Gary Marcus

advocates of #machinelearning, I am told that you all know that (current) #ML is limited. fair enough. but which limits are you willing to *publicly* acknowledge?https://twitter.com/NotSimplicio/status/1173373706674085888 …

Watch anyone of my talks of the last 4 years. Many even have the title "the power and limits of deep learning" More obvious answer: limitations are pretty much everything everyone in the field is doing research on: self-sup learning, model-based control, reasoning, causality...

agree with @ylecun on much of this list, and tried (among other things) to explain many of them (sometimes with slightly different terms) to the general public in http://rebooting.ai , because few seem to be widely understood or appreciated.

@yudapearl has of course done a great job bringing causality to the forefront of public attention. but overall i think there is a mistaken impression that all we need is more data and more compute, when in fact bolder steps are required.

There is a subtle but important difference between a) all we need is more data and more compute (which I don't know if any expert believes) and b) all we need are general methods like search and learning that leverage data and compute (which is what Richard Sutton argues).