Defenders argue manual review is essential for understanding training datasets.
Meta’s new workforce reduction strategy is make SWEs manually label and generate training data. Meta gets valuable data and doesn’t have to pay severance when the workers get bored and quit.
@__apf__ Fulltime! Forcefully assigned. It's why so many devs at Meta are actively searching for new jobs. There's around 5,000 of them reassigned for FT data labeling
Users highlight data as AI's real competitive moat rather than models, showing optimism about Meta reassigning engineers to manual data labeling.
Bad take, you absolutely need to look at the data, manually
Just learned:
Software engineers used to do manual data labeling at Scale AI while Alex Wang was CEO. After he left, new leadership joined, and were HORRIFIED to learn this. Stopped it ASAP
Now at Meta, software engineers are assigned manual data labeling... see the pattern?
@Skiminok @ArneKoehn data labeling, or creating labeled data?
@yoavgo @ArneKoehn I understand where you're coming from (and agree with that charitable view) – but it's literally the situation. About 5000 regular Meta SWEs with past "exceeds expectations" ratings have been forcefully reassigned to a new org where data labeling is their *only* job.
@yoavgo @ArneKoehn I understand where you're coming from (and agree with that charitable view) – but it's literally the situation. About 5000 regular Meta SWEs with past "exceeds expectations" ratings have been forcefully reassigned to a new org where data labeling is their *only* job.
@pjreddie i wonder if there are a few enterprising individuals that that train custom models to surreptitiously do the labelling until shogun wang tells them to seppuku
@yoavgo @ArneKoehn The latter. Creating SWE-related RL environments and tasks.
@Skiminok @ArneKoehn data labeling, or creating labeled data?
@__apf__ @GergelyOrosz They want these workers to quit. Then they don’t have to pay severance. And they get valuable training data as a side benefit! A win-win for Meta
@GergelyOrosz !! that's so wild it's hard to believe. how can that payroll math on cost of labeling possibly pencil out for them?
@GergelyOrosz The AI industry loves talking about models. The real moat is often the data. 🚀
@Skiminok @ArneKoehn seems very legit to me, its a software engineering project.. no?
@yoavgo @ArneKoehn The latter. Creating SWE-related RL environments and tasks.
Defenders argue manual review is essential for understanding training datasets.
Meta’s new workforce reduction strategy is make SWEs manually label and generate training data. Meta gets valuable data and doesn’t have to pay severance when the workers get bored and quit.
@__apf__ Fulltime! Forcefully assigned. It's why so many devs at Meta are actively searching for new jobs. There's around 5,000 of them reassigned for FT data labeling
