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Artificial Intelligence Overview
spiderfarmer writes: "Well, it feels slightly odd to suggest one of my own articles, but here goes. I've recently completed a brief overview of the current state of AI. The article concept was focused on Cyc, but scope creep being what it is, I ended up doing an overview of the entire field. Some of the Slashdot gang were fairly helpful in pointing me towards experts who would talk to me and towards white papers and books I might not have otherwise found. So, I thought they might be interested in how I put all the information together."
The scientist's explanation took one paragraph, and even sounded like it had a goal - allow a machine to use an encyclopedia to gain new information in a useful manner. This is an important step to an A.I. that can interact with people - you can then train it on reference materials, and have it "understand" them at a certain level.
This scientist is NOT mistaken - he would have to know that "common sense" does not equal "the human brain's inate ability to make sense of the culture it grows in". If I had to draw a distinction, "common sense", as you are describing, is static, tuned to one culture, while the "common ability" is semi-dynamic, able to learn, but (maybe) unable to unlearn.
You could try to fake common sense, by programming your own cultural assumptions into the program, subjecting it to cultural stimulus, and fine-tuning the program. Or you could attempt to program "common ability", train it on cultural materials for a few years, and try to tune the program to build it's own "common sense" in a way that is more like a human. I think these scientists are trying to do the latter.
I'm not sure what your tangent about post-modernism and 1984 have to do with A.I. - are you just making a rant about scientists who didn't get the memo that we are in post-modern times?
An interesting question is if human intellegence can be removed from the human - does it take eyes to understand the phrase "I've got the blues"? Does it take a parent to understand why many grade school teachers are women and most world leaders men? Does it take walking upright, starting at a tiny height and getting bigger, to understand skyscrapers? Or does that just take a penis?
Now, I'm using the "white-space" sense of understand - to be sympathetic to the person who has the blues, to feel an unexplained shame when the president is caught sleeping with a women not his wife, to feel an exhiliration driving into a new city. Can these be simulated in a computer without a body and a human's lifetime? Can these things be removed, still leaving a "human" intellegence? If we interacted with this intellegence, would we say it passed the Turing test? Would we want to interact with it?
Perhaps that's one level of A.I. above where this guy is aiming. It would be extremely useful just to have an intellence with a little of the human ability. You could train it on, for instance, medical journals. A doctor could then descibe symptoms, research, or an interest, and get summaries or references to the library. Once you trained it in the basics, you could burn it to a CD, send it to a doctor, who could then train it for his specific interests. Think of it as a very limited secretary, who requires some training and aclimation, but is still smarter than a PC.
This is probably the best A.I. can do for a few years - get to the point where you can train an A.I. for a particular subject, then meaningfully interact with those interested in the subject - like a very bad librarian. It's only when the clones come out in force that you can hook a computer up to a fetus, and do some real human A.I. training.
To go further, I posit that emotions are merely emergent behaviours of relatively simple systems, that seem to manifest complex behaviour. Just because we can't see the true motivation behind an emotion or decision, doesn't mean that the process was particularly complicated.
Examples: taking measurements of temperature in a region over 50 years and trying to predict climate change is statistical problem, while analysing samples of minerals in an area to try to find oil or gas is data mining. (as, presumably, mineral composition does not change over time so only single sample from each point is taken)
The basics to get self-aware systems is to define a self looping reasoning for the system. That is, the system must be able to observe it's OWN thoughts not only what is happening outside. It must be able to react and change it's thoughts by it's own thinking. That is very important. That also means you need to create a basic language system of some sorts for those underlying systems.
All in all it's complex but very interesting problem. Complex as in figuring out the basic underlying system and the defining parameters. If we get those done correctly, the AI will 'grow' and build upon them like a learning human would, by interacting with it's surroundings and with it's own thoughts.
