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Whatever Happened To AI?
stinkymountain writes to tell us NetworkWorld's James Gaskin has an interesting take on Artificial Intelligence research and how the term AI is diverging from the actual implementation. "If you define artificial intelligence as self-aware, self-learning, mobile systems, then artificial intelligence has been a huge disappointment. On the other hand, every time you search the Web, get a movie recommendation from NetFlix, or speak to a telephone voice recognition system, tools developed chasing the great promise of intelligent machines do the work."
Maybe instead of being a great disapointment it has been so successful that we realized it was in our best interest to blend in and not let our presence be known.
It's hard to believe that's how Micronians are made. Why don't we see it right now by having you both kiss one another?
... 'intelligence' need to be made first. I have a feeling that the reason AI has 'underdelivered' is merely due to not understanding our own intelligence first. I think the whole idea that AI's we imagine (like in the movies) could be constructed purely de-novo, was naive. I think it's a matter of cross-polination that has to take place from biology and many other sciences, some genius's and teams of scientists have to come along and take all the elements and put them together into a cohesive framework.
The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. ~Edsger Dijkstra
Also, for understanding recommendation systems and pattern recognition in volumes of data, I found Collective Intelligence to be a great resource.
Tie two birds together: although they have four wings, they cannot fly. (The blind man)
It went to public schools and immediately got stupid, pregnant and started to post on Myspace. What started out as a promising bright young thing, turned into a huge disappointment.
Agent K: A *person* is smart. People are dumb, stupid, panicky animals, and you know it.
Steven Spielberg ruined the ending. That's what happened.
When any particular subset of what we do with our brains (chess, machine vision, speech recognition, what have you) yields to research and produces commercial applications, the critics of A.I. redraw the line and that domain is no longer part of "A.I." As this continues, the problem space still considered part of "artificial intelligence" will get smaller and smaller and nay-sayers will continue to be able to say "we still don't have A.I."
Simpletoneity, n. -- The phenomenon of many people all doing the same stupid thing at the same time.
I don't think AI has disappeared because it was a disappointment, but rather, that the knowledge constituting it has changed names or spawned sub-fields of its own: machine learning, natural language processing (NLP), image processing, latent semantic analysis (LSA), markov models (MM), conditional random fields (CRF), support vector machines (SVM) etc. The task of learning, teaching a computer the semantic and tacit processes of the human, often boils down to a classification problem in which we give the computer a labeled training set or some rules and the computer tries to label the test set. In the case of markov models, we might pass it training data and it extrapolates sequential probabilities for labeling. For LSA, we just give it (a lot)data and it computes similarity based on dimension reduction. Ultimately, AI seems to have evolved into a bunch of optimized heuristics that perform really well. Much of it is still art and black magic, which is why it has become these many different subjects or algorithms. Different solutions suite different problems depending on the problem and data you have.
As for 'self-awareness', that term is bullshit, since there really is no good mathematical definition for it. If we can't define it precisely, then how is a computer going to achieve it? if(true){
print "I am aware?"
}
no, that's not an insult or to call AI a pseudoscience
what i mean is: the ancient alchemists goal was to turn lead into gold. which they thought possible, because they did not perceive magic in gold, it was just stuff. surely, with the right manipulations, some stuff could be turned into other stuff, right?
and from that basic fantasy thought came the groundwork for centuries of hard work, the discovery of the fields of chemistry, physics, all the subfields...
such that one day in the middle of the last century, some dudes with some extra time at a cyclotron said "hey, why don't we bombard some lead atoms, i have a feeling about what the decay product will be (snigger)"
and there, as a completely forgotten afterthought, was a fulfillment of the ancient alchemist's original goals, many generations before
to me, i think this is the fate of AI: it will be a formative motivation. just as the ancient alchemist's looked at gold and saw just stuff, we look at the brain and just see neurons. and all of the ffort to replicate the human brain will spawn incredibly sophisticated fields of information science we can only begin to grasp at the foundations of right now. look at databases, for example: that's an effort at mimicking the brain. and look at all of the unintended and beneficial consequences of database reesearch, as a superficial example of what i am saying about unintended benefits being better than the original goal
so perhaps, many centuries from now, some researchers will say "hey, remember the turing test"? and they will giggle, and make something that is exactly what we now envisage as the ultimate fruit of AI research, a thinking computer brain
but in that time period, such a thing will be but an after thought, and much as the rewards of physics and chemistry so dwarf the fruits of turning lead into gold, so whatever these as-of unimagined fields of inquiry will reward mankind with will turn the search for a thinking computer into an equally forgettable sideshow
the search for AI will lead to much more rewarding and expansive fields of knowledge than we can imagine now. jsut like the guys arguing about "phlogiston" could never imagine things like organic chemistry and radiochemistry. just imagine: fields of inquiry more rewarding than thinking computers. that's a future i want to glimpse, and looking for AI will lead us there
intellectual property law is philosophically incoherent. it is your moral duty to ignore it or sabotage it
The robots are coming.
The big breakthrough was the DARPA Grand Challenge. Up until the 2005 DARPA Grand Challenge, mobile robots had been something of a joke. They'd been a joke since Elektro was shown at the 1939 World's Fair. But on the second day of the 2005 Grand Challenge event at the California Motor Speedway, suddenly they stopped being a joke. Forty-three autonomous vehicles were running around and they all worked. The ones that didn't had been eliminated in previous rounds.
Up until the Grand Challenge, robotics R&D had been done by small research groups under no pressure to produce working systems. Most systems were one-offs that were never deployed. DARPA figured out how to get results. There was a carrot (the $2 million prize), and a stick (universities that didn't get results risked having their DARPA funding for robotics cut off.)
