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I’d argue, that it sometimes adds complexity to an already fragile system. Like when we implement touchscreens instead of buttons in cars. It’s akin to how Tesla, unlike Waymo, dropped LIDAR to depend on regular videoinputs alone. Direct control over systems without unreliable interfaces, semantic translation layer, computer vision dependancy etc serves the same tasks without additional risks and computational overheads.

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masterspace@lemmy.ca ⁨1⁩ ⁨day⁩ ago

I’d argue, that it sometimes adds complexity to an already fragile system.

You don’t have to argue that, I think thats inarguably true. But more complexity doesn’t inherently mean worse.

Automatic braking and collision avoidance systems in cars add complexity, but they also objectively make cars safer.

Direct control over systems without unreliable interfaces, semantic translation layer, computer vision dependancy etc serves the same tasks without additional risks and computational overheads.

But in this case, Waymo is still having to do that. They’re still running their sensor data through incredibly complex machine learning models that are somewhat black boxes and producing semantic understandings of the world around it, and then act on those models of the world. The primary difference with Waymo and Tesla isn’t about complexity or direct control of systems, but that Tesla is relying on camera data which is significantly worse than the human eye / brain, whereas Waymo and everyone else is supplementing their limited camera data with sensors like Lidar and Sonar that can see in ways and situations humans can’t and that lets them compensate.

That and that Waymo is actually a serious engineering company that takes responsibility seriously, takes far fewer risks, and is far more thorough about failure analysis, redundancy, etc.

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