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magic9mushroom  If you're going to downvote me, and nobody's already voiced your objection, please reply and tell me  3mo ago (thezvi.wordpress.com)   1802 thread views

Danger, AI Scientist, Danger

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Zvi Mowshowitz reporting on an LLM exhibiting unprompted instrumental convergence. Figured this might be an update to some Mottizens.

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Diffusion models work for text too. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10909201/

The blending of concepts that we see in MidJourney is probably less to do with the diffusion per se as with CLIP - a building block within diffusion. CLIP aligns a language model with an image model. Moving concepts between different representations helps with concept generation. There's a lot being done with 'MultiModal models' to make the integration between different modalities work better.

'Self play' is relevant for text generation. There is a substantial cottage industry in using LLMs to evaluate the output of LLMs and learn from the feedback. It can be easier to evaluate whether text 'is good' than it is to generate good text. So multiple attempts and variations can lead to feedback and improvement. Mostly self play to improve LLMs is done at the level of optimising prompts. However the outputs improved by that method can be used as training examples, and so can be used to update the underlying weights.

https://topologychat.com is a commercial example of using LLMs in a way inspired by chess programming (Leela, Stockfish). It does a form of self play on inputs that have been given to it, building up and prioritising different lines. It then uses these results to update weights in a mixture of experts model.

Here's the quote from Geoffrey Hinton:

"why is a compost heap like an atom bomb? And GPT-4 says, well, the timescales and the energy scales are very different. That’s the first thing but the second thing is the idea of a chain reaction.

So in an atom bomb, the more neutrons around it, the more it produces, and in a compost heap, the hotter it gets, the faster it produces heat and GPT-4 understands that. My belief is when I first asked it that question, that wasn’t anywhere on the web. I searched, but it wasn’t anywhere on the web that I could find. It’s very good at seeing analogies because it has these features. What’s more, it knows thousands of times more than we do. So it’s gonna be able to see analogies between things in different fields that no one person had ever known before.

That may be this sort of 20 different phenomena in 20 different fields that all have something in common. GPT-4 will be able to see that and we won’t. It’s gonna be the same in medicine. If you have a family doctor who’s seen a hundred million patients, they’re gonna start noticing things that a normal family doctor won’t notice."

From transcript at https://medium.com/@jalilnkh/geoffrey-hinton-will-digital-intelligence-replace-biological-intelligence-fc23feb83cfb of the video.

Diffusion models work for text too.

Last I checked, diffusion models work at all for text but they don't work very well. More specifically, text diffusion models remind me quite strongly of the classic-style Markov chain text generators that used to be popular for generating amusing locally-coherent-globally-word-salad text. Here's the best concrete example of this I can point to (italicized text is the prompt, normal text is continuation, examples sourced from this JDP tweet, whole tweet is excellent but somewhat OT here):

Diffusion model:

This whole dream seems to be part of someone else's experiment.

In the end, I think the only way to get the full benefit of the dream is to get up and walk around.

A lot of the other strands of the theory of dreams, especially the one called yncrest are known to the person who writes this blog. Consequently, I've tested his reactions via a series of brief, televised interviews with the British Prime Minister, Margaret Thatcher.

“I don't care what anybody says. I'm not going to waste my time with that. The study was conducted in the high-flow, low-flow and quiescent periods of a butterfly species and thus provides evidence for the first three of the four hypotheses listed in the original paper. The only one not covered is the "need to change the rate of capture", where we now know that this is largely a policy instrument - and particularly relevant to Sudanese geese.

GPT-2:

This whole dream seems to be part of someone else's experiment. This is usually hard to see, since you're conscious that mental shortcuts are normal. But you're definitely moments away from being normal again, as it's obvious that you are being coached to think like that by another person and doing our own thing. Why are we having such thoughts when we're paying no attention because that's already taking place?

I'm totally appalled by what these key players were doing, yet I explain it in a very large way. After 2 minutes, I make my way through high teaching circles, recognizing that there is more to learning than just to learn.

Few other teachers would air this incessantly their students' struggles recount the stories of their own memories and teachers'. Whatever it takes is the big fat slut shaming.

All these legendary trainees made the same point:

Cognitive functional aspects: Bayesian modeling, learn science way. And the most important part is: Like the coming of a hurricane, the book is mucking between science and morals.

Twitter Mentions of the first book: Kent

Now obviously in the limit as computational power and training data volume go to infinity, diffusion models and transformer models will generate the same text, since in the limit they're pulling from the same distribution with minimal error. But in the very finite regime we find ourselves in, diffusion models "spend" their accuracy on making the text locally coherent (so if you take a random 10 token sequence, it looks very typical of 10 token sequences within the training set), while transformer LLMs "spend" their accuracy on global coherence (so if you take two 10 token sequences a few hundred tokens apart in the same generated output, you would say that those two sequences look like they came from the same document in the training set).

The blending of concepts that we see in MidJourney is probably less to do with the diffusion per se as with CLIP

Agreed. Obvious once you point it out but I hadn't thought about it that way before, so thanks.

'Self play' is relevant for text generation. There is a substantial cottage industry in using LLMs to evaluate the output of LLMs and learn from the feedback.

Notably, Anthropic's Constitutional AI (i.e. the process by which Anthropic turned a base LLM into the "helpful, honest, harmless" Claude) process used RLAIF, which is self play by another name. And that's one big cottage.

The blending of concepts that we see in MidJourney is probably less to do with the diffusion per se as with CLIP

Thanks! I'm not strong on diffusion model and multimodal models, I'll do some reading.

'Self play' is relevant for text generation. There is a substantial cottage industry in using LLMs to evaluate the output of LLMs and learn from the feedback. It can be easier to evaluate whether text 'is good' than it is to generate good text. So multiple attempts and variations can lead to feedback and improvement. Mostly self play to improve LLMs is done at the level of optimising prompts. However the outputs improved by that method can be used as training examples, and so can be used to update the underlying weights.

https://topologychat.com is a commercial example of using LLMs in a way inspired by chess programming (Leela, Stockfish). It does a form of self play on inputs that have been given to it, building up and prioritising different lines. It then uses these results to update weights in a mixture of experts model.

Again, thank you. I haven't come across this kind of self-play in the wild, but I see how it could work. Will investigate further.

That may be this sort of 20 different phenomena in 20 different fields that all have something in common. GPT-4 will be able to see that and we won’t. It’s gonna be the same in medicine. If you have a family doctor who’s seen a hundred million patients, they’re gonna start noticing things that a normal family doctor won’t notice."

This is exactly what I was hoping for from LLMs, but I haven't been able to make it happen so far in my experiments. GPT does seem to have some capacity for analogies, perhaps that's a fruitful line of investigation.

Yes diffusion models work for text too. The difference between a collated set of pixels (ie an image) and a collated set of letters (ie a word or sentence) is purely conceptual. From an algorithmic perspective it's all just "tokens". However, this overlap in operation doesn't mean that they are not very different beasts under the proverbial hood.

By way of analogy, a conventional piston engine, a turbine engine, and an electric motor attached to a battery may all accomplish the base task of "make the vehicle go" but they have different trade-offs, use cases, and operating principles. Point being that similar output does not equal similar function.

As someone who has actually spent some time "in the trenches" as it were, designing algorithms and writing code to execute them, I am in broad agreement with @Corvos's take. The opening of Mowshowitz's essay comes across as ignorant, lazy, and plainly self-serving and nothing that follows really challenges that first impression of him.

The link from Hinton is better but seems make a lot of similar mistakes. It seems clear to me that both are far more interested in driving engagement through hyperbole than really exploring or helping others understand the underlying questions and theories.

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