One of the main problems with ML/NN is it often works like magic, aka the trick works as long as audience doesnt know the secret behind it. Its fascinating to gullible audience, mundane bordering on boring to practitioners.
>able to generalize various game logic like collision/friction with vehicles and also learns aspects of rendering such as a proper reflection of the sun on the back of the car
id did none of that, what this model did is learn all the frames of video and their chronological order according to the input.
> impossible task of "splitting a car in two" to try and solve a head-on collision.
it played back both learned versions at once, like reporting confidence of round thing being 50% ball and 50% orange.
My Tiger repelling rock^^^^^^leopard detection model works great on all animal pictures ... until you feed it a sofa https://web.archive.org/web/20150703094328/http://rocknrolln...
>able to generalize various game logic like collision/friction with vehicles and also learns aspects of rendering such as a proper reflection of the sun on the back of the car
id did none of that, what this model did is learn all the frames of video and their chronological order according to the input.
> impossible task of "splitting a car in two" to try and solve a head-on collision.
it played back both learned versions at once, like reporting confidence of round thing being 50% ball and 50% orange.