Comment by themgt | Hacker News Reader

Comment by themgt | Hacker News Reader

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"How am I talking to you right now?" Sussman asked. Sussman seemed to be talking about the shared symbolic values being held in the conversation, and at this point I started to understand. "Sure, when you're running the program, that whole thing is a black box. So is your brain. But you can explain to me the reasoning of why you did something. At that point, being able to inspect the symbolic reasoning of the system is all you have." And, Sussman explained, the propagator model carries its symbolic reasoning along with it.

I dunno. Are we talking about true "hard AI"? Because really, how much can Sussman or anyone introspect their own thought processes? We can tell ourselves stories about motivation and symbol manipulation, but those stories themselves are just more symbol manipulation. I'm not sure a sentient symbol-manipulator ever really "gets to the bottom" of true introspection of their own thought processes, including counterfactual thoughts they might have had.

This seems like an overly mechanistic approach, although perhaps workable in "soft AI"

You can usually come up with an explanation for why you did something. You don't have to "get to the bottom" of your own thought processes to do this: you just need to be able to reconstruct the symbolic manipulation part. This seems like a good thing for an AI to be able to do -- especially a truly "hard" AI that you'd trust to run things at a high level.

Humans often come up with an explanation for why they did something that's simple, consistent and also completely untrue. They sometimes even believe their own explanations. We may run into the same problem with an AI here - it may be, purposefully or not, providing us with untrue explanations.

Indeed, people even offer explanations when tricked into believing they had chosen the opposite outcome to the one they actually picked [1].

[1] http://researchgate.net/publication/6745688

> participants fail to notice mismatches between their intended choice and the outcome they are presented with, while nevertheless offering introspectively derived reasons for why they chose the way they did

I, for one, think that would be a much more fascinating problem than implementing neural nets where the representation of the underlying data is heavily obscured.

If only it is possible. The only powerful enough model for reasoning that comes to my mind are bayesian networks, and those will suffer from the same problems as neural nets - nodes, edges and values may not be related to any meaningful symbolic content that would be useful to report. It again seems in line with human mind; we invent symbols to describe some groups of similar concepts, with borders being naturally fuzzy and fluid.

If the AI tells us: "I did it because #:G042 and #:G4285 belong to the same #:G3346 #:G4216, while #:G1556 and #:G48592 #:G4499 #:G22461 #:G48118", I don't think we'll have learned anything useful.

My thoughts as well.

This is why I feel some people will never "let" AI happen. They want 100% certainty. Neural nets scare them. Despite the fact that real, human intelligence carries no certainty. You either have intelligence, and creativity, and uncertainty, and accidents. Or you have computers and rigid logic.

The interesting thing there is, when a space rocket malfunctions, when a car wrecks, or the stock market goes crazy... and it's a computer involved, it's always human error at the end of it. And, thus, intelligence that caused the problem. Computers mindless do exactly the thing they were given to do.

You either have intelligence, and creativity, and uncertainty, and accidents. Or you have computers and rigid logic.

Why does it have to be such a strict dichotomy? What are the specific tradeoffs being made between creativity and rigid logic? Is there really no way to pick something in the middle?

The interesting thing there is, when a space rocket malfunctions, when a car wrecks, or the stock market goes crazy... and it's a computer involved, it's always human error at the end of it. And, thus, intelligence that caused the problem.

Individual humans aren't always at the end of it. Sometimes you can trace the problem further, to the incentives applied to the human by some larger organization, society or system. Are human organizations also intelligent?

I think we're still very far away from a definition of "intelligence" that would let us answer all these questions. It's not clear that current AI research is bringing us any closer to that definition.

My point wasn't to start a philosophical debate on the term "intelligence". My point was, the intelligence that was recognize in humans (our only real guidepost at all, in terms of "AI") is closer to neural nets than the symbolic and expert systems of the AI winter. And that human intelligence is fuzzy and you have to accept the good and the bad.

It seems more inline with Justification based/Truth maintenance systems than hard AI.

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