(Artificial) Mind Meld
Posted by
timothy
on Sat Sep 16, 2000 01:20 AM
from the it's-pronounced-"eye-gore" dept.
from the it's-pronounced-"eye-gore" dept.
Reader tewl points to this Wired article about a collaboration between the OpenMind project headed by Push Singh of MIT's Media Lab and Chris McKinstry's Mindpixel project. Neat to see these complementary projects getting along despite criticism each might have for the other. From the article: "The OpenMind and the Mindpixel projects will tie their databases together 'at the back end.' This means that any user data entered into either of the projects will be accessible by the other."
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(Artificial) Mind Meld
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garbage in, garbage out (Score:3)
Here's a selection of data people have entered into mindpixel (rank as true or false, to validate them).
eat him?
okay, some of them are good, but they are all supposed to be context independent, and something that everyone would agree on. That means no opinion, no political campaigning, no paradoxes. If 10% of mindpixels database is complete garbage, of course it's never going to succeed. If it doesn't have an answer, people are just screwing up the system by entering it.
Hmm... (Score:3)
However, I'd rather try to gather money to buy out/opensource cycorp than re-implement everything they've done in the past 16 years; they have a huge knowledge base already built, and a lot of code, and CYC can already do some interesting reasoning. (I know there isn't much there, but read what articles you can find; it's fascinating stuff)
And only using yes/no facts for data is just stupid; the computer needs to do some reasoning, and have some structure, otherwise, it would all just take too long! That's about as stupid as 'the table method' in AI. Even simple AI's can't necessarily be represented like that, so I hope there's more to it that I just missed.
...and for those people who think computers inherently will never be able to reason: go home; you aren't welcome here. I'll argue with your facts, but I won't cater to your prejudices.
---
pb Reply or e-mail; don't vaguely moderate [ncsu.edu].
I don't mind... (Score:3)
More AI projects journaled in Wired (Score:3)
From the old school, there's always AOLiza [wired.com]. She's not smart, she's not even that pretty, but she's the one all the guys want to talk to...
Kevin Fox
Media Lab is mostly hype (Score:4)
Negroponte, the Lab emphasized computers and multimedia.
Ten years later it began its silly season with "Things that
Think" (chips in shoes or clothing that communicate with
the wearer, for example). But just then the Internet
materialized out of nowhere and caught the Lab with its
micropants down. Judging from its website, by now the MIT
Media Lab has made up for lost time by promoting projects
that expand e-commerce.
More interesting than anything the Lab has ever produced
is the fact that it's funded by big business. The Lab's annual
budget in 1995 was $25 million, mostly from 95 corporate
sponsors, half of which are overseas. While the Lab claims
that sponsors cannot dictate the research, it's also true
that grad students have to sign a nondisclosure agreement
before receiving aid, and sponsors often fund research that
is proprietary. Given this history, it's not surprising
that since the Internet arrived, the Lab has been chasing
the dot-com rainbow. But one has to ask: What about the
public sector? Where's the vision? Does anyone at the Media
Lab care?
This OpenMind project smells more like a rat than a mouse.
A computer knows only one thing, and it's the only thing
it is likely to ever know without insanely massive databases,
along with bloated fuzzy-logic programs that go by the name
of "artificial intelligence," but are really thinly-disguised
variants of brute force.
A computer knows this: one is not equal to zero.
Slashdot should try to stay clear of trendy hype backed by
big bucks. That includes Wired magazine, which received
start-up money from Nicholas Negroponte.
Wrong way to go about it (Score:3)
I took a look at both of the projects: Open Mind associated text strings with pictures (discribing a picture, discribing a picture's contets, and so on), or one text string with another (explaining a fact, giving an example of a relation, explaining cause an effect, and so on). Mindpixel gets a collection of statements/questions in the form of text strings, and tries to get a consensus on whether the statement is true or false (or if the answer to the question is true or false).
But this seems to me to be the wrong way to go about it. While these projects will collect massive amounts of data, all that data is is associations between text strings. All they'll be able to do is detect that there's certain connections/correlations between certain words, and certain collections of words. This way of doing AI assumes that intelligence is just a bunch rules and mechanisms for manipulating symbols, with the symbols somehow representing chunks of information.
But what if you took these vast stores of information and replaced each word with word with some gibberish: "vut" replaces "car", "folp" replaces "clock", and so on. All the relations between words, and groups of words, remains exactly the same, but no human could understand it; all of the meaning would go out of it, because the meaning is being suplied from the outside, by the humans knowledge of what certain strings of letters mean.
However, if you were somehow to do the same scrambling to the vocabulary of a human's mind, so that this (formerly English speaking) human now used "vut" for "car" and "folp" for "clock", other people would eventually be able to understand and communicate with him; all of the meaning and information has stayed the same, it's just the labels that have changed. But for something like Open Mind or Mindpixel, the words aren't labeling anything; there's just relations between meaningless strings of characters.
The above argument is a (rather bad) summary of the argument that Douglas Hofstadter makes in the book Fluid Concepts and Creative Analogies [indiana.edu]. Anyone interested in AI should read this book. Douglas makes a very compelling argument that diving straight away into things like words and sentences is getting much to far ahead of ourselves, and that we first need to make tiny baby steps in AI before we can attempt to make an AI that really uses human languages.
Suppose you were an idiot. And suppose that you were a member of Congress. But I repeat myself.