Archive for the ‘Data Mining’ Category

Artificial Intelligence and Sports

Thursday, February 8th, 2007

A couple of days ago Indianapolis won the Superbowl – just as predicted by an Electronic Arts Simulation. The simulation software had been fed with the latest data about all the players involved and they had the game AI fight it out. In the past some of the simulated outcomes were not that close to the final scores, but they still did a fairly decent job in 2005 and 2007.

There is more and more statistical decision making in baseball as well, the most famous example being the Miami-Orlando series in the 1997 playoffs.

Interesting…

Just got back from NIPS 2006

Tuesday, December 12th, 2006

Just got back in town from the NIPS conference. I’ve been to a couple of Machine Learning conferences before, but this was my first time at NIPS. A couple of papers were very interesting (you can download them at books.nips.cc) :

  • Manifold Denoising
    Matthias Hein, Markus Maier
  • Fundamental Limitations of Spectral Clustering Methods
    Boaz Nadler, Meirav Galun
  • Learning with Hypergraphs: Clustering, Classification, and Embedding
    Dengyong Zhou, Jiayuan Huang, Bernhard Schoelkopf
  • Recursive Attribute Factoring
    David Cohn, Deepak Verma, Karl Pfleger

However, I found the single-track style of the conference boring at times. My interest in the latest results from fMRIs etc. is low right now, so at times there was nothing to do, but mingle or just do nothing. At ICML there is always at least one conference-track that is interesting to me. The poster sessions at NIPS were very interesting, though.

The workshops were more interesting than the conference. Only the room-sizes were misallocated. Some workshops (the one with the big rooms) were rather empty, and the ones I attended were overcrowded. And, of course, the traditional workshop summarys at the end of the workshop were funny. The ones that stuck out in my mind the most were Man vs. Bird from the Acoustic Processing Workshop and the novel applications for the non-linear dimensionality reduction with their swiss-roll video. I got a few new ideas from the workshops that maybe will work out.

Also there were no T-Shirts. At this years ICML plenty of free t-shirts were given out – unfortunately during the reception, which forced everybody to carry their T-Shirts around during the entire reception (it looked very amusing, though) – yet at NIPS all we got was a mug… 🙂

Last, but not least, I’ve heard about the legendary NIPS partys from my friends and had some high expectations :-). Friday night I attended the GURU-party from Garry’s Unbelievable Research Unit, Saturday was the legendary Gatsby-party. Both partys were rather disappointing, so I actually went to check out the nightlife in Whistler instead. The bars and clubs I found were pretty quiet as well. Uh well… I’ve heard from people that Whistler had less people than last year around that time.

Data Mining of Social Networks

Saturday, September 30th, 2006

I just returned from the ECML Data Mining Workshop and one talk I found particularly interesting. In the talk Network-based marketing: Identifying likely adopters via consumer networks (S. Hill, F. Provost and C. Volinsky) presenter reported on a successfull marketing campaign. Rough summary from the talk: A phone company was introducing a new service and from past experience they had twenty-something marketing segments for people that were likely to buy that they would write to, call or otherwise inform of the new service. Since the phone company has access to the call records they extracted a list of friends these likely buyers frequently call and started marketing to these people as well. The cool part is that the response rate from the friends was about 3 times higher than the likely-buyers response rate (or was it even the buy-rate). That said, so many companies now started to collect (or have available to them) social networks data, e.g. Skype (now EBay), Google (GMail invites), MySpace, Facebook etc. Most likely this will change the ways of advertising quite a bit. Sidenote: the companys lawyers felt this is legal, because the company owns that call data. Interesting how this is legaly different from the NSA-survailance-program the US government has been doing.

 

Data mining used to find new materials

Sunday, August 27th, 2006

 I just read an Eureka Alert (see also ZDNet’s blog)mentioning that a couple of researchers at MIT found new, potentially useful crystal structures with AI and Data Mining techniques. You can find the abstract of their paper here. I’ve seen randomness and Genetic Algorithms around alot lately (such as the self-reconfigurable-modular-robot/) and a robot that can do bioinformatics experiments (DNA sequencing) all by himself (link?). I think that this is a very useful application of AI. However, it is only an application of the scientific knowledge. It’s fast testing based on the current physical models and insights. It automates science to an extend, but does not come up with new insights. It’s more data without more people to add an interpretation. For example, it took a few years before somebody found an application for Teflon.

I haven’t seen this around (will search again), but what would be really interesting is an algorithm that can form a new hypothesis (e.g. a differential equation) based on outcomes from Physics experiments. An algorithms that explains the data and forms a theory. It’s probably harder to build than regression algorithms…