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AI Going Nowhere?
jhigh writes "Marvin Minsky, co-founder of the MIT Artificial Intelligence Labratories is displeased with the progress in the development of autonomous intelligent machines. I found this quote more than a little amusing: '"The worst fad has been these stupid little robots," said Minsky. "Graduate students are wasting 3 years of their lives soldering and repairing robots, instead of making them smart. It's really shocking."'"
It can pick me out in a crowd, and it can show a number of emotions, such as surprise, anger, and boredom.... yawn.
"There are more pleasant things to do than beat up people." --Muhammad Ali
AI Founder Blasts Modern Research
By Mark Baard
Story location: http://www.wired.com/news/technology/0,1282,58714, 00.html
02:00 AM May. 13, 2003 PT
Will we ever make machines that are as smart as ourselves?
"AI has been brain-dead since the 1970s," said AI guru Marvin Minsky in a recent speech at Boston University. Minsky co-founded the MIT Artificial Intelligence Laboratory in 1959 with John McCarthy.
Such notions as "water is wet" and "fire is hot" have proved elusive quarry for AI researchers. Minsky accused researchers of giving up on the immense challenge of building a fully autonomous, thinking machine.
"The last 15 years have been a very exciting time for AI," said Stuart Russell, director of the Center for Intelligent Systems at the University of California at Berkeley, and co-author of an AI textbook, Artificial Intelligence: A Modern Approach.
Russell, who described Minsky's comments as "surprising and disappointing," said researchers who study learning, vision, robotics and reasoning have made tremendous progress.
AI systems today detect credit-card fraud by learning from earlier transactions. And computer engineers continue to refine speech recognition systems for PCs and face recognition systems for security applications.
"We're building systems that detect very subtle patterns in huge amounts of data," said Tom Mitchell, director of the Center for Automated Learning and Discovery at Carnegie Mellon University, and president of the American Association for Artificial Intelligence. "The question is, what is the best research strategy to get (us) from where we are today to an integrated, autonomous intelligent agent?"
Unfortunately, the strategies most popular among AI researchers in the 1980s have come to a dead end, Minsky said. So-called "expert systems," which emulated human expertise within tightly defined subject areas like law and medicine, could match users' queries to relevant diagnoses, papers and abstracts, yet they could not learn concepts that most children know by the time they are 3 years old.
"For each different kind of problem," said Minsky, "the construction of expert systems had to start all over again, because they didn't accumulate common-sense knowledge."
Only one researcher has committed himself to the colossal task of building a comprehensive common-sense reasoning system, according to Minsky. Douglas Lenat, through his Cyc project, has directed the line-by-line entry of more than 1 million rules into a commonsense knowledge base.
"Cyc knows that trees are usually outdoors, that once people die they stop buying things, and that glasses of liquid should be carried right-side up," reads a blurb on the Cyc website. Cyc can use its vast knowledge base to match natural language queries. A request for "pictures of strong, adventurous people" can connect with a relevant image such as a man climbing a cliff.
Even as he acknowledged some progress in AI research, Minsky lamented the state of the lab he founded more than 40 years ago.
"The worst fad has been these stupid little robots," said Minsky. "Graduate students are wasting 3 years of their lives soldering and repairing robots, instead of making them smart. It's really shocking."
"Marvin may have been leveling his criticism at me," said Rodney Brooks, director of the MIT Artificial Intelligence Lab, who acknowledged that much of the facility's research is robot-centered.
But Brooks, who invented the automatic vacuum cleaner Roomba, says some advancements in computer vision and other promising forms of machine intelligence are being driven by robotics. The MIT AI Lab, for example, is developing Cog.
Engineers hope the robot system can become self-aware as they teach it to sense its own physical actions and see a causal relationship. Cog may be able to "learn" how to do things.
Brooks pointed out that sensor technology has reached a point where it's more sophisticated and
He has a complete disregard for the question of where the AI engine will run. If an AI is to be of any more use than a curiousity then those "little autonomous robots" must function in a viable manner so that the AI has something to do when it comes to "life".
