I'm personally most interested in approaches to AI not based as heavily on dataset-collecting. There's a now almost standard method of: 1) first curate a nice, large dataset (often explicitly labeled by humans), then 2) carefully engineer a model architecture that has good performance on the problem represented by that dataset. Example: dataset is ImageNet, problem is tagging images. Obviously there's a lot of practical value in that, and it's a perfectly reasonable thing to research and use, but that's only one specific kind of inference.
Quite possible. I do have a recent PhD in AI, but it's a big field and I can't claim to know more than a small percentage of it.
The problem with the dataset-first approach is that humans are still providing a lage part of the intelligence: defining the domain, defining what good performance on it looks like, carefully designing model architectures, collecting and labeling large datasets, etc. This is fine for narrow task-specific problems, but is not really the end-all of AI, and does not even seem to work well on all well-defined tasks. As an example of another kind of inference, how about mathematical reasoning? I purposely pick one here that is seemingly very formal; should be possible for a computer to do it. Mathematicians are somehow able to invent conjectures and prove theorems without first being exposed to terabytes of labeled mathematical facts. The scientific method is kind of an even messier version of this. Or to take something laypeople do, people can usually learn games to at least a passable level from just a handful of playthroughs, not AlphaGo-style millions of plays (imagine if you had to play even 1,000 games of MT:G before you got the basic hang of it...).
All this kind of stuff is quite well-represented in the literature though, if you mean the scientific literature. Pop-press AI writing tends to cover a pretty specific subset of what's going on at AI conferences.
