[randomsearch]

I think he’s (deliberately? Give him the benefit of the doubt) misinterpreted Hofstadter’s use of the words “processing text”.

What he means, which seemed quite obvious to me, is that the machine is not reading text, building up a semantic interpretation of the sentence in the way a human does. That’s because a neural net is a simple pattern recognition machine that does not work in the same way as the brain. It doesn’t have the immense life experience to draw upon (probably relatively easy to fix) but more importantly it doesn’t have a concept of what a sentence actually means.

A neural network doesn’t have an understanding of what “double” means. It just pattern matches a translation.

I think there’s some serious symbolic reasoning going on in the brain, which neural nets don’t yet perform. It all feels like shallow syntactic matching right now, rather than semantic reasoning.

Unfortunately I don’t know how the brain works, so you end up in an absurd argument where people say “the brain is just a neural net” and it’s impossible to completely refute their claims, just as if someone said “the brain is a very large lookup table”. Well, from what I see it does seem that the brain is much smarter than that, but I can’t be certain without knowing what that extra kicker is. So whilst such an assertion seems terribly simplistic and self-evidently insufficient, it is difficult to argue with.

[mojuba]

“The brain is just a neural net” plus real life experience, in fact years and years of experience. Pretty much every example that automated translation gets wrong is when a real life context is required. Our language is not just a sequence of words and sentences, it almost always implies some contextual klnowledge. Where two humans have siginficantly different backgrounds they may have difficulty understanding each other for the same reason. A total lack of real life experience on one of the sides makes it even worse: it produces barely comprehensible near-nonsense.

[YeGoblynQueenne]

One problem with any attempt to map human intelligence to different types of artificial intelligence is that we only have a very precious few such types of AI, so there may be any number of things we're missing.

It's a case of not having the right analogies. We liken various bodily systems to machines: the heart is a pump, the lungs are funnels, the kidneys are filters, etc. These work up to some point because we understand both sides of the analogy well enough- we understand how the heart works, to the extent that it works like a pump and we understand how pumps work, etc.

But with intelligence we don't have this luxury. We don't understand how intelligence works, yet we draw an analogy not just with computers ("the brain is a computational device") but with specific types of computer programs. However, there are, literally, an infinite number of different computer programs and an unknown number of them could produce results similar, or even identical, to our intelligence.

Of course understanding intelligence is basically coming up with a good model of it. But that must be preceded by a good understanding of how intelligence works, which we currently don't have. Instead, what some researchers do, is that they take their arbitrarily chosen favourite AI model and try to find a way to argue that it's "like" human intelligence.

Neural networks are particularly guilty of this sort of thing. The whole idea of connectionism is to mimic the way the brain does intelligence, however we don't know what that is, so we've just come up with a complex machine that can optimise systems of functions, instead (I mean the set of neural network architectures). Then, when this machine turned out to be good at doing what it was designed for, optimising systems of functions, we claimed this as proof that it's actually doing what the brain does. That's a very circular way of thinking.