I did say most people living in Kashmir. Last time I checked, Kashmir wasn't Jammu or Ladakh.
289 karma · joined October 15, 2014
Interesting part is that a smart computer could resolve ambiguities in a human description of program desired in a way similar to humans. Missing part to this idea is that unless it's perfect, it would need a way to explain choices and results and probably some dialog system to iteratively improve upon how it resolved ambiguities.
E.g, make me an app like Uber but for cats -> here is a version you can play with -> oh I don't really like the cat icon, can we change it -> sure, which of these would you like ; etc etc
Computer requires ever changing human context, and maybe could have individualized context. So it has to learn over time. The point is to maximize efficiency of getting things made: how little needs to be specified before computer gives you something you accept / how fast can you go from some vague idea in your head to working acceptable software.
You should at least be able to get as good as an arbitrarily large number of people who lived a very similar life to yours and were put in a time bubble where you could talk with them to have them write software for you, assuming they're organized very well and highly motivated and great programmers and all that.
If you're instead politically aligned with most people living in Kashmir, it's not promoting terrorism, you see it instead as showing solidarity with the cause of freedom. It may be hard to understand, but most of the hundreds of thousands of people protesting in Kashmir right now are not in favor of terrorism. Instead they are incensed by what they see as the decades of Indian oppression and aggression, and Burhan Wani is only used as a symbol of something like "a young son of Kashmir who was driven to madness and made the ultimate sacrifice for freedom".
Facebook's removal of posts is perceived as political censorship by a lot of people politically aligned with Kashmiri independence who are disgusted by terrorism in the same way the US public is.
The Facebook censorship team probably just doesn't have anyone from Kashmir working there, and does not have a good enough grasp of Kashmiri public opinion.
It is really not in line with Facebook's mission of promoting openness to be censoring these posts, and especially to be banning accounts who are posting what they see as legitimate political opinion (when their beliefs and feelings about terrorism are in line with US public opinion)
Lots of brilliant young people who speak English but end up working dead end jobs because of the political situation's impact on the economy. And they want to live with their families, so they don't leave the area.
One of countless places all over the world where remote work over the internet would be life changing for millions, yet barely anyone does it: because the company to bring it to them doesn't exist yet
"turdurine" sounds nice to me; "amamanus" does not
All this is described at the top. I guess you didn't read that.
That sounds like a good approximation: although it's more judging if the word is spelled like it sounds versus if it has just one obvious spelling.
Similar ideas: check social media account availability, check domain availability, see how many Google search results show up, find similar sounding existing brand names
First off - used this code for training the models: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Very very easy to setup and train; highly recommend playing around with your own training data (just a text file!)
This project's code: github.com/shariq/burgundy
Styled and deployed the website about a year ago at a hackathon; it then used a nice wordlist with hand picked words. (repo/wordserver/old_burgundy_words.txt)
Few days ago: got the server to start training a bunch of models (~200), with randomized parameters, using the original wordlist as the training data. (repo/rnn/rnn.py:forever)
Yesterday: woke up at 3 AM after my sleep schedule rolled around, started exploring the output of models trained to different numbers of epochs and run at different temperatures. Subjectively looked at the outputs, decided some model/epoch/temperature tuples were horrible, got rid of those. Wrote a few different scoring functions (just using intuition for what kinds of bad outputs seemed to be commonly occurring) to score the model/epoch/temperature tuples. Got the top ~10 scoring tuples from each scoring function, plus added some additional interesting ones along the way, and then used a pronunciation scoring function (repo/rnn/pronounce.py) to select the top 5 of all of these. Funny enough, the top 5 tuples all used different models and a varying range of temperatures (i.e, not the same model from different epochs, and picking the right temperature significantly improved how well the model performed) (repo/rnn/explore.py)
Since the models would still occasionally output words which were completely unpronounceable, I put some code on top of the models which would generate a bunch of words then discard the bottom 1/3rd of unpronouncable words. A significant portion of generated words from these models also started with a "c" or "b" for some reason: gave those a high chance of being discarded. Short words were also uninteresting, and extremely long words would occasionally show up: added probabilistic filters for length. Finally, initialization time of LuaJIT is very high, so I had the server keep a pool of words which gets reseeded as it runs out. (repo/rnn/rnnserver.py)
If you want to train your own word generator and you need some pointers, would love to help: @shariq
Pretty sure my laptop does this :P
:p
- Shariq