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There is an interesting blogpost on the Official Google blog today, about seeing stars:
We’ve long believed that personalization makes search more relevant and fun. For nearly five years, we’ve been tailoring results with personalized search. Today we’re announcing a new feature in search that makes it easier for you to mark and rediscover your favorite web content — stars. With stars, you can simply click the star marker on any search result or map and the next time you perform a search, that item will appear in a special list right at the top of your results when relevant. That means if you star the official websites for your favorite football teams, you might see those results right at the top of your next search for [nfl].
So it sounds to me like this is a sort of bookmarking. What it not as obviously, however, is what this sentence means: Continue reading…
A NYT books article about Kasparov and chess, and the relationship between humans, machines, and decision processes is making the Twitter rounds today. I don’t have time at the moment to write a long comment about it, but I do want to point out that it supports a position that I’ve been taking on this blog for some time now:
This experiment goes unmentioned by Russkin-Gutman, a major omission since it relates so closely to his subject. Even more notable was how the advanced chess experiment continued. In 2005, the online chess-playing site Playchess.com hosted what it called a “freestyle” chess tournament in which anyone could compete in teams with other players or computers. Normally, “anti-cheating” algorithms are employed by online sites to prevent, or at least discourage, players from cheating with computer assistance. (I wonder if these detection algorithms, which employ diagnostic analysis of moves and calculate probabilities, are any less “intelligent” than the playing programs they detect.)
Lured by the substantial prize money, several groups of strong grandmasters working with several computers at the same time entered the competition. At first, the results seemed predictable. The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.
The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.
This result seems awfully similar to some of the other results I’ve reported on in the past. Continue reading…
Via Greg Linden, I came across this interesting quote from Eric Schmidt about the obligation to help newspapers succeed:
Finally, Eric claimed Google has a moral duty to help newspapers succeed:
Google sees itself as trying to make the world a better place. And our values are that more information is positive — [...]
I’ve been playing around with some old TREC data over the past few days and completely by chance I came across this document. I find it interesting because storytelling is a good metaphor for what we as researchers do when we construct interactive information seeking systems. The document is short enough that I think [...]
Last month, in reaction to the “Unreasonable Effectiveness of Data” paper that made the rounds, Stephen Few from the Business Intelligence community wrote an interesting post:
The notion that “we need more data” seems to have always served as a fundamental assumption and driver of the data warehousing and business intelligence industries. It is true that a missing piece of information can at times make the difference between a good or bad decision, but there is another truth that we must take more seriously today: most poor decisions are caused by lack of understanding, not lack of data. The way that data warehousing and business intelligence resources are typically allocated fails to reflect this fact. The more and faster emphasis of these efforts must shift to smarter and more effective. Although current efforts to build bigger and faster data repositories and better production reporting systems should continue, they should take a back seat to efforts to increase the data sense-making skills of workers and to improve the tools that support these skills.
This is a point that I wholely subscribe to, and an aspect of which I encountered the other day when attempting to use web search engines to satisfy my “hidden cafes in prague” information need. Continue reading…
Music Information Retrieval continues to be an excellent place to play around with the intersection of search, recommendation, user-guided exploration, and explanatory (transparent) algorithms.
First, check out the announcement of Music Explaura from Stephen Green at Sun Research. Stephen writes:
Continue reading…
It has now officially been ten years since Christopher Locke, Doc Searls, and David Weinberger wrote the Cluetrain Manifesto, rekindling and reminding us of the centuries-old notion that markets are conversations between people, buyers and sellers. The following are a few of the Manifesto’s points that resonate with me:
Continue reading…
PBS has an interesting article on the new media gatekeepers and the need for transparency in the process by which they promote media. Here is an excerpt:
The problem for these new gatekeepers is that they are providing the old editorial functions, but there’s a key difference between the way they operate and the [...]
At SXSW this year, Paul Lamere of The Echo Nest and Anthony Volodkin of Hype Machine engaged in a head-to-head panel about the utility of:
- Using computer algorithms (e.g. collaborative filtering, tag-based, content-based, etc.) to automatically recommend music, versus
- Using computers to (a) connect people who can directly recommend music to each other and (b) provide contextually relevant information around any shared songs
Perhaps I don’t fully understand the full subtlety of the conflict, but I find myself wondering: Why can’t you do both?
Continue reading…
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