I understand his frustration in general progress. But, those grad students are building a strong foundation for their later work that may very well meet the goals he is espousing. No need to have design flaws in implementation down the road because the engineer wasn't properly educated in physical design as well as logical design.
-Rusty
The Master (Angelo Rossitto) in Mad Max Beyond Thunderdome, "Not shit, energy!"
Yes, the man is quite brilliant, and possibly the most important voice in the field. That being said, he's also a self-important jerk. Intelligent Systems (what people in the field call AI) aren't where *he* thinks they should be, and he regularly complains about it.
Does it bothers you that AI is going nowhere?
Je t'aime Stéphanie
.. it was a really bad movie.
I don't see NON-ARTIFICIAL intelligence progressing a whole hell of a lot either...
It's not the AI which is going nowhere. It's the traditional approaches to AI such as Minsky's symbolic logic which are not going anywhere. Seach google for Henry Markram, Maass, Tsodyks. Their research seems very promising.
Well...
One could argue that our brains are just synapses firing. Each one on it's own knows absolutely nothing. However, it's the SYNERGISTIC effects of all the synapses working together that creates our brain, which allows us to reason, etc (Note: This is without religion getting in the way, I'd personally not go there...)
Steve
> You'll never have real, true intelligence
:-)
Define "real, true intelligence"
> You can try to simulate that, but so far
> simulation consists of what amounts to a
> gazillion 'if' tests
That's what tradiditonal AI school is doing. Yes, you are correct. It won't go anywhere. On the other hand spiking neural networks are very promising. Search google for "liquid state machine". These researches are making progress novadays, not Minsky.
Humans measure intelligence by gauging replys to questions that have quantified answers. Giving an advanced computer a IQ test is sinply a matter of recalling the appropriate information from memory to answer. The true form of AI isnt about intelligence, its about reason. But how do you teach a computer to respond with an answer to a question that the computer has never encountered before... When we build a machine that can answer a question based on incomplete imput we will have made the first step in creating a machine that can "think"
reply to MYCROFTXXX@lunaauthority.com
Fire in the hands of the village idiot is no tool, but a weapon of mass destruction
How about we more thoroughly study and understand how human intelligence operates before we even presume to design something that imitates or rivals it in depth and complexity.
The problem with that is that no one really *knows* how the brain works beyond a very, very basic and limited understanding. No one has ever been able to satisfactorily create/reproduce one. There's more going on than just synapses in there, that much most scientists can agree on. What they don't agree on is *what* else is going on in there.
My journal has hot
Come off it, stop making escuses. Robitics and AI are two entirely different branchs. AI is almost pure math and computing, and robotics is an engineering discipline. Why are AI researching building little robots?
Building any form of AI system is not easy, but copping out of it by building toys is not the answer. We already have platforms for AI; they're called line terminals. Things like pattern matching do not require a fully autonomous robot, after all.
Minsky is right; whats new to come out of actual AI research in the last 30 years?
No one has ever been able to satisfactorily create/reproduce one.
:).
My parents had no problem producing a brain; four in fact. Maybe I'll create some myself some day. I could tell you how but I'd need a signed note from your mom
Creating brains isn't hard; creating artificial brains is.
lysergically yours
"As soon as we solve a problem," said Pollack, "instead of looking at the solution as AI, we come to view it as just another computer system."
;)
This is the most interesting comment to me. Because we understand the nature of the process that produces supposedly 'intelligent' results, (and we don't understand the same process in ourselves), we perhaps rightly just view the resulting system as just an application.
Seems like Minsky is throwing all his toys out of the pram because he doesn't want to admit to what everyone else has been saying for a while: that whether a computer can think is at best an astonishingly difficult question to answer and at worst meaningless. I'm a grad student who's just spent a year looking at computational linguistics and semantics (amongst other things), and the most debilitating restriction on the semantic side of things is the problem of so-called 'AI-completeness', which essentially says that if you solve this problem you have a, externally at least, thinking computer. Really simple things like anaphora resolution are AI-complete in the general case. If we could have solved this problem by now, I think it's fair to say we would have done, given its massive importance. However, we know that the brute-force case is ridiculously intractable, and we can't figure out how to do it any more cleverly. Roger Penrose argues that this is due to the fundemental Turing-style restrictions that we place on our notion of computing. Until we get a paradigm shift at that level, we're likely never to solve the general case.
