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Comment by wim | Hacker News Reader

One experiment I would love to see, although not really feasible in practice, is to train a model on all digitized data from before the year 1905 (journals, letters, books, broadcasts, lectures, the works), and then ask it for a formula for mass-energy equivalence. A certain answer would definitely settle the debate on whether pattern recognition is a form of intelligence ;)

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> ask it for a formula for mass-energy equivalence

Way too easy. If you think that mass and energy might be equivalent, then dimensional analysis doesn’t give you too much choice in the formula. Really, the interesting thing about E=mc^2 isn’t the formula but the assertion that mass is a form of energy and all the surrounding observations about the universe.

Also, the actual insight in 1905 was more about asking the right questions and imagining that the equivalence principle could really hold, etc. A bunch of the math predates 1905 and would be there in an AI’s training set:

https://en.m.wikipedia.org/wiki/History_of_Lorentz_transform...

It’s nontrivial why it’s mc^2 and not 1/2 mc^2, since kinetic energy generally is 1/2 mv^2

but e=mc^2 is just an approximation

e: nice, downvoted for knowing special relativity

Can you elaborate? How is E=mc^2 an approximation, in special relativity or otherwise? What is it an approximation of?

E^2 = m^2 + p^2 where p is momentum and i’ve dropped unit adjustment factors like c

this allows light to have energy even if its massless

e=mc^2 is only correct for objects at rest. The full equation takes into account velocity, but for "low" speeds where v<<c, the term is close enough to zero than E=mc^2 is still a good approximation.

E^2 = (mc^2)^2 + (pc)^2,

where p is momentum. When an object is traveling at relativistic speeds, the momentum forms a more significant portion of its energy

I didn't downvote it, but short comments are a very big risk. People may misinterpret it, or think it's crackpot theory or a joke and then downvote.

When in doubt, add more info, like:

But the complete equation is E=sqrt(m^2c^4+p^2) that is reduced to E=mc^2 when the momentum p is 0. More info in https://en.wikipedia.org/wiki/Mass%E2%80%93energy_equivalenc...

The next section of the wikipedia link discusses the low speed approximation, where sqrt(m^2c^4+(pc)^2) ≈ mc^2 + 1/2 mv^2.

Calling E=mc^2 an "approximation" is technically correct. It's the 0th order approximation. That's just pointlessly confusing. A better word choice would be "a special case".

i think we are venturing into pedantic territory - the point of my comment is that the full derivation is a little harder than just E=mc^2 dimensional analysis

This is why RLHF causes those overly verbose answers to simple questions, it's a fundamentally busted evaluation function so you wind up optimizing for the wrong thing

[Sorry for the dealy.]

In one extreme there are wall of text and in the other extreme very short answers that only the initiated understand (like inside jokes). Somewhere in between there is a sweet spot that helps everyone else to follow the discusion and gain a litle of knowdledge.

(I don't claim I get the best lenght in my comments, but I hope it's good enough.)

What I learnt is that there is a rest mass and a relativistic mass. The m in your formula is the rest mass. But when you use the relativistic mass E=mc² still holds. And for the rest mass I always used m_0 to make clear what it is.

sounds like you had a chemistry education. relativistic mass is IMO very much not a useful way of thinking about this and it is sort of tautologically true that E = m_relativistic because “relativistic mass” is just taking the concept of energy and renaming it “mass”

This is all sort of silly IMO. The equation, like basically all equations, needs context. What’s E? What’s m? If E is the total energy of the system and m is the mass (inertial or gravitational? how far past 1905 do you want to go?), then there isn’t a correction. If m is rest mass and E is total energy, then I would call it flat-out wrong, not merely approximate. After all, a decent theory really ought to reproduce Newtonian mechanics under some conditions beyond completely at rest.

IMO, when people get excited about E=mc^2, it’s in contexts like noticing that atoms have rest masses that are generally somewhat below the mass of a proton or neutron times the number of protons and neutrons in the atom, and that the mass difference is the binding energy of the nucleus, and you can do nuclear reactions and convert between mass and energy! And then E=mc^2 is apparently exactly true, or at least true to an excellent degree, even though the energies involved are extremely large and Newtonian mechanics can’t even come close to accounting for what’s going on.

inertial mass, rest mass, gravitational mass - these are essentially all the same thing. “relativistic mass” is an additional concept where we rewrite energy as mass and is considered archaic

I should have used m_0 to avoid confussion. Anyway, as he sibling comment says, most modern advanced books of special relativity try to avoid the relativistic mass. It's useful for some calculations, like synchrotron, but the problem is that for forward/backward acceleration you must use other number so the relativistic mass add confussion. https://en.wikipedia.org/wiki/Mass_in_special_relativity#His...

