Could this be an experiment to show how likely LLMs are to lead to AGI, or at least intelligence well beyond our current level?

If you could only give it texts and info and concepts up to Year X, well before Discovery Y, could we then see if it could prompt its way to that discovery?

▲ ben_w 12 hours ago | parent | next [-]

> Could this be an experiment to show how likely LLMs are to lead to AGI, or at least intelligence well beyond our current level?

You'd have to be specific what you mean by AGI: all three letters mean a different thing to different people, and sometimes use the whole means something not present in the letters.

> If you could only give it texts and info and concepts up to Year X, well before Discovery Y, could we then see if it could prompt its way to that discovery?

To a limited degree.

Some developments can come from combining existing ideas and seeing what they imply.

Other things, like everything to do with relativity and quantum mechanics, would have required experiments. I don't think any of the relevant experiments had been done prior to this cut-off date, but I'm not absolutely sure of that.

You might be able to get such an LLM to develop all the maths and geometry for general relativity, and yet find the AI still tells you that the perihelion shift of Mercury is a sign of the planet Vulcan rather than of a curved spacetime: https://en.wikipedia.org/wiki/Vulcan_(hypothetical_planet)

▲ grimgrin 11 hours ago | parent | next [-]

An example of why you need to explain what you mean by AGI is:

https://www.robinsloan.com/winter-garden/agi-is-here/

▲ opponent4 11 hours ago | parent | prev | next [-]

> You'd have to be specific what you mean by AGI

Well, they obviously can't. AGI is not science, it's religion. It has all the trappings of religion: prophets, sacred texts, origin myth, end-of-days myth and most importantly, a means to escape death. Science? Well, the only measure to "general intelligence" would be to compare to the only one which is the human one but we have absolutely no means by which to describe it. We do not know where to start. This is why you scrape the surface of any AGI definition you only find circular definitions.

And no, the "brain is a computer" is not a scientific description, it's a metaphor.

▲ strbean 10 hours ago | parent | next [-]

> And no, the "brain is a computer" is not a scientific description, it's a metaphor.

Disagree. A brain is turing complete, no? Isn't that the definition of a computer? Sure, it may be reductive to say "the brain is just a computer".

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opponent4 10 hours ago | parent | next [-]

Not even close. Turing complete does not apply to the brain plain and simple. That's something to do with algorithms and your brain is not a computer as I have mentioned. It does not store information. It doesn't process information. It just doesn't work that way.

https://aeon.co/essays/your-brain-does-not-process-informati...

	▲	strbean 8 hours ago \| parent \| next [-]
		> Forgive me for this introduction to computing, but I need to be clear: computers really do operate on symbolic representations of the world. They really store and retrieve. They really process. They really have physical memories. They really are guided in everything they do, without exception, by algorithms. This article seems really hung up on the distinction between digital and analog. It's an important distinction, but glosses over the fact that digital computers are a subset of analog computers. Electrical signals are inherently analog. This maps somewhat neatly to human cognition. I can take a stream of bits, perform math on it, and output a transformed stream of bits. That is a digital operation. The underlying biological processes involved are a pile of complex probabilistic+analog signaling, true. But in a computer, the underlying processes are also probabilistic and analog. We have designed our electronics to shove those parts down to the lowest possible level so they can be abstracted away, and so the degree to which they influence computation is certainly lower than in the human brain. But I think an effective argument that brains are not computers is going to have to dive in to why that gap matters.
	▲	nearbuy 7 hours ago \| parent \| prev \| next [-]
		That is an article by a psychologist, with no expertise in neuroscience, claiming without evidence that the "dominant cognitive neuroscience" is wrong. He offers no alternative explanation on how memories are stored and retrieved, but argues that large numbers of neurons across the brain are involved and he implies that neuroscientists think otherwise. This is odd because the dominant view in neuroscience is that memories are stored by altering synaptic connection strength in a large number of neurons. So it's not clear what his disagreement is, and he just seems to be misrepresenting neuroscientists. Interestingly, this is also how LLMs store memory during training: by altering the strength of connections between many artificial neurons.
	▲	anthonypasq 9 hours ago \| parent \| prev \| next [-]
		ive gotta say this article was not convincing at all.
	▲	Closi 9 hours ago \| parent \| prev \| next [-]
		A human is effectively turning complete if you give the person paper and pen and the ruleset, and a brain clearly stores information and processes it to some extent, so this is pretty unconvincing. The article is nonsense and badly written. > But here is what we are not born with: information, data, rules, software, knowledge, lexicons, representations, algorithms, programs, models, memories, images, processors, subroutines, encoders, decoders, symbols, or buffers – design elements that allow digital computers to behave somewhat intelligently. Not only are we not born with such things, we also don’t develop them – ever. Really? Humans don't ever develop memories? Humans don't gain information?
	▲	mistermann 9 hours ago \| parent \| prev [-]
		[dead]

