Just a minor thing I want to clarify about the Weather Report [1] - it's framed in kind of a negative light in the article ("sparse updates"), but we've been updating it as frequently as we find a meaningful improvement in a relevant dimension. Since we launched it in February it has averaged about one update per month, as frontier labs keep racing forward!
You interpreted this backwards. Software companies offer consulting when their product cannot stand on its own. See Palantir, Salesforce, etc.
They are successful companies, yes, but not successful products. The product needs to be instantiated and maintained by sales engineers and consultants and customized into something so bespoke that it’s hardly the company’s product anymore.
I found this to be exactly right, and in my work I’ve come up with a taxonomy of constraint mechanisms which I keep in mind when guiding agents: generative to constrain the output of the model, interpretive to constrain how the model ‘understands’ code, and elicitative to help it ask the right questions of users.
Full write up is here: https://www.research.autodesk.com/blog/constrain-agent-not-u...
Disclaimer: I'm a former StrongDM employee
hard agree. But i don't think this is sufficient. Even formal verification has its limitations.
> AI works on data. The better the data, the better the likelihood of a desirable outcome. Code is data. If you have bad code, no matter how awesome the model you let loose on it, you can't get as good a result as if you had good code to start with. This principle has been well known in AI/ML circles since the 20th century.
hard agree. but also RL data is shaped differently than SFT data that has driven the majority of AI/ML innovations since ~2000, and its where there's so much room for innovation still. e.g. ImageNet was all just hand-labeled answer pairs.
> it's not a skill issue, it's an effort/laziness/rigor issue
I'm sorry but this feels like a semantic argument - the point of "skill issue" is "you didn't put in the effort or learn the techniques"