Yes, neural nets are useful in some applications, but the hard fact of the matter is that they have been completely oversold. If you have a messy problem to solve that you have no clue how to solve, chance are that a neural net won't do it. You can waste weeks of CPU time trying to train the thing, and you will never be able to get the error down to anything usable.
I suppose the main point to keep in mind is that, NNETs are a special kind of AI. I've always preferred to think of them as... artificial stupidity programs. Yes, they can learn (if you know what you are doing), but, damn, will they take a long time, even if you DO know what you're doing. Of course, perhaps someday better supervised training algorithms will come along, but right now the bread and butter of the trade is the back-propagation error correction scheme. It is highly effective for some problems, but it always takes a long time for the network to converge to something useful.
As far as the processor in the article, it sounds very interesting. If it can be made for cheap, great. Though I don't think that it is going to show up in handhelds and other small devices as much as the article seems to imply. Training is what takes so horribly long with neural nets. But the great thing about them is that once they're trained, they will spit out answers in no time at all, because are usually feed-forward, and hence not iterative. For many applications on handhelds, offline training will be fine.
You're really right about the compiler performance issues. Code compiled with gcc on Alpha machines is ridiculously slow--there is a need for some REAL work on optimization. Since most of what's in a Linux installation is compiled using gcc, an Alpha Linux installation tends to crawl as well. The horrible consequence of all this is that many people wanting to use their alpha boxes for intense floating-point calculations end up installing Windows NT--a criminal thing to have to do with such nice machines.
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Yes, neural nets are useful in some applications, but the hard fact of the matter is that they have been completely oversold. If you have a messy problem to solve that you have no clue how to solve, chance are that a neural net won't do it. You can waste weeks of CPU time trying to train the thing, and you will never be able to get the error down to anything usable.
I suppose the main point to keep in mind is that, NNETs are a special kind of AI. I've always preferred to think of them as... artificial stupidity programs. Yes, they can learn (if you know what you are doing), but, damn, will they take a long time, even if you DO know what you're doing. Of course, perhaps someday better supervised training algorithms will come along, but right now the bread and butter of the trade is the back-propagation error correction scheme. It is highly effective for some problems, but it always takes a long time for the network to converge to something useful.
As far as the processor in the article, it sounds very interesting. If it can be made for cheap, great. Though I don't think that it is going to show up in handhelds and other small devices as much as the article seems to imply. Training is what takes so horribly long with neural nets. But the great thing about them is that once they're trained, they will spit out answers in no time at all, because are usually feed-forward, and hence not iterative. For many applications on handhelds, offline training will be fine.
You're really right about the compiler performance issues. Code compiled with gcc on Alpha machines is ridiculously slow--there is a need for some REAL work on optimization. Since most of what's in a Linux installation is compiled using gcc, an Alpha Linux installation tends to crawl as well. The horrible consequence of all this is that many people wanting to use their alpha boxes for intense floating-point calculations end up installing Windows NT--a criminal thing to have to do with such nice machines.