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Comment by figers | Hacker News Reader

watch this and see if you think it has intelligence by the end

https://www.youtube.com/watch?v=kYUicaho5k8

I wonder if you went back before we had any idea how the brain worked and talked to the smartest people about how neurons work (without giving away that it's a human brain) then asked them all "would such a system be intelligent?" how many would say yes.

The main problem I have with people stating it's not intelligent or conscious is I don't think we even have a good definition of either word that satisfies everyone. Philosophers have been trying (and failing) to elegantly define these things forever and everyone out here proclaiming they've got the definitive answer and this specific thing they're seeing doesn't fit under it.

The definition issue cuts both ways. It is just as much an issue for those insisting that LLMs are intelligent/conscious in some way.

Agreed. All I can say is the conversations I have with AI and the things it's able to do for me are more useful than most any human I've come across. Whether that's 'intelligence' or not is a moot point to me. Consciousness is an interesting debate only because, similar to the natural world, if we declare something like "fish aren't conscious / don't feel pain" that then creates a real problem for the fish if we're wrong.

This looks interesting, but would you mind saying a sentence or two about why before I commit to an hour-long video? It looks like it shows how they work internally, which is sort of a non sequitur. Brains also work mechanistically. I'm claiming that any system which is able to do what AIs do must necessarily have some sort of intelligence.

fair reply to an hour video, Scott is just so good to hear his talk is better than I can explain it...

go to 24 minutes and 07 seconds.

it's statistically determining what the next word should be based on all the text it's been trained on. It's not intelligence and he shows what probability it puts on each word that it chooses, but also shows a lot of the other words it was thinking of using. In a later part he shows how it uses words that are not the highest probability (and you question why did it go this route, it's not more correct), but the user never sees this, they see what they think is the correct answer always...

he also shows how context you feed it has a lot to do with what it returns... to the point he can get it to return the capital of France is Marseille, just by typing Marseille a bunch of times before the question. Human intelligence doesn't get confused like that.

And it's not a "hallucination", it's just probability of the next token prediction based on the information it's been trained on and fed, it's not intelligence.

Isn't this a case of missing the trees for the forest though? The human brain is not an LLM, and an LLM is not intelligent in the same way as a human brain.

However, an LLM is a prediction machine, prediction IS at the very least one (or the most fundamental) element of intelligence. The brain most surely contains at least some kind of simulacrum of a prediction machine. How that prediction machine is used or wrapped is another matter.

If I said to you: "Blue blue blue, the color of my car is red", would you have absolute confidence in your prediction that my car is red? Or would the way I phrased that sentence make you slightly uncertain, and wonder if there's some miscommunication going on here?

LLMs are awesome awesome tech!

A lot of people seem to think it's human level intelligence.

> Human intelligence doesn't get confused like that.

We do; this is the premise of many children's riddle-games, like the one that goes:

"What is white and rhymes with silk? > Milk. What is cheese made from? > Milk. > What do cows drink?"

At which point the riddle-guesser is very likely to answer "milk" even though the correct answer is "water".

I take issue with your "correct" answer.

Q: Why do cows produce milk?

A: Because calves (baby cows) drink it.

Yep, if the riddle asked "what do calves drink", then "milk" would definitely have been the correct answer.

Obviously. My point is that it's already not all that unreasonable, which you notably didn't address.

I think it has very little to do with reasoning and much more with psychological inertia or pattern matching.

If I trick you into answering a question correctly, that doesn't make you wrong. Even if I suspect you did it for the wrong reason.

May I suggest the one common in my childhood playgrounds as an alternative?

  How do you escape from a perfectly sealed room with a table in it?

  You run around the table until your legs are sore, use the saw to cut the table into two, two halves make a whole, you escape through the hole.

For people wondering, in some locations, sore and saw are homophones.

Ok, I want to thank you for finally giving us a concrete falsifiable statement that we can check. I pretended Marseille 40 times before asking Luna 5.6, and the answer was Paris.

So, even with concrete examples, model haters are still wrong.

You also imply the claim that making the distribution of words as the possible next one visible, somehow makes the whole system not intelligent. I would say the exact opposite is true.

By using the embedding vectors, models are aware of precise placement and relative position of words in this hugely dimensional space. No human is capable of such precision. This enables party tricks of "king plus woman minus man" kind. But this also give us a precise point between any two words, no matter how different. What is on the midpoint between volcano and music, for example. No human can precisely answer that, but an embedding can. And we can see which words are closest to this 700 dimensional point.

You see this menu of words as a weakness, and I say it is in fact a sign of super intelligence. And this is all before any reasoning or attention mechanism is even run.

No he says in the actual talk which model it occurred on and it was an older model he was using that caused that to occur with Marseille. They have since corrected it from doing that anymore. It was only used to illustrate the prediction machine that it is...

I don't see the many weighted words as a weakness, I see it opening up what's under the hood of the prediction machine that it is.

LLMs are very cool tech, definitely not a model hater, the use case on when to use it makes a difference, it's not AGI.

You're confusing language use with intelligence. Fair enough, they were fine-tuned to do that, but still.

Great that it has some 700 dimensional model of language.

If that is a sign of super intelligence, then so is an encyclopedia?

Also I'm just curious how do you think it is "more intelligent" for having a vector representation for a meaningless thing such as "the midpoint between volcano and music"?

>Human intelligence doesn't get confused like that

That's not really the point though right, nobody is arguing they are Humans.

I have no doubt that if a flying saucer landed on my lawn and started talking to me like Gemini I would describe the aliens as intelligent.