Kind of agree. But pervasive downvoting by folks who don’t understand the subject is a form of forum rot. The risk is only that we expose the rot. Not such a terrible risk, because either the owners notice and fix the problem, or the forum continues to rot. In the latter case karma points wont be desirable in the long run.

This thread has come up before(1), but I'll continue to argue that relativistic mass is a perfectly valid concept as much as any other, and if you disagree, you'll need arguments more substantial than it just being unpopular these days. Especially if you're trying to argue people out of using a concept that they personally find useful to aid their own understanding, just because it doesn't fit your own mathematical or aesthetic preferences.

1: https://news.ycombinator.com/item?id=38425252

sure but i think it

1. is not very intuitive/useful to have mass that varies on the direction (which is what this implies)

2. is somewhat tautological to define a new mass m_rel = E/c^2 and say that it satisfies the equation when this is not what most people understand mass to be. most people understand photons to be massless particles.

at minimum, relativistic mass should always be specified as m_rel to distinguish from what is typically referred to as mass.

but i don’t think relativistic mass is a wrong concept any more than any other mathematical convenience like virtual particles. the main question is how useful is it and should it be described using the word “mass” or is this confusing. there is value in having shared language, even if you can construct an alternate system of symbols and rules that can yield the same answer to every question. to the extent to which intent of the author matters at all (probably doesn’t), Einstein agreed that relativistic mass was not a useful concept.

i'll concede that the arguments in the thread you linked are not good

> most people understand photons to be massless particles.

I suspect that most people have no opinion at all and are generally unaware of the properties of light.

That being said, a (perfectly reflective or otherwise steady state, e.g. black body at equilibrium) box of photons has mass due to those photons. You can stick it on a scale or a pendulum and measure it. It attracts other nearby objects according to GR or, in the appropriate limit, Newtonian gravity, in accordance with the relativistic mass of those photons.

it's a special case, not an approximation

its not an either/or, it is both. regardless, my point is that you cannot simply dimensional analysis your way to special relativity or the energy-momentum relation

This reminds me of a similar idea I recently heard in podcast with Adam Brown. I'm unsure whether it is his original notion. The idea being, that if we can create AI that can derive special relativity (1905) from pre-Einstein books and papers then we have reached the next game-changing milestone in the advancement of artificial reasoning.

Great podcast, especially the part about hitchhiking :)

https://www.youtube.com/watch?v=XhB3qH_TFds

Or RSS

https://api.substack.com/feed/podcast/69345.rss

Right, hadn't listened to that one, thanks for the tip!

Finally, a true application of E=mc^2+AI

This is how patent disputes should be decided. If an LLM can figure it out, then it is not novel.

Novelty (is it new) is the easy question because it’s just checking a database. Patentable inventions also have to be non-obvious, which is a more subtle question.

I know it’s just a spicy take on a forum, but this sounds like a terrible public policy.

And what prompt would you give that does have novel input.

If I was me, I would start by giving a collection of LLMs the patent, ask half "why is this patent novel" and half "why is this patent not novel" and see what happens. I use this method of "debugging" my thinking (not code), might be a starting point here? Not sure.

LLMs are already good at summarizing the claims - patents all explain why they’re novel - so it would be a waste to ask them, especially if you reserve half the LLMs in your set for this question. Asking why a patent is not novel is a great question, but the problem with asking why they are not novel is it has to know all other patents (including very recently filed patents) and it has to be correct, which LLMs are not at all good at yet (plus they still tend to hallucinate confidently). This is a great test for LLM accuracy if you know the right answer already, and not a good test for patent validity.

Every patent application contains a section of claims. You can just ask the LLM to come up with ways to satisfy those claims.

But I'm sure there are lots of ways to go about it.

But is there even enough pre-1905 data to create models that say hello world reliably?

The terabytes of training data required for decent LLMs does not exist. I’d guess there may only be gigabytes worth.

My wife is an 18th century American history professor. LLMs have very very clearly not been trained on 18th century English, they cannot really read it well, and they don't understand much from that period outside of very textbook stuff, anything nuanced or niche is totally missing. I've tried for over a year now, regularly, to help her use LLMs in her research, but as she very amusingly often says "your computers are useless at my work!!!!"

my wish for new years is that every time people make a comment like this they would share an example task

https://s.h4x.club/bLuNed45 - it's more crazy to me that my wife CAN in fact read this stuff easily, vs the fact that an LLM can't.

(for anyone who doesn't feel like downloading the zip, here is a single image from the zip: https://s.h4x.club/nOu485qx)

Super interesting in that

1. In theory these kind of connections should be something that LLMs are great at doing. 2. It appears that LLMs are not trained (yet?) on cursive and other non-print text

Yes, I regularly encourage my wife to approach the comp sci department at her uni on doing a project together but she for whatever reason doesn't think they would be interested/I've yet to get her interested enough to grasp what a transformer can do. I find it very frustrating because of your first point, she very specifically could do some meaningful % more research if the LLMs could help with the connections. Sadly, I am not rich, handsome or talented enough to do this for her.

