A grand unified theory of AI
web.mit.edu
web.mit.edu
For people familiar with inverse methods, what they basically have here is a generalized inverse solving engine that obeys the laws of probability.
Of course, right now, this approach ("solving AI by running programs backwards") is a bit slow, but some startups are rethinking the entire computing stack ( http://www.naviasystems.com ) in an attempt to rectify that. [I'm one of the people at said company]
This project doesn't make any progress on that, but nor was that its goal. The whole "grand unified" business seems to just be editorializing by the author.
Why use ad hoc schemes when you can just maintain a probability distribution?
Now you could setup a wide range to test cases with various loads, temperatures faulty sensors etc. Or you can figure out a reasonable approximation by hand based on Fuzzy logic and ship it.
Note: your solution must run on a 4bit 32khz cpu with 400 bytes of ram.
Fuzzy logic is not about creating actual intelegence just a quick and dirty aproach that happens to be useful. When selling bread makers you are vary limited in your development budget and the HW you send to people. So yea it's overly simple add hock solution but it's also cheap.
It's really just a special case of Bayesian inference: p(A calls B "tall" | B is a 6'1 man) is a combination of what you know about who is called tall in general and what you know specifically about who A thinks is tall. Unfortunately, for some reason many linguists don't like thinking in these terms, so it is easier to communicate with them using fuzzy logic vocabulary than Bayesian inference.
As far as I know, thats not true. Fuzzy logic is meant to encapsulate the idea that someone is "sort of" tall.
I haven't studied fuzzy logic or degrees-of-Xness, do you know how useful that ends up being in practice?
However, I remember reading studies that seemed to indicate that apes/chimps use fuzzy logic. I don't remember who wrote it or how they tested it, but it seemed fairly convincing at the time.
So, I guess I'd say its not so useful now (at least not as an independent concept) but if its true that humans use it, it might become useful in the future.
In the end though, fuzzy logic isn't going to solve your problems for you, at least not alone. The way you use the fuzzy logic is going to be much more important.
However, it should be noted that statistical inference is not a necessarily an effective learning approach given that it was created to deal with random variables and the world we are trying to understand has many non-random, orderly aspects.
Humans aren't good at doing the things that statistics is good at but statistics isn't good at doing the things humans are good at. Just as an example, a person can indeed act effectively in uncertain but somewhat ordered environment but virtually no human being can tell you anything like the probability distribution of the events which they deal with in daily life.
So basically, we do indeed need new approach different from both the probabilistic and the pure-logical approaches. But problem is that melding these various existing approaches into something coherent and usable is far more easily said than done. One clear problem with any such system is that the complexity explodes for a formal specification which involves both probability and logical process.
I suggest people call their approach "a general theory" after they do something impressive with it. We're waiting.
Perhaps the intended title of article was "There Ought to Be A General Theory Of AI". That I'd agree with...
Fuzzy logic is just like binary logic only it allows for partial truth.
Probability relates to how likely something is to happen.
To take an example (I didn't make this up, but I don't remember the source):
If you take a series of data points to determine whether or not I am in my living room at 7:00 on any given evening and determine that the probability is 50%, that means that I am in my living room 50% of all nights.
However, if you give me a 50% fuzzy logical value of being in my living room, this means that I am lying in the doorway between my living room and my bathroom, such that exactly half of my body is in one place and half of my body is in another.
These are two different things and the mechanisms do not apply at all to the same problem sets.
http://en.wikipedia.org/wiki/Fuzzy_logic#A_new_way_of_expres...
In other words, "Fuzzy logic" can mean anything vague related to numbers. In other words, it's just a buzz word that was trendy in the eighties for quantifying something without any particular logic behind it. In other words, it is crap.
I mean, seriously, the "discovery" of Fuzzy Logic involved no original or interesting mathematical machinery whatsoever, it just involved y Lotfi Zadeh coining a word to cover ad-hoc quantifying processes. It's the flimsiest of "pop" mathematics and it hasn't had much following for a while now. Sure you can "use" it in the sense that still engage ad-hoc quantification but you could do that before Zadeh came around.
This will be good reading on the bus tomorrow as I ride off to the coding salt mines (where crappy programmers go to die).
This SMBC on science reporting seems apropos: http://www.smbc-comics.com/index.php?db=comics&id=1623
I haven't followed Cyc in quite a while, but I think they tried incorporating some probibilistic reasoning. I wonder if they ever gave a shot at incorporating exception assertions? Using the analogy of birds, penguins are birds, but the bird assertion of flight does not apply.
Anyone find any more substantive info relating to this article?
If I understand this journalist's description of what the AI folk are talking about, 'Church' is the old weighting strategy again. Since we don't really understand how our 'tacit knowledge' develops (or -doesn't-), this model may result in something similar.
It's ground-breaking IFF the computer can really resolve 'reality' without continual hand-holding. To my knowledge this hasn't been achieved yet (how many years are we into the CYC project now?), but certainly it makes sense to use some rules that 'seed' growth. It may require our best intuition to create that seed.