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Weak-to-Strong Generalization

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181–190 of 203 posts

Re: Weak-to-Strong Generalization

#181

Earlier quoted context omitted.

>Kids learn through supervised learning. Children don't develop strong language skills without parents or other people to correct them when they use language incorrectly. Untrue. Many cultures don't much speak to their children and they turn out just fine. It's fairly evident Language learning is primarily unsupervised. https://www.scientificamerican.com/article/parents-in-a-remo...

That doesn't say that parents don't correct kids, just that they don't speak to their infants. The initial words are learnt that way, but I don't think you master language without anyone to correct you when you make mistakes.

This is simply not true. I am very well read in language acquisition research, and no evidence supports that children would need corrections to get language right.

Re: Weak-to-Strong Generalization

#182

I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence. You can't model and predict the weather just by training on the outputs of the weather system (whether it rained today, whether it was cloudy yesterday, and so on). You have to train on the inputs (air curr…

> I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence. This is irrelevant because OpenAI's definition of AGI [1] doesn't imply similarity or equivalence to humans at all: >artificial general intelligence (AGI)—by which we mean highly autonomous systems that o…

> Most of your intelligence is not actually yours.

You’re confusing knowledge and intelligence

Re: Weak-to-Strong Generalization

#183

I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence. You can't model and predict the weather just by training on the outputs of the weather system (whether it rained today, whether it was cloudy yesterday, and so on). You have to train on the inputs (air curr…

> what are those inputs?

In many many casea the inputs are other humans, delivered via text written by humans and read by other humans who react by writing more text affecting how other humans will respond etc etc.

Yes there are plenty of cases where the inputs are not captured in the large text corpora we have, but this insight does explain why LLMs even approximate the ability to do intelligent things

Re: Weak-to-Strong Generalization

#184
post #170

Earlier quoted context omitted.

I wouldn't call those equipment "autonomous" though, definitely not "highly autonomous". But more importantly - yes, you're right, we have built machines that are superhuman in various ways - and they have replaced most jobs. We have adapted in the past to different jobs. Some people are worried that this time we won't have any new jobs to adapt to, which is a real possibility. (Some are also worried about the inhere…

> I wouldn't call those equipment "autonomous" though, definitely not "highly autonomous". Why? Tractors run and harvest mostly on their own. They don't do it 100% on their own, but neither did the definition above, they remove basically all human work needed from farming. https://www.youtube.com/watch?v=QvFoRk4JsPc > But more importantly - yes, you're right, we have built machines that are superhuman in various ways…

> Why? Tractors run and harvest mostly on their own.

This is incorrect. Most tractors are still human operated. The automated ones still frequently stop and need remote operator intervention via camera review.

Re: Weak-to-Strong Generalization

#185
post #86

Earlier quoted context omitted.

Human intelligence itself is shaped by our interaction with outputs. Our learning and understanding of the world are profoundly influenced by the language, behaviors, and cultural artifacts we observe. Think about the process of a child learning a language. The child does not have direct access to the "inputs" of linguistic rules or grammar; they learn primarily through observing and imitating the language output of…

> The child does not have direct access to the "inputs" of linguistic rules or grammar; they learn primarily through observing and imitating the language output of others around them. I would argue that that learning is always contextualized by visual and spatial information about the real world (which is what our language is meant to describe). And (equally importantly) the child gets real-world feedback on their de…

Visual and spatial feedback aren't terribly difficult, though - we already have video-game training gyms, from Atari to GTA. If multimodality and feedback are the main barriers to full AGI, I expect we'll be there soon.

Re: Weak-to-Strong Generalization

#186

Earlier quoted context omitted.

> I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence. This is irrelevant because OpenAI's definition of AGI [1] doesn't imply similarity or equivalence to humans at all: >artificial general intelligence (AGI)—by which we mean highly autonomous systems that o…

> Most of your intelligence is not actually yours. You’re confusing knowledge and intelligence

Compressed and abstracted knowledge is intelligence. Your intelligence is mostly formed by the quality training material, not just conditioned on it. Most of your ability to reason about the world and predict things, most of the abstractions you use, most of your emotional responses, etc. A simple concept of acceleration took the work of ancient philosophers to figure out. Even stateful counting in a positional system. Even the concept of a "concept" is not yours. Only a tiny bit of your reasoning depends on your actual biological capabilities and the work you did personally, as opposed to the humanity as a superorganism.

Re: Weak-to-Strong Generalization

#187
post #86

Earlier quoted context omitted.

