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Though that's also what makes humans so good at solving problems as well, it turns out.
Also, slight tangent: but I do find the "clanker" insult kind of funny. I feel like it counter-intuitively makes the models sound cooler than they are, if anything. I love clankin' shit.
Next time you get a new and a fresh and an inspiring idea, and you spend hours solving a unique problem nobody has ever done before. You can take comfort in the fact that a few months later some lame and uninspiring developer can write the same problem in a prompt and get the plagiarism machine to steal your work, just in a more lame and uninspiring way.
That may very well be true now. And in fact, this was true of more rudimentary calculations early on in computing history, where humans were definitely more efficient, particularly for more abstract mathematics. But Moore's Law comes at you fast. Even without more efficient compute, it's rather wild how much more efficient models are becoming these days just from algorithmic and training improvements.
So, maybe for now, certainly. Are you confident that will be the case in 5-10 years? And is that really your barometer for success?
>And when a human learns these things they usually remember how to, and are able to extrapolate that knowledge into new and fresh problem spaces.
That is certainly a limitation for now, but plenty of academic research is being done on how to address that in a more individualized way. That said, the models also have the advantage of synthesizing learnings from user interactivity back into a future release and essentially applying that globally, which is pretty neat.
There's also some cool techniques to sort of bridge the gap today, like compound engineering.
>Next time you get a new and a fresh and an inspiring idea, and you spend hours solving a unique problem nobody has ever done before. You can take comfort in the fact that a few months later some lame and uninspiring developer can write the same problem in a prompt and get the plagiarism machine to steal your work, just in a more lame and uninspiring way.
But that's the thing: it's becoming pretty clear that the "plagiarism machine" can probably take that same problem in a prompt, having never been trained on my code, and still solve it.
In that case...maybe it doesn't feel great to have someone copy my idea. But that is certainly not plagiarism in the way you mean it. And when you put ideas out into the world, you can't be certain that someone else won't copy and remix it into something new. That's kind of how the world works already, but we're just seeing the barrier to entry decline.
Yes, I am. I am very confident that general purpose digital computers will never be more efficient then human minds in generating moderately complex code.
Why am I so confident... Well, it has been over 10 years since AlphaGo beat top go player Lee Sedol. AlphaGo was able to beat the a world class go player by doing several thousands orders of magnitude more computations then Lee Sedol, and it did so by spending several orders of magnitude more energy then the top human go player. Today, over 10 years later, the top go machines are able to beat world class go players much easier, but still do so using the exact same strategy of outcomputing the humans with thousands of orders of magnitude more computations, and spending orders of magnitudes more energy.
Things did not change in the past 10 years, I see no reason why it should change 10 years from now.
Has it not? Why do you say that?
Also, do we still require a Deep Blue sized supercomputer for chess? :)
But regardless, compute will get to a point where human level intelligence close to as efficient as we are. You could argue it already is today, when you factor in the resources that the average person in the west already uses in terms of their overall impact on the planet.
I can just as well describe the future evolution of the internal combustion engine and claim it will get more and more efficient and eventually we will be able to burn oil so efficiently that our personal vehicles can fly through the atmosphere at twice the speed of sound.
There is limitations to digital computers just as there are limitations to internal combustion engines. Our brains are not digital computers. When we use our brains we don’t just do a bunch of linear algebra.
This is a silly comparison. There is a certain quantity of energy stored in oil, so we know what peak efficiency looks like. We don't actually know what amount of energy is required to solve certain problems. We quite literally have models with quite a bit of capability that can run locally on a phone today, right alongside Stockfish, for example.
And this is to say nothing of work happening now on new hardware approaches, such as Normal Computing's work on thermodynamic matrix math: https://www.normalcomputing.com/blog/a-first-demonstration-o...
That said, this feels like a strange tangent: I'm not sure it's that important that the models be as energy efficient as a human brain. We don't avoid cars because they're less energy efficient than our legs. ;)
This matters because unlike cars LLMs are only doing stuff we can already do using our brains, just several orders of magnitudes less efficiently. Cars can at least take us distances we would never be able to using our muscles. In comparison, if I need to compile CPython into a WASM binary I can simply download a library that does it, or copy paste code in a few seconds, for a million billionth of the energy it takes an LLM to do the same. Except when I download the library or copy-paste the code I (hopefully) attribute the original author and give them credit for their work.
I'm suggesting that while LLMs are bounded by physical reality, that you actually don't know what that bound is. Just a few years ago we would have thought it a fantasy to have a conversational model run on a phone.
Even if you could compute it now, that would still be tied to current architectures. With appropriate incentives, we'll continue developing hardware to make these models more efficient to execute. It's very likely that you'll be able to run a Fable caliber coding model on your phone in the next five years.
>This matters because unlike cars LLMs are only doing stuff we can already do using our brains, just several orders of magnitudes less efficiently. Cars can at least take us distances we would never be able to using our muscles.
But that's not largely true of cars. The majority of trips are five miles or less and could easily be replaced with a bicycle. While I might personally use a bicycle, the majority choose a car to save a bit of time and effort.
So, please continue to enjoy your car, and I will continue to enjoy ready access to an LLM for a variety of other tasks. My inference energy costs are almost certainly less than your vehicle usage. ;)
OK then - do it, faster.
> You can take comfort in the fact that a few months later some[...] developer can [solve] the same problem [using your work]
Isn't that what collaboration and sharing software is supposed to be all about?
On the other hand: "Stop trying to make 'clanker' happen! It's not going to happen!"
"AI slop" caught on but "clanker" did not.
It caught on, sure, but not exactly in the way I expected. The wild popularity of "slop" as a term for AI eventually gave way to the genericization of the word "slop" to mean "content of low quality, regardless of source", and is seemingly being used as just a derogatory term for anything that people dislike (particularly by folks in left leaning communities). For example, I've seen people refer to (clearly human written) commentary from some political commentators as "slop".
You comment kind of reinforces the idea by the fact that you have to now say "AI slop" specifically to disambiguate it. It's kind of a fascinating little turn.
The earliest OED2 citation of "slop" for the sense "figurative. Nonsense, rubbish; insolence" is 1952. Slop was slop long before "AI slop" was coined, and AI slop is slop from an AI.
We're a society built by thought and good-will engagement. We won't get out of our "rules for thee" with less thought and less good-will engagement.