Hi, I'm the blogger. I just want to add a very simple statement: In order to create a model of the world, the machine learning substrate has to have the capac

Hi, I'm the blogger. I just want to add a very simple statement: In order to create a model of the world, the machine learning substrate has to have the capacity to cover the observed dynamics. Hard not to agree with this, almost sounds like a tautology. Now let's try to derive conclusions: - World dynamics is full of multi-scale interactions (e.g. in vision illumination of a single pixel depends on the whole scene and the whole scene depends on many tiny details). To capture that, the machine learning substrate has to allow low level representations access high level stuff. Hence feedback all over the place. Which is exactly what is seen in the biological cortex. This is not recurrent layer made of LSTMs, this is a FULLY RECURRENT system. This will not be achieved with any franken-neocognitron deep network neither with MSE, nor adversarial nor even triple adversarial loss function. This requires a new approach and together with several colleagues after a few years of continuous and intense thinking and modelling we have proposed a solution: http://blog.piekniewski.info/2016/11/04/predictive-vision-in-a-nutshell/ http://blog.piekniewski.info/2016/11/04/predictive-vision-in... As well as a full paper https://arxiv.org/abs/1607.06854 https://arxiv.org/abs/1607.06854 Now, I'm not saying this is all done. This is just a beginning of a really exciting research adventure and many things look very promising. It will require however for the AI field to get out of a pretty "deep" local minimum it is in right now.