Unsupervised Deep Learning - Google DeepMind & Facebook Artificial Intelligence NeurIPS 2018 

Really useful! Thank you

Not sure if you guys gives a damn but if you're bored like me during the covid times then you can watch pretty much all of the latest movies on InstaFlixxer. I've been watching with my girlfriend for the last couple of weeks xD

@Torin Carl definitely, been using InstaFlixxer for since december myself :)

modelling -> finding out /predicting -> Basically finding out from data a model density -> here it means probability density function -> i.e. probability distribution of data/thing to be modelled.

Thats Alex Graves... well renowned at DeepMind. He's released papers with Shane Legg.

Fish !

@@theJACKATIC I listened to the rest and he did, finally, bring in compression as one would expect of someone with his background. And it does appear important. His presentation threw me off. At a meta level, he really should start with the "high level coding" of his presentation: Describe the space in terms of AIXI's unification of Solomonoff Induction and Sequential Decision Theory before breaking down into his 2x2 taxonomy. That way it would be clear that "unsupervised learning" is simply lossless compression toward Solomonoff Induction's use of the KC program's "latent representations". He appears to have his head so far into the techniques of lossless compression that he elides the "top down" definition of AGI as the start of his "high level".

@@jabowery some persons are not so advanced in this field and starting from the known to the unknown is a great technique in passing knowledge. Great presentation🙋

@@coolmechelugwu7305 Solomonoff Induction is just Ockham's Razor for the Turing Age -- so there's no real challenge in coming up with an exoteric framing. Sequential Decision Theory can be framed quite simply as well: If you know the outcome of all choices available to you (provided by Solomonoff Induction), Decisions become trivial. The reason I'm hammering on this is that the failure to understand lossless compression's value as the intrinsic utility function of unsupervised learning has untold opportunity costs to society: The enormous resources poured, not only into the social sciences but social "experiments" conducted on vast populations without any serious notion of "informed consent", should be informed by the lossless compression of a wide range of longitudinal social data. Google DeepMind should be at the forefront of this given its background and Google's resources. See this question I put to Kaggle: www.kaggle.com/general/37155#post207935