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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

#181

Post by Manjuuu »

Workaccount2 wrote: post #132
Manjuuu wrote: post #71

Earlier quoted context omitted.

The same confidence of those already planning for the advent, that will never be, of AGI. I see a lot of unmotivated enthusiasm for the "new" thing, the opinions of Hinton should be considered valid if the reasoning makes sense, not just because the source is Hinton. No idea why he said that it's possible in 10 years. Not trying to bash anyone, I just meant normal people, outside of the field. Enthusiastic nonsense i…

We have these guys calling themselves "experts on intelligence" just because they know the structure of the components of the neural net. It's like neurologists saying they are experts in consciousness because they know how neurons function and grow. Thankfully doctors don't have nearly the same levels of hubris as tech bros.

Exactly.

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

#182

Post by simon_000666 »

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 now get calculations correct is a fallacy. It’s still just predicting the next word, it’s just that it’s now got a post processing step that is converting those next words into code and parroting the output. It maybe be able to now answer 4567*9876 correctly (using the human hardcoded wolfram alpha engine) but it still does not fundamentally comprehend why 1+1=2 - like my 5 year old can.

Until it can generate its own internal neural networks to for example learn to logically reason about calculations we are still far from AGI. Also those calling for more data are misguided - less data, more sophisticated architectures than transformers are the only way to avoid the stochastic parrot trap.

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

#183

Post by Sinidir »

_yb2s wrote: post #65
marcosdumay wrote: post #42

Earlier quoted context omitted.

I believe you will quickly run into a barrier due to the small size of the context window. And increasing the context window gets harder and harder the larger it is. There's a sibling talking about internal memory. That's how our brains solve the issue. AFAIK, nobody knows how to train something like it.

You are right, but it is unclear to me where this limitation will fall in terms of actual abilities. GPT-4 can divide large tasks up into logical steps and instructions that can be worked on independently by other instances, and it can create "compressed" condensed explanations of it's own state that can be stored externally and repeated back to it, or passed back and forth between instances. With the unreleased 32,0…

That just gave me the idea that there could be a main instance that keeps a summary of the problem (List of paragraphs for each subproblem) and other instances that hold the larger text that these summaries are based on. Then on demand the main instance could get needed extra information for a subpoint back. Kinda like having forgein keys in a databas with which you can join in the relevant row!

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

#184

Post by TedDoesntTalk »

rlt wrote: post #176
michaelmrose wrote: post #159

Earlier quoted context omitted.

An actual intelligence would have a mental model of the world deeper than the connection between one token and the next and a model of the human beings he was interacting with and an expectation of effecting that model to achieve actual differences in the observed world. In other words Google translate doesn't actually speak Chinese.

> An actual intelligence would have a mental model of the world deeper than the connection between one token and the next Why are you so confident in this? We don't really know how human "intelligence" works. Whose to say it's not doing something similar? It's a bit like saying an audio file doesn't contain "music" because it's just a binary representation of the original work.

> We don't really know how human "intelligence" works

Or cepholapod intelligence. Or magpie/raven/crow intelligence.

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

#185

Post by UncleEntity »

roflyear wrote: post #139
_yb2s wrote: post #75

Earlier quoted context omitted.

I'm not sure why you think that? When I give it programming tasks, GPT-4 will ask all kinds of clarifying questions. Here is a trivial example: p: I want you to solve for x, giving me a numeric value, given the formula ax^2 + bx + c = 0. r: To find the numeric value of x for the quadratic equation ax^2 + bx + c = 0, we need to use the quadratic formula: x = (-b ± √(b^2 - 4ac)) / (2a) However, you haven't provided spe…

First, these are trivial examples. I would not classify "give me the values to this math formula" as clarifying questions. Second, in response to "I'm not sure why you think that?" ask GPT why it often does not ask clarifying questions. It will explain to you why! Third, I just asked GPT the most vague question on the planet: "I am trying to create a function that takes a value, and gives me a result using that value…

It gave you exactly what you asked for.

Be like complaining that someone used “foo” as a function name instead of looking up the exact function call you wanted.

Ask a vague question get a vague answer, seems reasonable.

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

#186

Post by jmfldn »

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.

It all depends on your definition of AGI as this term seems to be used with wildly different meanings. Before I can agree on any statement about AGI, I need to know what it is I'm being asked to agree / disagree on.

Fwiw though, using this kind of mashup approach of gluing lots of things together, I suspect you'll get something that appears to match at least some people's definition of AGI.

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

#187

Post by bjackman »

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 wonder if the first tribe to be annihilated by bronze weapons were like "that stuff is just like stone but more malleable, don't see what the big deal is".

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

#188

Post by Escapado »

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 English. I am going to integrate GPT-4 into a new product where it provides tangible value for non technical users. To be useful it does not need to be sentient or able to iterate on its own architecture.

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

#189

Post by SanderNL »

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…

Your 5yo does not understand 1+1. You yourself do not understand it. Entire careers were spent trying to pin it down. It is basically its own branch of mathematics.

I understand your point, but I am struggling to see why it matters. This seems more and more an argument like “cars are not horses”. I know they are not but does it matter? Cars are superior for our use cases.

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

#190

Post by trifurcate »

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…

Is this some kind of copypasta? Too many tropes all at once. "GTP" on top of all this is too on the nose.
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