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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."
Noun ; 1. The current scientist scam, which has replaced the older artificial intelligence scam with its more robust resistance to criticism and even more byzantine theories.
:)
Actually, cognitive science does not replace AI. The goal of cognitive science is to figure out how our brain works on a functional level. Where neurology studies the actual chemical reactions and neural activity, cognitive science studies how the "hardware" works to achieve our thought processes.
One good example is how the brain works out an image of the mismash of neural impulses going through the retinal nerves. The resolution of the eye is actually quite low, and the "pixels" aren't ordered in any linear fashion. The brain does an enormous amount of processing to form an actual image. This is why babies can't see, even though the optics work. The brain needs to develop the processing algorithms in order to make sense of all the information coming in.
Of course, all of this is theory, and subject to scientific dispute
.: Max Romantschuk
But doing so doesn't relieve you of your responsibility to think too.
I have been pwned because my
The Annals of Improbable Research, the humor magazine for scientists, once had an article entitled "Advances in Artificial Intelligence". After the title and author affiliations, the page was appropriately completely blank...
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.
Googlefight "Slashdot Troll" against "BSD is dying" 303:229. BSD thus cant die.
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.
Mod me down and I will become more powerful than you can possibly imagine!
to detect dupes!
Having a system combine both symbolic logic systems and sensory systems is mentioned in the article as a major focus of research today, but I wonder why this has been split so specifically...maybe someone can help me to understand.
The point at which an understanding of body position is integrated with an overall structure of behavior leading towards a goal seems a mirage, since this isn't necessarily the way animal systems work. The best recreation of natures flexibility in "simple" systems that I've heard of comes from Mark Tilden's analog systems that are controled by tight-loops of feedback that very closely model reflex circuits, but that are capable of recovering from intense deformations of "perfect positioning".
Now, obivously, reflex systems can only go so far, when you have a bot that you want to decide path across a room, there has to be a symbolic understanding of its environment. But it seems to me, from my (albeit very limited) understanding of insect / lower-animal inteligence, that most insects don't actually work up a full symbolic understanding of their surroundings, they just have some sort of sense of direction towards a goal (think moths to light) and then they start the reflex circuits firing to move towards it. I can understand having an end goal of having a full cognitive system comparable to human understanding of the world, but it seems like people might be overshooting the process a bit. We need a greater understanding of the simple systems before we can hope to frog-leap to the big stuff.
To dispute my own point though, I feel its fair to say that the "simple" systems of the animal brain are already currently being modeled to the point that prosthesis for the brain might just be within reach. The success of an artificial hipocampus will prove that modeling the brain isn't necessarily understanding the brain, but it might be easier to learn the systems from our artificial models than the real ones.
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The ultimate goal of the RoboCup project is by 2050, develop a team of fully autonomous humanoid robots that can win against the human world champion team in soccer
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Now THAT's a goal.
Maybe we'll see humanoid robot referees in sports. That should stop any dissent from the players
Player: C'mon ref, that was never in a million years a f**king penalty !!
Ref: You have 3 seconds to comply..
$ strings FTP.EXE | grep Copyright
@(#) Copyright (c) 1983 The Regents of the University of California.
Johnny five is alive!
This is my sig, there are many like it, but this one is mine...
A more detailed summary is available here and this is the project web site.
Compared to proprietary systems such as Ai's HAL, Meaningful Machines Knowledge Engine, and Lobal Technologies LAD, EBLA is the only system to incorporate grounded/perceptual understanding of language.
I was disappointed by the 5 articles I read and stopped reading. It basically reads like a catalog of the projects and techno-terms that are being performed with very little actual content.
Basically each one boiled down to: our lab does the XYZAB project and we're studying this system.