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Comment by SimianSci | Hacker News Reader

When someone is expected to be wizened and does not have the knowledge to keep up with the needs of those around them, they in turn become Shamanistic in their practice.

The speed of improvement on these models has been incredible and has outpaced the learning speed of humans and put many experts into these Shamanistic roles.

I think the operative means of addressing this is to recognize that we can only learn so quickly, but we are still called to improve our knowledge and understanding to a higher level. Since the improvement of these models is neither logorithmic, nor exponential, we currently occupy a space in time in which the models are currently smarter on average than we are as a collective whole.

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Algorithms and data that emulate responses aren't smart.

A 5 year old knows if you want to wash your car, you need to take it to the car wash.

Can a 5 year old write a substantial program on spec, that passes the requirements and given tests, in a few minutes?

If not, then perhaps this comparison is not the be all end all.

"A ship is useless, it can't drive over land..."

5 year olds and ai both have jagged intelligence.

also its AI not "artificial code generation intelligence" . Ship is your view of the product to shoehorn into something specific.

But it demonstrates that LLMs struggle with basic reasoning. A criticism of LLMs is that they're imitating without a understanding of what they're doing and without a clear plan, so this inability to solve a simple logic puzzle is very relevant. If LLMs didn't struggle with reasoning problems then something like ARC-AGI wouldn't exist.

Its a question designed to fool the AI. It's like saying that a person doesn't understand the limitations of reality when they fall for a magic trick.

These aren't the same thing.

You can't fool an AI because it isn't using its own judgement.

You can fool an AI as evidenced by them being fooled. They demonstrably appear to be working through a problem and get fooled by the wording of the problem. If you think differently, merely asserting it is not the way to convince people that what they see is wrong.

You can't fool an inanimate object.

An LLM is (effectively) just a really, really elaborate "choose your own adventure" book.

It's not "working through" problems, it's just tracing a route through an pre-defined information space. It's not actually thinking, it just does a good impression of it.

>You can't fool an inanimate object

You can't constrain foolability to just the animate objects.

Foolability is based on intelligent processing.

To what degree such an intelligence needs to be developed is not defined at all (we can, for example, trivially fool a doog using one of many tricks, and a dog is hardly general intelligence).

Thus far, the inteligent processing we knew concerned animate objects. But we now have developed software/hw combos that exhibit intelligent processing. Is it enough to actually be intelligent or to be fooled?

We don't know. We do know it's enough to appear intelligence, convince people that it is intelligent, and to appear to be fooled.

But even if we think of LLM AI as mere mechanistic process with no emergent intelligence, who said one can not fool a mechanistic process? We can fool even simple pre-LLM gaming AI systems (based on simplistic heuristics) just fine.

They simply appear to "exhibit intelligent processing". The intelligence is in what created the data. It's a surface that's being traversed in a complex way. The LLM doesn't 'understand' that surface in any way, it just traces a path on it and reguritates apparent understanding. I'll grant it's eerily convincing.

'Fooling' something, essentially to deceive or trick, is defined as causing someone to believe an untruth. LLMs don't hold beliefs (neither do mechanistic processes), and they aren't a someone.

You can widen out the definition of the word but that generally makes language weaker - interestingly, semantic drift is a big issue for LLM's.

We have different opinions, and that's fine. Have a good day.

Please explain why the mechanism of the LLM generating output precludes it from being able to be fooled without using use tautologies or reducing to substrate for explanation.

'Fooling', essentially to deceive or trick, is defined as causing someone to believe an untruth - at least in British English it does.

LLMs don't hold beliefs (neither do mechanistic processes), and they aren't a someone.

You can widen out the definition words but that generally makes language weaker - interestingly, semantic drift is a big issue for LLM's.

You're describing a tool.

Tools can do very useful things, but they aren't intelligent.

referring to John Woolridge's recent talk? "the car wash is only 200 feet away, should I drive or walk?"

His slide showed Opus 4.6 saying "walk". I couldn't get 4.6 to do that.

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