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Yeah - Tesla software has two parts, a vision system and a driving system. The vision system turns the camera image into a description of what is in the image (

Yeah - Tesla software has two parts, a vision system and a driving system. The vision system turns the camera image into a description of what is in the image (road, cars, people, etc), and the driving system actually controls the car based on the vision input and route it has been given. Up until now the driving part had been hand coded in C++, but now it is a neural net that has learnt what would a human do in similar road situation (not sure what they trained it on). Is this an improvement? Who knows - should certainly be different. This doesn't mean the car suddenly got smart and now understands what it is looking at - it just means it's using a different set of rules for when to switch lanes, brake, etc.

cdumler 3y ago
I would argue reasonable to believe it will be notably better until it is catastrophically wrong. The issue with neural nets is that they can be really well trained within the bounds of training set; however, when faced with novel, there is no underlying abstraction inference on which to revert. Look instances where autonomous cars are disable or have weird responses when people modify signs[1]. The driving will be better, but the when the information sits squarely outside of the norm (art on the road, abstract images that defy normal depth cues, etc), the car will do very unexpected things. As humans, we have this issue already. We call them "optical illusions." Optical illusions are really brain neural net failures where there are competing narratives between evaluations of the information provided by our senses we conclude invalid results. We don't have the ability to process absolute light values, so we infer colorspace by relative color within our 3D mental model, which can result in inferring a difference in color where there is none. 1. https://spectrum.ieee.org/slight-street-sign-modifications-can-fool-machine-learning-algorithms https://spectrum.ieee.org/slight-street-sign-modifications-c... 2. https://en.wikipedia.org/wiki/Checker_shadow_illusion https://en.wikipedia.org/wiki/Checker_shadow_illusion

cdumler 3y ago

I would argue reasonable to believe it will be notably better until it is catastrophically wrong. The issue with neural nets is that they can be really well trained within the bounds of training set; however, when faced with novel, there is no underlying abstraction inference on which to revert. Look instances where autonomous cars are disable or have weird responses when people modify signs[1]. The driving will be better, but the when the information sits squarely outside of the norm (art on the road, abstract images that defy normal depth cues, etc), the car will do very unexpected things. As humans, we have this issue already. We call them "optical illusions." Optical illusions are really brain neural net failures where there are competing narratives between evaluations of the information provided by our senses we conclude invalid results. We don't have the ability to process absolute light values, so we infer colorspace by relative color within our 3D mental model, which can result in inferring a difference in color where there is none. 1. https://spectrum.ieee.org/slight-street-sign-modifications-can-fool-machine-learning-algorithms https://spectrum.ieee.org/slight-street-sign-modifications-c... 2. https://en.wikipedia.org/wiki/Checker_shadow_illusion https://en.wikipedia.org/wiki/Checker_shadow_illusion