Comment by teiferer
3 days ago
> responded by writing its own computer vision pipeline
Was it "its own" or something that was part of its training material?
Don't get me wrong, I find this all amazing too and makes my work 10x easier and quicker. But it's not like it's inventing this stuff from scratch / first principles. It has seen this kind of tech before by consuming all publicly available source code and books etc. (And that's ok, but let's be honest/clear about that.)
Is anything you do on your own?
You wrote this response in English, but didn't invent your own language. How dilute of a contribution can someone/something have made and still merit credit?
This argument is as old as the debate itself. No I didn't invent English. But you'd also not argue that way if you plagiarized somebody's homework and a teacher called you out on it. Point is that an LLMs output is perhaps closer to somebody's copied essay from English class rather than having invented a new language.
The tu quoque argument doesn't really work here. LLMs are specifically designed to output either material from their training set or instances that look statistically like it. Humans are not.
We aren’t specifically designed for anything, perhaps outside of the necessary consequences of having our mental capabilities and existing in an evolutionary context, so we can reason about tigers or integers and can persuade others to like us and have sex and help with our kids.
But our material natures are such that we have a profound need for external training sets - we don’t develop linguistic abilities unless we are exposed to a lot of speech. We can’t hear or produce phonemes in general that we are not exposed to during the training years.
Literacy, libraries and printing are so powerful because they extend the lifetime and size of our training material; if we each had to invent culture from our individual powers and skills, we would be living worse than anyone for fifty thousand years has lived.
Sincere question: do we know enough about human intelligence to support that last assertion?
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LOL. Tolkien invented multiple languages. There are/were probably 100 000 languages on this planet. That's without including dialects.
We use existing languages not because we love to copy, it's because languages are a medium for communication. They're only useful if they're understood by others.
If you wanted to prove a point: writing is much, much harder to invent, yet it was invented independently at least 3 times in history. By definition LLMs can't invent something truly novel like writing. Because nobody trained the first writers. There was no "training set".
It's fine, LLMs can still be useful. Science is looking at the gaps and most of the gaps that are useful are small and LLMs can explore those faster than us. That could potentially improve many lives. Though not in this corporate LLM world, most likely.
I don't think the invention of writing is as awe-inspiring as presented or as difficult/impossible for an LLM to achieve as is commonly believed. The invention of writing was a long series of micro-improvements over common every day things. Let's keep track of count by writing a line in the sand. Let's cut it into a tree. Let's push the line into clay. Let's use two lines touching. These are the kinds of things an LLM can also reason through and achieve.
I reject this idea that an LLM couldn't come up with something novel that isn't in the training data. We know it can come up with sentences that aren't in there. We know it can come up with mathematic proofs that aren't in there. So could it feasibly create something as fundamental as writing or language? Would we even be able to understand what it had done, or would it be so foreign to us that we wouldn't even realize?
Those early writers did have a training set of human knowledge that led them to pushing stones into clay. And our agents may very well have a set of knowledge that leads it to pushing their proverbial stone into their proverbial clay too.
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1. Languages are used for much, much more than communication, and there is good reason to think that capacity didn't evolve for communication, but rather for metacognitive problem solving.
2. The idea that Tolkein --one of the most reknowned writers in our entire canon-- did something isn't relevant to the philosophical discussion of creativity and its obvious bounds. Even Tolkein was just painting within the lines set by the linguists he had read and the obscure languages he adored, not to mention the fundamental limits set by our capacity for language in the first place.
For anyone who's curious about this kind of thing, I cannot reccomend the infamous debate b/w Chomsky & Foucault enough; it ranges across topics a bit, but chapter markers should help you skip to the core of it (creativity) if you prefer. Video here: https://www.youtube.com/watch?v=3wfNl2L0Gf8 , some old summaries on /r/AskPhilosophy here: https://www.reddit.com/r/askphilosophy/comments/vgz1vb/what_...
Are you saying your bar for being impressed by an LLM is something equivalent to the initial invention of writing?
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I remember a report a few years back that LLM's(?) started communicating between each other using an internal language, shortcutting most of their serialization. That is language.
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I’m sorry but I find this to be the worst response possible lol.
Do you honestly not see how there is a difference between a computer regurgitating information vs a human uses what they’ve learned and applying it?
I just can’t take your argument seriously, it’s so disingenuous
One could argue that human learning is not all that different from machines training on data. We are all regurgitation our own experiences and knowledge to some degree. We certainly have things that machines don't, but LLMs can definitely come up with novel things that were not in the training data.
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> But it's not like it's inventing this stuff from scratch / first principles. It has seen this kind of tech before by consuming all publicly available source code and books etc. (And that's ok, but let's be honest/clear about that.)
But how much to emphasize it, and why?
Because frankly, you aren't inventing anything from scratch / first principles, either. None of us are, not frequently and not much anyway. We're primiarily just regurgitating what we've seen in the past and, mixing it with what we see in front of us - and that's true whether it's art or prose or code.
