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Inside Google's Deep Dreams project
- sabujp 11y agotldr; it helps us to perhaps understand how we recognize the world around us, esp. when we're dreaming or on psychadelics
- jmcmahon443 11y agoReally enjoyed the humble approach to describing the engineering mindset. Also a great look into how awesome Google management is for letting their software engineers explore spontaneous interests.
- daveguy 11y agoAlso a great look into how awesome Google management is for letting their software engineers explore spontaneous interests+. + at 2am at home :)
- michael_h 11y agoMy company lets me spend 66% of my time on whatever I want.
- pearjuice 11y agoTry self employment, you will approach 100%.
- jacquesm 11y agoNot if you want to stay afloat. 100% of your time will go to serving your customers, saving for a downturn, doing admin, customer acquisition, staying in touch with old customers who are not currently in the market but who may ping you for the occasional question and so on. The other 100% of your time you can spend on whatever you want.
- paki123 11y agoWhat exactly is going on? Are we all tripping?
- lawpoop 11y agoQuestion for the HN audience: what exactly would Deep Dream do it if were trained on 'everything'? When it came out, people asked why its output was full of puppyslugs, and the answer was "Because it was trained primarily on a corpus of dog pictures." Well, suppose that it was trained on a corpus of pictures of 'everything'. What would its output look like then? Would they look more or less like the input image?
- Joof 11y agoMy guess is that the network wouldn't recognize as much stuff effectively and might not look as identifiable. Like an ADHD brain with too much stuff going on. Might look really cool actually; I'd try it.
- thenewwazoo 11y agoI'm not a CNN guy, but your question can't really make sense given my understanding of neural networks. In short, the output step of a NN has to include a classifier. That is, there's a matrix of probabilities that the NN generates. The higher a given probability, the more likely the input is to match the category associated with that value. For example, a NN trained to distinguish "light" from "dark" may output a matrix with value [0.3, 0.7]. To train a CNN on "everything" means you have to have an arbitrarily large output matrix. Can you list every possible category of everything? Probably not. Even if you could take a swing at it, it's hard to get enough data (and time!) to train the net on each category. Small datasets result in overfitting of the training data, and poor overall performance. How big a data set would you need to properly train a sufficiently-sized CNN with an arbitrarily-sized classifier?
- nl 11y agoThe question makes perfect sense. Word2Vec does a pretty good job of encoding the entire English language. Autocoders are a thing, and back in 2012 Google trained one against YouTube and it learnt to recognise cats. The state of the art has progressed a lot since then. See https://googleblog.blogspot.com.au/2012/06/using-large-scale-brain-simulations-for.html https://googleblog.blogspot.com.au/2012/06/using-large-scale...