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I'm not talking about detection, I'm talking about 3D reconstruction and making judgements on whether X will be able to pass by Y without hitting it. ML has al
by snovv_crash 10y ago
I'm not talking about detection, I'm talking about 3D reconstruction and making judgements on whether X will be able to pass by Y without hitting it.
ML has all the same weaknesses that humans do in that they are easily fooled and can't accurately project outside of their trained parameter space. Without an accurate physics model and an integrated comprehension of the geometry involved we are basically planning to put autistic pigeons in charge of driving our cars.
- TheSpiceIsLife 10y agoThat seems like a bad idea. But don't computers have the upper because they can do both simultaneously and weight the inputs appropriately?
- usrusr 10y agoBut the nice thing about computers is that you can feed the results of the high level ML classifier into the mm accurate geometry detector. You can only determine whether X will be able to pass by Y if you already know that there is an X and a Y. Humans and pigeons will identify an X and a Y and will then try to use the same method to classify the scene as either "will pass" or "won't pass". Computers can do the fuzzy thing for identification, but don't have to use the same method for "will pass"/"won't pass". They can switch to geometric measuring and simulation for that.
- dfox 10y agoIn order to accurattely ascertain whether thing X will fit into Y hole you have to know accurate geometry of both which is not something that can be reliably determined from results of the fuzzy classification pass. When humans want to know this they either trust their fuzzy judgement and try it or measure the X and Y in question with tape measure.