Expose it to which data exactly? Say it's picking up on some obscure phenomena that is relatively rare, yet very important. How would you, completely unaware of this, be able to collect more relevant data when the majority of the data you collect would not have this element?
But that's a problem with the data, not with the functioning of the net. The whole thing hinges on the premise that you will be able to tell 'sense' from 'nonsense' when you see it, even if you don't know exactly why it labeled 'sense' as 'sense' and vv.
If you are simply amassing data without purpose then what you could do is to try to visualize the layers of the network to see if any surprising features turn up but that will only work if the features are obvious enough to stand out and if that were the case I would suspect we would not have this discussion in the first place.
Computers excel at: remembering stuff forever and speed.
So any kind of improvement that a neural net or any other solution would bring to the table over a human would likely fall in either one of those categories, either the computer is faster at solving the problem, or its ability to remember and apply a vast amount of data to the problem will give it a (slight) edge over what a human could do, or maybe it will be just enough to reach parity.
It will not tell you what is and what is not significant in the input, though, with enough samples you with your slower but much superior intellect might be able to draw new and far-reaching conclusions once you are exposed to the data in sufficient quantity yourself that a pattern suggests itself.
That's in a way a very nice collaboration between man and machine, each doing what they are best at.
A key element in something like this being at play would be the computer consistently being at odds with experts in the field but being right more often than not about those cases. That would be an excellent opportunity to wake up to the possibility that there is something that should be obvious and noticeable but that still got missed.
> Computers excel at: remembering stuff forever and speed.
> So any kind of improvement that a neural net or any other solution would bring to the table over a human would likely fall in either one of those categories, either the computer is faster at solving the problem, or its ability to remember and apply a vast amount of data to the problem will give it a (slight) edge over what a human could do, or maybe it will be just enough to reach parity.
> It will not tell you what is and what is not significant in the input, though, with enough samples you with your slower but much superior intellect might be able to draw new and far-reaching conclusions once you are exposed to the data in sufficient quantity yourself that a pattern suggests itself.
I think that its too early to say how AI will be deployed in practice -- whether it will augment or replace human roles. The ambition is certainly to produce enough "intelligence" to supersede a large fraction of human decision making.
As we seek to make computers more powerful, robust, and versatile ("AI"), it seems like we are pushed towards more organic computational structures. If the trend continues, it would imply that the closer we get to AI, the less their strengths and weaknesses would resemble computers of yesteryear. The interesting possibility is that one might be able to have an interpolation between the strengths of humans and computers.