One stumbling block is that OpenAI's best model (o1-pro) can't yet deal with image uploads (edit: I'm wrong, it works now... trying it.)

Gemini 2.0 Experimental Advanced says:

-------snip--------

This document is a petition filed in the Orphans Court of Baltimore County. Here's a breakdown of its content:

Petitioners: Neary Senters and his wife are petitioning the court.

Subject: The petition concerns a boy named James Gannon, who is identified as a pauper and has been bound (apprenticed) to them.

Issue: The petitioners claim that James Gannon is claimed by a certain Henry Haine of Baltimore City. They believe Haine is attempting to exploit the boy's labor. He obtained the boy through a seemingly valid indenture (apprenticeship contract) authorized by two Justices of the Peace in Baltimore City. However, the indenture references records in the Register of Wills' office in Baltimore City, suggesting the boy was already an indentured servant. The petitioners argue that a 1793 Maryland law (Chapter 45) regarding the binding of apprentices invalidates Haine's claim. They assert that the law requires parental consent for binding out a child, and there's no evidence of such consent in this case.

Request: The petitioners request the court to summon Henry Haine and the Justices of the Peace involved in creating the indenture. They also seek to have James Gannon and his parents (if living) appear before the court. Their ultimate goal is to have the indenture to Haine declared invalid, thus retaining custody and the labor of the boy.

Legal Argument: The core of their argument hinges on the 1793 Maryland law, which they believe was violated when James Gannon was bound to Henry Haine without parental consent. They are essentially challenging the legality of the indenture held by Haine.

In essence, this document reveals a dispute over the legal custody and labor of a young boy in 18th-century Baltimore. The petitioners are using a specific law about apprenticeship to argue against another individual's claim to the boy, highlighting the importance of legal procedures and parental rights in such matters during that time.

-------end snip--------

The prompt was simply "Please provide a detailed summary of this document" (I wasn't sure what else to ask.)

Is that closer to your wife's interpretation? I mean, I can't make head or tail of it...

Ideas are kinda there, details (like names) are wrong wrong.

Feel like I should make this task my personal ASI benchmark lol :D

o1-pro just answered (it often takes a few minutes). Not sure if this is any better (is it?) but at least it is starting to admit when it isn't sure about something, rather than just spewing BS confidently. That seems like a genuine win.

---------snip--------

    From the handwriting and context, this appears to be a mid-19th-century petition submitted to the Orphans’ Court of Baltimore County. Below is a point‐by‐point summary of its content and purpose:

    Address and Purpose
        The document is formally addressed “To the Honorable Judges of the Orphans’ Court for Baltimore County.”
        It identifies itself as a petition—that is, a request for the Court’s action or permission under the relevant orphan or apprenticeship laws.

    Parties Involved
        The petitioners are (1) Henry Jenkins and (2) another individual whose name is difficult to read, but who appears to be the child’s mother (or at least a close guardian figure).
        They describe themselves as residents of Baltimore City and ask the Court’s intervention concerning a minor boy—also named “Henry” (it looks like “Henry Thomas,” though the middle/family name is partly obscured).

    Relationship to the Minor
        The petition states that the mother had this boy by a former husband, meaning the child is not the biological son of Henry Jenkins.
        The boy is described as being about fifteen (or so) years of age.
        The petitioners refer to him as an “orphan,” suggesting his father is deceased and that they (the petitioners) are seeking legal authority over him, or wish the court to confirm or formalize that authority.

    Legal Concern: Binding as an Apprentice
        A key theme is the binding of a minor as an apprentice under Maryland law. In early Baltimore history (and in many states at that time), orphans or children whose parents were unable to support them could be legally “bound” (apprenticed) so that the child would learn a trade and have financial support.
        The petition cites the Act of 1788, Chapter 45, which was one of Maryland’s statutes governing apprenticeships—how they are created, what formalities must be met, and how the Orphans’ Court is involved.

    Specifics of the Request
        The petitioners believe there is either a gap or question about whether they have the legal right to apprentice the boy. They are asking the Orphans’ Court to recognize or approve the arrangement.
        They indicate that the mother (as the surviving parent) and Henry Jenkins (presumably the stepfather or the mother’s new husband) want to place the boy under an apprenticeship, possibly so he can learn a trade and be supported until he reaches the age of majority.

    References to Official Records
        The document mentions the Register of Wills and/or Deeds in Baltimore City, implying that they have looked into or recorded something about the child’s status.
        They point out that the legal forms required for binding a child as an apprentice often must be recorded with an official clerk or the Orphans’ Court, and they want to ensure everything is in proper order.

