… the progress made in statistical machine learning (presumably the brand of AI that most LWers care about) and cognitive science in the last 20 years… And I’m not talking about impressive-looking results that dodge around the real issues, I’m talking about fundamental progress towards resolving the key problems in artificial intelligence.
Could you point me towards some articles here? I fully admit I’m unaware of most of this progress, and would like to learn more.
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 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.
Could you point me towards some articles here? I fully admit I’m unaware of most of this progress, and would like to learn more.
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