3 ms·
I'm guessing a lot of you have read Neuromancer; I liken generative-adversarial networks to be a lot like neuromancer-wintermute networks :) Imagine Robert Axe
by pizza 10y ago
I'm guessing a lot of you have read Neuromancer; I liken generative-adversarial networks to be a lot like neuromancer-wintermute networks :)
Imagine Robert Axelrod's iterated artificial intelligence agents tournaments [0], reloaded; let's pit GANs with LSTM cells, deep-q-networks, style-transferring networks, random forest algos, MCMC algorithms, word2vec architectures, etc. against one another, if only to see possible new failure modes of tit-for-tat.
[0] https://en.wikipedia.org/wiki/Tit_for_tat#Background https://en.wikipedia.org/wiki/Tit_for_tat#Background
- hiddencost 10y agoThe second half of your post reads like word salad made from headlines about ML. They're all words, but the fact that you thought they form a thought with semantic content makes me worried. We need fewer MBAs coked out of their mind meddling with this stuff. The advancements that are interesting in ML mostly come from deep statistics and information theory and very sophisticated analysis, not combining the most popular black boxes that you read a medium post about.
- pizza 10y agoI think you might be over-fitting a bit with your model there