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Unpredictable abilities emerging from large AI models

quantamagazine.org

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Re: Unpredictable abilities emerging from large AI models

#291

Post by YeGoblynQueenne »

saiojd wrote: post #258
datpiff wrote: post #93

Earlier quoted context omitted.

> According to NVidia, LLM sizes have been increasing 10X per year for the last few years. Clearly this cannot continue, as the training costs will exceed all the compute capacity in existence. The other limit is training data, eventually you run out of cheap sources.

I would not be so sure about compute capacity? Neural network architectures are still in their infancy, it is very likely that more efficient approaches exist.

We've had neural nets since 1943. The architectures are not "in their infancy", new architectures have been developing for decades, even entire neural net paradigms (feed-forward nets, recurrent nets, recursive nets, etc etc.). Their scale has also been increasing ever since Hinton and friends rediscovered backprop in the '80s. Neural nets are positively ancient at this point, not "in their infancy"!

I don't know why people just keep repeating this complete fantasy as if it were true. Where does it originate from, I wonder? I suspect someone said something like that on social media, their post went viral, and now all of the internet is reverberating with this thing. It's a meme, yes?

Re: Unpredictable abilities emerging from large AI models

#292

Post by pixl97 »

Jensson wrote: post #196
whimsicalism wrote: post #174

Earlier quoted context omitted.

I find the phrase "statistical analysis" a frustrating one nowadays as it seems to have become a signal for "I hold a particular philosophy of the mind". I don't understand this use of "statistical" as a diminutive to describe these models. Why can't incredibly complicated behavior be emergent from matrix multiplication subject to optimization in the same way that our biological matter has developed complicated emerg…

> I find the phrase "statistical analysis" a frustrating one nowadays as it seems to have become a signal for "I hold a particular philosophy of the mind". LLMs are trained to reproduce human text, that is different from for example AlphaGo that is trained to win Go games. Trained to reproduce data is what we mean with a statistical model, trained to win is how we got superhuman performance before, while trained to r…

This is reductive in the sense is that things like AlphaGo were bad at Go for a very long time, and then with more compute power and algorithm changes suddenly they were far better. And the problem space for go is absolutely huge it is still an insignificant portion of the problem space for knowledge.

Re: Unpredictable abilities emerging from large AI models

#293

Post by mark_l_watson »

zzleeper wrote: post #284
mark_l_watson wrote: post #144

Nice write up! I have been using classic back-prop neural networks since the 1980s, and deep learning for the last 8 years. This tech feels like a rocket ship that is accelerating exponentially! I am in my 70s and I don't work much anymore. That said, I find myself spending many hours in a typical day doing what I call "gentleman scientist" activities around Large Language Models. I was walking this morning with a no…

Hi Mark, One question out of academic curiosity: I'm exploring ways to use these tools for research projects in econ and am struggling to see a good angle. For instance, suppose I have lots of PDF reports on how firms have evolved on each quarter (10,000 reports or any other number beyond what I can read). Can LLMs be used to spit out variables based on these reports? EG: indicator variables (optimistic-vs-pesimistic…

Look at the LangChain and Llama-Index (used to be called GPT-Index) projects that make smaller projects that need to use a large amount of text data do-able. There is also support for reading PDF files (and many other data sources), and pre-computing embeddings. If you spend a short while looking at example code in the documentation, find something that is similar to your requirements (e.g., semantic search, conversational chat about a set of documents, etc.), and build on that.

Re: Unpredictable abilities emerging from large AI models

#294

Post by YeGoblynQueenne »

blurbleblurble wrote: post #112
maxdoop wrote: post #23

Earlier quoted context omitted.

I have fun on these HN chats responding to comments like yours . It’s just fancy auto complete to you? You honestly can’t see the capability it has and extend it the future? What’s that saying about “it’s hard to get someone to understand something when their salary depends on their not understanding it”.

I feel very frustrated with these takes because instead of grappling with what we're going to do about it (like having a conversation) it's a flat, dismissive denial, and it isn't even grounded in the science, which says that "memory augmented large language models are computationally universal". So at the very least we're dealing with algorithms that can do anything a hand written program can do, except that they've…

>> I feel very frustrated with these takes because instead of grappling with what we're going to do about it (like having a conversation) it's a flat, dismissive denial, and it isn't even grounded in the science, which says that "memory augmented large language models are computationally universal"

That's not what "the science says", it's the title of an article that someone put on arxiv.

The article has no theoretical results, just a shoddy empirical demonstration of... something. The author claims that the something is an LLM simulating a Turing machine. But, is it? Really?

Well, here's how the article concludes:

>> Hopefully the reader has been convinced by this point.

"Hopefully" is not how you show computational universality of a neural net architecture. This is how:

https://www.sciencedirect.com/science/article/pii/S002200008...

i.e. with maths. That article you link to is a bunch of hooey.

