[supern0va]

AI models decompose problems down into tiny pieces that exist in their training data, so in a sense, you're correct.

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.

[runarberg]

The amount of computations for a human to do the same tasks is thousands of orders of magnitudes less. 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 how the first person to run CPython in WASM did that, and that is why the plagarism machine can now do the same (only a thousand times more lame and uninspiring).

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.

[supern0va]

>The amount of computations for a human to do the same tasks is thousands of orders of magnitudes less.

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.

[runarberg]

> Are you confident that will be the case in 5-10 years?

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.

[supern0va]

>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? :)

[runarberg]

What has not change is the strategy of throwing a gargantious amount of computations at the problem. If anything we throw more computations at more problems now than in 2016 (and in 1997 for that matter). The underlying technology is pretty much the same, just more parameters, more calculations, etc. Yes every individual calculations takes less power now then in 2016, but we make up for that by making millions of millions of more calculations, even for simpler tasks.

[supern0va]

Sure, but there will be an upper bound after which we will be close to human level performance on the vast majority of tasks, and then at that point the focus becomes efficiency (or a continuing road to superintelligence for some tasks).

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.

[scubbo]

> The amount of computations for a human to do the same tasks is thousands of orders of magnitudes less.

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?

[wlonkly]

On one hand, "clanker" has good steampunk vibes.

On the other hand: "Stop trying to make 'clanker' happen! It's not going to happen!"

"AI slop" caught on but "clanker" did not.

[supern0va]

>"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.

[wlonkly]

But "slop" has meant low-quality stuff for a very long time. See also "swill", both analogies to pig feed.

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.

[Chu4eeno]

"Slop" originated on /pol/ but I'm not gong to try to tread the needle by of the rules by trying to explain it without being offensive or triggering some filter: The first related term here: https://en.wiktionary.org/wiki/AI_slop#English

[blackqueeriroh]

You have this backwards, as Simon could tell you. In fact, Simon coined “AI slop” to mean “low quality AI output.”

[simonw]

I didn't coin it myself, but I did help amplify it at the moment it started to take off.

[calvinmorrison]

claiming you aren't robophobic is the first sign of being a robophobe.