And I'm sure that Minsky is aware that attempts to solve constrained domain inference and understanding have been taking place for a good long time now. I just don't see why he's so upset that the field of 'AI' (which is a nebulous catch-all term at best) has shifted its focus to things that we stand a cat in hell's chance of solving, and that have important theoretical and practical applications (viz. machine learning). Replicating human thought is not the be-all and end-all, and you can argue that it's not even that useful a problem.
Robots, though, I agree with. Can't stand the critters
Henry
i don't do sigs. oops.
I very much enjoy the works of Markram and Tsodyks. What they mainly analyze is how two nerve cells can interact with each other. They showed how they change their connection weights and how the timing of spikes, nerve impulses, affect how neurons connect to each other and how they transmit information.
While these studies tell us a lot about the underlying biology they do not tell us what these modes of information transmission are used for. For years it had been known that synapses have complex nonlinear properties. Biology pretty much does not constrain what functions neurons compute.
Thats why I do not believe that such studies will bring us nearer to real AI anytime soon. The algorithms coming from these systems are severely underconstrained. A lot of modelling has followed the pioneering works of Markram and Tsodyks, one of them being Maas. All these algorithms are very fascinating and might yield insight into the functioning of the nervous system.
The algorithms however are lightyears from being applicable to real world problems. The field of AI is old and in some sense quite mature. None of the "biologically inspired" algorithms today can compete with state of the art machine learning techniques.
Googlefight "Slashdot Troll" against "BSD is dying" 303:229. BSD thus cant die.
* One might claim LTP or LTD as some sort of neuronal knowledge. Ok, that's fair, but my point stands if you apply it to the building blocks of neurons. Do ion channels "know"? Do amino acids? It's turtles all the way down.
Give me Classic Slashdot or give me death!
Although mentioned in a (lone) paragraph in the article, I don't know why we haven't heard more about the Cyc project. Lenat's premise that you can't have intelligence without knowing the millions of "obvious" things about the world, aka, "common sense" seems intuitively right.
If you reply, do so only to what I explicitly wrote. If I didn't write it, don't assume or infer it.
That's all there is to it. Minski is just sore that his theories from 30 years ago aren't proving themselves, and the decentralized models being implemented by his rivals at MIT (e.g. Rod Brooks and his graduate students) are demonstrating remarkably sophisticated behaviors and advancing the state of the art.
Ask some 3-year-old kids which way is up, and they will all know, but they won't be able to define it. Yet, since computers don't have bodies, they don't have anything like the semicircular canals that we have, which act as gyroscopes and give us an intuitive, non-intellectual sense of which way is up.
Trying to program intelligence purely with software puts the researcher at a disadvantage, since even the most fundamental rules and attributes of things (fire is hot, water is wet) have to be explicitly entered as constant variables.
Once robotics advances to the point where mobility, vision, and speech recognition can be taken for granted, then AI can be programmed as an add-on.
Body first, mind second - That's how animals evolved on this planet, and it's how, I believe, Rodney Brooks approaches this field.
every stain tells a story
"Graduate students are wasting 3 years of their lives soldering and repairing robots, instead of making them smart. It's really shocking."
Yeah, much more shocking than the -- decades -- he (and others in the 'hard AI' camp) have been spending? They've made oh-so-much more progress, haven't they?
Rodney Brookes made more progress with his robots in the early nineties than the whole hard AI camp did in 3 decades. I remember seeing a documentary once comparing this huge robot which used a traditional procedural program to navigate through a boulder-strewn field. It took about 3 HOURS to decide where to put its foot next. Meanwhile, little subsumption architecture-based robots were crawling around like ants, in real-time. (Oh, and some of them had to learn to walk from first principles every time they were turned on -- only took about half an hour!) That's the most damning evidence of the failure of hard AI I can think of.
