Posted by Anon84 20 hours ago
You wont get anything materially useful, apart from an intuition, from using different terminology.
1. English specification of the system and its goals
2. Formal, mathematical specification of the system and its goals
3. Mathematical proof the system met its goals
4. Code implementing that system with a proof, often informal, that the code embodied the informal and formal design.
5. Tests of all functionality to prove the design and code worked.
6. Independent analysis of the above to find flaws in them.
7. If the implementation has concurrency or parallelism, more errors can occur due to that. There were techniques to mitigate it.
Each step found problems in systems made by professionals. Mathematical work and coding both have a long history of errors in their on fields. Combining them increases the likelihood of error.
Further, in machine learning, we see lots of papers with math that doesn't lead to anything useful. Most papers claiming advances over prior architectures don't pan out in later testing. So, there will be skepticism when people make performance claims like the authors'. The best response is just independent testing.
https://huggingface.co/spaces/galilai-group/prh_demo
> A man in a black shirt and hat with a yellow neckerchief has his hand raised.
no hand raised
> A white sheep sitting on top of a cake.
no its just cake sheep . sheep is not sitting
> A man is standing on the beach while holding his surfboard.
Its not just a man
infact all the examples are wrong.
Similar to zero knowledge proof (ZNP) and its use cases?
They do seem to have some really interesting training efficiency improvements, though. The paper is at least worth a read.
It's possible that you both hit upon or recalled the same cool phrase, especially if the "proving" part was pre-decided and you were thinking of making an incremental improvement.
I was talking to a friend about an idea I had, to build something, he said, "yes! I'm doing that too", then I said "with this specific tooling because is ideal ... " .. he said .. "me too! same tooling", I said "what a coincidence!! amazing ...wait a minute, have you been talking with Claude about this? " ... of course we both did.
My opinion is that that LLMs are influencing us more that we think, and it makes us reach the same conclusions and making us think it was our idea, or that we were the ones steering the conversation.
Perhaps you feel that Claude stole your phrase but I don't think 2 days is enough for that phrase to make it to a re-training, I think it might be more subtle and worse, perhaps you wrote that phrase influenced by the conversation, and the other person did the same.
Just a theory, I can't prove it. But if you think about it, LLMs conversation are the most plausible choices, so it is logical that we all users of this machine are going to tend to converge on the most plausible way of thinking and talking.
No offense, but perhaps that rhyme that you though it was original, it is actually the most plausible in that context.
The fun part is extending it to internal development at corporations. Company A thinks they’re doing interesting competitive work, but are actually being quietly shepherded to build exactly the same things in more or less the same ways as their competitors.
The weirdness of human individuals (even setting aside amorphous concepts like “taste”) is an asset because it provides occasion for actual differentiation, and that noise may turn out to be necessary for the emergence of value in the long run.
Or it might be just pure chance, or might be that you are right, check your settings, in chtatgpt there is a check (activated by default ) that states you agree to help improving other conversations. I’m not sure the same is true in Claude.