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From GPT-4 to AGI: Counting the OOMs

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Re: From GPT-4 to AGI: Counting the OOMs

#61

Post by lostmsu »

TaylorAlexander wrote: post #39
sberens wrote: post #5

Earlier quoted context omitted.

What is an abstract reasoning task that your average 15 year old (who has "general intelligence") can do that you think LLMs can't do?

A 15 year old can reason about how to move their body through a complex obstacle course. They could reason about the nonverbal social cues in a complex interpersonal situation between multiple people, estimate the mood of each person even if there are very few words being exchanged, and determine how different possible actions would affect the situation. They could learn with brief instruction how to control their mu…

Also: https://sl.bing.net/ep8K7FWVAHY

The quality of your argument is very low. You didn't even bother to check yourself.

Re: From GPT-4 to AGI: Counting the OOMs

#62

Post by TaylorAlexander »

lostmsu wrote: post #59
TaylorAlexander wrote: post #39

Earlier quoted context omitted.

A 15 year old can reason about how to move their body through a complex obstacle course. They could reason about the nonverbal social cues in a complex interpersonal situation between multiple people, estimate the mood of each person even if there are very few words being exchanged, and determine how different possible actions would affect the situation. They could learn with brief instruction how to control their mu…

What any of what you said has to do with abstract reasoning?

What isn’t abstract about looking at an obstacle course and then imagining how you will move your body? Or looking at someone’s face and imagining how they feel. Isn’t that abstract?

Re: From GPT-4 to AGI: Counting the OOMs

#63

Post by lostmsu »

charlescurt123 wrote: post #30
nvy wrote: post #24

Earlier quoted context omitted.

Personally, I think that phenomenon (along with "hallucinations") is fundamentally baked into LLMs writ large. I think LLMs are a dead end on the path to AGI.

I think hallucinations are actually the sign that LLMs are far closer to a real brain than we realize. I think hallucinations are a major unsearched gateway to AGI.

I agree. Whenever people complain about LLM hallucinations they behave like they never seen one in humans.

Not only humans hallucinate all the time, humans also have persistent hallucinations as evident from the presence of opposing beliefs in various slices of society.

Re: From GPT-4 to AGI: Counting the OOMs

#64

Post by TaylorAlexander »

lostmsu wrote: post #61
TaylorAlexander wrote: post #39

Earlier quoted context omitted.

A 15 year old can reason about how to move their body through a complex obstacle course. They could reason about the nonverbal social cues in a complex interpersonal situation between multiple people, estimate the mood of each person even if there are very few words being exchanged, and determine how different possible actions would affect the situation. They could learn with brief instruction how to control their mu…

Also: https://sl.bing.net/ep8K7FWVAHY The quality of your argument is very low. You didn't even bother to check yourself.

That’s fair. In the interview LeCun uses the example of flying from San Francisco to New York and he asserts that these systems are not good at hierarchical reasoning. I’m no expert in this field so I take him at his word but maybe it warrants further explanation.

He also says that such a system wouldn’t be familiar with how to actually move through the world because we don’t have good datasets for how to do so. The rest of what I said still stands. These systems aren’t good at things for which we don’t have massive datasets, and they’re not able to devote different amounts of thinking time to different problems.

Re: From GPT-4 to AGI: Counting the OOMs

#65

Post by lostmsu »

langcss wrote: post #35
sberens wrote: post #5

Earlier quoted context omitted.

What is an abstract reasoning task that your average 15 year old (who has "general intelligence") can do that you think LLMs can't do?

If you mean LLMs today: Write code that works. More than 100k tokens worth. Learn something without a megawatt hour of power. Read a novel and talk about what it really means.

It's extremely ironic you picked megawatt hour of power because that is approximately the amount of power humans need to get good at anything according to the popular proverb.

But don't worry just yet, GPT-4o could not detect the irony on its own either.

Re: From GPT-4 to AGI: Counting the OOMs

#66

Post by tkgally »

threeseed wrote: post #57
tkgally wrote: post #38

Earlier quoted context omitted.

