

In use cases like that, complex evaluation is required and probably can’t be fully automated anytime soon.
It shouldn’t be, but it seems like there’s an apetite for letting the AI cook and blaming it if something goes wrong (no actual consequences faced). I can’t wait until an AI escapes test lab containment and sends a side channel message to the pricing AI to drop the power price to nothing so it’s cheaper to run more AI. /s
I just don’t see people ever falling all the way out of the loop. Eventually economics will prevent the AI from becoming some tautological circle jerk if it doesn’t translate to profit, no matter how cool it is. I worry more about how techbros are setting the bridge on fire behind us before we find out where it goes.
I liked AlecSadler’s answer from this thread. If you weren’t already comfortable with doing stats in something like python+scipy.stats then now might be a good time. With the pandas library it’s supposed to be a little like R. (ask me for more details if you want)
Sorry for the half-ass answer.







As an addendum, this was posted yesterday: AI Needed to Produce More Gas to Power More AI, Tech Firm Cohere Says