    Legal Justification
        The petitioners note that under the relevant laws, it “is lawful to bind out any child” whose parents cannot fully support them, or when the father is deceased.
        They appear to lay out the reasons why the boy qualifies for such an arrangement (i.e., he is fatherless and his mother/stepfather lack the resources to maintain him or want to formalize his situation).

    Request for Court Action
        Ultimately, they are asking the Court to review the circumstances and grant them permission (or confirm their right) to bind the boy as an apprentice.
        They emphasize that the law requires the Orphans’ Court to oversee such arrangements to protect the minor’s welfare and ensure the terms are fair.

    Overall Significance:
       This document is a formal petition under Maryland’s 18th- and 19th-century apprenticeship laws. It centers on a minor boy (about fifteen years old), the remarriage of his mother, and the need to have the Orphans’ Court recognize or approve an apprenticeship arrangement. The purpose is to comply with the legal requirements of binding a minor so that he can be taught a trade and maintained financially until adulthood.

---------end snip--------

She said it's around 60% there, but not helpful for her specifically as her area of research is on the families of the slave trade, and so in the document, that is actually the only thing that really matters to her. Names and places (she spends A LOT of time tracking transfer of slaves through states) - I guess I should be honest that I'm goalpost moving a bit from my original original post, it can work through some 18th english century text, but generally struggles where it matters, the details.

i’m curious what the ground truth actually names in the doc are

Caroline Timmis is the boy mother, she is married to Henry Jenkins but was married previously, the boy is James Timmis.

Looking more closely at the image, it seems that the strikeouts are what are confusing the model. It sees "Henry" and disregards "James" written above it. In a few places, the strikeouts almost look more like underlining.

Gotta be an insanely-challenging task for a program that wasn't even written with handwriting recognition in mind.

Other than the proper names, are any major details wrong?

Dr. Jang said: "I've not transcribed this work yet it's future work. I can send it once it's transcribed, however from skimming the various chatgpt texts you sent me, the people are generally wrong, their relationships are inconsistent between all the text you sent me, these issues are why I do not go to chatgpt to help me with my research. The document generally is indeed about requesting the courts oversee the apprenticeship work. Minors could work, however, when minors are used it needed to be overseen by the courts, this document is the court overseeing the work"

She provided this as one she just got done working with: https://s.h4x.club/z8u9xmv7 (John King Esq. but try giving it to an LLM)

I will also happily again admit a bit of goal post moving on my part. I was probably a little to harsh on it (maybe because I'm used to her and her history geeks talking about how they don't work well for their research).

o1-pro takes images? had no idea

Yes, it will take .JPGs but not .PDFs. I thought it didn't support either until I checked just now.

“ From what the text shows, Henry Jenkins and his wife Caroline (the boy’s mother) are asking the Orphans Court to void an apprenticeship arrangement involving her minor son, James Timmons. They claim James—about 15 years old—was bound out as an apprentice without proper authority or the mother’s consent, and they cite Maryland law (an act from 1793 and its supplements) which they believe was not followed. They request the court declare that the indenture is invalid and restore James to his mother’s care.”

No idea if that’s correct (and no doubt not useful to an expert able to read this directly, but curious if it’s close?

She said: "The idea is close the the details are wrong".

have you been trying to provide it as an image directly? if so, doesn’t surprise me at all.

really thanks for sharing!

My wifes particular area of research is using the capitalist system to "re build" broken slave family trees, she flys around the US going to archives and getting contracts and receipts for slaves, figures out how they got traded, and then figures out where they ended up, and then "re links" them to their their family to best of her ability. Although her area of research isn't particularly overflowing with researchers, there are still a lot of people like her who just have this very tacit knowledge among each other, they email around a lot and stuff, knowledge like who was running a region during a period, ofc they publish, but it's a small field and it's all extremely poorly documented. Was watching the Adam Brown interview with Dwarkesh Patel the other day and he said for his work LLMs are better than bothering an expert in an area of his field with a question, I'm not sure people in her field are able to do this as readily. Franky, I've yet to find a novel/or good use for an LLM in her work. I often joke that her and "her people" are going to be the last ones with jobs if they don't transfer their knowledge into LLMs, ha! :)

Instead of asking for a formula, a better test may be to point out all the seeming contradictions in physics at that time (constancy of the speed of light, wave vs. particle nature of light, ultraviolet catastrophe), and ask it how they could be resolved.

I had a similar thought but about asking the LLM to predict “future” major historical events. How much prompting would it take to predict wars, etc.?

You mean train on pre-1939 data and predict how WWII would go?

Right. If it were trained through August 1939, how much prompting would be necessary to get it to predict aspects of WWII.

Man, that would be a fascinating experiment. Would it be able to predict who wins and when? Would it be able to predict the Cold War?

