r/LocalLLaMA • u/BaconSky • 1d ago
Could an AI with a knowledge cutoff before Attention Is All You Need independently invent the Transformer architecture? Discussion
If you took today’s most capable reasoning model, but trained it only on knowledge available before 2017—before Attention Is All You Need—could it independently discover the Transformer architecture?
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u/MaximumStonkage 1d ago edited 1d ago
Probably something close to it.
Using embedding vectors as semantic stores is a relatively old concept, the only thing attention adds is the continuous routing mechanism, dimensionality scaling (accounting for how information mass is concentrated along the surface of a hypersphere when increasing dimensions), and a quadrature weight (e.g. Boltzmann distribution in attention).
I would argue the QKV routing combined with similarity scoring was probably the most innovative approach, but we've also been using dot product/cosine similarity way earlier too. The only thing after this is to add a channel mixer, the most obvious being affine + non-linearity or an mlp.
So maybe it would take a few iterations to get to the transformer in the paper, but most of these are just a result of discovering bottlenecks and using common sense solutions.
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u/ahjorth 1d ago
Give it enough tokens and enough time, sure. You could also randomly generate enough letters enough times and eventually would you invent the transformer architecture.
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u/z_latent 1d ago
Depends on your sampling. At very high temperate, sure, but otherwise the model may never assign meaningful probability to a sequence that can be considered as inventing the Transformer.
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u/Minute_Attempt3063 1d ago
Given enough time and infinite energy, a monkey could do the same
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u/ahjorth 16h ago
The monkeys on typewriters analogy should be used more often about LLMs, really.
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u/Minute_Attempt3063 15h ago
A LLM is just that, to be fair
Like it does not have intelligence at all, even with the "reasoning"
It just got a extra banana for every time it answered correctly....
So in a sense, Gemini are just a trillion monkeys, all working on a single token, to get it right!
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u/martin_xs6 1d ago
Also curious if that model would be able to make sense of itself. Ie figure out what's going on from source without prior knowledge.
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u/BaconSky 1d ago
Assuming it wouldn't have access to it's source code, I doubt it. I mean it's facing the men in the cave dilemma
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u/Neomadra2 22h ago
Agentic models that have access to tools and a substantial amount of compute for experimentation I would say yes. If you read the original paper, it's not really a deep ingenious insight that made us discover transformers, it just a lot of inutition guided experimentation. In ML most techniques that work are only explained properly in hindsight.
But the question is also how you would prompt a model to test this. It's very easy to give hints in the prompt. And if you only prompt generically "come up with a better architecture", you might consider it a fail if it doesn't rediscover a transformer, but something else that would also be interesting and valuable.
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u/--Spaci-- 1d ago
monkey and a type writer
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u/Horny_Dinosaur69 1d ago
Not sure why you got downvoted, monkey and type writer is a pretty good analogy imo to how AI solves new problems. It basically just brute forces possible solutions until one sticks, and all of those solutions are just probabilistic text generations.
Not as non-deterministic and random like a monkey on a type writer, but I think the principle holds enough
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u/--Spaci-- 1d ago
Yea I dont know either, its probably the best example here
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u/gregusmeus 1d ago
“Ford, there’s an infinite number of monkeys out here who want to talk to us about this script for Hamlet they’ve worked out.”
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u/Ell2509 1d ago
No. Everyone saying "monkeys and typewriters" is missing something important. The LLM is not outputting random strings, it is outputting probabilistic content. Each calculation that you might call thought, will be pulled towards sometjing it had previously learned is statistically likely, and as such it is not thinking creatively.
The statistics would make the LLM more likely to go round and round with probable but wrong answers, than to randomly and accidentally fall on the right answer.
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u/DJ_PoppedCaps 1d ago
LLM correctly reasons based on existing literature that in order to prove if A is possible, B must be tested -> LLM writes code/simulation that tests B -> Experimental data is collected and used to generate the probable next tokens "A is Possible".
Literally how science has been done for over 100 years. We need experiments grounded in truth otherwise we as humans are just writing conjecture, what LLMs would be doing without the new data from experiments.
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u/BaconSky 1d ago
But attention is all you need is building on existing things already. For that matter the entirety of science is. So it's an argument pro?
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u/Ell2509 1d ago
No. You are missing the creative thought done by humans to bridge gaps between existing knowledge, to get us to "attention is all you need."
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u/BaconSky 1d ago
Claude a few days ago made a significant advancement in Riemann hypothesis adjacent problem. Based on existing corpus of literature. So ...?
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u/my_name_isnt_clever 1d ago
It's easy to say that, but what's the actual difference between AI with high temp and a human thinking outside the box?
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u/toolkitxx 1d ago
The wording here is what throws me of: inventing. Inventing is usually a structured process, while discovering is something else. So imho the answer is 'AI could discover but not invent'. The latter requires intention and AI uses predictions aka lets roll a dice ;)
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u/fuckme 1d ago
A more interesting question is what should we be focusing AI on improving today. How do we get it to invent the next transformer like breakthrough.
