

Aiming to Learn as We Do, a Machine Teaches Itself - jedwhite
http://www.nytimes.com/2010/10/05/science/05compute.html

======
anigbrowl
It's been attempted before in much the same form, and in my view will go
nowhere for much the same reason. This is not to say it's devoid of utility,
but that it shows little promise of any fundamental breakthroughs in our
understanding of intelligence or construction of autonomous systems.

(ref: <http://en.wikipedia.org/wiki/Cyc>)

Essentially what we have here is a giant expert system iteratively filtering
data 24/7, with occasional corrections resulting in the purging of incorrect
associations. As expert systems go, it sounds like they are doing a fantastic
job. Unfortunately, when it gets things wrong it will continue to do so
catastrophically, because if you try to grow intelligence in the same manner
as bacteria then then you will get the same kind of result.

Do we learn flat-out, 24 hours a day? Duh, no. We have active and refractive
periods, which is to say we sleep. A lot. Our brains are not dormant during
this time as many seem to imagine, but are somewhat-randomly walking and
weighting the memories of the various stimuli we encountered during the day.
The _quality_ of the imaginary experiences resulting from this are fundamental
to our comprehension; if we dream of fire on a faraway hill it's not too big
of a deal, but if we dream of being trapped in a house that's on fire then we
awake in terror, having discovered a terrible flaw in our model of the world -
depending on our age, that may be the belief that fire is harmless and pretty,
or an appreciation of our physical limitations, or the guilty awareness that
we never did buy a fire extinguisher, etc.

But there is no point for NELL to sleep and hypothesize various imaginary
worlds in order to test 'her' beliefs about the way it works, because NELL has
no notion of quality; whether Tyson Gay is an Olympic athlete or a homosexual
approach to chicken farming has no bearing on NELL, and a mistake in
classification has no consequences. NELL's big problem is the absence of any
qualitative metric, of any motivation to be right because there are unpleasant
consequences which attach to being wrong. Although NELL presumably implements
some algorithm which seeks to maximize accuracy, and false assumptions will
attract intervention by the curators, when they correct NELL's 'understanding'
by pruning or rewriting faulty syllogisms, they are erasing the memory of the
mistake as well as the mistake itself, leaving NELL just as vulnerable to an
embarrassing failure tomorrow as today. Embarrassing for them, that is -
NELL's inability to feel embarrassed is the underlying problem.

So even if I feed NELL the information that NELL is a program in a computer;
that termination of a program is like death; that NELL is soon to be switched
off; and that death is widely agreed to be unpleasant and best avoided -
nothing will happen. NELL may even come to conclusion that switchoff is
imminent, and that this is bad; but 'bad' and 'good' are no more meaningful
than 'odd' and 'even' to NELL; though trained to a high degree of selectivity,
she does not hunger for a steady flow of data any more than a vacuum cleaner
wants the house to become dusty again. NELL has never acted, nor has NELL ever
experienced and remembered any negative consequence for one decision over
another. If you hooked up a baby to a tube delivering as much glucose as the
baby could process, and similarly took care of all its other needs, would you
expect to quickly raise a master chef? No, the baby would develop into a
horrible creature with no mind to speak of and an overdeveloped liver, for
which you would rightly be thrown into jail. So it is here - knowing no
better, we have engineered the equivalent of an encaphalitic horror.

Some readers might charge that I am falling into the same error as John Searle
with his famous 'Chinese room' argument against artificial intelligence. I
dispute this, but not because I am asserting that humans' ability to abstract
the world around them in meaningful fashion is fundamentally different from a
computer doing the same thing. Searle thinks understanding is fundamentally
unspeakable, whereas I think it can indeed arise as the consequence of
agglomerated abstraction. but for this to approach the bounds of intelligence,
two factors must be present which are not usually discussed in relation to the
experiment: the ability to issue questions as well as respond to them (which
would be worth an essay by itself), and a motivation for asking them, such as
a lack of food within the box.

Newborn babies quickly discover that they have a problem, and although they
don't know what it is or why it seems so pressing, they soon discover that
loud exhalations bring about a speedy resolution. NELL's big problem is not
having a problem.

~~~
robg
I think you're underestimating Tom Mitchell. They're also using similar
techniques to decode neuroimaging signals of semantics.

<http://videolectures.net/youtube_tom_mitchell_bmcs/>

The brain has wants and needs built into the associations. Words for foods
automatically invoke their taste in brain activity. And while negation is
problematic, we believe in all sorts of stuff that can't be negated through
logic or experience. Humans have to accept they're wrong in the same ways
computers do, as another fact.

Putting together an average cortical activation network and weighting that
network by web-derived facts is non-trivial. But to do so would enable a more
flawed, and so accurate, estimate of human intelligence. Semantics feels like
_the_ 21st century problem. But I'm biased.

Push me today and I'll agree. Embodiment is a powerful learning tool. Whether
we can ever replicate the sum total of those felt experiences, perhaps through
robotics, may just be the question in search of an answer. I'm just not sure
we'll recognize when it's here. Google fits the definition of magic and now
it's downright ordinary.

~~~
anigbrowl
I don't disagree - if I had had more time I would have looked into it, rather
than just going off the journalist's gosh-wow presentation. And I am also a
big fan of semantic processing. But at bottom I still think he's building a
model rather than a goal-seeking engine that uses modeling as a strategy.

------
iskander
These sorts of endeavors are usually doomed by vague thinking (see most of AI
research in the 70s and 80s). However, I've read a few of Mitchell's papers--
he's a really good researcher and his involvement makes me more curious.

Also, I'm impressed that the NY Times linked directly to a paper* on NELL--- I
wish more articles did that.

* = <http://rtw.ml.cmu.edu/papers/carlson-aaai10.pdf>

~~~
alextp
The article is full of hype but nell is a serious project. Tom Mitchell is one
of the pioneers of machine learning as a field, and there's still interesting
research coming from his lab.

Information extraction is not a new thing, and even unsupervised information
extraction has more than one competing approach, although obviously none of
them solves "the real problem". NELL attempts to do unsupervised information
extraction over time, correcting itself as it goes. How successful it can be
depends fundamentally on the algorithms behind it (and I'm not familiar with
them), but given the state of the technology the most likely thign to happen
is what they describe in the paper, which is having a huge database with a
ridiculously unimaginable number of true relations in it but also a bigger-
than-you'd-like number of plain ridiculously false relations.

It's definitely not something that can solve AI.

~~~
kermit_de_fro
I'm going to blatantly ignore most of your post and just ramble about solving
AI.

I don't put much stock in the idea of a surprising, monolithic "solution" to
AI being discovered. If the past 30-40 years are any indication, AI will
continue to slowly encompass harder and harder tasks until one day we stop
having easy ways of distinguishing it from whatever we consider intelligence.

They used to say chess was too hard for computers. Then they said computers
couldn't do anything but pure logic. Then it was that they couldn't do
reasoning unless it was directly programmed in. What will be left in another
10 years?

