>I too would like to know why cars have wheels when helicopters have shown that rotors work well. We should have abandoned wheels ages ago since they are clearly old and with that inferior.
I said cost/benefit and your example is about an expensive way to do what a car can do. You get some points for speed but lose big points on affordable transportation. The world has settled on the car.
> And that app will have issues deciding if it sees a couch or a leopard. Or do you expect a child to correctly train its neural net?
It will train a neural net sooner than it will learn 3d computer vision.
> And yet we have mars probes with a build in color table. Why do you hate geometry?
I said that I was a skeptic against deep learning an in favor of the old ways, so why do you claim that I hate it? Its inadequacy regarding the current and possibly future state of computer vision technology is not an expression of my feelings. Its a mere observation.
> I said cost/benefit and your example is about an expensive way to do what a car can do.
Sometimes neural net running on a sever rack full of GPUs is also overkill, including the cost/benefit. You get bonus points for buzzwords thought.
> It will train a neural net sooner than it will learn 3d computer vision.
And you would trust it to run as required? I admire your courage. Unless the person selecting the training data knew what they were doing I wouldn't. I certainly wouldn't trust a child to get it right without being trained itself.
> I said that I was a skeptic against deep learning an in favor of the old ways, so why do you claim that I hate it?
Your mention against "building chessboards" for basic calibration as if that was in any way hard or even necessary. Calibration of sensors on mars is already a solved problem. I don't understand why you would think otherwise.
> I said cost/benefit and your example is about an expensive way to do what a car can do. You get some points for speed but lose big points on affordable transportation. The world has settled on the car.
No, the world uses the one that makes sense for the particular circumstance.
I said cost/benefit and your example is about an expensive way to do what a car can do. You get some points for speed but lose big points on affordable transportation. The world has settled on the car.
> And that app will have issues deciding if it sees a couch or a leopard. Or do you expect a child to correctly train its neural net?
It will train a neural net sooner than it will learn 3d computer vision.
> And yet we have mars probes with a build in color table. Why do you hate geometry?
I said that I was a skeptic against deep learning an in favor of the old ways, so why do you claim that I hate it? Its inadequacy regarding the current and possibly future state of computer vision technology is not an expression of my feelings. Its a mere observation.