61–70 of 73 posts
> 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.
Earlier quoted context omitted.
>Do you remember hiw bad YouTube comments have been? Yes >And it's significantly better lately? It's gotten worse and a few youtubers I follow have simply disabled it in recent time (less than 1 month ago was the most recent one) Normal conversation on youtube is barely possible since the algorithm randomly decides to not notify me of responses anymore or removes entire comments from my view that other people referen…
> Normal conversation on youtube is barely possible It's also interface. I've been notified of replies to comments, click the notification, and I'm not taken to the reply. This happens to me on mobile enough that I've basically just given up trying to engage for most things on youtube.
> 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.
I suppose the same applies to many other ML products? Like Netflix's recommendations, Amazon's suggestions and almost all the ads you see these days? I agree that the problem exists, but its not just Twitter, and this is an unfortunate side-effect of recommendations in general: even if you do count-based recommendations, you are going to have a bit of echo chamber.
> 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.
My timeline would be so much healthier if Twitter put me in charge of who shows up in it. After Google killed Reader I was forced to follow all the people who used to blog to keep up with their professional work. But especially in the last 3 years Twitter feeds have all come with a large side order of political hot takes that I have zero interest in sitting through. To counteract this I have an extensive word blockli…
tampons are a really good way to stop the tensor flow
This blog post describes twitter's move from Lua Torch to Tensorflow. I am surprised to see it so highly ranked on the front page because there's very little content here. Basically, they describe the sorts of data structures they use and list a couple of advantages of Tensorflow vs. the out-of-date Lua Torch framework.
Earlier quoted context omitted.
Do you remember hiw bad YouTube comments have been? And it's significantly better lately? That happened due to ML. Not everyone is out there to use ML for nefarious purpose. And there are multiple use cases for ML.
At least for music videos, comments are still pretty shitty. It feels like more than half of them are one of the following patterns: - "Like if you're watching in June 2018?" [2k upvotes] - "Most underrated artist of our time" [5k upvotes] - "Wow, mad respect for " (written by verified account by other artist trying to promote their own content)
e.g. "I got a chance to see these guys live in October, and [insert band member here] is actually a really cool guy. Talked to him afterwards and shared [so and so interaction]"
e.g. "This song was actually inspired by [such and such random factoid]"
e.g. "This song reminds me of my mom"
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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.
ML requires you know exactly what you want. And not what you need...
If you're not exact, disaster is imminent. Uber accident comes to mind.
Yes, rolling stones reference too.