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Davidzheng 3 hours ago | parent | prev [-]

probably not actually turing complete right? for one it is not infinite so

▲ ben_w 10 hours ago | parent | prev [-]

Cargo cults are a religion, the things they worship they do not understand, but the planes and the cargo themselves are real.

There's certainly plenty of cargo-culting right now on AI.

Sacred texts, I don't recognise. Yudkowsky's writings? He suggests wearing clown shoes to avoid getting a cult of personality disconnected from the quality of the arguments, if anyone finds his works sacred, they've fundamentally misunderstood him:

  I have sometimes thought that all professional lectures on rationality should be delivered while wearing a clown suit, to prevent the audience from confusing seriousness with solemnity.

- https://en.wikiquote.org/wiki/Eliezer_Yudkowsky

Prophets forecasting the end-of-days, yes, but this too from climate science, from everyone who was preparing for a pandemic before covid and is still trying to prepare for the next one because the wet markets are still around, from economists trying to forecast growth or collapse and what will change any given prediction of the latter into the former, and from the military forces of the world saying which weapon systems they want to buy. It does not make a religion.

A means to escape death, you can have. But it's on a continuum with life extension and anti-aging medicine, which itself is on a continuum with all other medical interventions. To quote myself:

  Taking a living human's heart out without killing them, and replacing it with one you got out a corpse, that isn't the magic of necromancy, neither is it a prayer or ritual to Sekhmet, it's just transplant surgery.

  …

  Immunity to smallpox isn't a prayer to the Hindu goddess Shitala (of many things but most directly linked with smallpox), and it isn't magic herbs or crystals, it's just vaccines.

- https://benwheatley.github.io/blog/2025/06/22-13.21.36.html

▲ markab21 12 hours ago | parent | prev [-]

Basically looking for emergent behavior.

▲ water-data-dude 11 hours ago | parent | prev | next [-]

It'd be difficult to prove that you hadn't leaked information to the model. The big gotcha of LLMs is that you train them on BIG corpuses of data, which means it's hard to say "X isn't in this corpus", or "this corpus only contains Y". You could TRY to assemble a set of training data that only contains text from before a certain date, but it'd be tricky as heck to be SURE about it.

Ways data might leak to the model that come to mind: misfiled/mislabled documents, footnotes, annotations, document metadata.

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gwern 10 hours ago | parent [-]

There's also severe selection effects: what documents have been preserved, printed, and scanned because they turned out to be on the right track towards relativity?

	▲	mxfh 9 hours ago \| parent [-]
		This. Especially for London there is a huge chunk of recorded parliament debates. More interesting for dialoge seems training on recorded correspondence in form of letters anyway. And that corpus script just looks odd to say the least, just oversample by X?

▲ alansaber 12 hours ago | parent | prev | next [-]

I think not if only for the fact that the quantity of old data isn't enough to train anywhere near a SoTA model, until we change some fundamentals of LLM architecture

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andyfilms1 12 hours ago | parent | next [-]

I mean, humans didn't need to read billions of books back then to think of quantum mechanics.

	▲	alansaber 12 hours ago \| parent \| next [-]
		Which is why I said it's not impossible, but current LLM architecture is just not good enough to achieve this.
	▲	famouswaffles 12 hours ago \| parent \| prev [-]
		Right, what they needed was billions of years of brute force and trial and error.

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franktankbank 12 hours ago | parent | prev [-]

Are you saying it wouldn't be able to converse using english of the time?

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ben_w 12 hours ago | parent | next [-]

Machine learning today requires an obscene quantity of examples to learn anything.

SOTA LLMs show quite a lot of skill, but they only do so after reading a significant fraction of all published writing (and perhaps images and videos, I'm not sure) across all languages, in a world whose population is 5 times higher than the link's cut off date, and the global literacy went from 20% to about 90% since then.

Computers can only make up for this by being really really fast: what would take a human a million or so years to read, a server room can pump through a model's training stage in a matter of months.