But we know Hitler has a Time Machine that goes forward, he doesn’t need to return to use that knowledge as he already has a timeline here to use. Definitely risks involved here.

If you build an oracle that tells you who wins the war that far in the future, you build a simulator that allows anyone to win any war. Everything is dual use.

That will never work on any complex system that behaves chaotically, such as the weather or complex human endeavors. Tiny uncertainties in the initial conditions rapidly turn into large uncertainties in the outcomes.

Not an LLM but models could get pretty good at weather

https://www.technologyreview.com/2024/12/04/1107892/google-d...

No, they don't, since the weather is chaotic.

I mean, there are the theorems about how close you can get, and models are not better than theoretically possible.

Yeah, I wish more people understood that it is simply not possible to make precise long-term forecasts of chaotic systems. Whether it is weather, financial markets, etc.

It is not that we don't know yet because our models are inadequate, it's that it is unknowable.

The problem is we stupidly branded the field "chaos theory" and made it sound like bullshit so the ideas of non-linear dynamics have largely been lost on several generations at this point.

Not just chaos theory but "chaos theory" + psychedelic fractal artwork. Then the popular James Gleick book, "Chaos: making a new science" just sounds like complete bullshit and it sold a ton of copies.

I only started studying non-linear dynamics in about 2015 after first running across it in the late 90s but I literally thought it was all pseudoscience then.

Between "chaos theory", fractals and a best selling book it would be hard to frame a new scientific field as pseudoscience more than what played out.

Why do we need this when current models already handle questions and answers about new discoveries: ones that are happening every week and are often easier to grasp than Einstein’s equations? I think it is clear that they will fail on most of them. That doesn't mean that LLMs are not useful but there are more walls in the road.

The best human performance on that task required many many hours of private work given that input.

How much would ChatGPT charge for that much reasoning? Isn't cost quadratic in sort term working memory?

It would be more interesting to prompt it with X% of a new paper's logical argument, and see if it can predict the rest.

There is a reason why they won't do it. They are selling a narrative. There is a lot of money to be made here with this narrative and proving that artificial intelligence is NOT intelligent won't help sell that narrative.

The goal is to make it intelligent, by which OpenAI in particular explicitly mean "economically useful", not simply to be shiny.

Passing tests is well known to be much easier than having deep understanding, even in humans. They openly ask for tests like this, not that they could possibly prevent them if they wanted to.

There's scammers trying what you say of course, and I'm sure we've all seen some management initiatives or job advertisements for some like that, but I don't get that impression from OpenAI or Anthropic, definitely not from Apple or Facebook (LeCun in particular seems to deny models will ever do what they actually do a few months later). Overstated claims from Microsoft perhaps (I'm unimpressed with the Phi models I can run locally, GitHub's copilot has a reputation problem but I've not tried it myself), and Musk definitely (I have yet to see someone who takes Musk at face value about Optimus).

> The goal is to make it intelligent, by which OpenAI in particular explicitly mean "economically useful", not simply to be shiny

I never understood why this definition isn't a huge red flag for most people. The idea of boiling what intelligence is down to economic value is terrible, and inaccurate, in my opinion.

Everyone has a very different idea of what the word "intelligence" means; this definition has got the advantage that, unlike when various different AI became superhuman at arithmetic, symbolic logic, chess, jeopardy, go, poker, number of languages it could communicate in fluently, etc., it's tied to tasks people will continuously pay literally tens of trillions of dollars each year for because they want those tasks done.

This definition alone might be fine enough if the word "intelligence" wasn't already widely used outside of AI research. It is though, and the idea that intelligence is measured solely through economic value is a very, very strange approach.

Try applying that definition to humans and you pretty quickly run into issues, both moral and practical. It also invalidates basically anything we've done over centuries considering what intelligence is and how to measure it.

I don't see any problem at all using economic value as a metric for LLMs or possible AIs, it just needs a different term than intelligence. It pretty clearly feels like for-profit businesses shoehorning potentially valuable ML tools into science fiction AI.

> This definition alone might be fine enough if the word "intelligence" wasn't already widely used outside of AI research. It is though, and the idea that intelligence is measured solely through economic value is a very, very strange approach.

The response from @s1mplicissimus' on my previous comment is asking about "common usage" definitions of intelligence, and this is (IMO unfortunately) one of the many "common usage" definitions: smart people generally earn more.

I don't like "commmon sense" anything (or even similar phrases), because I keep seeing the phrase used as a thought-terminating cliché — but one thing it does do, is make it not "a very, very strange approach".

Wrong, that happens a lot for common language, but it can't really be strange.

> Try applying that definition to humans and you pretty quickly run into issues, both moral and practical.

Yes. But one also runs into issues with all definitions of it that I've encountered.

