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HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

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Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#231

Post by simon_000666 »

bjackman wrote: post #187
simon_000666 wrote: post #182

ChatGTP/4 is to AGI what pepper’s ghost is to holography. It’s a parlor trick, even if you add plugins or the ability to call other hugging face ML models - it’s just a parlor trick with fancier bells and whistles. All it is doing is using stochastic gradient descent to predict the next word in a sequence based on an enormous sophisticated training set designed to amaze people. Thinking it has advanced because it can…

I find this so bizarre. Every time someone demonstrates a new way in which models are capable of a wider array of tasks than expected someone goes "it's just predicting tokens". It's such a big "just". You are just firing neurons. The stock market is just supply and demand. The internet is just a bunch of computers talking through 50 year old protocols that don't work very well. Everything is just something else! I w…

Pepper’s ghost is also impressive when you see it for the first time. They’ve enhanced it do entire concerts now with dead music stars on stage for huge audiences. Has it helped us get any closer solve holography, will I be able to have a Star Trek style hologram roaming round my house because of pepper’s ghost?

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#232

Post by simon_000666 »

bjackman wrote: post #203
mmiliauskas wrote: post #196

Earlier quoted context omitted.

Except "this is just" is sprinkled all over NNs, DL and in turn of ChatGPT. Actually they pride themselves on "this is just". So your argument is probably more accurate for the other camp, or at least as accurate for the other camp as well.

I'm not sure what you're getting at here but I'll try to respond. My argument is that "this is just" is meaningless as a way to assess the impact of a technology. If AI researches say, "this is just X and it can do Y!" then fine, that's just framing for "look: Y". When stochastic parrot guys say "this is just X, what's impressive about that?" it throws me for a loop coz they are are refusing to engage with Y.

I think we disagree about what Y is. My point is that Y is not that different from materially what is possible with a slack bot from circa 2015. Essentially chatgtp is a less efficient way to get to the same outcomes that were already possible. The trick is that it appears to be something it’s not - AGI.

I like your bronze sword analogy. From my point of view chatgtp is not a bronze sword, it’s a Stone Age sword that someone has painted bronze. It has value because people realize the advantage that a true bronze sword would have in a battle. However, when you actually put it through it’s paces you quickly realise it offers no actual value over what came before.

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#233

Post by simon_000666 »

qolop wrote: post #211
simon_000666 wrote: post #182

ChatGTP/4 is to AGI what pepper’s ghost is to holography. It’s a parlor trick, even if you add plugins or the ability to call other hugging face ML models - it’s just a parlor trick with fancier bells and whistles. All it is doing is using stochastic gradient descent to predict the next word in a sequence based on an enormous sophisticated training set designed to amaze people. Thinking it has advanced because it can…

>> It's still just predicting the next word Predicting the next word is a much deeper problem than people like you realise. To be able to be good at predicting the next word you need to have an internal model of the reality that produced that next word. GPT-4 might be trained at predicting the next word, but in that process it learns a very deep representation of our world. That explains how it has an intuition for c…

“To be able to be good at predicting the next word you need to have an internal model of the reality that produced that next word.”

Now that’s an interesting claim - that I would deeply dispute. It learns from text. Text itself is a model of reality. So chatgtp if anything proves that in order to be good at predicting the next word all you need is a good model of a model of reality. GTP knows nothing of actual reality only the statistics around symbol patterns that occur in text.

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#234

Post by istjohn »

YeGoblynQueenne wrote: post #155
famouswaffles wrote: post #152

Earlier quoted context omitted.

Is this your argument? Really? I'm Just surprised honestly. All of the straw man arguments, this is the best you can come up with? Man do better. GPT-4 would have a better response than this.

I don't understand your derision. Where is the "straw man"? I showed you "output that clearly demonstrates reasoning and understand[ing]" exactly as you said. How do you explain it, if not by saying it understands, and it can reason? Are you really just going to cop out and avoid engaging seriously with my question? How do you explain the output above except as reasoning and understanding?

