Management technique we learned early on: short-term machine learning deadlines can be set based on inputs (e.g. high-quality execution on a set of experiments) but not outputs (e.g. reaching some level of performance). Science does not bend easily to the wishes of managers.
“Are there any software engineers that switched into a machine learning role and found it a lot more stressful due to deadlines combined with the uncertainty of research?” Discussion: old.reddit.com/comments/ulsuzn

May 11, 2022 · 1:53 AM UTC

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Replying to @gdb @hardmaru
And then there are paper revisions where you have a few weeks to run some additional experiments on some remotely related methods. Of course, the methods don't share their code so you have to scramble to get it coded up asap so that experiments finish running in time.
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Replying to @gdb
Big tech companies are obsessed with roadmaps and annual planning processes You can't write down a plan to invent something, because if you could, you would already have the invention How much do you think existing business processes hold companies back with respect to ML?
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Replying to @gdb
Arguing this with managers who don’t understand ML but who know OKRs is a real pain.
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Replying to @gdb
Especially DL work. Non technical / no common sense managers really suck.
Replying to @gdb
I am sure no-one games models to meet targets... It's not like a manager would ever know. Smart managers would not set stupid targets... they would understand the problem, the model and be actively helping.
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Replying to @gdb
This exactly. Living in a non-deterministic world is stressful. It's why leading AI teams need a deeper understanding of the processes and risks, with far greater latitude given to milestones and goals
Replying to @gdb
I think ppl too often forgot experiment is different from building products and that makes a huge difference when you have to setup deadlines or project trajectories
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Replying to @gdb @gentry
This can be said of most interesting problems.