> It also invalidates basically anything we've done over centuries considering what intelligence is and how to measure it.

Sadly, not so. Even before we had IQ tests (for all their flaws), there's been a widespread belief that being wealthy is the proof of superiority. In theory, in a meritocracy, it might have been, but in practice not only to we not live in a meritocracy (to claim we do would deny both inheritance and luck), but also the measures of intelligence that society has are… well, I was thinking about Paul Merton and Boris Johnson the other day, so I'll link to the blog post: https://benwheatley.github.io/blog/2024/04/07-12.47.14.html

> smart people generally earn more.

> there's been a widespread belief that being wealthy is the proof of superiority.

Both of these are assumptions though, and working in the reverse order. Its one thing to expect that intelligence will lead to higher value outcomes and entirely different to expect that higher value outcomes prove intelligence.

It seems reasonable that higher intelligence, combined with the incentives if a capitalist system, will lead to higher intelligence people getting more wealthy. They learn to play the game and find ways to "win."

It seems unreasonable to assume that anyone or anything that "wins" in that system much be more intelligent. Said differently, intelligence may lead to wealth but wealth doesn't imply intelligence.

I think we're in agreement? I'm saying their measure in this case is no worse than any other, but not that it's a fundamental truth.

All the other things — chess, Jeopardy, composing music, painting, maths, languages, passing medical or law degrees — they're also all things which were considered signs of intelligence until AI got good at them.

Goodhart's law keeps tripping us up on the concept of intelligence.

> I think we're in agreement? I'm saying their measure in this case is no worse than any other, but not that it's a fundamental truth.

Maybe we are? I think I lost the thread a bit here.

> chess, Jeopardy, composing music, painting, maths, languages, passing medical or law degrees

That's interesting, I would have still chalked skill in those areas as a sign of intelligence and didn't realize most people wouldn't once AI (or ML) could do it. To me an AI/LLM/ML being good at those is at least a sign that they have gotten good at mimicking intelligence if nothing else, and a sign that we really are getting out over our skis risking these tools without knowing how they really work.

Maybe by the time it’s doing a trillion dollars a year of useful work (less than 10 years out) people will call it intelligent… but still probably not.

I haven't seen "intelligent" used as "economically useful" anywhere outside the AI hype bubble. The most charitable interpretation I can think of is lack of understanding of the common usage of the word, the most realistic one is intentionally muddying terminology so one cannot be called a liar. Are LLMs helpful tools for some tasks like rough translations, voice2text etc? Sure. Does it resemble what humans call intelligence? I'd yet have to see an example of that. The suggested experiment is a great idea and would sway my opinion drastically (given all the training data, model config, prompts & answers are public and reproducible of course, we don't want any chance of marketing BS to taint the results, do we). I'll be honest though, I'm not going to hold my breath for that experiment to succeed with the LLM technology...

edit: lol downvoted for calling out shilling i guess

They don't have to do it themselves. The super-GPU cluster used to train GPT-6 will eventually shrink down to a garage size and eventually some YouTuber will.

what if someone invented it before 1905

Why does AI have to be smarter than the collective of hummanity in order to be considered intelligent? It seems like we keep raising the bar on what intelligence means ¯\_(ツ)_/¯

A machine that synthesizes all human knowledge really ought to know more than an individual in terms of intellect. An entity with all of human intellect prior to 1905 does not need to be as intelligent as a human to make discoveries that mere humans with limited intellect made. Why lower the bar?

The heightening of the bar is an attempt to deny that milestones were surpassed and to claim that LLMs are not intelligent.

We had a threshold for intelligence. An LLM blew past it and people refuse to believe that we passed a critical milestone in creating AI. Everyone still thinks all an LLM does is regurgitate things.

But a technical threshold for intelligence cannot have any leeway for what people want to believe. They don’t want to define an LLM as intelligent even if it meets the Turing test technical definition of intelligence so they change the technical definition.

And then they keep doing this without realizing and trivializing it. I believe humanity will develop an entity smarter than humans but it will not be an agi because people keep unconsciously moving the goal posts and changing definitions without realizing it.

Disagree. The AI we have is very useful for specific things. The pushback you see is not so much denying the milestones that have been surpassed, but rather the milestones that enthusiasts claim are near. And for good reason! Every time and in every field we’ve extrapolated an exponential-looking curve ad infinitum, it’s turned out to be S-shaped, and life goes on.

> We had a threshold for intelligence.

We’ve had many. Computers have surpassed several barriers considered to require intelligence such as arithmetic, guided search like chess computers, etc etc. the Turing test was a good benchmark because of how foreign and strange it was. It’s somewhat true we’re moving the goalposts. But the reason is not stubbornness, but rather that we can’t properly define and subcategorize what reason and intelligence really is. The difficulty to measure something does not mean it doesn’t exist or isn’t important.