When the data isn't there, reading what it does have really quickly isn't enough.

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wasabi991011 12 hours ago | parent | prev [-]

That's not what they are saying. SOTA models include much more than just language, and the scale of training data is related to its "intelligence". Restricting the corpus in time => less training data => less intelligence => less ability to "discover" new concepts not in its training data

	▲	franktankbank 11 hours ago \| parent [-]
		Perhaps less bullshit though was my thought? Was language more restricted then? Scope of ideas?

▲ armcat 12 hours ago | parent | prev | next [-]

I think this would be an awesome experiment. However you would effectively need to train something of a GPT-5.2 equivalent. So you need lot of text, a much larger parameterization (compared to nanoGPT and Phi-1.5), and the 1800s equivalents of supervised finetuning and reinforcement learning with human feedback.

▲ dexwiz 12 hours ago | parent | prev | next [-]

This would be a true test of can LLMs innovate or just regurgitate. I think part of people's amazement of LLMs is they don't realize how much they don't know. So thinking and recalling look the same to the end user.

▲ Trufa 12 hours ago | parent | prev | next [-]

This is fascinating, but the experiment seems to fail in being a fair comparison of how much knowledge can we have from that time in data vs now.

As a thought experiment I find it thrilling.

▲ Rebuff5007 12 hours ago | parent | prev | next [-]

OF COURSE!

The fact that tech leaders espouse the brilliance of LLMs and don't use this specific test method is infuriating to me. It is deeply unfortunate that there is little transparency or standardization of the datasets available for training/fine tuning.

Having this be advertised will make more interesting and informative benchmarks. OEM models that are always "breaking" the benchmarks are doing so with improved datasets as well as improved methods. Without holding the datasets fixed, progress on benchmarks are very suspect IMO.

▲ nickpsecurity 8 hours ago | parent | prev | next [-]

That is one of the reasons I want it done. We cant tell if AI's are parroting training data without having the whole, training data. Making it old means specific things won't be in it (or will be). We can do more meaningful experiments.

▲ mistermann 9 hours ago | parent | prev | next [-]

[dead]

▲ feisty0630 12 hours ago | parent | prev [-]

I fail to see how the two concepts equate.

LLMs have neither intelligence nor problem-solving abillity (and I won't be relaxing the definition of either so that some AI bro can pretend a glorified chatbot is sentient)

You would, at best, be demonstrating that the sharing of knowledge across multiple disciplines and nations (which is a relatively new concept - at least at the scale of something like the internet) leads to novel ideas.

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al_borland 12 hours ago | parent [-]

I've seen many futurists claim that human innovation is dead and all future discoveries will be the results of AI. If this is true, we should be able to see AI trained on the past figure it's way to various things we have today. If it can't do this, I'd like said futurists to quiet down, as they are discouraging an entire generation of kids who may go on to discover some great things.

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skissane 11 hours ago | parent | next [-]

> I've seen many futurists claim that human innovation is dead and all future discoveries will be the results of AI.

I think there's a big difference between discoveries through AI-human synergy and discoveries through AI working in isolation.

It probably will be true soon (if it isn't already) that most innovation features some degree of AI input, but still with a human to steer the AI in the right direction.

I think an AI being able to discover something genuinely new all by itself, without any human steering, is a lot further off.

If AIs start producing significant quantities of genuine and useful innovation with minimal human input, maybe the singularitarians are about to be proven right.

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thinkingemote 10 hours ago | parent | prev [-]

I'm struggling to get a handle on this idea. Is the idea that today's data will be the data of the past, in the future?

So if it can work with whats now past, it will be able to work with the past in the future?

	▲	al_borland 8 hours ago \| parent [-]
		Essentially, yes. If the prediction is that AI will be able to invent the future. If we give it data from our past without knowledge of the present... what type of future will it invent, what progress will it make, if any at all? And not just having the idea, but how to implement the idea in a way that actually works with the technology of the day, and can build on those things over time. For example, would AI with 1850 data have figured out the idea of lift to make an airplane and taught us how to make working flying machines and progress them to the jets we have today, or something better? It wouldn't even be starting from 0, so this would be a generous example, as da Vinci way playing with these ideas in the 15th century. If it can't do it, or what it produces is worse than what humans have done, we shouldn't leave it to AI alone to invent our actual future. Which would mean reevaluating the role these "thought leaders" say it will play, and how we're educating and communicating about AI to the younger generations.