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The New AI: Where Neuroscience and Artificial Intelligence Meet
An anonymous reader writes "We're seeing a new revolution in artificial intelligence known as deep learning: algorithms modeled after the brain have made amazing strides and have been consistently winning both industrial and academic data competitions with minimal effort. 'Basically, it involves building neural networks — networks that mimic the behavior of the human brain. Much like the brain, these multi-layered computer networks can gather information and react to it. They can build up an understanding of what objects look or sound like. In an effort to recreate human vision, for example, you might build a basic layer of artificial neurons that can detect simple things like the edges of a particular shape. The next layer could then piece together these edges to identify the larger shape, and then the shapes could be strung together to understand an object. The key here is that the software does all this on its own — a big advantage over older AI models, which required engineers to massage the visual or auditory data so that it could be digested by the machine-learning algorithm.' Are we ready to blur the line between hardware and wetware?"
For such a blatant, transparent, promotional, hyperbolic "story", I wish soulskill would at least throw in a sarcastic jab or two to balance out the stench a bit.
Agreed. This story smells of the usual Google hype.
I think it's great that there is more research in this area, but "The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI" suggests that Google is at the forefront of this stuff. They're not. Look at the Successes in Pattern Recognition Contests since 2009. None of Ng, Stanford, Google or Silicon Valley are even mentioned. Google's greatest ability is in generating hype. It seems to be the stock-in-trade of much of Silicon Valley. Don't take it too seriously.
Generating this type of hype for your company is an art. I use to work for a small company run by a guy who was a wiz at it. What you have to understand is that reporters are always looking for stories, and this sort of spoon fed stuff is easy to write. Forget about "Wired". The guy I knew could usually get an article in NYT or WSJ in a day or two.
I'd mod you up if I could, but I think I can help out with a few points instead:
1) There is no concrete constructive definition of intelligence yet, and I think anybody at a higher level in the field knows that. Establishing that definition is a recognized part of AI research. Intelligence is still recognized comparatively, usually related to something like the capability to resolve difficult or ambiguous problems with similar or greater effect than humans, or can learn and react to dynamic environmental situations to similar effect as other living things. Once we've created something that works and that we can tangibly study, we can begin to come up with real workable definitions of intelligence that represent both the technological and biological instances of recognized intelligence.
4) Modern ANN research sometimes includes altering the morphology of the network as part of training, not just altering the coefficients. I would hope something like that is in effect here.
What precisely are those long-standing problems?
I ask because I actually know people who are starting to demonstrate the rudiments of intelligence using simulations of ~100,000 neurons.
Per upthread, that's a long way from a brain, and in fact we don't even know how all of the brain is wired, let alone how it works. But you might want to consider this and this and this.
If they're attempting the impossible, you should let them know not to waste their money.
Sheesh, evil *and* a jerk. -- Jade