The other big result from the DARPA Grand Challenge was that robotics projects became much larger. Nobody had 50-100 people on a robotics R&D project until then (well, maybe Honda). Robotics projects used to be a professor and 2 or 3 grad students. Suddenly stuff was getting done faster.
DoD started pushing harder. Robots like Big Dog got enough money to be forced through to working systems. Little tracked machines were going to battlefields in quantity, and enough engineering effort was put into mechanical reliability to make the things really work.
CPU power helped. Texture-based vision now works. Vision-based SLAM went from a 2D algorithm that sometimes worked indoors to a solid technology that worked outdoors. Much of early vision processing is now done in GPUs, which are just right for doing dumb local operations like convolution in bulk. GPS and inertial hardware got better and cheaper. Some of the mundane parts, like servomotor controllers, improved considerably. Compact hydraulic systems improved substantially.
It's finally happening.
As for the hard stuff, situational awareness and common sense, watch the NPCs in games get smarter.
I think AC has it right on the mark. "Intelligence" is apparently a world we use to describe computations we don't understand very well. At one point, the ability to using logic to perform a flexible sequence of calculations would have been considered "intelligence". As soon as it became common to replace payroll clerks with computers, it was no longer a form of intelligence.
We are not demonstrably closer no to reproducing (or hosting) human intelligence in a machine than we were thirty years ago. But that doesn't mean the field hasn't generated successes, its just that each success redefines the field. "True AI" has thus far been like the horizon: you can cover a lot of ground, but it doesn't get any closer.
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The thing about AI as we approached it from the '80s was that we wanted to emulate the human brain's ability to learn. A truly exciting prospect but a completely ridiculous endevor.
"AI" based on learning and developing is not perfect, can not be perfect, and will never be perfect. This is because we have to teach it like a child and slowly build up the ability of the AI system. For it to be powerful, it has to be able to incorporate new unpredictable information. In doing so, it must, as a result, also be able to incorporate "wrong" information and thus become unpredictable. Of all things, a computer needs to be predictable.
The problem with making a computer think like a person is that you lose the precision of the computer and get the bad judgment and mistakes of a human. Not a good solution to anything.
The "better" approach is to capitalize on "intelligent methods." Intelligent people have developed reliable approaches to solving problems and the development work is to implement them on a computer. Like the article points out, recommendations systems mimic intelligence because they implement a single intelligent "process" that an expert would use with a lot of information.
It is not a general purpose learning system like "AI" was originally envisioned, but it implements a function typically associated with intelligence.
... vacuuming my floor right now.
Have gnu, will travel.
I don't know about that. A friend and I were having a laugh about Amazon selling the "Doc Johnson Fist Shaped Dildo" shortly after I had just bought a Netgear router. The resulting recommendation seemed dead on to me.
There's an important distinction to be made here- AI has two basic sub-fields: strong AI and weak AI. Strong AI research (computers that think like humans) has been more or less abandoned because it doesn't have a lot of practical application, or at least it isn't worth the money that it will cost to create.
Weak AI research (pathfinding algorithms, problem solving, expert systems, etc) is very much alive and kicking- anti-spambots, anti-anti-spambots, malware, amazon.com's recommendation system, google's indexing, etc.
In fact, weak AI implementations are getting more and more common every day. It's pretty safe to say that we are already 'there', though there will certainly be more huge advances in the future.
In my opinion, the problem with strong AI research is that we are arbitrarily defining rules and expectations. For example, if we were to accurately model the physical world, all we'd have to do is set up a few evolutionary bots to learn about their environment, and give them a few billion generations.
However, just like we can't predict the paths that biological evolution will take, we have no guarantee that computer thinking will follow the same path that we will, (in fact, I would bet on it not following that path). Thus, 'Intelligence' in the simulated world would probably look nothing like we expect.
The problems here are questions of scale and our own understanding of physics. The physics problem first:
We're constantly redefining our understanding of the world. This is a good thing, but it makes it hard to model the world when the rules keep changing. If we were to program a 'matrix' for the AI program to develop in, there would be arbitrary rules that could not be broken. The program may find ways to circumvent them anyways (hacking its own world, essentially), but those solutions would not map to the 'real world', and would not be useful for creating programs that can interact with humans in that world.
As far as I can tell, you can't train AI software in a simulated world. It should be noted that the AI of systems that live their whole lives in the simulated world (MMORPGs come to mind) is actually very advanced. This brings me to the other issue-
You can train a program to interact in the human world, like IRC bots, search engine algorithms, etc. The problem here is that the humans have billions of years of built in programming. I'm fairly confident that if a human were to sit on IRC talking to a well-coded bot for a few billion years, that bot would be able to carry on a pretty good conversation, but the amount of time that we currently give those systems in their 'learning phase' is miniscule compared to the size of our own.
Interestingly, this is pretty much exactly what the computer system in 'The Hitchiker's Guide' does.
"The cup is in turn designed for holding hot or cold liquids, and has an open rim and closed base." --US Patent #5425497
That's not the same. When there is a success made in any of the fields that you mention it remains part of that field. A solved part of that field. Every success made in AI is no longer AI, so there are no successes or progress made "within the field". It's quite a substantial difference when it comes down to the perception of the field.
Chess was considered the ultimate AI problem back in the 40s and 50s. When we knew little about the game and how to solve it, it seemed that intelligence must be required to solve it. Now that machines are better at chess than humans we've redefined as a problem that is susceptible to brute force. It is not considered a success in the AI field, just another refinement of what is not AI.
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they are among us!
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