Here's what GPT-4 would have written:

og_kalu, I understand the point you're trying to make regarding the potential intelligence of GPT-4 and the connection with the philosophical zombie, but I believe there are some important distinctions to consider.

First, it's important to recognize that the goalposts for artificial intelligence have indeed been shifting, and for good reason. As our understanding of intelligence grows, so does our ability to build systems that can mimic it. However, this doesn't necessarily mean that a given AI system, like GPT-4, has truly achieved general intelligence. Instead, it might simply be that our models are becoming more sophisticated and better at solving specific tasks.

The philosophical zombie argument, on the other hand, is concerned with subjective experience and consciousness, rather than intelligence. A philosophical zombie is a hypothetical being that is behaviorally and functionally identical to a human being, but lacks subjective experience. The debate around the philosophical zombie is more about the nature of consciousness and whether it can be separated from intelligence, rather than the intelligence itself.

Now, regarding your assertion that true distinction shows in results, it's true that GPT-4 and similar models have shown impressive capabilities. However, it's crucial not to confuse correlation with causation. Just because an AI system can generate outputs that seem to demonstrate reasoning and understanding, it doesn't necessarily mean that it possesses true understanding. It might simply have learned to generate outputs that are highly correlated with human-generated responses, without any actual understanding or reasoning taking place.

In summary, while it's true that AI systems like GPT-4 are becoming more advanced and able to generate seemingly intelligent responses, it's important to differentiate between the appearance of intelligence and genuine understanding. Furthermore, the philosophical zombie argument is primarily concerned with consciousness, not intelligence, so it may not be entirely relevant in this context.

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#235

Post by [deleted] »

simon_000666 wrote: post #182

ChatGTP/4 is to AGI what pepper’s ghost is to holography. It’s a parlor trick, even if you add plugins or the ability to call other hugging face ML models - it’s just a parlor trick with fancier bells and whistles. All it is doing is using stochastic gradient descent to predict the next word in a sequence based on an enormous sophisticated training set designed to amaze people. Thinking it has advanced because it can…

[deleted]

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#236

Post by adhocmobility »

simon_000666 wrote: post #233
qolop wrote: post #211

Earlier quoted context omitted.

>> It's still just predicting the next word Predicting the next word is a much deeper problem than people like you realise. To be able to be good at predicting the next word you need to have an internal model of the reality that produced that next word. GPT-4 might be trained at predicting the next word, but in that process it learns a very deep representation of our world. That explains how it has an intuition for c…

“To be able to be good at predicting the next word you need to have an internal model of the reality that produced that next word.” Now that’s an interesting claim - that I would deeply dispute. It learns from text. Text itself is a model of reality. So chatgtp if anything proves that in order to be good at predicting the next word all you need is a good model of a model of reality. GTP knows nothing of actual realit…

You are being given a chance to dispute it. Give an example of a problem that any human would be easily able to solve but GPT4 wouldn't.

>> "good model of a model of reality"

That is just a model of reality. Also, a "model of reality" is what you'd typically call a world model. Its an intuition for how the world works, how people behave, that apples fall from trees and that orange is more similar to red than it is to grey.

Your last line shows that you still have a superficial understanding of what its learning. Yes it is statistics, but even our understanding of the world is statistical. The equations we have in our head of how the world works are not exact, they're probabilistic. Humans know that "Apples fall from the _____" should be filled with 'tree' with a high probability because that's where apples grow. Yes, we have seen them grow there, whereas the AI model has only read about the growing on trees. But that distinction is moot because both the AI model and humans express their understanding in the same way. The assertion we're making is that to be able to predict the next word well, you need an internal world model. And GPT4 has learnt that world model well, despite not having sensory inputs.

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#238

Post by simon_000666 »

adhocmobility wrote: post #236
simon_000666 wrote: post #233

Earlier quoted context omitted.

