The term used for the popular recent solutions is "deep learning". To be more specific, the most effective solutions are "guided deep learning". The term "guided" means that the important inputs and outputs are partly chosen by humans trying to tune the learning process. Progress has been rapid in just the last two or three years. Image recognition, for instance, an extremely tough area until very recently, is now pretty much solved. In this area, the next frontier to be cracked is totally independent learning without any need for humans to be involved. Such a breakthrough may or may not be achieved quickly.
Another very interesting area of research is how to deal with imperfect information. Where large amounts of data is available, and that data can unambiguously be used to determine a correct solution (such as moves in chess, or analysis of MRI scans for tumor analysis) artificial intelligence can already surpass the performance of any human if the AI system is given sufficient training. With AIs that must deal with imperfect information (especially prediction of what humans or other AIs might do) progress is being made, but the best humans are generally still superior to the best AIs. Examples are playing poker, and stock market decisions (though the latter is still heavily AI assisted).
Still a major problem for AIs is where limited clearly relevant data to guide decision making is available. Clearly, humans rely on a lot of peripheral experience to suggest a plan of action. The actions taken may be imperfect, but at least there is a basis for the decision. Before AIs can be made equally (or hopefully more) adept, the process needs to be better understood.