
A grand unified theory of AI - fogus
http://web.mit.edu/newsoffice/2010/ai-unification.html
======
stochastician
The idea here is using "Stochastic Functional Programming" (see some of the
work by Goodman, Mansinghka, Roy, and others at <http://web.mit.edu/vkm/www/>
). Basically, you write down your AI problem in a "forward" direction,
suggesting how the data came to be. You then "fix" the outputs. The engine
generates a distribution on "possible program histories" that preserves the
statistical properties you care about.

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]

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mojuba
I suspect that, like any other simplistic and too-obvious-to-be-interesting
theory, this one too will never go beyond the question of whether birds fly.
Just a slightly improved expert system. And good luck in entering all the
rules of the world and watching your "AI" failing at the most simple questions
such as "what time is it?" or "what was my previous question?" - questions
that even kids can answer. Shame it's published under such a monumental title
at mit.edu.

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muxxa
The general approach seems to pretty obvious. Surely similar approaches have
been tried? It superficially reminds me of fuzzy logic.

~~~
jey
The hard part is finding the 'right' (universal, tractable) prior. And there's
nothing "fuzzy" about probability theory:
<http://en.wikipedia.org/wiki/Bayesian_inference>

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.

~~~
rdtsc
I think he meant fuzzy logic derived from fuzzy set theory. It was pioneered
by Lotfi Zadeh. It has gained more ground in Japan than the rest of the world.
Here is the wiki on it: <http://en.wikipedia.org/wiki/Fuzzy_logic>

~~~
jey
Right -- I'm just saying that fuzzy logic is crap (at least compared to proper
statistical inference; maybe there are other, better, uses for fuzzy sets).

Why use ad hoc schemes when you can just maintain a probability distribution?

~~~
jxcole
Fuzzy logic is not crap, and the fact that you are comparing it to statistics
in this manner shows that you have little understanding of either.

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...](http://en.wikipedia.org/wiki/Fuzzy_logic#A_new_way_of_expressing_probability)

~~~
jey
False. Maybe you're thinking of frequentist statistics?

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jxcole
I think this is the actual paper if anyone is interested:

<http://www.mit.edu/~ndg/papers/churchUAI08_rev2.pdf>

~~~
owyn
Good find. Relevance ++!

This will be good reading on the bus tomorrow as I ride off to the coding salt
mines (where crappy programmers go to die).

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Estragon
I attended a tutorial by Noah Goodman last year. It's an interesting idea, but
there's not much there in the way of implementation, at this stage, and it's
not clear to me how that implementation is going to work. For instance, he was
talking about doing MCMC to recover semantics from natural language using a
kind of "blurring" of the observed text. That is, he didn't have a way to
derive a reasonable initial parse in which the observed text would have a
reasonable probability, so he intended to look for parses in which "nearby"
texts had high probability. Then presumably he intended to do some kind of
simulated annealing which would progressively tighten up the neighborhood of
acceptable text. Of course, the devil is in the details of "nearby..."

~~~
bravura
Based upon this article, I am also not sure what is really new here.
Probabilistic AI has been around since the 1980s (see Judea Pearl:
<http://en.wikipedia.org/wiki/Judea_Pearl>).

~~~
stochastician
There's a lot of new interest in probabilistic programming languages, which is
what Noah et. al. have developed. See <http://probabilistic-
programming.org/wiki/NIPS*2008_Workshop> for a workshop from about a year ago
that explored the space. Probabilistic programming languages enable you to
express concepts such as recursion and universal quantification in a way that
Pearl's graphs don't easily allow.

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nollidge
What's this thing do that my spam filter doesn't?

This SMBC on science reporting seems apropos: [http://www.smbc-
comics.com/index.php?db=comics&id=1623](http://www.smbc-
comics.com/index.php?db=comics&id=1623)

~~~
_delirium
It makes for a handy mental filter, though: claims to have found the grand
unified theory of AI can be treated with the same piles of salt as claims to
have found a grand unified theory of physics, or to have solved the P/NP
question (not impossible, but unlikely in any given instance).

~~~
Jach
It's also somewhat comforting that this is coming from MIT. This guy doesn't
seem anywhere near full-blown general intelligence (whose risks he probably
hasn't even considered) if he's only barely discovered Bayesian Inference...

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jackfoxy
Interesting only insofar as this article had nothing new, yet MIT found it
timely to publish. Hasn't bayesian inference been around for a while?

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.

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drcode
Hmmm... this article is too breezy to figure out if there is any substance
behind this claim. Unfortunately, "church programming" is a dead end on
Google, for obvious reasons.

Anyone find any more substantive info relating to this article?

~~~
sah
<http://projects.csail.mit.edu/church/wiki/Church>

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mark_l_watson
The Church probalistic extentions to Scheme
(<http://projects.csail.mit.edu/church/wiki/Church>) look interesting enough.
I am not sure how practical things like the fuzzy list equality (using
Levenshtein distance) would be, but still really interesting ideas.

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teeja
A lot of what we learn and know as human beings is not the result of
deliberate heuristic strategy, but simply what we call "intuition" ... which I
usually interpret as unconscious pattern-seeking based in experience -- spread
out over a considerable amount of time so that the result "cooks out" like a
slow stew.

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.

