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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"

#271
post #270

Earlier quoted context omitted.

You acknowledged above that consciousness isn't what LLM is and you likely understand that the poster was referring to that... The broad strokes you use here are exactly why discussing LLMs are hard. Sure some people dismiss them because it isn't general AI but having supporters dismiss any argument with "passes the Turning test" is equally useless.

No you have misunderstood. As I wrote above: "But you are not making a technical argument here unless you can define "understand" in technical terms. This is a matter of semantics." I said the nature of their argument is not technical, since they are not dealing with technical definitions, but I did not dismiss their argument altogether. I clarified and restated their own argument for them in clearer terms. LLMs are…

I stand by my point that people using synonyms for consciousness being told "LLM knows true better than humans do" is bad for discussion.

The core issue is their "knowledge" is too context sensitive.

Certainly humans are very context sensitive in our memories but we all have something akin to a "mental model" we can use to find things without that context.

In contrast LLM has knowledge defined by that context quite literally.

In either case my original point on using true and false is that LLM can hallucinate and on a fundamental design level there is little that can be done to stop it.

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

#272
post #270

Earlier quoted context omitted.

No you have misunderstood. As I wrote above: "But you are not making a technical argument here unless you can define "understand" in technical terms. This is a matter of semantics." I said the nature of their argument is not technical, since they are not dealing with technical definitions, but I did not dismiss their argument altogether. I clarified and restated their own argument for them in clearer terms. LLMs are…

I stand by my point that people using synonyms for consciousness being told "LLM knows true better than humans do" is bad for discussion. The core issue is their "knowledge" is too context sensitive. Certainly humans are very context sensitive in our memories but we all have something akin to a "mental model" we can use to find things without that context. In contrast LLM has knowledge defined by that context quite l…

LLMs can outperform humans on a variety of NLP tasks that require understanding. Formally, they are designed to solve "natural language understanding" tasks as a subset of "natural language processing" tasks. The word "understanding" is used in the academic context here. It is a standard term in NLP research.

https://en.wikipedia.org/wiki/Natural-language_understanding

My point was to show that their thinking, reasoning and language was flawed, that it lacked nuance and rigor. I am trying to raise the standards of discussion. They need to think more deeply about what "understanding" really means. Consciousness does not even have a formal universally agreed definition.

Sloppy non-rigorous shallow arguments are bad for discussion.

> LLM can hallucinate and on a fundamental design level there is little that can be done to stop it.

That's a separate issue. They generally don't hallucinate when solving a problem within their context window. Recalling facts from their training set is another issue.

Humans sometimes have a similar problem of "hallucinating" when recalling facts from their long term memory.

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

#273
post #272

Earlier quoted context omitted.

I stand by my point that people using synonyms for consciousness being told "LLM knows true better than humans do" is bad for discussion. The core issue is their "knowledge" is too context sensitive. Certainly humans are very context sensitive in our memories but we all have something akin to a "mental model" we can use to find things without that context. In contrast LLM has knowledge defined by that context quite l…

LLMs can outperform humans on a variety of NLP tasks that require understanding. Formally, they are designed to solve "natural language understanding" tasks as a subset of "natural language processing" tasks. The word "understanding" is used in the academic context here. It is a standard term in NLP research. https://en.wikipedia.org/wiki/Natural-language_understanding My point was to show that their thinking, reason…

Except that if you narrow to a tiny training set you are back to problems that can be solved almost as quickly with full text search...

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

#274
post #272

Earlier quoted context omitted.

LLMs can outperform humans on a variety of NLP tasks that require understanding. Formally, they are designed to solve "natural language understanding" tasks as a subset of "natural language processing" tasks. The word "understanding" is used in the academic context here. It is a standard term in NLP research. https://en.wikipedia.org/wiki/Natural-language_understanding My point was to show that their thinking, reason…

Except that if you narrow to a tiny training set you are back to problems that can be solved almost as quickly with full text search...

Narrow to a tiny training set? What are you talking about now? That has nothing to do with deep learning.

GPT-3.5 was trained on at least 300 billion tokens. It has 96 layers in its neural network of 175 billion parameters. Each one of those 96 stacked layers has an attention mechanism that recomputes an attention score for every token in the context window, for each new token generated in sequence. GPT-4 is much bigger than that. The scale and complexity of these models is beyond comprehension. We're talking about LLMs, not SLMs.

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

#275
post #274

Earlier quoted context omitted.

Except that if you narrow to a tiny training set you are back to problems that can be solved almost as quickly with full text search...

Narrow to a tiny training set? What are you talking about now? That has nothing to do with deep learning. GPT-3.5 was trained on at least 300 billion tokens. It has 96 layers in its neural network of 175 billion parameters. Each one of those 96 stacked layers has an attention mechanism that recomputes an attention score for every token in the context window, for each new token generated in sequence. GPT-4 is much big…

I misread context window as training set and thought you were switching to SLMs. My mistake.

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

#276

Earlier quoted context omitted.

Also spoilers

No? Nothing is spoiled in that scene.

This scene is at least 7 hours in, at episode 1 no one ever expects Maeve, the sex worker to be such a focal point in the series.

Her being the first to wake up this way and become more conscious is a huge spoiler.

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

#277

Earlier quoted context omitted.

No? Nothing is spoiled in that scene.

This scene is at least 7 hours in, at episode 1 no one ever expects Maeve, the sex worker to be such a focal point in the series. Her being the first to wake up this way and become more conscious is a huge spoiler.

I strongly disagree, but to each their own.

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

#278

Earlier quoted context omitted.

Yeah, it's a very silly article with wrong mathematical reasoning. Hinton is quite obviously talking about a much more information-theoretic approach to the process, but he's phrasing it in people-friendly terms. What's a little more concerning to me is that people are reading and upvoting it. I think, because I have hopes and aspirations about working on some very hard problems and communicating them to the public a…

Yeah I agree with that for sure. It’s so strange how the majority of the research folks appear to be on this ‘new shiny’ mentality at the expense of fundamentals. Especially for how new this field is, relatively. It’s not exactly like we’re all tapped out. Probably not even of low hanging fruit.

Yeah, if you read Hinton's long backlog... It's mindblowing. So many fresh concepts, just left there in the dust.

Highly encourage. There's a goldmine in there, I thinksies. <3 :')))))))))

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

#279

Earlier quoted context omitted.

I think the analogy is something like: if you have a simple distribution over all words, then that's just word frequency. Obviously not a good predictor. The 'information' necessary to predict the correct next word contextually is just not there if you're predicting words in a vacuum. In order to be practically useful and predict the right words _in context_, the model must be conditioning off of more of the sentence…

That's not information theoretic, that's just conditional probability.

You might want to take another look at Shannon's paper, lol, this statement is quite contradictory. Probability _is_ the backbone of information theory, dude! It's quite incredible.
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