Like all mottos , it ' s good to have one that will last for a bit longer than a few years I like where you are coming from with the blog post , but it might not be necessary to invoke AI winter to identify things current approaches can ' t dodon ' t do well , and for people to work towards solving them with various new approaches in addition to continuing to push current lines of research . If anything , increase of RD money in the current hype cycle should enable multiple lines of research though of course , results in basic research are not guaranteed to arrive on a schedule . I think it would be great to have explicit lists and I ' m sure many already have of things where current models don ' t do wellbreak down . Things likeonline learningunsupervised learninggenerative models that exhibit an understanding of the system generated music or sentences that don ' t sound randomcrapComputational resources are becoming a bottleneck again too training deep networks used to take monthsweeks before GPUs today there are similar issues training very large datasets , or for things like using evolutionary algorithms to search , train and benchmark new network architectures it is computationally very expensive . Will need new types of hardware that is orders of magnitude faster to accelerate these , and progress in hardware moves slow . Physicalanalogoptical computing could be orders of magnitude faster for certain things but is a good while away . Also , I know some machine learning practitioners are not so keen on biomimicry , but the brain is the only current example of intelligence we have . To u n derstandinterfacesimulate physical neural networks , we also need orders of magnitude faster hardware and much better instruments to better understand how the brain really works . Approaches like neural lace seem promising but getting results will take years . b r a i n i m p l a n tspiesonindividualneurons 1 . 17713