Bloomberg reports that Tesla also accuses Yatskov of misrepresenting his experience and skills on his resume. Bloomberg also says that Yatskov declined to comment.

“This is a case of unlawful withholding of trade secrets by an employee who, during his short time at Tesla, has already demonstrated that he lied and then lied again by providing a ‘dummy’ device to try to cover their tracks,” Tesla said. writes in the file, reports Bloomberg.

CEO Elon Musk has been teasing Tesla’s supercomputer project, called “Dojo,” since at least 2019. Last summer, the company finally explained the project in more detail, setting a goal of using AI to analyze massive amounts of vehicle data, ideally resulting in a safer and more refined autonomous driving experience. The computer, which offers 1.8 exaflops of performance and 10 petabytes of NVME storage running at 1.6 terabytes per second, trains using video from eight cameras inside Tesla vehicles running at 36 frames per second.

Tesla claimed last year that while this approach generates a huge amount of data, it is still more scalable than building high-definition maps around the world. At the time, Tesla said the system worked best in sparsely populated areas where cars could mostly drive uninterrupted. Still, the company also touted some early successes in denser areas, including Dojo’s ability to learn new types of traffic warnings, pedestrian collision detection, and poor pedal applications (accidentally pressing on the accelerator instead of the brakes).

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