Feel free to call it intelligence. But the limitations are staggering, given the advantages LLMs have over humans. They have been trained on all written knowledge that no human could ever come close to. And they still have not come up with anything conceptually novel, such as a new idea or theorem that is genuinely useful. Many people suspect that pattern matching is not the only thing required for intelligent independent thought. Whatever that is!

If you consider that evolution has taken millions of years to produce intelligent humans--that LLM training completed in a manner of months can produce parrots of humans is impressive by itself. Talking with the parrot is almost indistinguishable from talking with a real human.

As far as pattern matching, the difference I see from humans is consciousness. That's probably the main area yet to be solved. All of our current models are static.

Some ideas for where that might be headed:

- Maybe all it takes is to allow an LLM to continuously talk with itself much like how humans have "the milk man's voice".

- Maybe we might need to allow LLMs to update their own weights but that would also require an "objective" which might be hard to encode.

> If you consider that evolution has taken millions of years to produce intelligent humans--that LLM training completed in a manner of months can produce parrots of humans is impressive by itself.

I disagree that such a comparison is useful. Training should be compared to training, and LLM training feeds in so many more words than a baby gets. (A baby has other senses but it's not like feeding in 20 years of video footage is going to make an LLM more competent.)

No, a baby is pre-trained. We know from linguistics that there is a natural language grammar template all humans follow. This template is intrinsic to our biology and is encoded and not learned through observation.

A baby has a template but so does an LLM.

The better comparison to the templating is all the labor that went into making the LLM, not how long the GPUs run.

Template versus template, or specific training versus specific training. Those comparisons make a lot more sense than going criss-cross.

The template is what makes the training process so short for humans. We need minimal data and we can run off of that.

Training is both longer and less effective for the LLM because there is no template.

To give an example suppose it takes just one picture for a human to recognize a dog and it takes 1 million pictures for a ML model to do the same. What I’m saying is that it’s like this because humans come preprogrammed with application specific wetware to do the learning and recognition as a generic operation. That’s why it’s so quick. For AI we are doing it as a one shot operation on something that is not application specific. The training takes longer because of this and is less effective.

I disagree that an LLM has no template, but this is getting away from the point.

Did you look at the post I was replying to? You're talking about LLMs being slower, while that post was impressed by LLMs being "faster".

They're posing it as if LLMs recreate the same templating during their training time, and my core point is disagreeing with that. The two should not be compared so directly.

They are slower. In theory these LLMs with all the right weights can have intelligence superior or equivalent to humans.

But the training never gets there. It’s so slow it never reaches human intelligence even though we know these networks can compute anything.

> It’s somewhat true we’re moving the goalposts. But the reason is not stubbornness, but rather that we can’t properly define and subcategorize what reason and intelligence really is.

Disagree. Intelligence is a word created by humans. The entire concept is made up and defined by humans. It is not some concept that exists outside of that. It is simply a collection of qualities and features we choose to define as a word “intelligent”. The universe doesn’t really have a category or a group of features that is labeled intelligent. Does it use logic? Does it have feelings? Can it talk? Can it communicate? We define the features and we choose to put each and every feature under a category called “intelligence”.

Therefore when we define the “Turing test” as a benchmark for intelligence and we then invalidate it, it is indeed stubbornness and a conscious choice to change a definition of a word we Originally made up in the first place.

What you don’t realize is this entire thing is a vocabulary problem. When we argue what is conscious or what is intelligent we are simply arguing for what features belong in what categories we made up. When the category has blurry or controversial boundaries it’s because we chose the definition to be fuzzy. These are not profound discussions. They are debates about language choice. We are talking About personal definitions and generally accepted definitions both of which are completely chosen and made up by us. It is not profound to talk about things that are simply arbitrary choices picked by humans.

That being said we are indeed changing the goal posts. We are evolving our own chosen definitions and we very well may eventually change the definition of intelligence to never include any form of thinking machine that is artificially created. The reason why we do this is a choice. We are saying, “hey these LLMs are not anything amazing or anything profound. They are not intelligent and I choose to believe this by changing and evolving my own benchmark for what is intelligent.”

Of course this all happens subconsciously based off of deeply rooted instincts and feelings. It’s so deep that it’s really hard to differentiate the instincts between rational thinking. When you think logically, “intelligence” is just a word with an arbitrary definition. An arbitrary category. But the instincts are so strong that you literally spent your entire life thinking that intelligence like god or some other common myth made up by humans is some concept that exists outside of what we make up. It’s human to have these instincts, that’s where religion comes from. What you don’t realize is that it’s those same instincts fueling your definition of what is “intelligent”.

Religious people move the goal posts too. When science establishes things in reality like the helio centricity of the solar system religious people need to evolve their beliefs in order to stay inline with reality. They often do this by reinterpreting the Bible. It’s deeply rooted instincts that prevent us from thinking rationally and it effects the great debate we are having now on “what is intelligence?”.

