[yters]

My understanding is the practical benefit of machine learning and control systems is mostly due to simple models. Not the fancy "deep" models currently in vogue. An added problem is the high dimensional models are essentially black boxes, and are probably significantly overfitting the data, hence all the adversarial type research.

Why does the mapping of biological neural networks to silicon substrate imply the human mind is a computer?

[stevenhuang]

> Why does the mapping of biological neural networks to silicon substrate imply the human mind is a computer?

Is this a rhetorical question or something, because it seems to me you've answered yourself there. I mean, if the mapping works, what else should it imply besides the consequent?

[yters]

The implication requires a further premise that the mind is reducible to the brain, which we do not know to be true.

[feanaro]

Not just the brain, but the whole body and, even more generally, to phenomena that can be described by physics. Unless you are trying to argue for a non-physical (i.e. magical) soul, the argument is sound.

[yters]

Right, why assume the mind reduces to physics? This is usually how people argue for AGI being inevitable, but assuming the mind reduces to physics is a big assumption. Perhaps we have a physical soul.

[feanaro]

Why not? Everything so far had been reduced to physics; sometimes to at-the-time undiscovered physics.

[jononor]

Deep models on GPUs have made using machine learning on images tractable. Manufacturing is one industry which is expected to benefit a lot from this, using computer vision heavily for automation, quality control and robotics.

[yters]

I'd be curious to see the actual ROI on this claim.