Re: Unpredictable abilities emerging from large AI models

#295

Post by [deleted] »

guns wrote: post #91
mpalmer wrote: post #59

I'd like to see posts on LLMs written from a different perspective. For me, the surprise comes not from the sudden emergent capability of language models, but that the understanding (and synthesis!) of ideas encoded in language has succumbed to literally nothing more than statistical analysis. Or at least come that much closer to doing so. That it bears so close a resemblance to actual thinking says more about the im…

This is what Stephen Wolfram concludes in a recent article about ChatGPT: > The specific engineering of ChatGPT has made it quite compelling. But ultimately (at least until it can use outside tools) ChatGPT is “merely” pulling out some “coherent thread of text” from the “statistics of conventional wisdom” that it’s accumulated. But it’s amazing how human-like the results are. And as I’ve discussed, this suggests some…

[deleted]

Re: Unpredictable abilities emerging from large AI models

#296

Post by pixl97 »

coldtea wrote: post #209
lamp987 wrote: post #135

Earlier quoted context omitted.

"your comment confidently states this is unfixable - presumably based on the frequency you've seen similar text on the internet. why should anyone believe the veracity of your statement? " no its because GPT is based on transformers.

and you aren't? Aren't you just a function of your input and memories (stuff you've read, sensory input) as run through/managed by some neural network? What makes you think the rest isn't just emergent properties? And what makes you think you can't hook up the LLM with some algorithms or layers that handle some of the rest behavior of what your brain does?

Yep, the idea of grounding seems interesting to me. Everything in a LLM is just a statistical dream at this point with no 'reality basis' at this point. I wonder if it's possible to give the language model grounding points of things that are real and building a truth model from that.

Re: Unpredictable abilities emerging from large AI models

#297

Post by fulafel »

HyperSane wrote: post #280
bandofthehawk wrote: post #239

Earlier quoted context omitted.

Was he wrong to oppose the Vietnam war?

He supported the communist though. He also supported Pol Pot. You can predict his opinions of foreign policy if you understand that he bases them on the "US is evil" axiom and therefore anyone who opposes it is good, no matter how actually evil they may be.

The Pol Pot thing seems debunked: https://www.abc.net.au/news/2011-07-01/brull---the-boring-tr...

The communism support doesn't seem to quite stick either if WP is to be believed: https://en.wikipedia.org/wiki/Political_positions_of_Noam_Ch...

In Britannica and WP he's labeled an anarcho-syndicalist, so seems to be against both capitalism and noncapitalist authoritarian systems.

Re: Unpredictable abilities emerging from large AI models

#298

Post by pixl97 »

great_psy wrote: post #228
maxlamb wrote: post #34

Earlier quoted context omitted.

I’m guessing one is data. The limit would be once you’ve trained a LLM on all public (or even private) data. Sure you can still make some improvements or try to find some additional private data but still, a fundamental limit has been reached.

I think that’s actually really not a limit. We don’t teach babies by throwing lots of data at them, instead we teach them by giving them useful data. The loop I see is: - train on a lot of existing data - run out of useful data - people use ai and give feedback (we’re here) - perform reinforcement learning on the data collected Loop over the last two steps. There is already more than enough data available, it’s just…

Reality throws a lot of unfiltered data at a baby. Now I guess you can consider things like gravity and pain useful data because of the consequences of violating them. But it's this data that grounds the baby in the world it exists in.

Re: Unpredictable abilities emerging from large AI models

#299

Post by pixl97 »

FrojoS wrote: post #71
maxlamb wrote: post #34

Earlier quoted context omitted.

I’m guessing one is data. The limit would be once you’ve trained a LLM on all public (or even private) data. Sure you can still make some improvements or try to find some additional private data but still, a fundamental limit has been reached.

Good point. But isn't the next logical step to allow these systems to collect real world data on their own? And also, potentially even more dangerous, act in the real world and try out things, and fail, to further its learning.

This is likely exactly what we will do... which is very questionable when you may have an unaligned paperclip maximizer hidden in there.

Re: Unpredictable abilities emerging from large AI models

#300

Post by pixl97 »

EForEndeavour wrote: post #70
tarruda wrote: post #44

Earlier quoted context omitted.

That was what I thought until a few months ago when ChatGPT was released. I never cared much about LLMs because it always felt like a brute force method to solving problems. What I'm seeing now is that some kind of intelligence seem to emerge from these models, even though under the hoods it is just a bunch o matrix multiplications. Who's can say for sure that our own brains doesn't work similarly? Maybe human intell…

It doesn't matter to me whether the intelligence is "really emergent" or "just a simulation." Two things are true: 1. Solving all kinds of nontrivial problems posed in text format is extremely useful, no matter how it works under the hood. This means lots of people will use it, and it will change how people work 2. The more convincing the illusion of intelligence, consciousness, even sentience and personhood, the mor…

LLMs currently have no continuous learning feedback loop, yes we can train prompts and make them temporarily smarter...

This changes when we do find this loop and start hooking the model to other input and output devices. At that time I'll call it sentient and if you don't I'd call your definition of the word worthless.

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