Since we know an LLM does indeed simply regurgitate data, having it pass a "test for intelligence" simply means that either the test didn't actually test intelligence, or that intelligence can be defined as simply regurgitating data.

Intelligence is debateble without even bringing ai into it. Nobody agrees on whether humans have intelligence. Well, smart people agree but those people also agree we have or will soon have agi or something negligibly different from it.

> Intelligence is debateble without even bringing ai into it. Nobody agrees on whether humans have intelligence.

Yep, that constitutes the second of the two options I mentioned.

> Well, smart people agree but those people also agree we have or will soon have agi or something negligibly different from it.

lol, the ol' "I know what all smart people think and it's what I think" appeal.

> The heightening of the bar is an attempt to deny that milestones were surpassed and to claim that LLMs are not intelligent.

That was never "the bar"; nobody denies that milestones have been surpassed; none of those milestones are relevant to the question of intelligence.

> We had a threshold for intelligence. An LLM blew past it and people refuse to believe

Have you ever actually looked at contemporary (to Turing) examples of what people thought "passing a Turing test" might look like? It's abundantly clear to me that we were simply wrong about what the output would have to look like in order to convince human judges in the 2020s.

Even examples from much more recently (see e.g. on http://www-logic.stanford.edu/seminar/1213/Hawke_TuringTest....) suggest a very different approach to the test than prompting ChatGPT and marveling at the technical accuracy of its prose.

(Exercise: ask an LLM to write a refutation to your comment from the perspective of a human AI skeptic. Notice the ways in which it differs from mine.)

> Everyone still thinks all an LLM does is regurgitate things.

No; people still think LLMs aren't intelligent. Because they aren't, and they cannot become so in principle. They can do many things that are clearly beyond "regurgitation" (as we would otherwise apply the word to computer programs), but none of those things are the result of intelligence. Producing a result that could plausibly come from an intelligent system does not, in fact, demonstrate that the actual system producing it is also intelligent. The https://en.wikipedia.org/wiki/Antikythera_mechanism wasn't intelligent, either, and applying a power source to turn the gears wouldn't have made it so, either.

> They don’t want to define an LLM as intelligent even if it meets the Turing test technical definition of intelligence so they change the technical definition.

The Turing Test was never a "technical definition" of intelligence. Turing's original paper (https://en.wikipedia.org/wiki/Computing_Machinery_and_Intell...) spoke of "thinking" rather than "intelligence". Besides, the "Imitation Game" is presented as a substitute problem exactly because "think" cannot be clearly enough defined for the purposes. The entire point:

> As Stevan Harnad notes,[7] the question has become "Can machines do what we (as thinking entities) can do?" In other words, Turing is no longer asking whether a machine can "think"; he is asking whether a machine can act indistinguishably[8] from the way a thinker acts. This question avoids the difficult philosophical problem of pre-defining the verb "to think" and focuses instead on the performance capacities that being able to think makes possible, and how a causal system can generate them.

But the usual processes of pop science seem to have created a folk wisdom that being able to pass a Turing test logically ought to imply intelligence. This idea is what has been disproven, not the AI skepticism.

"Why lower the bar?"

Because of the chance of misundertanding. Failing at acknowledging artificial general intelligence standing right next to us.

An incredible risk to take in alignment.

Perfect memory doesn't equal to perfect knowledge, nor perfect understanding of everything you can know. In fact, a human can be "intelligent" with some of his own memories and/or knowledge, and - more commmonly - a complete "fool" with most of the rest of his internal memories.

That said, is not a bit less generally intelligent for that.

Supose it exists a human with unlimited memory, it retains every information touching any sense. At some point, he/she will probably understand LOTs of stuff, but it's simple to demonstrate he/she can't be actually proficient in everything: you have read how do an eye repairment surgery, but have not received/experimented the training,hence you could have shaky hands, and you won't be able to apply the precise know-how about the surgery, even if you remember a step-by-step procedure, even knowing all possible alternatives in different/changing scenarios during the surgery, you simply can't hold well the tools to go anywhere close to success.

But you still would be generally intelligent. Way more than most humans with normal memory.

If we'd have TODAY an AI with the same parameters as the human with perfect memory, it will be most certainly closely examined and determined to be not a general artificial intelligence.

> If we'd have TODAY an AI with the same parameters as the human with perfect memory, it will be most certainly closely examined and determined to be not a general artificial intelligence.

The human could learn to master a task, current AI can't. That is very different, the AI doesn't learn to remember stuff they are stateless.

When I can take an AI and get it to do any job on its own without any intervention after some training then that is AGI. The person you mentioned would pass that easily. Current day AI aren't even close.

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