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1. vannev+14[view] [source] 2025-04-08 19:44:13
>>todsac+(OP)
I would argue that Lenat was at least directionally correct in understanding that sheer volume of data (in Cyc's case, rules and facts) was the key in eventually achieving useful intelligence. I have to confess that I once criticized the Cyc project for creating an ever-larger pile of sh*t and expecting a pony to emerge, but that's sort of what has happened with LLMs.
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2. baq+3j[view] [source] 2025-04-08 21:29:24
>>vannev+14
https://ai-2027.com/ postulates that a good enough LLM will rewrite itself using rules and facts... sci-fi, but so is chatting with a matrix multiplication.
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3. joseph+cm[view] [source] 2025-04-08 21:53:49
>>baq+3j
I doubt it. The human mind is a probabilistic computer, at every level. There’s no set definition for what a chair is. It’s fuzzy. Some things are obviously in the category, and some are at the periphery of it. (Eg is a stool a chair? Is a log next to a campfire a chair? How about a tree stump in the woods? Etc). This kind of fuzzy reasoning is the rule, not the exception when it comes to human intuition.

There’s no way to use “rules and facts” to express concepts like “chair” or “grass”, or “face” or “justice” or really anything. Any project trying to use deterministic symbolic logic to represent the world fundamentally misunderstands cognition.

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4. woodru+Zb3[view] [source] 2025-04-09 20:45:47
>>joseph+cm
> Any project trying to use deterministic symbolic logic to represent the world fundamentally misunderstands cognition.

The counterposition to this is no more convincing: cognition is fuzzy, but it's not really clear at all that it's probabilistic: I don't look at a stump and ascertain its chairness with a confidence of 85%, for example. The actual meta-cognition of "can I sit on this thing" is more like "it looks sittable, and I can try to sit on it, but if it feels unstable then I shouldn't sit on it." In other words, a defeasible inference.

(There's an entire branch of symbolic logic that models fuzziness without probability: non-monotonic logic[1]. I don't think these get us to AGI either.)

[1]: https://en.wikipedia.org/wiki/Non-monotonic_logic

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5. joseph+Tx3[view] [source] 2025-04-09 23:30:41
>>woodru+Zb3
Which word will I pick next in this sentence? Is it deterministic? I probably wouldn’t respond the same way if I wrote this comment in a different mood, or at a different time of day.

What I say is clearly not deterministic for you. You don’t know which word will come next. You have a probability distribution but that’s it. Banana.

I caught a plane yesterday. I knew there would be a plane (since I booked it) and I knew where it would go. Well, except it wasn’t certain. The flight could have been delayed or cancelled. I guess I knew there would be a plane with 90% certainty. I knew the plane would actually fly to my destination with a 98% certainty or something. (There could have been a malfunction midair). But the probability I made it home on time rose significantly when I saw the flight listed, on time, at the airport.

Who I sat next to was far less certain - I ended up sitting next to a 30 year old electrician with a sore neck.

My point is that there is so much reasoning we do all the time that is probabilistic in nature. We don’t even think about it. Other people in this thread are even talking about chairs breaking when you sit on them - every time you sit on a chair there’s a probability calculation you do to decide if the chair is safe, and will support your weight. This is all automatic.

Simple “fuzzy logic” isn’t enough because so many probabilities change as a result of other events. (If the plane is listed on the departures board, the prediction goes up!). All this needs to be modelled by our brains to reason in the world. And we make these calculations constantly with our subconscious. When you walk down the street, you notice who looks dangerous, who is likely to try and interact with you, and all sorts of things.

I think that expert systems - even with some fuzzy logic - are a bad approach because systems never capture all of this reasoning. It’s everywhere all the time. I’m typing on my phone. What is the chance I miss a letter? What is the chance autocorrect fixes each mistake I make? And so on, constantly and forever. Examples are everywhere.

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6. woodru+4G4[view] [source] 2025-04-10 13:15:44
>>joseph+Tx3
To be clear, I agree that this is why expert systems fail. My point was only that non-monotonic logics and probability have equal explanatory power when it comes to unpredictability: the latter models with probability, and the former models with relations and defeasible defaults.

This is why I say the meta-cognitive explanation is important: I don’t think most people assign actual probabilities to events in their lives, and certainly not rigorous ones in any case. Instead, when people use words like “likely” and “unlikely,” they’re typically expressing a defeasible statement (“typically, a stranger who approaches me on the street is going to ask me for money, but if they’re wearing a suit they’re typically a Jehovah’s Witness instead”).

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