As others here have said, what good is a brain until we get a useful BODY working? Manueverability and acute senses are a must before an artificial intelligence can do anything useful, or learn from its environment effectively.
ERROR 144 - REBOOT ?
If Marvin wants to know why AI hasn't made major strides in the past 30 years (and, by the way, I would say that it has) he should look no further than his own bullying, arrogant approach.
Promising subfields like perceptrons were intentionally quashed by him... he went out of his way to strangle investment and research in areas he considered to be a dead end. We're not literature majors: we can't just all say the same thing in a party over wine and cheese and call it progress.
Even bad ideas, when well explored, can give new meaning and better approaches to a field. And since this is research, noone knows the correct answer: even a dumb-seeming idea may turn out to be the right one-- or give us clues about features the right answer needs to have.
Of course we've had major advances in AI. One of the challenges of AI, as the article points out, is that once something is well understood, it is defined as being outside the AI field. Computer vision, face recognition, voice and speech recognition. Conversation engines like SmarterChild. No, this isn't HAL, but they are good, positive steps in the right direction.
I read about artificial brains in a book somewhere. To be able to make an artificial brain we first need to know how to make a lot of positrons, and how to keep those away from electrons. Then we can make a brain.
-- Cheers!
I took Minsky's class last year, and let me tell you, the article couldn't print 75% of the irate stuff he has to say about AI, MIT, and life in general. We once spent an hour class session listening to Misky rant about modern science fiction and random things he didn't like about his Powerbook. In fact, most of his classes were extended rants about something or other (you zealots will be happy to know that he too, hates the Microsoft).
He comes across as affable but bitter. I found it strange that though he cointually complains about the leadership of the AI lab, he and his protege Winston were in control of it for some ~30 years without making any groundbreaking progress. In fact, Minsky's latest work "The Emotion Engine" is simply a retread of his decades-old "Society of Mind." I suspect that now that Brooks and the new guard are moving in, the old guard is looking for someone blame its lack of results on.
Please mod the parent up.
:-(
I am an AI researcher and the parent poster is speaking truthfully.
The main challenges in AI at the moment do not concern building the physical robots -- e.g. a piece of kit on wheels with IR sensors or such things.
The main challenges in AI concern applying some very complicated math to solve problems like pattern recognition, density estimation and other forms of machine learning.
It seems to me that a large number of AI PhD students spend their lives tinkering with the mechanics and electronics of the robots that will ultimately be used to test their algorithms. This is wasted time; a good electronics graduate should be able to do the tinkering, it shouldn't require a prospective AI PhD student to do it.
I can see the point in the PhD student learning a little about the hardware that they want to run their algorithms on (so that they know the limitations and common problems with real hardware), but they should not spend all their time doing that and wasting the opportunity to spend their time contributing to their field (i.e. AI, not mechanics or electronics).
That said, many AI labs do not have the funding to be able to pay full time hardware technicians, so in many cases the PhD student *has* to do the tinkering
"The noble art of losing face will one day save the human race"---Hans Blix
You can't get that much out of most humans!
the preceding comment is my own and in no way reflects the opinion of the Joint Chiefs of Staff
Here's some perspective from an MIT AI lab grad student who's been inspired by both Minsky and Brooks. (Minsky is on my Ph.D. committee.)
"AI has been brain-dead since the 1970s."
I agree, unfortunately. At least, what was traditionally meant by "AI" has been brain-dead. There is very little focus in the field today on human-like intelligence per se. There is a lot of great work being done that has immediate, practcal uses. But whether much of it is helping us toward the original long-term goal is more questionable. Most researchers long ago simply decided that "real AI" was too hard, and started doing work they could get funded. I would say that "AI" has been effectively redefined over the past 20 years.
"The worst fad has been these stupid little robots."
