If you don't know the sampling regime that generated the data, you might as well give up. The only solutions to this seem to be collecting and providing all the

If you don't know the sampling regime that generated the data, you might as well give up. The only solutions to this seem to be collecting and providing all the data to ML analysis, or using provenance to inform the training procedure, which requires human expertise. However, with a more general AI, we would be able to tell it "this is where and how the data were collected" and it could make the necessary inferences. Fully general AI would also be able to ask the right questions and make reasonable guesses on its own. Everything you do now, and nothing like anything that's been developed. To me the weak point of this article isn't in the thesis (of course everyone can agree that general intelligence is more useful than narrow, all else being equal) but that there was nothing said about how to get there. The only reason ML is currently resurgent is because it we've figured out how to do something that works, while general intelligence has proven beyond our reach for 60+ years. Edit: actually it seems to be "classical AI" and hybrid approaches and I guess for more details one would need to read the book. I guess articles like this are worthwhile to temper expectations of what's possible with the current crop of technologies for those not in the field, which could help prevent another winter due to overinflated expectations.