Slashdot Mirror
Recent Advances in Cognitive Systems
Roland Piquepaille writes "ERCIM News is a quarterly publication from the European Research Consortium for Informatics and Mathematics. The April 2003 issue is dedicated to cognitive systems. It contains no less than 21 articles which are all available online. In this column, you'll find a summary of the introduction and what are the possible applications of these cognitive systems. There's also a picture of the cover, a little robot with a very nice looking blue wig. And in A Gallery of Cognitive Systems, you'll find a selection of stories, including links, abstracts and illustrations (the whole page weighs 217 KB). There are very good pictures of autonomous soccer robots, swarm bots, cognitive vision systems, and more."
The great thing about the recent development in so-called cognitive systems is that they start to address more real problems. The time of toy problems is over. It is not enough to just follow a line. Only the challenge from the real world can make algorithms in any way "clever" or meaningful.
This is why I find it truly inspiring that so much research is going into these systems these days.
Sadly however most of neuroscience these days is still far from these questions. Most electrophysiologists that for example study the visual system show it trivial stimuli such as bars or gratings. In some sense a system can only show its capability when the stimuli are rich enough.
Nevertheless there is clearly a move these days towards larger more interesting problems even in neuroscience. We should be inspired by the works of the roboticists.
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Actually, I'd say that not very many are doing that.
The goal of all the cognitive scientists I've met is to make machines think, just as with A.I. In fact, I've always heard, and was told in my AI class, that A.I. is a branch of cognitive science.
However, there are many approaches to machine thinking that are not considered part of A.I.:
neural networks, SVMs, computer vision (signal interpretation), modeling.
So what does A.I. cover then? Well, it's not exactly well defined. If you read A.I. textbooks, you'll find the full of lots of different things. Some would go so far as to even include those things I mentioned that aren't normally considered part of A.I. However, in general, I would say that A.I. is the field that is concerned with
1) Solving the search problem (searching for a solution in a large set of possibilities)
2) Doing it with heuristics.
I'd like to take a moment to note that a famous computer vision paper came out in the 80's that documented a method called Marr-Hildreth, which was for finding edges in images. They created it by using the same technique that eyes use (laplacian of a Gaussian for edge detection - they studied cats to find this out).
A few years later someone improved upon it by throwing out the model completely and NOT doing it the way that people do (Canny).
Cognitive scientists are usually more concerned with getting the machines to do what we want than they are with modeling human thinking techniques.
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While this is all very interesting and becoming more practical for everyday use, we don't hear enough about the stuff that's related but not quite bleeding edge. We know there are people trying to create intelligent systems such as for language understanding and intelligent web searching, but it seems we don't hear much about them. I'm wondering if it's because most of that is being done within corporations while much of this bleeding edge research is done by universities.
Developers: We can use your help.
Reading this reminds me of my cognitive neuroscience/AI prof Lev Goldfarb. He began our course by telling us that very, very little has been accomplished in the fields of Cog Sci and AI, and that he is possibly the only one who has brought a real contribution to the table: a formal language ("real science") for working in this field. His "Evolving Transformation System" or ETS provides methods for measuring symbols and the differences between them, and lays the groundwork for modelling cognitive processes.
Compare this to any of the fake sciences, which can easily be itentified because they have the word "science" in them. Social Science, Cognitive Science, and so on, which talk about phenomenon but fail to create formalisms to describe them (like physics does for physical phenomena, for example.)
He's eccentric, but is he right? I don't know. You can read a summary of his work here. I never dived into this field enough to learn whether he was a revolutionary or just a big talker. I'd be interested to hear what other slashdotters have to say.
In all matters of opinion, our adversaries are insane. -Oscar Wilde
I don't mean to slight the progress made, and I also didn't mean to criticize all AI researchers.
Perhaps a better way to describe what I was getting at is that there's an unfortunate feedback effect that happens with these advanced applications, where: researchers say things which excite the general public because they describe things that sound amazing and desirable; researchers notice said excitement and connect that with increased funding; researchers exploit excitement by attaching loaded buzzwords like "AI" to all sorts of vaguely related research projects. But what the public heard or believed initially, is never actually delivered, and what is delivered doesn't seem nearly as exciting as the original vision. If not for this effect, the first "AI crash" would never have happened.
Of course, what I've described is to some extent how the promition of just about any project or product works. The difference with advanced applications like AI is that the ultimate end goals - which are often brought up as justifications for the work - are so far from achievability that expectations are dashed much more than usual when the projects finally reach some kind of fruition - if they ever do. Much of the audience then feels as though it was burned, and could care less about the fact that "real" AI is so much harder than any other software that's been developed to date. They simply perceive that what was "promised" was not delivered.
Like public companies which learned to carefully manage their earnings so as to remain in line with Wall Street expectations, researchers in these fields need to be careful about expectations management if they're going to promote their projects publicly - unless they have something concrete they're going to be delivering in a finite and predictable timescale.
I do think a term like "Cognitive Systems" is much less likely to suffer from these kinds of problems. Many things which could reasonably be called cognitive systems research could not, without significant qualification, really be called artificial intelligence research.