Comment by pyridines
9 hours ago
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.
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.
> 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.
5 replies →
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.