Minsky's attitude towards the direction the MIT AI lab has taken (Rod Brooks's robots) is well-known. And I agree that spending years soldering robots together can certainly take time away from AI research. But personally, I find a lot of great ideas in Rod's work, and I've used these ideas as well as Marvin's in my own work. Most importantly, unlike most of the rest of the AI world, Rod *is*, in the long run, shooting toward human-level AI.
Curiously, just last month I gave a talk at MIT, tited "Putting Minsky and Brooks Together". (Rod attended, but unfortunately Marvin couldn't make it.) The talk slides are at
http://www.swiss.ai.mit.edu/~bob/dangerous.pdf.
In particular, I shoot down some common misperceptions about Minsky, including that he is focused solely on logical, symbolic AI. Anyone who has read "The Society of Mind" will realize what great strides Minsky-style AI has made since the early days. I also show what seem like some surprising connections to Brooks's work.
- Bob Hearn
Think of it. Only a stupid robot is a good robot.
Before you yell flamebait or troll, let me explain.
I have been following the progress of various AI technologies, including neural nets and adaptive logic networks, for many many years now. Years ago perceptions were first developed and it was shown that they could learn simple patterns. Perceptrons were basically two layers of software simulated neurons. They worked, and researchers were fascinated and worked on them regularly.
Minsky, being the "highly regarded" and "leader" in AI, wrote a paper that proved that these perceptrons could never learn more complicated patterns, and threw a bunch of math at the reader. So people stopped. After all, there was a mathematical proof that perceptrons weren't going anywhere. Research skidded to a halt for decades because of Minsky.
Of course, then someone developed the (gasp!) THREE layer perceptron/neural net and sure enough with the right formula it could learn much more complicated tasks.
Minsky, in my opinion, does this regularly. The problem is, that he has a reputation in the industry as being a leader (I'm not sure why).
He's already lost us two or three decades of research because of his "leadership" -- I am terrified that he might cost us more development into the future.
Where could we have been if Minsky wasn't always going around half cocked, screaming that he is right? "Robots are useless!" is history repeating itself and him trying to get more press. Keep developing guys, just ignore the peanut gallery. There's always someone who says it can't work (ahem, Minsky) -- it can and it will.
The baby's fine -- please stop sending business cards.
People who do perform illusions and escape tricks have been doing things mostly the same way for decades. Magic tricks may change slightly, but all the basic principles and tricks are the same. There's no real evolution, just adaptation to please the crowd.
Now, with that in mind, let's look at artificial intelligence. AI has always been about trying to convince an audience that a machine is thinking. This is demonstrated by the very existence of the Turring test and many products (such as the Aibo, Furby, etc) that try to mimick emotions. If the audence is entertained, amused, or convinced, the AI is considered good. Bad AI is when the audience can see right through it.
Artificial intelligence is magic. It's a trick. It's an illusion.
It is no surprise then that AI hasn't really advanced. The trades of showmen are practically unchanged for hundreds of years. Razzle-dazzling an audience involves technological advances, but it remains unchanged. Even in the cases where "artificial intelligence" is used to aid in medical diagnosis ("expert systems") or manufacturing are really only following man-made logical structures. The computers aren't thinking, they're only doing what they're told to do, even if indirectly. The end result is impressed people who think the machine is smart.
Of course, you don't have to take my word for it. If you want to see how badly AI is going nowhere, I hightly recommend reading The Cult of Information by Theodore Roszak. While his focus is not on the fallicy of AI, it covers it in context with the much broader disillusionment of computers by society.
Now, what does AI need in order to progress? Probably AI creating other AI. Something with a deeper embodiment of evolution. As long as it's man-made, it will never be intelligent, just following a routine. Of course, I am going to stop right here... I am not qualified to offer a solution these obstacles.
Join Tor today!
"How the hell am I ever going to be able to download my brain into these damn little robots if they don't hurry up and make them smarter? I running out of time, dammit!!!"
