Twitter has become a tragedy.
Twitter meets TensorFlow
11–20 of 73 posts
Re: Twitter meets TensorFlow
#12This 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 headches of making it adequately general for a few years before I’ll even start caring about grokking it for my use cases.
In the meantime, creating build tooling, data pretreatment tooling and deployment tooling is pretty valuable for me to understand business considerations and make sure all my modeling & experimentation aren’t just time wasting ivory tower projects, particularly in terms of customizing performance characteristics on a situation-to-situation basis, free to design the deployed system without a constraint to a particular serving architecture.
It also makes me very disinterested in applying to work for the Cortex team, because even though the article is talking about DeepBird v2 as a means to free ML engineers to do more research, it seems pretty obvious that there’s a huge surface area of maintenance and feature management for this platform. Your job is probably going to be less about research, which is scarce work that people compete over anyway.
Possibly attractive for people who just like deep C++ platform building, which is an internal drive not often found in people wanting to solve business problems with ML models.
Re: Twitter meets TensorFlow
#13[deleted]
Re: Twitter meets TensorFlow
#14Brought my following down to 9 accounts last week and enjoying the flow again. I have always used Twitter passively, only re-tweeting 3-4 interesting stories a day. Hoping TensorFlow for healthy conversations does not spoil this sort of 24/7 breaking news from trusted sources, now. What I fear the most, is having my flow made of one tweet from sources I choose & one tweet TensorFlow suggests to me.
Re: Twitter meets TensorFlow
#15> 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.
Re: Twitter meets TensorFlow
#16> 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.
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.
Re: Twitter meets TensorFlow
#17> 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’m not even sort of interested in what Twitter’s heavy-handed definition of “healthy conversations” are. I dare say they’re acting well outside of their wheelhouse. Their job is to provide a platform for discussion, not whatever that mess of corpspeak I just read is.
Their job is make money. The sense of entitlement people have about social media is just wild.
Re: Twitter meets TensorFlow
#18> 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.
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.
How are yt comments any better now?
Re: Twitter meets TensorFlow
#19Earlier 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.
How are yt comments any better now?
It's not perfect, but it's way better. The spam is mostly gone. Inane comments are lower in priority.
Re: Twitter meets TensorFlow
#20This 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.