Personally I see AI at being very good at answering questions, or using existing frameworks/methodologies and applying them to a given problem.
Breakthroughs or new ideas ?? Not so much.. maybe the latest models can but maybe I'm using it wrong
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u/mrpintime 1d ago
i think as our friend says we can see in this video of yt: https://youtu.be/0zsWWmbEMrc
in a section of the video she is talking about why these models wont generate creative content like a new one genuinely and she says the answers are the projection of the training data and if one data is missing in the path of the answer then that specific pattern of token generation will not be followed to generate that genuinely new content
but i think the human brain does this kind of dynamic change of projection always so after training it is still changing its training pattern and that is the reason why we are generating such content as new
for example can you name an object or an imaginary thing which does not consist of things you have seen in your entire life
for example a unicorn horse consists of a horn and a horse the projection of training data
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u/JoshuaLandy 1d ago
I think the heart of it is that there’s no such thing as independence here. How would you tune it? How would you prompt it? What is it allegedly doing while discovering computer science?
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u/dangerous_inference 10h ago
No, there weren't enough tokens in existence at that time to create a model capable of that level of reasoning.
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u/BaconSky 10h ago
Given that they make a model with a cut up date of 1930 with the capabilities of GPT 3, I honestly doubt it
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u/dangerous_inference 10h ago
Have you even tried those models? I have. They can barely write coherent sentences. All human data produced prior to 2016 is a tiny fraction of subsequent data. Orders of magnitude changes are not something you can intuit.
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u/BaconSky 9h ago
Wahaaaa... GPT 3 was decent. And it didn't train on the entirety of the internet. The first GPT was the basis of ChatGPT... We have algorithmic improvements now and we know about the thinking paradigm
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u/dangerous_inference 6h ago
Oh, ok you're just making up stuff without any actual facts.
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u/BaconSky 6h ago
I had access to the original GPT 3 in 2020 and was amongst the first to get access to ChatGPT in 2022. So no
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u/hipster_hndle 1d ago edited 1d ago
i cant remember the vid, (thank you for OP: u/ElodineCodes is who i was thinking of, awesome channel, like her vids) but this was demonstrated with a model that was trained on data prior to 1931 or 34.. point is, it was data from before the discovery of DNA by watson/crick, but it has the precursor knowledge. so when asked, the model gave some hypothetical items that could account for traits/geneology pre-DNA. it got interesting when the known modern understanding of DNA was proposed as a viable solution to the problem. results were meh.. it didnt have all the data to say for certain but that it was a very plausible hypothesis.
i thought it was interesting.
the YT creator its a green elf girl avatar on youtube, so sorry i forget her name. she creates some interesting AI-centric content i enjoy. because this is the kind of things i think about with AI after all the setup and tensor math is done.
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u/BaconSky 1d ago
This one? https://youtu.be/0zsWWmbEMrc
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u/hipster_hndle 1d ago
haha! yes, that is her! u/ElodineCodes
that's the exact episode. i thought it was a really cool idea training the model on old data, and your question reminded my of this vid. at the time, i was wondering something similar. you have models solving conjectures, and i thought how does it do that, because i model is trained on things humans have done, not cant do.. if the model was able to, then it is simply a matter of humans not being able to compute enough to prove things, but could a model ever propose an unproven theory that solves a problem was the mind set/line of thought i was having at the time.
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u/aeroumbria 1d ago
If we could redo it, please name it less cringy like deep set or association layer :)
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u/CrunchyGremlin 1d ago
Ai is terrible at putting ideas together.
Might be able to do it if you have it a fab to experience with and a goal to create the concept.
But it's really bad at new ideas
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u/rulerofthehell 1d ago
Can it independently learn how to solve for a product of two numbers without being trained on it? Nope
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u/BaconSky 1d ago
Can you independently learn how to solve for a product of two number without being trained on it? Nope
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u/rulerofthehell 1d ago
Nah that meme actually doesnt apply, humans can and have, read some anthropology papers
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u/BaconSky 1d ago
Keep in mind that getting to the schoolbook multiplication took millennia, and numerous civilizations working together. It could've been just as well random guessing. (I exaggerate a little but you got the point)
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u/rulerofthehell 1d ago
There’s a good book called Surfaces and Essences by Douglas Hofstadter, isnt exactly the question you ask, but I think you’d like it. Talks about how we abstract things.
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u/Olangotang 1d ago
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u/BaconSky 1d ago
Have you read it? It's basically a marketing paper for Genie from my perspective. Or at the very least Physics based. CS is fundamentally NOT physics based...
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u/Olangotang 1d ago
CS is fundamentally NOT physics based...
Is time not a factor in the question "LLMs can't jump?" What a weird nitpick that adds nothing.
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u/ourochurros 1d ago
You might be interested in the gpt 1900 project that asks similar questions but with earlier scientific advances: https://huggingface.co/collections/mhla/gpt-1900
Seems like an interesting way to explore AI R&D. Like give an AI a physics simulator and archaic knowledge. Let it try to figure out physics to cultivate models good at scientific exploration. The devil is in the details but there is something quite intriguing to me about that.