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But it will be much more accurate. The neural network will learn to take into account air resistance and wind, for instance. Depending on how much data you give

But it will be much more accurate. The neural network will learn to take into account air resistance and wind, for instance. Depending on how much data you give it and the resolution, it might even learn to model air turbulence, spinning and wobbling of thrown objects, etc. It will be a lot more accurate then the simplistic physics model. And sure, it doesn't output a nice simple equation. But in most real problems, there aren't nice simple equations to output. Physics is sort of a fluke, where the fundamental laws of nature happen to be so simple we can do that. In something like biology, you are never going to find a simple equation that explains a biological system. They are complicated messes of millions of interacting pieces. Even if we knew how everything worked, we don't have the computing resources to accurately simulate it anyway. And we are actually seeing neural networks be useful in these areas. They are able to predict what chemicals are more likely to be useful drugs. If you restricted yourself to using only simple mathematical models, you would never get these results. This very article is about the very difficult domain of image processing. Why should we expect the statistical distribution of photographs to be simple and explainable by simple math? Finding an equation that accurately models the shape of a human face is pointless and misguided. Before NNs came along, they were doing crazy stuff like handcoding complicated heuristics that detected dark spots that might be eye sockets. There's nothing elegant about that. Physicists are really blessed to have a domain where they can ignore stuff like air resistance. Where the data really does fit simple equations. And as the parent comment says, you can interpret neural networks. You can observe the activation behaviors, and discover what features it's learned to detect eye sockets, or chemicals with certain properties. You can see how the output changes as the inputs change, and fit a simpler model to that.

nightski 9y ago
But that's precisely the problem isn't it? It's hard to know when it's picking up additional details or when it's overfitting to noise.
- jacquesm 9y ago
  Why is that hard? Expose it to data it hasn't seen yet and see if the results check out.
  - nightski 9y ago
    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?
    - jacquesm 9y ago
      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.

nightski 9y ago

But that's precisely the problem isn't it? It's hard to know when it's picking up additional details or when it's overfitting to noise.

jacquesm 9y ago
Why is that hard? Expose it to data it hasn't seen yet and see if the results check out.
- nightski 9y ago
  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?
  - jacquesm 9y ago
    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.