Human Level AI's Killer Application - Interactive Computer Games, John E Laird and Michael van Lent American Association for Artificial Intelligence AI Magazine Summer 2001 pp 15-25
My summary of the above - the AI in games might not be too hot (some would dispute with the academics about that but let it go), but game environments themselves are complex enough to pose a challenge for state-of-the-art AI researchers.
"The problem with that is that no one really *knows* how the brain works beyond a very, very basic and limited understanding. No one has ever been able to satisfactorily create/reproduce one. There's more going on than just synapses in there, that much most scientists can agree on. What they don't agree on is *what* else is going on in there."
That's what Minsky is getting at. Few people are working on that problem.
Research talent in universities seem to be striving for business solutions. But IMO, such research should be primarily done by businesses, not AI labs. Universities should create new science.
(Bits On Our Mind, an exhibition of some undergrad and graduate computer science work) ...and I headed STRAIGHT for the nematode booth. You see, I had heard that some clever Cornellian had created a simulation of the entire neural network of a nematode. The way I saw it, there was nothing else there that could possibly be more interesting than that.
:P
So I found myself standing in front of a computer screen. It was a worm swimming through water! In 3D! In real time! After I pushed my jaw shut, I began to ask the genius student some questions...
"Is that real-time?" "Well, actually, no, that is a 10 second looping clip that took a week to calculate."
"Well, I see a neural map there. Is that complete?" "Well, actually, no, that is a simplified version of the real nematode nervous system, on the order of about 1 simulated neuron to 10 actual neurons."
"So you simulate neurons! That's awesome. Let's see the code." (He proceeds to flip through 4-5 pages of very sophisticated-looking mathematical equations to describe the behavior of ONE neuron.)
What a let-down! No wonder Minsky is pissed, real AI is HARD!
We will soon have hardware that has the number of connections or processing power of a human brain. The problem is nobody's come up with the software to run on it. In humans this is what makes the brain more than big organ ... the "soul" if you are religiously inclined. Maybe a human soul can be reduced to nothing more than a program with an enourmous propesity to learn and adapt over years of training / habituation ... say from the years 0 to 18.
There are many authors that have written and demonstrated that the brain probably doesn't function as a mass of context-free predicate logic rules -- including my favorite, Hubert Dreyfus.
Dreyfus argument is old, and its rebuttals are well-known. Consider that symbolic systems are not limited to context-free predicate logic.
The progress of AI is uncertain, but it is certain that there's no future for symbolic logic AI.
It is not certain for me.
Both connectionist and symbolic approaches may succeed if given enough time. However, I think that obsession with neural nets of many people here is of the same nature that obsession of numerous early aviation enthusiasts with wind-flipping devices. Certainly you can mimic mechanics of nature with some effort, but there are usually better ways to do the job.
Lisp is the Tengwar of programming languages.
So Minsky has an opinion, he expresses it, he provides data, calculations and evidence to support it.
People just accept it, and progress is delayed.
Why is it his fault that there are so many followers? If anything is to be blamed is that these researchers just blindly follow whatever he's saying rather then take a good critical look at what is going on.
If his math and theory "proved" that an area of AI was a dead end, and it wasn't, his math/theory was wrong. It is a sad state when nobody dare challenge the status quo.
What Minsky is actually flaming about here is the damage done to the field by the original "AI winter" and the real possibility of a new AI winter starting in a few years.
;-)
.sig below).
"AI winter" is the name given to the collapse of strong AI as a business model in the mid 80's - expert systems and symbolic AI in general didn't deliver on their promises, and so the money went away. As a guy who got his doctorate in AI in 1985, I can tell you all about it.
One of the major causes of AI winter was researcher hubris - lots of people hacked up systems that appeared to solve 80 percent of certain complex problems and then said "all that stands between us and a complete solution is money and time". For many of those systems, solving the last 20 percent would have taken 2000 percent of the time, if it could have been done at all. The tragedy of AI winter, though, is that basically all of symbolic AI was abandoned, though some of it is creeping back out into the light with obfuscated syntax (see my
What Minsky sees here is a lot of people heading down the same path, but with neural nets and small robots instead of expert systems. The new systems are doing some interesting things relative to the old symbolic AI systems (though they do have the advantage of 20 years of Moore's law to help them). But, will they scale up? Right now, nobody knows. If they don't, the last thing the field needs is another cycle of overpromise/underdeliver/abandon.
