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Today I learned that Markov chain generation = "AI." Shame on Ars staff for conflating this parlor trick as "artificial intelligence," and failing to explain h
by strgrd 9y ago
Today I learned that Markov chain generation = "AI."
Shame on Ars staff for conflating this parlor trick as "artificial intelligence," and failing to explain how this rather simple process works.
For fucks sake, they find the raw screenplay of a movie like Knight Rider, regex out the crap, download a Markov chain generator off of github, and train their model on the text. And of course by "training", I mean they run a single command to process the text document, and wait. Why this is described as a "...long short-term-memory recursive machine-learning algorithm" probably has to do with the fact that Ars has their hand in promoting these short films.
- gliptic 9y agoThey describe it like that because it's a long short-term memory recurrent neural network, which is a machine learning algorithm. How exactly were you able to infer that they were lying and were instead using a Markov chain from a few snippets of dialogue?
- RodericDay 9y agodefending hype-building like this is why we keep having AI winters
- orthoganol 9y agoProbably because they trained an LSTM model to learn conditional probabilities between words and then sampled using markov methods to actually generate the sequences. It's actually pretty standard and not particularly innovative, technically, in that you can google already existing tutorials on exactly how to do that (even in Keras - forget lower level stuff). At any rate, this doesn't work well, typically. I'm sure they generated a bunch of sample sequences and cherry picked ones that made even a little sense for inclusion in their script.
- deleted 9y ago[deleted]
- JustFinishedBSG 9y agohttps://en.wikipedia.org/wiki/Long_short-term_memory https://en.wikipedia.org/wiki/Long_short-term_memory
- minimaxir 9y agoThe tutorials of which are all extensions of the char-rnn project. http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ A large amount of AI-generated text just swaps in the source dataset, generates the text, calls it AI, and gets many blog posts written about "artificial creativity."
- themadstork 9y agoThat's definitely how I started, but I'm working with my own LSTM now. Here's a bit about my process from last year: https://medium.com/artists-and-machine-intelligence/adventures-in-narrated-reality-6516ff395ba3 https://medium.com/artists-and-machine-intelligence/adventur... https://medium.com/artists-and-machine-intelligence/adventures-in-narrated-reality-part-ii-dc585af054cb https://medium.com/artists-and-machine-intelligence/adventur...
- minimaxir 9y agoThose write-ups are very neat! Thanks!
- themadstork 9y agoI'm one of the creators, and I assure you we did not use Markov. I trained the LSTMs myself.
- jlarocco 9y agoFirst of all, the article doesn't mention Markov, and there are a lot of ways to generate text, so you may be jumping to conclusions. Second, the concept of AI is so fragmented, varied, and overloaded, that who's really to say Markov text generation isn't a simple AI? And TBH, there's not much difference between Markov chains and most AI and machine learning algorithms. The math's a little different, but they're almost all based on probability and statistics (and sometimes searching) and none of them really understand anything.