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When someone authors a paper, is it on others to proove the author did not use their work as inspiration? No, it is on the author to give credit where it is due. You guys are acting as if it its legal issue, when it is not.

Sorry but this is a misconception: these models are both capable of complete novelty and of plagiarism. For a concrete example, image diffusion models have been shown to reproduce many existing images nearly 100% exactly, yet clearly, they can also create new ones.

A model being trained on lots of irrelevant information does not mean relevant information was not used.

Any degree of tracking what people do is unprivate. Every single web interaction you perform is tracked. All LLM companies store all your conversations by default. Do you need more examples?

This definitely feels like the correct reading unless there is information we were not provided with. If the problem were unimportant, there would be no debate that this is not OK...

Its very easy for OpenAI to answer, yes or no, if the model they used trained on their chats.

There is no redundancy, the keyword is repeated for affective impression.

Consider the following example, read this letter while paying close attention: https://xcancel.com/PierrePoilievre/status/18835154856582268...

Notice how often the keyword repeats. It is not repeated to communicate information, but to communicate affective state.

If you don't understand how it works, imagine the above letter instead said "Trump", and read it again.

Don't focus on what the letter says, instead on how it feels. Actually go and read it again, start to finish. Don't read past this line until you did.

Do you see how each time the keyword repeats, it affects your emotional state just a bit more? Can you start to feel it?

I'm assuming you hate Trump, but if you don't, just pretend you do. Then read the same letter one more time, imagine it said "Trump" instead and that the Republicans, which you really hate, just got re-elected. Do read it one more time.

You must understand that the keyword is repeated for affective impression.

Now read the letter again, one last time, slowly. Pay attention to the keyword, and how you feel each time it repeats. Imagine it said really "Trump", and that you really hate him, and what he did to your government.

The keyword does repeat, but there is no redundancy. It is repeated for affective impression. It only feels surreal because you are emotionally disinvested.

If you actually read the letter over and over, and really imagine how it feels, as if it were of deep concern to you, then you will understand. You seem smart, so I'm sure you will understand.

The keyword is repeated for affective impression.

LLMs write one word at a time, and I do too.

It is really not that complicated: words are chosen to lead somewhere.

Imagine a checkers engine then.

Normal boring CS scientific work. Just running running my experiments, reproducing other papers, etc. A lot of it does involve the agent waiting for some computation, but the fact that it resumes independently when I'm sleeping is kind of the point (+ usually I have several running in parallel).

I'm not trying to cure cancer, although I do hope people who are use LLMs. ;)

"You most likely can't utilize the workstation 24/7. Your work hours will be concentrated into 6-10 hours per day."

I have agents running 24/7 doing research, in fact I would argue this how they will be used for most programming tasks in the near future. For chatting, I agree local inference makes no sense. But for tasks that run continually, I'm not so sure. Personal computers took a while, local inference will too, but I think it will happen.

This whole thing immediately reads as Claude generated, making it hard to take seriously.

Why do these results contradict existing serious attempts at benchmarking LLMs? Namely: https://artificialanalysis.ai/ https://arena.ai/leaderboard/agent

Clickbait doesn't have to be false, it has to be shocking. Being false is one way of being shocking. "Stop doing X!" -- really now?

I agree to some extent about the jargon (Claude has a bigger vocabulary that me, if it knows a useful word I don't I'm fine with learning it), but often times the way information is laid out across sentences just doesn't make any reasonable sense. At least its consistent in the ways its atrocious, sure, but like...

Sure, and the parent comment's position is that they dislike it. Its purpose is to advocate against clickbait titles becoming normalized in the scientific community.

Yes I can, no problem. First figure out how consciousness works and tell me, then I'll be able to explain if the software is ethical or not. Its simple really.

Haven't seen him no ;)

Yes, I think it really depends on the task. Clearly we won't be willing to spend 1000x on tokens for our everyday needs. But what about for the police monitoring conversations? I'd very much like my chats to stay encrypted, but there have been laws pushing against this. Technology like this provides a real solution i.e. you can scan chats for keywords without actually reading them, and get a warrant if you find something.

The top comment mentions a ~10^3 overhead. Am I crazy (or already old?) for thinking this is not a very large constant?

Have all the skeptics in this thread somehow forgot about Moore's law?

One trick is to simply as for a fast answer when talking to a high effort model, when working interactively. Sounds stupid but I do this all the time and it works. Just tell it you are working interactively now and need ultrafast answers with no thinking.

I would be really curious to know as well, why effort is linked to cache as its quite inconveniant. Is it possible the token used to indicate effort is only passed once at the start, not per thinking trace, or quite simply that different efforts have different model weights?

Yeah I agree, I was just saying I really doubt the author of that blog post was trying to take credit for the ideas they present.

You're reading this the wrong way I think, citations aren't given because its obviously a pedagogical article about well established stuff. Much like you wouldn't give citations in a blog post explaining calculus.

"The oral system scales poorly. One examiner can only hear so many students in a day."

I'm not joking: you could have an AI quiz the students and test their understanding. That way the tests go deeper into testing the understanding by following through on the answers, and are standardized and fair for everyone. I don't think we are there yet at all but I actually think its not insane for the future. I expect downvotes though/I don't think our generation would agree with this.

What's old is new :)

Yeah I'm not sure what happened but Reddit in particular is beyond saving, and I'm not saying it was ever great. I think we simply don't know how to create online communities that make any sense yet. I'd rather have second hand smoke than any more algorithmic "engagement" maximization at this point.

Yes I am, which is why I said that, its much easier to automate over the chaos with a coding agent than with a script. They are still far from doing anything productive on their own but its easy to imagine designing a search space and "letting them go", especially since coding agents are approaching the point of being able to reproduce papers reliably.

Yes but science is well-structures and practically designed about repeatability so its a lot easier to automate than "softer" disciplines.

Looks interesting. But does anyone really like the useState useContext etc. React model? Might as well make a better one if you are going to be compiling no?

No that's not what I meant, I meant exactly what I said above.

Thanks a lot for sharing! I'll try the discussion to unpack context, that sounds interesting. I end up having such discussions in separate chats anyways to understand what's happening so it makes sense to do it upfront.

I think you are correct, its so hard to judge the quality of research that we have been using publication record/count as a proxy. Ultimately, having papers being easy to write is good, provided we find a better way of judging quality.