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For some reason, the American military have been looking into the http://www.scn.org/~mentifex/ AI Home page predating the Mind project by seven years, as evidenced by the following logs of recent military accesses:
24/Jul/2001:10:59:39 - nipr.mil -24/Jul/2001:11:04:27 - nipr.mil -
24/Jul/2001:11:04:34 - nipr.mil -
24/Jul/2001:11:04:41 - usmc.mil -
24/Jul/2001:11:06:24 - nipr.mil -
24/Jul/2001:11:11:56 - usmc.mil -
29/Jul/2001:11:37:10 - navy.mil -
29/Jul/2001:11:40:56 - navy.mil -
30/Jul/2001:07:38:45 - arpa.mil -
07/Aug/2001:07:22:58 - pentagon.mil -
07/Aug/2001:14:44:12 - af.mil -
07/Aug/2001:14:44:16 - af.mil -
07/Aug/2001:14:48:19 - af.mil -
08/Aug/2001:11:21:48 - army.mil -
08/Aug/2001:11:22:02 - army.mil -
08/Aug/2001:22:18:15 - nosc.mil -
I used to work in AI Alley in Cambridge during the 80s. The industry got hyped to death by claims and demands on performance that wasn't possible. Lots of interesting things were done and lost during that timeframe. The thing is, we hadn't even reached artificial stupidity at that point. I'm not sure we have yet but we're closer in some domains. Until people unlink visions of Cherry 2000 and C3PO from the AI moniker, it's going to be tough getting people to set realistic goals. Just like the Lisp environment back then, I see the research and technology backdooring into products every day. More and more of the stuff that we did back then is making it out as "new technology" under Microsoft when we had it 20 year ago, but hyped it into obscurity. The industry seems to be coming out of it's own dark ages lately and I hope the media doesn't get back on the bandwagon to beat it back down. I thought the article was very good and you did a LOT of good research and didn't just edit together all the buzzwords you found. The response has been good in the community I still keep in touch with and you better watch out or you'll be changing people's minds about talking to journalists. Many of us wish you had been given more access to Cyc and their underlying knowledge engineering techniques to do that topic/aspect justice as well.
Good Job!
Since we do not know what consciousness is, any theory presented by Penrose is speculative. But that isn't the point of the book(s), anyhow.
However, his arguments against the possibility of a turing machine exhibiting understanding of mathematics are surprisingly strong. I'm still not convinced, but I can figure out reasons why all of my counter-arguments are irrelevant or implausible.
I was suprised that there was no information at all to be found regarding genetic programming. This method builds a large population of random computer programs and then refines them through genetic mutation to accomodate a specific task. Darwinian selection ensures that only the most fit programs survive, and less useful ones die off quickly.
I have been doing some work involving genetic programming lately, and have found it to be an amazing tool for finding creative solutions to complex problems. The problem domain I have been training my genetic program to solve is purely mathematical, but it seems to me that the technique could easily be adapted to find solutions to some of the tougher problems in AI, including but not limited to: data mining, natural language processing, and parellization.
I read somewhere (can't find the reference right now, sorry) that some work was being done whereby the genetic programs were being evolved that could themselves create neural networks. Each genetic program could be considered a template for creating a neural network. This seems to me like the most likely means of creating a software that could eventually pass a Turing test. I won't get into the self-conciousness debate here.
std::disclaimer<std::legalese> sig=new std::disclaimer; sig->dump(); delete sig;
The A.I. effort is often classified into two camps.
One is to approach human intelligence. This usually
implies conversational ability, since a hallmark of
human intelligence is language. This A.I. approach is
called "hard A.I.".
Soft A.I. looks at sub-problems, such as problem
solving, image understanding and so on.
Many of software inovations originated in A.I.
labs (e.g. interactive editors, bitmap graphics).
(During the early 80s these spinoffs were sometimes
confused with A.I.)
A problem with both kinds of A.I. is that its a
receding target. Once an important goal has been
reached, e.g. a chess computer that beats grand masters,
people write it off as a nice trick,
but not really A.I.
So I proprose what I call "interesting A.I.".
Two hallmarks of human intelligence are language
and curiosity. So if an A.I. could TELL us
something new and interesting on a regular basis,
then I would call it a success.
I suspect A.I.s will first arise in entertainment
computing: either as a robo-toy, a synthetic game
player, or synthetic actor in a film. This will be
a results of people's drive for challenging
creative play.