Maybe AI is just plain hard, and cracking it will take longer than one or two computer industry business cycles.
To a Lisp hacker, XML is S-expressions in drag.
"Al Going Nowhere?"
well duh!
what idiot made the lowercase L and uppercase I look the same?
but, since we're on the subject, did you know Al Gore invented the field of AI?
intellectual property law is philosophically incoherent. it is your moral duty to ignore it or sabotage it
Actually, they have made significant strides already in figuring out how the brain works. Check out the Levy Lab at the University of Virginia. They've trained a computer model of a rat's hippocampus to do all sorts of intelligent things, such as transitive inference, sequence completion/combination/disambiguatuion, goal finding, etc. While these are not difficult problems for humans to solve or hardcode into a program, the fact that a single network can do these different and sometimes contradictory things represents something that I would call intelligence. As far as I know, they don't plan on having a model of a human brain very soon, since U.Va. lacks NSA-scale compute servers, but even rat-level learning is pretty cool.
WARNING: there is a trojan on your
Minksy belongs to the old school of AI thinking. These guys believe that it is possible to make statements about intelligence itself, without considering the interactions of the organism/agent with its environment or the underlying architecture of the brain/CPU. I think that the total failure of this style of thinking to produce anything interesting in 50 years proves that this approach is sterile. Minsky laments the fact that graduate students build robots, but this activity exposes students to the challenges of constructing a device that must actually interact with the environment. It is ridiculous to assume that you could design a system capable of intelligent behavior without ever confronting the problems of sensors and actuators. Almost every part of the brain is devoted to processing raw sensory input or generating motor output. One cannot simply design an intelligent system without worrying about sensory input and behavioral output. The CYC project of Lenat has the laudable goal of teaching a machine "common sense" by hard coding a vast database of simple statements like "Trees cannot walk". This is a totally wrongheaded approach to learning and reasoning, and is typical of old school, hard AI.
We will only make progress in engineering intelligent, adaptive systems by studying actual examples of intelligent, adaptive systems, namely animals. Neuroscientists and psychologists are beginning to embrace the tools of mathematical modeling and simulation wo help explain nervous system structure and function. Computer scientists would do well to similarly embrace the work of experimental neuroscience.
Minsky is a dinosaur.
What's the difference between thinking and fooling people into believing you're thinking?
The same difference between cheating on a test in a subject you've never taken and actually knowing the material: as soon as you're asked a question that isn't on your crib sheet, the charade is over.
An AIBO has a pre-programmed set of behaviors, and any stimulus it isn't programmed to respond to will not have a realistic effect. The same is true of Loebner Prize winner Alice -- the only difference is the size of the pre-programmed response database. Ask it something it's never heard, and it will choke.
When these AIs are able to produce convincing responses to new stimuli, then I'll say that the difference between "fooling" and actually thinking has become irrelevent.
The enemies of Democracy are
If you were put inside a little white box where you had to flip millions of switches on and off according to certain simple rules, you would look like an idiot next to a computer. A computer can't walk around and recognize things, and doesn't know what an apple is, so what? In my opinion, machine intelligence should be focused on making computers able to make themselves better at what they do best. I'm not sure what a super intelligent computer system would be used for, and I don't think that I would even be able to imagine what would be possible. I would be interested to know what other people think about this idea. Most of the things that I can think of tie back into the "real" world somehow. What would a self-organizing non 3-dimension oriented intelligence be able to do?
Saying that AI is impossible because computers can't come into "our world" of three dimensions, or understand our literature is kind of intelligence chauvinism.
Stallman and co. hadn't spent so much of their time rewriting unix *GNU), instead of working on AI, like they were supposed to be doing. :-)
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