ah, to live in an era where one could freely type an em dash every two sentences

> in 18 comments no less than 3 different people are warning about doing something against the rules

I don't know how it is in your country but where I grew up it's well-known that the cops will come knock at your door fast if you transmit at power or frequency bands you shouldn't be transmitting.

That's just an eternally confusing bug of VS/cdb. On other IDEs such as QtCreator with clang or gdb it shows the break red dot exactly where the issue is.

I'd like to look into adding it to https://github.com/jcelerier/librediffusion for real time inference using tensorrt, do you think it's worth trying?

We’ve tried a lot of inference techniques but we’re not a real-time level yet (and believe us, we’ve done real time models!).

We’d like to open-source it soon (it’s just that it’s another can of worms - that we already went thru for Krea 2), precisely for people to try things like this. The Krea 2 community efforts have been incredible to watch from the inside.

Once Krea 3 is open, you should def try!

how do you feel about people that love going to pachinko

very badly

Who are you to judge other people's ways of enjoying their life?

Who do you have to be to do so?

not really an option when you're working on a large codebase where a bisect means that a lot of steps could be multiple-hours of compiles. E.g if on a simple 12-step bisect you get 6/8 with a full rebuild you've already spent an entire work day on this

if patterns can be put into words, why wouldn't the same process happen at the word level? do you think it's impossible for a LLM to write even a simple python program that will generate millions of plausible random sentences in the general problem space we aim to generate new concepts for, then filter and rank them semantically?

More generally, do you really think humans randomly come up with new ideas, in any act of creation, such as art, science, etc.? Why do you think art studies and stuff like Oblique Strategies exist? How long should one wait for a specific random human without any formal education in physics to have 50% chance of coming up with a new workable theory of cosmology?

As you say, putting a random sequence of plausible-sounding words together is a trivial task - even a non-LLM algorithm can do it. No, I don't think humans are unique - they also absorb the ideas available to them and re-elaborate on them to produce novel ones.

An LLM might come up with a sequence of words that express a novel idea/thinking pattern, but so can I on the spot right now. The question, though, is: is that idea any good? In the case of humans, maybe the first idea is good, the second ok-ish, but inevitably they get duller.

Most people come up with very few novel (good) ideas in their lifetime. The question, then, is whether a model can, unlike humans, *repeatedly* generate new ideas, continuously.

My guess/assumption is that NO, a model might come up with SOME ideas that are somewhat original [1], mixing the existing sources available to it (similarly to how a human would do it, just with more sources and more efficiently), but it cannot continuously generate novel ideas.

If that's true, I think there will be a space for humans to be thinkers, even if models are super smart, as a different unique perspective and a different reconfiguration of previous ideas will be more unique than raw intelligence.

Please note: I wasn't refusing the notion that LLMs don't produce anything original, just noting some fallacy in your example about word generation. The jury is still out on whether AI models will own thinking in the future - or will simply be partners to humans - and I don't pretend to have the answer.

[1] More should be said about what makes an idea original (even if derived from others), as opposed to a transposition of an existing concept. I admit I'm not a mathematician or anywhere near an expert on the topic, but from what I've read, the latest AI math achievements seem to belong to the latter case.

As Stalin said, quantity has a quality all it's own.

11000 agents explored one of the recent math problems solved by AI (can't remember exactly which one). About 3 of them had crucial insights which moved the problem forward. A 4th one also had one of the key ideas, but it's session got messed up and the idea was lost, and only noticed in retrospect.

Maybe 11000 top mathematicians working on it would have found the same ideas. But certainly not in days.

Even if LLMs generate novel ideas at 0.01 rate of humans, you can deploy vast amounts of them on even trivial problems, that humans would not bother with.

For biological reasons, human intelligence is kind of fixed and only very slowly advances by better compression and synthesis of the school curriculum.

There is no clear present bound on machine intelligence. One might argue that machines won't be able to get smarter than the material they were trained on (human output). I kind of doubt that. And certainly they can be much faster.

> I'm ready to bet that is untrue. Sure, no one can recite the code, but people can quickly scan the module and refresh their memory as well as understanding the new changes, even after a while. If not, collaboration would have been impossible.

I don't understand this part. When I was doing freelance consulting, my job was quite often "come to our office, scan this 40 million lines codebase and fix stuff for the next week". It was never a problem to read and get up to speed completely unknown codebases if you have basic programming ability. Much harder to do something that fits floaty business objectives though.

For one person that understands a proof of some matrix multiplication algorithm lowering its current complexity bound in an useful way (e.g. not with absurdly gigantic <= 2nd order polynomial term), there's likely thousands of companies willing to pay for the improved efficiency

And if anything that useful does come out of this output/"slop dump" then that might begin to make a strong argument for it. But every other time it was "that's the theorem done for the headline figuring out everything important and how to use it is an exercise for the reader".

Which language do you think doesn't need it? For instance for me I wish I had such a tool whenever I used any interpreted or JIT language, to understand clearly to which semantics whatever is happening maps and how their respective front-end optimizes. Things as simple as "a" + "b" in JavaScript: are you sure you know how every js runtime maps it? Do they fold the strings as an optimization during parsing ? Do they preallocate a larger native string type and push? Do they just do something that ends up an equivalent of std::u16string("a") + std:: u16string("b")?