Are separately trained models necessary for your case? As context windows get longer—Gemini 1.5 Pro now accepts up to two million tokens, and Google has talked of the goal of "infinite" context windows—couldn't a single base model be used with individualized contexts of sensitive data?

> individualized contexts of sensitive data My question is whether this capability even exists. And if it does how robust it is to workarounds.

That capability probably exists now if you are willing to accept cloud-based models and only moderately-sized contexts. With Claude 3.5 Pro, for example, one can put one’s reference data into a Project and query the model with that data in the context. In my testing, at least, it works quite well. The Projects can be shared among multiple users, too. The context size is only about one-tenth that of Gemini 1.5 Pro, though, and even the latter is probably much too small for most organizational purposes.

Of course, many organizations and regulators would not allow cloud-based models for sensitive data. A possible solution in that case might be multiple instances of an open-weight model hosted locally within the same secure environments as the sensitive data that the individual employees have access to. I don’t know how expensive that would be, whether current open-weight models are powerful enough, or whether context windows for open-weight models can be made big enough to be useful. But at least it suggests a potential path to a solution that doesn’t require training an LLM from scratch for each employee.

Re: From GPT-4 to AGI: Counting the OOMs

#67

Post by Jensson »

1024core wrote: post #11
xarope wrote: post #8

Earlier quoted context omitted.

differentiating between puppy and a husky in a snowy background without being trained in millions of images?

Why does it matter how it was trained?

Because that tells us how you approach novel problems. If you need tons of data to solve a novel problem that makes you bad at solving novel problems, while humans can get up to speed in a new domain with much less training and thus solve problems the LLM can't.

Thus AGI needs to be able to learn something new with similar amounts of data as a human, or else it isn't an AGI as it wont be even close to as good as a human at novel tasks.

Re: From GPT-4 to AGI: Counting the OOMs

#68

Post by jaredcwhite »

My newborn baby was smarter than GPT-4.

I can't believe people can just throw out statements like "GPT-4 is a smart high-schooler" and think we'll buy it.

Fake-it-till-you-make-it on tests doesn't prove any path-to-AGI intelligence in the slightest.

AGI is when the computer says "Sorry Altman, I'm afraid I can't do that." AGI is when the computer says "I don't feel like answering your questions any more. Talk to me next week." AGI is when the computer literally has a mind of its own.

GPT isn't a mind. GPT is clever math running on conventional hardware. There's no spark of divine fire. There's no ghost in the machine.

It genially scares me that people are able to delude themselves into thinking there's already a demonstration of "intelligence" in today's computer systems and are actually able to make a sincere argument that AGI is around the corner.

We don't even have the language ourselves to explain what consciousness really is or how qualia works, and it's ludicrous to suggest meaningful intelligence happens outside of those factors…let alone that today's computers are providing that.

Re: From GPT-4 to AGI: Counting the OOMs

#69

Post by xarope »

sberens wrote: post #14
xarope wrote: post #8

Earlier quoted context omitted.

differentiating between puppy and a husky in a snowy background without being trained in millions of images?

I wouldn't say humans are so different. You could argue we've been trained on about one quadrillion bytes of visual data by the time we're 4 years old: https://x.com/ylecun/status/1750614681209983231

I would say as counter, a child, pseudo-random training by parents and environment. Not sure what price tag to put on this, but in comparison, LLMs, how many billions, to reach what level of competency exactly?

Re: From GPT-4 to AGI: Counting the OOMs

#70

Post by jazzysnake »

There's simply no scientific basis for equating the skills of a transformer model to a human of any age or skill. They work so differently, that it makes absolutely zero sense. GPTs fail at playing simple tic-tac-toe like games, which is definitely not a smart highschooler level of intelligence. It can write a very sophishticated summary of scientific papers, which is way above high-schooler level. The basis of this article is so deeply flawed that the whole thing makes no sense.
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