
Artificial Addition (2007) - apsec112
http://lesswrong.com/lw/l9/artificial_addition/
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bainsfather
A nice thought-parody of arguments about creating General AI.

It would be nice to have an updated version because the option 'we just need
more computing power' (and training data) is now working pretty well for many
machine learning domains (i.e. deeplearning).

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adrusi
Sure, throwing more computing power at a problem that's already conceptually
solved, more or less, is going to yield results. But that won't help for
creating general ai because it's not conceptually solved.

~~~
btilly
A surprising number of the things that have been solved in the past decade
with more computing powers were things that were not conceptually solved, and
which we had no good ideas how to solve.

A good example is Google Translate. We stopped trying to "understand" language
and just threw a large dataset at pattern matching. And it worked amazingly
well.

For other things we throw ideas of "this might improve what we can already do"
and it winds up effectively solved, whether or not we understand it. This
happened, for example, with Alpha-Go.

So "conceptually solved" and "AI can do it" are pretty much disconnected.

~~~
adrusi
I see now that "conceptually solved" is just too ambiguous. Translation is
conceptually solved, in the sense that I meant to evoke, to the extent that
it's solved. We have all the conceptual understanding necessary to translate
between human languages. We don't have a full conceptual model of language,
but we discovered that that's not necessary for translation.

Suppose a team of reseachers were given a computing platform a trillion times
more powerful than the best systems available today, and a trillion dollars to
build a set of training data. Would they be able to create a general AI in a
predictable timeframe? If not then there's at least some area where we need to
develop conceptual understanding. My intuition says that this is the case.

If you think that general AI can, in fact, be developed the same way as google
translate, then what do you think the training data would look like? I'd
imagine that the translation training set looks something like a massive
Rosetta stone, though I haven't looked into it. Inputs associated with a valid
output. What would a set of input/output pairs look like for a general AI? Any
valid English query/statement/remark/essay with an "intelligent" response?
Like a chatterbot? Doesn't seem like that would work. Or maybe I'm wrong.
Maybe a chatterbot with ungodly amounts of computational resources and
training data would develop the ability to reason. Maybe I'm just not
appreciating how much that could change the game.

But it seems more likely that the first general AI will look more like
Alphago, where we started with a reasonably solid understanding of how to
approach the game, and then introduced deep learning where we discovered it
was the best approach. We don't have a reasonably solid understanding of how
to organize a general intelligence, and we don't know what it would take to
get there.

~~~
kobeya
> We have all the conceptual understanding necessary to translate between
> human languages.

Except that we don't. The original Google Translate used the conceptual
modeling approach to translation, and it was absolute shit for any non-closely
related language pairs. Google Translate (and Baidu, etc.) now uses deep
networks whose operations are rather opaque. We _don 't_ understand in detail
how they work. But that didn't stop us from building them.

~~~
adrusi
In context, that claim was an explanation of what I was trying to get at with
the phrase "conceptual understanding". We don't have a conceptual
understanding of language. We do have a conceptual understanding of how to
translate between human languages — Proof: we can translate between human
languages. If you think that's not adequate proof then you're disagreeing with
me on the semantics of "conceptual understanding". If you have an alternative
name for that then I'd appreciate it because I think my usage is confusing.

It's not a useless notion of "conceptual understanding" though, because while
being able to have a program complete a task implies having the conceptual
understanding necessary for that task, it's possible to have the conceptual
understanding but not be able to make a program, if, for instance, it would
require greater (within reason) computing resources.

~~~
kobeya
It does sound pretty useless because the same logic can be used to prove that
we have a conceptual understanding of general intelligence, because after all
we exist and we think.

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acobster
This reminds of Jeff Hawkins' book _On Intelligence._ He takes a similar
stance on the overarching/conceptual solution vs. more specialized solutions
that dance around the knowledge gap. He criticizes neural networks as not
sufficiently detailed to mirror the brain's behavior, and proposes the
Hierarchical Temporal Memory model, or HTM.

The hierarchy is crucial: as in the brain, there are different regions at
various levels of cognition, with higher regions responsible for greater
levels of abstraction. A moment of real learning/understanding is
characterized by a sudden, system-wide switch of signals between layers mostly
traveling _up_ the hierarchy (this input is confusing! I don't know what to do
with it!) to signals mostly cascading _down_ (AHA! I get what that means!
Let's do this in response).

It was written in 2004, so like this article it lacks some of the insight
we've gained over the past decade or so. But I think some of its general
insights are still relevant. Whether it's NNs or HTMs or some yet-to-be-
discovered algorithm that ends up being the driver of general AI, this article
definitely resonates. At the most general levels of AI, we are still in the
"confused" phase: viewing the state of AI as a whole system, our signals are
still largely questions going up the conceptual hierarchy, rather than
understanding cascading down.

