Topic Modeling

In my work with the IRIS project, I have had the opportunity to meet and work with people doing topic modeling. Sounds like an exciting subject. It is. Take Latent Semantic Analysis, tweak it to something called Latent Dirichlet allocation, add in gibbs sampling, chinese restaurant algorithms, and you've got a probabilistic means by which topics can be harvested from large bodies of information resources. Some interesting papers can be found at:
http://www.pnas.org/cgi/reprint/0307752101v1.pdf
http://eprints.pascal-network.org/archive/00000990/01/WS905BuntineW.pdf
http://cog.brown.edu/~gruffydd/papers/ncrp.pdf
http://cog.brown.edu/~gruffydd/papers/author_topics_kdd.pdf

In the Pay Library of the (near) Future, 5

In the Pay Library of the (near) Future, you'll be able to purchase or rent a book from any publisher as easily as you can get money from any bank's ATM today (even if you're overseas — systems in different countries will interact with each other). Some publishers, and some readers, will prefer this distribution channel to downloading. They'll carry their library in their wallet with their credit cards.