Comment by electrograv | Hacker News Reader

Comment by electrograv | Hacker News Reader

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> In us there is a shared language of representation that encodes experience.

What you express in the quote above is a very "computationalist" view to AI. FWIW, in contrast, I'm more of a "connectionist" [1]. This means that when I look at the same incredible "general intelligence" of humans -- this almost "synesthetic" ability to abstract and connect ideas -- I do not see a powerful translation and symbolic reasoning machine with some seemingly magical universal language buried deep within. In fact, I believe there can be no such language without severely compromising the reasoning system's generality.

Instead, I see a powerful "connection machine", where concepts, thoughts, language, etc. are all the result of some incredibly versatile connective learning/creativity process. Of particular importance, I see this connection machine thrive and prosper within systems where there are no axioms, no ground truth, no single agreed-upon notion of what separates "this" from "that" (you'll see this in humans when studying philosophy). The analogy to this notion from a computationalist perspective would be a machine whose instruction set, or core reasoning language, is always in a state of flux.

I will freely admit though that one view does not necessarily have any more explanatory power than the other; they both rely on some "magic" unknown assumptions. For a connectionist, the "magic" is the connective learning algorithm. For a computationalist, the "magic" is in this universal language/symbolic system.

Disclaimer: I'm by no means an AI expert (still have a lot more to learn!), but I always enjoy thinking/discussing these topics and the surrounding philosophy.

[1] http://en.wikipedia.org/wiki/Connectionism

Connectionism is not a predictive theory. Rather it is the manifestation of a depressingly common fallacy in science: assigning a sacred mystery[1] to as-yet unexplained phenomenon.

How does your connectionist interconnected networks of simple units actually give rise to general AI? Answer that and you'll have the "shared language of representation" that the OP was talking about.

[1] http://lesswrong.com/lw/iv/the_futility_of_emergence/

I completely agree that "super magic emergent intelligence" is not an explanation, but a mystery. But I think it's worth noting that the same applies for this "super magic universal language of representation" -- it's not an explanation, it's a mystery.

It's also important to realize that these aren't beliefs, or truth claims, or scientific claims. They're philosophical perspectives; no more, no less. They might guide the intuition, but have no bearing on the science itself. Someone who doesn't clearly understand the distinction between the philosophy and science of a topic may definitely risk either contributing to the "depressingly common fallacy in science" you mention, or risk misinterpreting a philosophical argument for a scientific one, and hence through blurred vision believe they see fallacy when in fact there is none.

One way of looking at it is both views are different philosophical angles of the same thing (or at least the same problem/mystery). A connectionist sees this conception of a "shared language of representation" as assigning a sacred mystery (what does this language actually consists of, precisely?) to an as-yet unexplained phenomenon, in the same way a computationalist sees this of the connectionist's learning algorithm (how does this learning algorithm work, precisely?)

The reason I highlight this philosophical symmetry is to emphasize that these are merely different intuitive mindsets developed towards approaching the common mystery of general intelligence.

The bottom line is so long as human-like "general intelligence" is a mystery (to the extent that we can't replicate it 100%+ effectively in computers), it's going to be an "unexplained phenomenon", and thus any theories developed around it will have some "magic" hole somewhere -- some key element devoid of predictive power. (Because if there were no such hole, then by definition, we'd already have it all figured out.)

FWIW my preferred language of representation is sparse activity of neurons over time :)

most intelligent human brains have been programmed by their parents and education systems for more than 10 to 20 years. Even if we have an as powerful ANN, it still take years to train it into something intelligent. Computational way might be well ahead of that. Thus we still haven't taught ourselves how to program our own brain.

1. Why should ANN train at the same rate as humans do? It's not unreasonable to expect that it will learn million times faster than any human.

2. While every single human must go through the 10-20 years learning process, there are no reasons to duplicate that process for AI. Connection diagram and weight matrix could be easily copied between ANNs, so that they can constantly build on top of each other knowledge.

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