
John McCarthy: Challenges to Machine Learning (2007) [pdf] - sgt101
http://jmc.stanford.edu/articles/appearance/appearance.pdf
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Animats
I had McCarthy as a professor in the 1980s, just about when it was clear that
logic-based AI had hit a wall. McCarthy was determined to put mathematical
logic as a formalism under the real world, and it just wasn't working.

He once described the "missionary and cannibals problem" in class. Then he
wrote it up in his notation for "circumscription", cranked the notation, and
the answer came out. I thought at the time, as he converted the problem to his
notation, "here's where the miracle occurs". That's the problem. Hammering the
real world into a formalism is very hard. Cranking the formalism is not
difficult to automate.

Mathematical logic works fine where the problem space can be formalized, such
as in software proofs of correctness. But trying to get to "common sense
reasoning" that way, which was McCarthy's goal, just didn't work. It was a
great disappointment to him.

AI as a field has suffered from a repeated problem - believing that the next
big development will lead to strong AI. Graph search, the General Problem
Solver, expert systems, and big databases of knowledge have all been touted as
the solution. In each case, the ceiling of what could be done rose a bit.
Machine learning isn't a panacea, but it, too, has raised the ceiling of what
can be done.

(It's personally frustrating that much of my career was during the "AI
winter", from about 1985 to 2005. Expert systems had failed, and machine
learning didn't work yet. I spent a lot of time trying to find AI in control
theory. Combining machine learning and control theory is at last working out,
which is why autonomous drones are now so agile. But it happened 15 years
later than I'd hoped.)

~~~
solve
> as he converted the problem to his notation, "here's where the miracle
> occurs"

Exactly what I see to be the future of all types of engineering, computer
control, and interaction - e.g. programming languages, circuit engineering,
database query languages, all types of problem solving.

The future of all of these is just to very directly describe to the computer
the problem you want to solve, in a way that is a very direct representation
of the way you think through the problem, with nearly zero translation between
the way we approach the problem and the way we communicate the problem to the
computer.

To me, this perfectly describes the evolution from C/Java to Perl to Python.
The shift from highly relational SQL to Document-based noSQL databases. The
shift from bladed weapons, to point & shoot guns. The way you interact with a
doctor or future doctor AI, and it will infer from your layman description
what you're really communicating and then synthesizes that into a possibly
multi-iteration search strategy for solving the problem.

Today, the ability of tech to achieve this still holds us back substantially.
Over time though, we'll continue to converge to a way of communicating our
creations and desires to computers, using some kind of language or interaction
that requires us to do no intermediate translation. In many places, I think
we're not actually that far off from this, for some tasks. Over time, our
interaction methods won't get more strange and hard to learn, but strangely
more familiar and natural.

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eli_gottlieb
It gets to properly be something worth calling "artificial intelligence" only
when you do _causal_ reasoning from data. And that's actually fairly new.

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soup10
It's well known that machine learning is currently useful for solving some
problems and irrelevant to others. As is the case with all existing ai
algorithms. ML is not strong ai and nobody has ever claimed it was, so it
seems to be an odd criticism.

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JadeNB
Of course the name in the title should be 'McCarthy'. Also, I think that the
rest of the title is misleading; the paper seems to be about what McCarthy
sees as the challenges _to_ the subject, rather than about his personally
challenging the subject.

~~~
dang
Fixed. Thanks.

