71–73 of 73 posts
Earlier quoted context omitted.
ML is like a genie in that it is very good at optimizing exactly what you tell it to, and so you need to think long and hard about what you're telling it to optimize, because it might turn out to be bad proxy for what you really wanted.
This is a super insightful analogy! Thank you.
> Machine learning enables Twitter to drive engagement, surface content most relevant to our users, and promote healthier conversations. One that wants to manipulate your mind, one that echochambers your discovery, one that censors arbitrarily.
More importantly allow opt out and configuration of stuffing feeds with politics and liked by those I follow.
But the idea is not to empower users but masticate users to provide more 'value' to Advertisers by selling them 21stC version of 'hits' - the false & forced engagement metric.
This all stems from the mentality that Twitter are serving the money not the user.
It is short-termism.
Hopefully crap-stuffing users will allowing the resurgence of a diverse market that necessitates open standards unlike our current hyper monopolies and their data silos.
This is very confusing and meandering. It gives flow charts and lists of steps that don’t map to my experience building deep learning models at scale, and spends a strange amount of time passive aggressively dismissing Lua Torch and extolling virtues of TensorFlow that aren’t very important. As with all of these purported pipelining systems, I’m skeptical and happy to let a bunch of other people deal with the headche…
[0] https://github.com/pantsbuild/pants/pull/5815 [1] https://github.com/twitter/scalding