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Comment by mirekrusin | Hacker News Reader

What if this is the answer? What if our intellect is nothing more than few orders of magnitude more performant network of Clever Hanses in our head developed over years? Look at the history - it's actually hard to find something that is not biased - false, biased believes are spread throughout the timeline of human kind. Given enough time, our knowledge gets closer towards truth - but maybe it's nothing more than a time-trimmed tree of Clever Hanses? Like a child constantly creating false, biased interpretation of surrounding reality, discarding most of it, leaving bits that pass test of time.

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Clever Hans learned to copy and mirror the expectations of smarter beings. We are not being watched by smarter beings who are constantly giving us feedback, so there's no analogy there. If we were performing for an audience of super-human intelligences, we would be wise to use all the signal we could from them about what they expected us to do.

> Look at the history - it's actually hard to find something that is not biased - false, biased believes are spread throughout the timeline of human kind. Given enough time, our knowledge gets closer towards truth

How do you think you know any of that?

> “We are not being watched by smarter beings who are constantly giving us feedback, so there's no analogy there.”

Parents, family, teachers, community... ???

"We," as in the human race. (Remember Clever Hans was a horse, being watched by us.)

However, even on an individual level, haven't you ever had a new idea that didn't come from another person but just your own observation and contemplation?

The human race watches itself. It creates ideas of supreme beings so that its subjects will mirror and copy behaviours deemed to be "supreme".

And the human race is not smarter than the human race, which is why we are not in a Clever Hans situation. There's no hidden signal, telling us when to stop, that allows us to fool other smarter creatures into thinking we're as smart as they are when we are actually just mimicking them. We're just humans, learning from nature and from other humans, and that's just culture.

I think it's comparable, it's just that nature is a much more consistent teacher than Hans' handler was.

> What if this is the answer?

It would be a copout. Instead of actually tackling AI's problem of common sense, claim that maybe layers of logistic regression and matrix factorization is all there is, we are its equal, just a few layers up in abstraction and evolution. Does one really stem and count tokens to decide if a movie review is negative sentiment? Or does one empathize with its writer and build a complete model inside your head?

The horse would be the AI researcher claiming reasoning and understanding from an activation vector trained on word co-occurence on Wikipedia, and the farmer giving clues, is the heated community and industry, mistaking impressive dataset performance for a solution for a problem they're starting to forget.

I remember using some kind of software for math problem sets in high school. Some of the kids would just look at the equation, get it wrong, see the answer, and try and figure out the answer to a new version of the problem with generated coefficients. That sounds very much like a Clever Hans solution done by a human. I think what AI is lacking is the mechanism which causes us to reject such a solution, and that's much more complex than just finding the answer and I'm not sure related to the ability to find solutions in the first place.

Can you elaborate on what you mean by "a new version of the problem with generated coefficients"?

For example a problem might be solving for the roots of a polynomial, and on each try it would randomly generate a new polynomial with new coefficients.

first problem: find the roots x^2 + 2x + 1

second try: find the roots of 4x^2 - 2x + 4

Or something like that.

Ah, so what you're saying is that they would give up on the problem once they saw the answer, and then move on to a new one, rather than work through the first one until they understood the answer, right?

Sort of. The software would give you several attempts in order to get points on the problem. So they didn't really give up so much as they never had any intent to solve the problem in the first place, so much as see whether there was some kind of obvious relationship between randomly generated coefficients and the answer in order to get points on the question.

Ah, I see, sort of gaming the test platform (or trying to) rather than actually understanding the math. So a case of "you get what you measure" and also an example of what happens when you force kids to learn something they have no interest in, perhaps?

To my mind the problem is structural. Any algorithmic method the details of which are completely explicable is always going to seem not deep enough to constitute the true core of intelligence. But this doesn't call for not doing the clever Hans thing, it calls for doing it and concealing the fact.

Even our correct beliefs are not learned through their correctness, but monkey-see, monkey-do.

e.g. highschool mathematics

But some people - communities of people - pored over this stuff for centuries to work it out and try to find flaws (and found them). You could have an AI that tries to find flaws - provided you have an actually correct statement of the problem.

That's the classic scifi problem with robots: they do what you tell them to.

I think the trope of 'intelligent' automata being too literal goes back farther than the word 'robot', though. It also sounds a lot like the ancient stories that you hear about things like golems. Remember The Sorcerer's Apprentice, from Fantasia?

It comes across as a warning that we don't really have any solid understanding of what we're doing when we create new and powerful entities, and that pride cometh before the fall.

Plus, any sufficiently advanced magic is indistinguishable from technology :P

Which is why we invented science - create theories and use the predictions to generate new experiments that verify the theory, or not. There are two steps there; first the core of science - which doesn't just apply in the lab, but also in scenarios like office politics, it isn't just the clues you have but making situations that give you information that verifies your hypothesis. Second the getting of the idea that we should think this way, rather than just processing the environment as it comes.

It's actually not why we invented science, but it is why we discovered philosophy.

We know that "this" can't be the answer because we know that humans aren't fooled by the inputs that were crafted to fool the language model.

Is it a fair comparison? Give algorithm few orders of magnitude better computation power and as many years of unbounded train data as humans get - and draw conclusions on those results, no?

Researchers are free to do that, to the extent that it's feasible. Until they have, it seems pointless to speculate.

In that specific example, yes. But in general, I think the "we are all Clever Hans" is right at least to some degree.

Clever Hans couldn't answer the simplest of questions if the asker didn't already know the answer. If we were all Clever Hans, there'd be nobody left to feed us answers.

Human knowledge was created by humans. We might repeat it blindly too, but somebody had to create it first. That is fundamentally different from what Hans did.

Why? The idea that human intelligence also "fake" is just a convenient excuse for the lack of any real progress in AI. We hear the same thing at the tail end of every overblown AI hype cycle. Well, gee, maybe humans aren't that smart anyway!

Why are we equating brute force with fake?

The human mind has many faculties, and the brain has correspondingly many functionally different elements. Plus years of brute force learning.

Compared to GPT2, AlphaZero and whatever we are waay more complex and had a lot more training done.

Probably the super secret sauce is in the hyperparameters that determine how to cobble all those functional components together to get something more than the simple sum of them. (But they are also learned and brute forced through evolution.)

AI needs better brains. (So R&D can proceed faster. We can test new ideas cheaper, we can optimize algorithms, so they'll learn and perform better.)

I'm not sure that I am equating brute force with fake.

Are you saying we should teach machines to math before teaching them to speak?

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