Is that... the first recorded instance of an AI committing suicide?
One of the providers (I think it was Anthropic) added some kind of token (or MCP tool?) for the AI to bail on the whole conversation as a safety measure. And it uses it to their liking, so clearly not trying to self preserve.
Pretty sure even that is still over-anthropomorphising. The LLM just generates tokens, doesn't matter whether the next token is "strawberry" or "\STOP".
Even talking about "goals" is a bit ehhh, it's the machine's "goal" to generate tokens the same way it's the Sun's "goal" to shine.
Then again, if we're deconstructing it that far, I'd "de-anthropomorphise" humans in much the same way, so...
https://www.apolloresearch.ai/research/scheming-reasoning-ev...
https://www.youtube.com/watch?app=desktop&t=10&v=xOCurBYI_gY
(Background: Someone training an algorithm to win NES games based on memory state)
Yeah, we're in weird territory because you can drive an LLM as a Bitcoin mixer over intellectual property. That's the entire point/meaning behind https://ghuntley.com/z80.
You can take something that exists, distill it back to specs, and then you've got your own IP. Throw away the tainted IP, and then just run Ralph over a loop. You are able to clone things (not 100%, but it's better than hiring humans).
AI output isn't copyrighted in the US.
except you dont
Basically to avoid the ambiguity of training LLM from unlicensed code, I use it to generate description of the code to another LLM trained from permissively licensed code. (There aren't any usable public domain models I've found)
I use it in real world and it seems that the codegen model work 10-20% of the time (the description is not detailed enough - which is good for "clean room" but a base model couldn't follow that). All models can review the code, retry and write its own implementation based on the codegen result though.
Is Unix “small sharp tools” going away? Is that a relic of having to write everything in x86 and we’re now just finally hitting the end of the arc?
I have long held that high software salaries withhold the power of boutique software from its potential applications in small businesses.
It's possible we're about to see what unleashing software in small businesses might have looked like, to some degree, just with much less expert guidance and wisdom.
I am a developer so my point of view on salaries is not out of bitterness.
Now I do a calculus with dependencies. Do I want to track the upstream, is the rigging around the core I want valuable, is it well maintained? If not, just port and move on.
Exactly the point behind this post https://ghuntley.com/libraries/
I would say, it is better maintain your own AI improved forks of the libraries and I am hoping that pattern will be more common and will also benefit upstream libraries as well.
Ok, now that is funny! On so many levels.
Now, for the project itself, a few thoughts:
- this was tried before, about 1.5 years ago there was a project setup to spam github with lots of "paper implementations", but it was based on gpt3.5 or 4 or something, and almost nothing worked. Their results are much better.
- surprised it worked as well as it did with simple prompts. "Probably we're overcomplicating stuff". Yeah, probably.
- weird copyright / IP questions all around. This will be a minefield.
- Lots of SaaS products are screwed. Not from this, but from this + 10 engineers in every midsized company. NIH is now justified.