Sometimes I instruct copilot/claude to do a development (stretching it's capabilities), and it does amazingly well. Mind you that this is front-end development, so probably one of the more ideal use-cases. Bugfixing also goes well a lot of times.
But other times, it really struggles, and in the end I have to write it by hand. This is for more complex or less popular things (In my case React-Three-Fiber with skeleton animations).
So I think experiences can vastly differ, and in my environment very dependent on the case.
One thing is clear: This AI revolution (deep learning) won't replace developers any time soon. And when the next revolution will take place, is anyones guess. I learned neural networks at university around 2000, and it was old technology then.
I view LLM's as "applied information", but not real reasoning.
Based on any reasonable mechanistic interpretability understanding of this model, what's preventing a circuit/feature with polysemanticity from representing a specific error in your code?
---
Do you actually understand ML? Or are you just parroting things you don't quite understand?
---
Way to go in showing you want a discussion, good job.
Now go read https://transformer-circuits.pub/2024/scaling-monosemanticit... or https://arxiv.org/abs/2506.19382 to see why that text is outdated. Or read any paper in the entire field of mechanistic interpretability (from the past year or two), really.
Hint: the first paper is titled "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet" and you can ctrl-f for "We find three different safety-relevant code features: an unsafe code feature 1M/570621 which activates on security vulnerabilities, a code error feature 1M/1013764 which activates on bugs and exceptions"
Who said I want a discussion? I want ignorant people to STOP talking, instead of talking as if they knew everything.
Quick question, do you know what "Mechanistic Interpretability Researcher" means? Because that would be a fairly bold statement if you were aware of that. Try skimming through this first: https://www.alignmentforum.org/posts/NfFST5Mio7BCAQHPA/an-ex...
> On the macro level, everyone can see simple logical flaws.
Your argument applies to humans as well. Or are you saying humans can't possibly understand bugs in code because they make simple logical flaws as well? Does that mean the existence of the Monty Hall Problem shows that humans cannot actually do math or logical reasoning?
The mere existence of a research field is not proof of anything except "some people are interested in this". Its certainly doesn't imply that anyone truly understands how LLMs process information, "think", or "reason".
As with all research, people have questions, ideas, theories and some of them will be right but most of them are bound to be wrong.