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I disagree with Geoff Hinton regarding "glorified autocomplete"

statmodeling.stat.columbia.edu

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Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#51

Post by karaterobot »

mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

> You would realize that all we are doing is using statistics to predict what the next item might be.

I agree that Hinton's original quote doesn't make sense to me either. I suspect he would leverage the phrase "really good" to explain the difference between ChatGPT and, say, a Markov chain. I think that's a little disingenuous, if that's how he means it, but I don't know if I'm right about that.

But I also do not agree that humans use statistics to predict what the next item in a series might be. As evidence, there is the classic example of asking people to predict the next coin toss in the series: "heads, heads, heads, heads, heads, heads, heads...". They'll either guess heads because it's come up so many times already, or because they assume the coin isn't fair, or tails because it's "overdue" to come up, but none of those are based on statistics per se.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#52

Post by civilized »

lordnacho wrote: post #39
civilized wrote: post #29

Earlier quoted context omitted.

LLMs don't have a concept of "best". Only most likely in what they've been trained on. I think LLMs ultimately just take imitation to a creative and sophisticated extreme. And imitation simply doesn't comprise the whole of human intelligence at all, no matter how much it is scaled up. The sophistication of the imitation has some people confused and questioning whether everything can be reduced to imitation. It can't.…

Ok, so now we need an example that separates humans from LLMs? I struggle to think of one, maybe someone on HN has a good example. Eg if I'm in middle school and learning quadratic equations, am I imitating solving the problem by plugging in the coefficients? Or am I understanding it? Most of what I see coming out of chatGPT and copilot could be said to be either. If you're generous, it's understanding. If not, it's…

It is very easy to separate humans from LLMs. Humans created math without being given all the answers beforehand. LLMs can't do that yet.

When an LLM can create math to solve a problem, we will be much closer to AGI.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#53

Post by troelsSteegin »

mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

From the article, Gelman's money quote is this: " So I’m not knocking auto-complete; I’m just disagreeing with Hinton’s statement that “by training something to be really good at predicting the next word, you’re actually forcing it to understand.” As a person who does a lot of useful associative reasoning and also a bit of logical understanding, I think they’re different, both in how they feel and also in what they do."

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#55

Post by TerrifiedMouse »

tayo42 wrote: post #6

This kind of was on my mind recently, I was wondering, when I speak am I just spitting out the next word that makes sense or is there more to it. I think there is for people, I can think before I speak, I can plan out my thought entirely before turning it into words. Each invocation of the chat bot/llm is a new set of probabilities. I can plan what my 2nd token output will be and stick to it. Llm models dont have the…

> when I speak am I just spitting out the next word that makes sense or is there more to it.

There is more to it. Specifically you are doing so to advance towards a specific goal. LLMs don't have goals. They just pick from a list of likely tokens - based on their training data - at random to generate the next token.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#56

Post by hackerlight »

mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

Why is "using statistics" mutually exclusive with "understanding"? It would help to carefully define terms. Note that "understanding" doesn't mean it's necessarily conscious.

These systems learn high-level representations/abstractions of concepts we humans also use as part of our cognition. The concept of an object, an intuitive physics, the role of specific objects. I don't criticize him for using the word "understanding" to describe this.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#57

Post by chpatrick »

im_down_w_otp wrote: post #36
Joeri wrote: post #27

Earlier quoted context omitted.

Nobody actually understands how humans actually understand something, just like nobody actually understands how LLMs do what they do. Everybody opining about it is doing just that: offering an opinion. Geoff Hinton’s opinion is worth more than someone else’s, but it is still an opinion.

I don’t know about the human part, but we absolutely understand how LLMs do what they do. They’re not magic.

We understand the architecture but we don't understand the weights.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#58

Post by nkingsy »

tayo42 wrote: post #6

This kind of was on my mind recently, I was wondering, when I speak am I just spitting out the next word that makes sense or is there more to it. I think there is for people, I can think before I speak, I can plan out my thought entirely before turning it into words. Each invocation of the chat bot/llm is a new set of probabilities. I can plan what my 2nd token output will be and stick to it. Llm models dont have the…

I think people get tricked by the forward pass mechanics into thinking a single generation is comparable to a human thought process.

I think we have a llm like mechanism we can employ and lean on as heavily as we like, but we also have an executive function, like thousands of specialized instant Boolean checks, which can adjust and redirect the big talky model’s context on the fly.

My hunch is it’s turtles all the way down. “Gut feelings” are hyper-optimized ASICS with tiny parameter counts, but all using the same intelligence mechanisms.

Extrapolating from that hunch, we are already witnessing AGI, and in fact we’ve started at the top.

I believe that current llms are actually far far superior to a human language center and current architectures are more than sufficient in terms of raw intelligence.

The challenge that remains is to understand, train, integrate, and orchestrate all the different flavors of intelligence that humans wield so elegantly and naturally as to make them opaque to our own understanding.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#59

Post by lordnacho »

overactor wrote: post #17
hopfenspergerj wrote: post #11

Earlier quoted context omitted.

I agree. We should prompt the model with the statement of the Riemann hypothesis. If the autocomplete is good, the model will output a proof.

No human can solve the Riemann Hypothesis. Why do you expect an AI to do it before you consider it able to understand things?

OTOH, doing something that only humans have done thus far would be a huge step in demonstrating understanding.

Does that mean when a computer outputs a new proof it understands?

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#60

Post by brookst »

famouswaffles wrote: post #50
im_down_w_otp wrote: post #36

Earlier quoted context omitted.

I don’t know about the human part, but we absolutely understand how LLMs do what they do. They’re not magic.

No we don't. No it's not "magic". No we don't understand what the black box is doing.

For some values of “we”
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