Last week I saw a BayCHI talk by Elaine Wherry of Meebo, in which she used the history of classical music as an analogy for the evolution of user interface and interaction design of web-based user interfaces. The parallels she established between the Baroque era and what we have experienced in the last decade of “Web 2.0” interfaces are compelling.

Baroque music arose during the Renaissance as a reaction to the impoverished musical forms characteristic of the middle ages, an era dominated by monastic chants and songs of Troubadours. The Renaissance brought a revolution in music-making technology with the invention of a range of musical instruments. Unlike the sparse music that preceded it,  Baroque music is characterized by a profusion of notes that, while initially interesting, tend to overwhelm and can render the composer’s melody unrecognizable.

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I had fun watching the Superbowl, and it was interesting to see a great defense contain a great offense. It was also fun to explain what was going on (on the field) to some attendees of our CIS2010 workshop who were more familiar with the other football. I hope they enjoyed it too!  But of course the Superbowl is not (only?) about football: there is the half-time show, and the ads. The most striking thing about the halftime show was that The Who are still sort of functional as a musical group. Who would have thunk it?

Some of the ads, however, have me a bit worried. In particular, there were two — the Audi and the Google — that triggered the latent George Orwell in me. Is it really a good idea (no matter how tongue in cheek) that the government have the power to coerce individuals’ behavior as shown in the Audi ad? While I am all for recycling, the mere premise that recycling should be motivated by threat rather than incentive strikes me as both perverse and subversive of our rights.  I guess I am not the only one with a negative reaction: Jeffery Goldberg calls it Gorewellian, while an eco-energy blog laments the Nazi allusions and the disservice to the green cause.

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I wanted to clarify the point I tried to make in my blog post about Bobo and LinkedIn’s use of faceted search. I ended that post with a confusing question about faceted search framework in Lucene, and was quickly reminded by Bob Carpenter and others that Solr provides that capability. My comment was poorly made.

My comment about facets was related to programming the interface rather than to retrieval algorithms. It seems to me that a good, interactive interface for faceted browsing is every bit as complicated to build as a  good engine for finding the facets in the first place. Lately I’ve been messing around with Javascript programming, and am getting frustrated by the seemingly unnecessary complexity of building web user interfaces that are both efficient and effective.

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Last week Jeremy and I attended the SSM2010 workshop held in conjunction with WSDM2010. In addition to chairing one of the panels, I got an opportunity to demonstrate an interface that I built to browse Twitter search results, to which Daniel alluded in his summary of the workshop. The system is described in a position paper (co-authored with Miles Efron) that has been accepted to the Microblogging workshop held in conjunction with CHI 2010.

The idea behind this interface is that Twitter displays its search results only by date, thereby making it difficult to understand anything about the result set other than what the last few tweets were. But tweets are structurally rich, including such metadata as the identity of the tweeter, possible threaded conversation, mentioned documents, etc. The system we built is an attempt to explore the possibilities of how to bring HCIR techniques to this task.

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For an article I’m writing for a well-known magazine I needed to get my hands on one of the new iPads for a few moments, pre-release. I went bottom-up, top-down, pretended to be a reporter, employed vague threats, etc. All to no avail. I suppose the powers-that-be have a good reason for this, but it is a mystery to me. I mean at this point, the cat is out of the bag! On the other hand, I’m not really in the target market (like these guys, I find Apple’s mobile devices far too restrictive — my particular pet peeve is having to subvert the OS just to mount as a drive). So maybe I’m not meant to understand.

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Twitter recently released some of its tweet-related code as open source. This is great news for those building applications on top of twitter, as it reduces the need to write the same code over and over. The released code  includes parser and HTML markup generator classes, and a Regex class that includes a bunch of Pattern instances. Code is available in Java and Ruby.

The examples seem straightforward to use, which means I will be using them!

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Last Wednesday Jeremy and I participated in the SSM2010 workshop organized by Ian Soboroff (NIST), Eugene Agichtein (Emory University), Daniel Tunkelang (Google), and Marti Hearst (University of California, Berkeley).  It was a full day of panels, discussions and poster presentations on a variety of topics related to search, to social media, and how to combine the two. In an earlier post, I wrote about one way that we can characterize the space, and Daniel did an excellent job of summarizing the workshop, which was also cross-posted  at BLOG@CACM.

I am still trying to digest all that I learned during the day, but I can say that one of the challenges was live-tweeting the event. I was one of several people who tweeted about what was happening in the panels and about the issues that were raised. Over 500 tweets were sent and resent with the workshop’s hashtag by people at the event and elsewhere. It was interesting to see other people pick up some of the topics and comment on them. In particular, several of my twitter friends who are not part of the SSM research community had commented on the tweets, and retweeted certain aspects of the discussion.

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Dave Bacon, who wrote the elegant overview of the research discussed in my New Year’s Day post, just published his review, joint with Wim van Dam, of Recent Progress in Quantum Algorithms. Bacon writes beautifully, and this piece is no exception.

Most people have heard of no more than two quantum algorithms: Shor’s factoring algorithm and Grover’s search algorithm. For five years after Grover’s algorithm, no one discovered a significantly novel quantum algorithm, only variations on Shor’s and Grover’s algorithms were found. The first truly new quantum algorithms were discovered starting in 2001. Now there are many quantum algorithms found using a variety of approaches, though the applications remain restricted.  My recent overview of quantum computing mentions many of these algorithms. Bacon and van Dam provide a more detailed, but still high level, view of these algorithms. They group the algorithms into four categories corresponding to different approaches: quantum random walks, wave packet scattering, finding hidden symmetries, and simulating quantum physics. I hope many of you will enjoy learning more about, in their words, “the benefits of … studying the notion of an algorithm through the perspective of the physical laws of the universe.”

In my post on quantum inspired classical results, I gave as one example Gentry’s recent discovery of a fully homomorphic encryption scheme. His beautiful work deserves its own blog post. Initially I approached his work with trepidation, worried that it would be so technical I would not understand anything without a lot of work. Others have mentioned not  having looked at his work for the same reason. That is a shame! While the details are technical, the key idea, bootstrappable encryption, is both a non-obvious approach and an easily understandable concept.  I remember smiling while I read the first couple of pages of his paper in response to the elegance and surprising simplicity of his approach.

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I’ve been messing with the Twitter search API, and I am here to whine about it. Overall, it’s a great feature, but it’s interesting that it imposes costs on the third-party client that the Twitter interface seemingly doesn’t share. For example, I can run a search and get back a bunch of results. When I do it from the Twitter web page, it gives me the option of drilling down and showing conversations when they come up in search results.

When I execute the same query using the API, however, there is no indication that a particular message was related to some other message in any way. Sure, I know who sent what to whom, but that’s not enough! Not only does the search API not tell me when a message is a reply, it doesn’t provide useful information to indicate a retweet, either.

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