~~~
AndrewKemendo
ANN's and HTM aren't incompatible. Deep nets are hierarchical. Here's a few
papers on the subject:

[https://arxiv.org/abs/1410.0736](https://arxiv.org/abs/1410.0736)

[https://www.cs.toronto.edu/~rsalakhu/papers/HD_PAMI.pdf](https://www.cs.toronto.edu/~rsalakhu/papers/HD_PAMI.pdf)

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joe_the_user
This is indeed a brilliant article, at least as far framing the problem with
some objections goes.

Of course, what's missing from hypothetical artificial arithmetic is simple
symbolic manipulation. And I am pretty sure that "More gofi", a more explicit
treatment of AI, is something that Noam Chomsky and other have as their
favorite bullet point on how to go further.

However, that too has been tried to whatever degree. If you had an overt,
tractable logical theory that explained all intelligent behavior, then yes
you'd have the missing ingredient of human intelligence. But unlike arithmetic
calculation, it seems unlikely that human intelligence has this quality.

Indeed, all human "dealing with the world" behavior together involves a black
box whose entirety is not subject to rational or logical reflection or
description but whose broad outline. A person can't give a complete-enough-to-
write-an-algorthim account of walking down the street recognizing things but
the person can likely give a good why the street light they see is a street
light - ie, heuristic black box behavior and logical/deductive behavior is
intimately tied within human behavior.

And this might give some clue what's missing modern AI.

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gumby
>Of course, what's missing from hypothetical artificial arithmetic is simple
symbolic manipulation. And I am pretty sure that "More gofi", a more explicit
treatment of AI, is something that Noam Chomsky and other have as their
favorite bullet point on how to go further.

Unlikely as Minsky (the prince of gofai) and Chomsky were, umm, hardly fans of
each other's work.

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mannykannot
I was going to say something, but I don't think I can comment on this post
without participating in that which is being parodied.

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VanillaCafe
> With the graphical-model insight in hand, you can give a mathematical
> explanation of exactly why first-order logic has the wrong properties for
> the job, and express the correct solution in a compact way that captures all
> the common-sense details in one elegant swoop.

What was the compact, correct way referring to here?

~~~
tomfitz
[https://en.wikipedia.org/wiki/Graphical_model](https://en.wikipedia.org/wiki/Graphical_model)

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posterboy
>the label "thirty-seven" is meaningful, not because of any inherent property
of the words themselves

either this "label" has no place in the story or it is not meant to be part of
the story, but then our number-words do have an inherent structure, so they
are easy to understand.

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VLM
I'd rephrase the last line as the lesson is more the classic early
optimization mistake. If you can't make something as smart as a fish then
don't worry so much about super-human levels of optimization.

The mathematically innumerate comments are almost a troll that if the thought
experiment worked and an artificial mathematician were invented, a significant
fraction of the real world would not recognize or agree with its results.

There's a very cruel saying about people should do what they're born to do,
with the dark insinuation that grandma the knitter should be locked in a
sweatshop to sew against her will or (insert trendy CS tech here) children
should be euthanized as a general policy until the tech gains significant
commercial traction. Anyway unless an AI is quite heavily socialized into our
culture what we're likely to grow might be ideally suited to research Klingon
Warp Engine Fields in the 24th century but to us the output is going to look
like hard to compress digital noise, so there's that problem.

