Tesla’s latest version of FSD had taught itself how to drive by processing billions of frames of video of how humans do it, Isaacson writes.
The"neural network planner" that Shroff and others were working on took a different approach."Instead of determining the proper path of the car based on rules," Shroff says,"we determine the car's proper path by relying on a neural network that learns from millions of examples of what humans have done." In other words, it's human imitation.
Shroff showed Musk instances where a neural network planner would work better than a rules-based approach. The demo had a road littered with trash cans, fallen traffic cones, and random debris. A car guided by the neural network planner was able to skitter around the obstacles, crossing the lane lines and breaking some rules as necessary."Here's what happens when we move from rules-based to network-path-based," Shroff told him.
Members of the team installed massive 85-inch television monitors in their workspace that displayed in real time how many miles the FSD cars were driving on average without interventions. They put a gong near their desks, and whenever they successfully solved a problem causing an intervention, they got to bang the gong.
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