Human intelligence itself is shaped by our interaction with outputs. Our learning and understanding of the world are profoundly influenced by the language, behaviors, and cultural artifacts we observe. Think about the process of a child learning a language. The child does not have direct access to the "inputs" of linguistic rules or grammar; they learn primarily through observing and imitating the language output of…

> Human intelligence itself is shaped by our interaction with outputs. Our learning and understanding of the world are profoundly influenced by the language, behaviors, and cultural artifacts we observe. I always thought that language definitely shapes our understanding of the world, but at much more fundamental level, I believe language falls apart to teach us anything. For example, there are words in dictionary tha…

> For example, there are words in dictionary that should not be defined using other words (but dictionary still do, this sort of circular reasoning is something I always have problem with)

You shouldn't. When you learn a foreign language dictionaries can be a great help and it doesn't matter if they use circular definitions. Well it can matter in a situation like Stanisław Lem described with his "sepulkas"[1], but even then the definitions were a red warning. Ijon Tichy just failed to understand it.

> you would not be able to do so unless the kid touches something really hot like boiling kettle to "feel" it

I learned English by reading books mostly. In most cases I had no easy access to a dictionary and inferred meanings of words by a context. In some cases I failed to infer meaning but nevertheless in each case I managed to get some idea about a word. I remember some surprises when I found a real meaning of a word and it was not exactly what I thought. Or even some ideas that felt new and inspiring for me before I connected them to ideas I learned long before that in my native language. I'm like an English LLM myself, because I never used English in a real world context, only to read texts and to write comments in English. To this very moment I cannot talk about some topics in Russian, because I do not know Russian words to talk about them.

All this experience led me to doubt an idea that you can understand language only by connecting it to reality. I believe you can. Your language will be disconnected from reality and it can be a disaster probably if you try to apply it to reality, but you can talk a lot, participate in philosophical debates and it doesn't matter your understanding is different. It is like redness of red: do you see red as I do and does it matter?

> There are things we just cannot understand through language.

We cannot link our senses with language without burns. But it doesn't mean we cannot understand. For example, you can watch other people touching really hot boiling kettles. If you never touched anything hot you will not understand their pain, but you'll know they are in a pain and you'll know of a special nature of that pain. You can learn all the important intricacies of interaction with hot objects by just watching. Or by reading. Your understanding will be limited still, but in a lot of cases it doesn't matter.

Though of course we coming very near to a debate what is understanding. Is it a human-centric definition, that boils down to "only a human can understand", or it is more relaxed and rely on a pragmatic idea, like does your understanding enables you to make right choices. If LLM doesn't have a body that can feel pain, it doesn't matter if LLM cannot know how pain feels.

[1] https://en.wikipedia.org/wiki/Sepulka

Re: Weak-to-Strong Generalization

#188

I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence. You can't model and predict the weather just by training on the outputs of the weather system (whether it rained today, whether it was cloudy yesterday, and so on). You have to train on the inputs (air curr…

Your take here converges with a long-standing debate in AI regarding embodiment.

Our natural world doesn't distinguish between "inputs" and "outputs" -- instead, all the causes, effects, and even our analysis of every process itself get jumbled into one physical world. As embodied actors, we get to probe and perceive that physical world, and gradually separate causes from effects in order to find better models. Where statistical ML, symbolic AI, and NLP have been more isolated disciplines from e.g. vision and robotics, the latter have argued that their ability to interact with a disorganized natural world would be essential for AGI.

More recently, these boundaries are breaking down with multimodal training. If an AI can learn image/text, text/text and image/image associations simultaneously, is it stepping beyond the world of "human outputs"? Will other modalities be essential to reach human+ capabilities? Or will learning relations between action and perception itself by critical?

Nobody knows yet! But IMHO, the right way to explore these tasks is by understanding what is necessary to succeed at specific tasks, not a generalized notion of AGI. We don't know truly where the limits are on our own ability to reason or extrapolate across modalities.

Re: Weak-to-Strong Generalization

#189

I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence. You can't model and predict the weather just by training on the outputs of the weather system (whether it rained today, whether it was cloudy yesterday, and so on). You have to train on the inputs (air curr…

Thanks for the inspiring discussion. I think the one`s output can be the other's input so I am still not sure if we can say that this can't become AGI

Re: Weak-to-Strong Generalization

#190
I wish they would define some of their terms.

> to align future superhuman AI systems to be safe has never been more important.

Align to who? Align to US citizens, OpenAI shareholders, align to what values?

What does safe mean? Pornography? Saying “fuck”, racial bias, access to private data?

I can understand OpenAI erring on the side of not rattling bells and training their LLMs to say “As an AI model I cannot answer that” but it’s horseshit to say that it is super aligned.

All alignment is alignment to X values but your X could be detrimental to me.

What is superalignment supposed to mean?

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