“To be able to be good at predicting the next word you need to have an internal model of the reality that produced that next word.” Now that’s an interesting claim - that I would deeply dispute. It learns from text. Text itself is a model of reality. So chatgtp if anything proves that in order to be good at predicting the next word all you need is a good model of a model of reality. GTP knows nothing of actual realit…

You are being given a chance to dispute it. Give an example of a problem that any human would be easily able to solve but GPT4 wouldn't. >> "good model of a model of reality" That is just a model of reality. Also, a "model of reality" is what you'd typically call a world model. Its an intuition for how the world works, how people behave, that apples fall from trees and that orange is more similar to red than it is to…

Can chatgtp ride a bicycle? Can you ride a bicycle? If you ‘d never rode on a bicycle before - do you think if you read enough books on bicycle riding, the physics of bicycle riding, the physics of the universe - you would have anywhere near as complete a model of bicycle riding as someone who’d actually rode on a bicycle before. Sure you’d be able to talk a great game about riding bicycles - but when it comes to the crunch, you’d fall flat on your face. That’s because riding a bicycle involves a large number of incredibly complex emergent control phenomena embedded within the marvel of engineering that is the human body - not just the small part of the brain that handles language. So call me when LLM’s can convert their ‘world models’ learned from statistics on human language use into being able to ride a bicycle first time. Until then I feel comfortable in the knowledge they know virtually nothing of our objective reality.

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#239

Post by lukeschlather »

_yb2s wrote: post #19
Workaccount2 wrote: post #8

I strongly suspect the first AGI will come sooner than expected on the back of a "glue" AI that can intelligently bond together a web of narrow AIs and utilities. I got access to the wolfram plugin for chatGPT, and it turned it from a math dummy to a math genius overnight. A small step for sure, but a hint of what's to come.

I agree, I suspect AGI is possible right now with a similar system only slightly more sophisticated than this one. The right "glue" for existing models, and plugins to existing data sources all coordinated in a system. GTP-4 would do the managing, and handling, and some simple template API and handler script would allow it to call instances of itself or other models, track recursion depth, and automatically remind GT…

I agree in general, though I think you're being too optimistic about GPT4 finding and fixing its mistakes. I haven't been using it much, but the only thing where it performed flawlessly was translating a simple song into Spanish. At the same time, GPT is a total sycophant and this makes it rather hard to work with. Whenever I give it feedback it agrees with whatever I have said and then proceeds to produce paragraphs of text which include several contradictions of the things we've "agreed" on.

I think the "stochastic parrot" idea is too harsh, but it does reflect that GPT is fundamentally incapable of looking at a small block of text, turning it into a set of logical facts, and recognizing if those facts are consistent or not. I think it is something that is possible to build with a refinement of GPT, but I'm not sure it can be done simply with glue code.

Re: HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace

#240

Post by simon_000666 »

Escapado wrote: post #188
simon_000666 wrote: post #182

ChatGTP/4 is to AGI what pepper’s ghost is to holography. It’s a parlor trick, even if you add plugins or the ability to call other hugging face ML models - it’s just a parlor trick with fancier bells and whistles. All it is doing is using stochastic gradient descent to predict the next word in a sequence based on an enormous sophisticated training set designed to amaze people. Thinking it has advanced because it can…

And while it may be true that it is far from AGI, I don’t think calling it a parlor trick does it justice. I used it this morning to set up a new workout routine for myself after having it write a little boilerplate typescript code to bootstrap 70% of a micro service I want to set up. My girlfriend who is studying react got a lot of value out of it by having compile errors explained to her. My mum uses it to practice…

Yeah I agree that’s fair, a parlor trick is perhaps a little harsh. ChatGTP can provide value - It’s arguable whether having done that ‘with classical’ methods could have been more efficient or whether the end result is as good - (btw careful with code - in my experience ChatGTP often thinks it knows what is wrong but is way off - something an experienced coder would notice immediately). Do you remember the tamagotchi? That also provided value to millions of people, many people thought of it as sentient even - was it? No - was it anywhere near AGI? No. If we can find good uses for the GTP models that were not possible or cost prohibitive before - then great. I think we just need to be clear - like the Tamagotchi - this is far from AGI and plugins/hugging face is not penultimate step before skynet.
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