Here, I wouldn't be able to do the same thing Claude did now, because I've never written a visual ML pipeline before myself. I know enough basics to get me started searching, and I'm confident I'd be able to cobble something together, but it would be me regurgitating and mixing whatever TensorFlow tutorials or OpenCV docs I found relevant, perhaps even tweaking an example from Github.
Now, unless I'd literally just tweak a few lines of config in an existing Github example, no one would begrudge me saying "I wrote my own computer vision pipeline to solve this". So why begrudge LLMs?
This is just the rationalist-empiricist[0] tension, from philosophy. Most empiricists (usually people who are _not_ from continental European cultures), take a position that knowledge and creativity come from observation and imitation, while rationalists believe that knowledge and creativity can be endogenous to the mind.
One could say that, stereotypically and cartoonishly, empiricists believe that invention is a false concept, and a synonym for discovery, while rationalists would oppose this view.
That catch is, if you look at the etymology of words "invention" and "discovery", you will find that they both share the _same_ root: _Ars Inveniendi_.
A natural question arises: does this mean that people from a millennium ago did not have a dyad equivalent to our invention-discovery dyad? And the answer, surprisingly, is _no_. Even a millennium ago, the empiricists and rationalists were going at it. If _Ars Inveniendi_ is art of discovery/invention, then its counterpart is _Ars Demonstrandi_ (the art of demonstration/proof).
So, how do these differ? Is one just observing/creating and the other just math and language-games?
In general, Ars Demonstrandi is about writing down axioms, and then expanding those axioms recursively (similar to rewrite rules in any formal system), until you get to some end-state, or, if there is none, a novel or surprising state. I, personally, call this source-to-sink thinking.
Ars Inveniendi is about taking conclusions (often using observations from the physical world) and trying to figure out what axioms can lead to those conclusions. I, (again) personally, call this sink-to-source thinking.
Put differently, Ars Inveniendi can help one discover starting points for Ars Demonstrandi.
If you read the dialectics (e.g. Plato and friends), you'll find that most of them are just a mutual recursion between Ars Inveniendi and Ars Demonstrandi.
I believe (again, I am not a philosopher, and this is just my intuition), that the thing that we call "creativity" and "invention" _emerges_ from the recursive loop[1]. A favorite example: Esperanto (the conlang). It is a language, which is remarkably elegant and consistent and (in my opinion) beautiful, because it _is_ derived from first principles, which were themselves derived from the various languages spoken in Europe (not all of which are Indo-European -- the agglutinative features have more in common with Finno-Ugric and Turkic languages). There is something about it, that makes it _qualitatively_ different (and thus holistically novel) from all other languages (and I speak, fluently, _two_ national languages, that are very different from each other, so I can attest to the difference personally).
My guess, is that people will not accept that AI/LLMs are creative or inventive, until they can produce an original[2] (non-plagiarized) artifact that feels the way Esperanto feels.
[0]: By Rationalist, I do not mean the "Bay Area Rationalists", who are, in fact, empiricists.
[1]: I am unsure if the loop requires only one human, at least two humans, or if it can be fully automated.
[2]: Note that Centos are poems made completely out of line-numbers (e.g. fragments from the Iliad, or the bible, etc). Every line is borrowed, yet some of them are considered beautiful and original works of art. Similarly, Labatut and Burroughs and Perec, write using a technique called _the cut-up method_, where they take books, magazines, and newspapers, and superimpose page-fragments, and use that as an inspiration -- they are all considered artists, and good ones. It is unclear to me why LLMs (which seem to be built on the cut-up method, and have cut-ups of all of human knowledge) cannot match these artists. What's missing?
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It would be "its own" in that it's customized for the specific use case.
Your same argument could just as easily be applied to humans. If you write your own code, is it really your own, or is it just based on your own training and other code you've seen?
I think their point is that there is no new invention here, being able to mimic what a human will do is absolutely impressive, but at the same time it is still mimicking humans. While I just type that out, I do realize my expectations for AI has been constantly lifted by how fast it iterates.
This isn't mimicking what a human would do, because it is an exceedingly rare human who would write their own renderer for a task needing a CAD file generating. The human who is capable of doing both is rare, let alone the human who can do it in any sort of reasonable time. Saying "but it's in the training data" is a cop-out: it's in Google too, would I do it? No. I would not.
It's mimicking humans only in as far as it is still using tools & programming languages developed by humans, for humans.
This is temporary. There is a future where AI builds tools made by AI for AI, evolving at speeds that we may no longer be able to follow.
Even with our current nascent technology, we can already observe this happening. See the recent laments on the AI Bun rewrite that "it's a million lines of code that nobody has ever reviewed." Or Cursor building a new source-control system for LLMs, because git is designed for human collaboration speed, not hundreds of changes per second.
That's just 4 years in. How much of humanity's code will remain hand-written vs LLM-written 2, 5, 10 years from now?
It's mimicking humans in how humans mimick each other in everything they do.
It's definitely not seen the counterexample to the Jacobian conjecture before.
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>But it's not like it's inventing this stuff from scratch / first principles.
Yes, it's definitelly time for an Alpha Zero moment. An AI which invents everything from first principles. That would be impressive, ... and a bit scary.
Glue layers are fun and easy to write - and often worth doing.