A good overview would fill up a post on its own, but some relevant topics are given below. I don't think any of it is behind a paywall, but if it is, let me know and I'll link to another article on the same topic. In cases where I learned about the topic by word of mouth, I haven't necessarily read the provided paper, so I can't guarantee the quality for all of these. I generally tried to pick papers that either gave a survey of progress or solved a specific clearly interesting problem. As a result you might have to do some additional reading to understand some of the articles, but hopefully this is a good start until I get something more organized up.

Learning:

Online concept learning: rational rules for concept learning [a somewhat idealized situation but a good taste of the sorts of techniques being applied]

Learning categories: Bernoulli mixture model for document classification, spatial pyramid matching for images

Learning category hierarchies: nested Chinese restaurant process, hierarchical beta process

Learning HMMs (hidden Markov models): HDP-HMMs this is pretty new so the details haven't been hammered out, but the article should give you a taste of how people are approaching the problem, although I also haven't read this article; I forget where I read about HDP-HMMs, although another paper on HDPs is this one. I think the original article I read was one of Erik Sudderth's, which are here. Another older algorithm is the Baum-Welch algorithm.

Learning image characteristics: deep Boltzmann machines

Handwriting recognition: hierarchical Bayesian approach, basically the same as the previous research

Learning graphical models: a survey paper

Planning:

Planning in MDPs: value iteration, plus LQR trees for many physical systems

Planning in POMDPs: I don't actually know much about this; my impression is that we need to do more work in this area, but approaches include reinforcement learning. A couple interesting papers: Bayes risk approach, plus a survey of hierarchical methods

Those are roughly my thoughts as well, although I'm afraid that I only believe this to rationalize my decision to go into academia. While the argument makes sense, there are definitely professors that express frustration with their position.

What does seem like pretty sound logic is that if you could get better results without a research group, you wouldn't form a research group. So you probably won't run into the problem of achieving suboptimal results from administrative overhead (you could always just hire less people), but you might run into the problem of doing work that is less fun than it could be.

Another point is that plausibly some other profession (corporate work?) would have less administrative overhead per unit of efficiency, but I don't actually believe this to be true.

I haven't read MacKay myself, but it looks like it hits a lot of the relevant topics.

You might consider checking out Tom Griffiths' website, which has a reading list as well as several tutorials.

Why use snail mail when you can use skype? My lab director uses it regularly to talk to other researchers.

"In the past" as in before they had alternative methods of long distance communication, or after?