I remember reading an article on Slashdot last year about researchers found that being more messy equates to being more productive. Unfortunately, not having taken i202 yet and not knowing about the value of PIM, I didn’t bother to bookmark it, so I ended up just Googling around to find something similar to what I remember reading. The general overview of the finding is that messy people are more productive because they save time by not using it for organizing information that needs to be constantly maintained. The link posted below isn’t the one that I read from before, but hopefully someone might have come across the same thing I read and bookmarked it…

http://www.cbsnews.com/stories/2007/01/28/sunday/main2405083.shtml

This is more of a link dump, but PIM is one of my favourite areas, and a lot of questions that I have explored came up in class today. I thought my fellow students would find these applications, technologies, and concepts interesting. These are all things that I use or have used at one point.

Rescue Time — for passive recording of on-computer activity (active application, with tagging/productivity scoring)
Cluztr — tracking (and publishing) all web pages visited
Attention Recorder — tracking all web pages visited
IPTC tagging — I’d call this one of the most underused technologies for PIM. Various apps available, add-ons for iPhoto, ACDSee, etc. Keep your descriptions, captions, photographer, tags, etc WITH your photos, so they are on your local copy and also added when you upload to flickr (only caveat is that they’re lost on edit).
Google Desktop — index/search of email, chat logs, web visits, etc across multiple computers
ScheduleWorld — an OpenML/Funambol service to synchronize calendar/to-do/contacts across multiple devices/people/apps
Wakoopa — tracking software usage
PhoneTag — voicemail-to-text transcription
EarthClassMail — have all your snail mail go to a central location and get it scanned online for you
RingCentral — virtual PBX to centralize and easily access phone numbers/voice mail anywhere

Also, it’s helpful to use a network attached storage drive, IMAP for email, SVN for file versioning, a scanner that does good one-touch OTR scanning of documents…

http://projectbamboo.uchicago.edu/

Not sure if you have heard of Project Bamboo, but it is a effort to find ways to utilize and incorporate technology into humanities research to advance the field(s).  Sponsored by the Mellon Foundation, the end goal is a proposal for an implementation strategy, including standards and the like.  My husband has been attending the most recent workshop on behalf of Blackboard (because they want a seat at the table as the standards are being set of course!!!) and it’s basically been a 202 extravaganza.  At the table?  Librarians, philosophers, artists, lit profs, computer scientists, even a few iSchool professors (Larson and Kansa), etc. This led to lengthy debates about the meaning of what they were actually trying to do, how explcitly they should define it, how to carve up their worlds, why the sky is blue, etc. One of the main things that they apparently kept coming back to was, of course, The Tradeoff.  Who does the work and who reaps the benefits.

Pretty cool stuff though, and hearing his recap (”classification”, “ontology”, “schemas”, “data interoperability”, “buzz”, “buzz”, “buzz”) was essentially like a mini-study session for the midterm.

If anyone is interested in contributing – especially those philosophers among us – there are links to join off of their site.

http://projectbamboo.uchicago.edu/join-us

I don’t know if people have seen Tagthe.net, but it’s pretty interesting.

It’s another site that tries to automatically generate tags based on a URL or a block of text, but this one also tries to group these tags into categories, “topic,” “content type,” “person,” “title,” “location,” and “language.”

Like most auto-tagging sites, sometimes it works and sometimes it doesn’t.  Here’s this blog’s generated tags:

You wanted some tags? Here you are!

http://blogs.ischool.berkeley.edu/i202f08

topic

talk    Knowledge    analysis    Gruber    book    Web    Permalink    google    information    content

content type

text/html

person

Bill Schilit    Nathaniel Wharton    Bob    Nick Doty

title

INFO 202 Fall 08 Blog

language

english

James’s blog post reminded me of another company from Japan that presented at TechCruch50 in September called GazoPa. GazoPa uses proprietary image analytics technology to extract information such as color and shape from images and then identifies similar pictures from a pool of about 50 million different images found around the web.

I registered and played around by uploading a few images and noticed that the image search engine is keen on color and will retrieve a bunch of photos that have similar color patterns or palettes. A picture of a white puppy on a green pasture will generate anything from birds and horses to frogs and spiders as long as there is sizable green and white within the frame of the picture.

A more complex picture with a hipster-ish looking girl looking blasé surrounded by text and images (pulled from a magazine) generated a much more random, some seemingly non-sequitur crop of photos, that ranged from a picture of Christina Aguilera, a cat, a construction site and some others. But even with that, the color tones remained within the same range of the original uploaded image. The service needs work with better identifying shapes. In addition I wish they had an added feature of identification beyond the link at the bottom of the retrieved images. Under the photo of Christina Aguilera, I’d like for it say Christian Aguilera.

Apparently a similar service was developed by AltaVista ten years ago but was abandoned shortly. GazoPa hopes for better chances of success due to the ubiquitiy of digital and phone cameras these days and its large database.

With widespread use, I wouldn’t go so far as say that it would render keywords and tags used for image search obsolete but it would definitely be an additional method we can use when we go about searching for images.

Bob’s mention of the ads for “glushko” in class today reminded me of my own experience with Google ads and disambiguation.

I have pet rats, and when Google was first putting ads in the right column of Gmail, any emails I sent about my pet rats ended up with Google ads for rat exterminators or rat poison. Needless to say, the ads were misplaced and also rather traumatic, as I didn’t want to poison my pets.

But after a few months, I noticed that the mix of ads began to change from exterminators and poison to pet rat food or litter or rescue societies for pet rats. Now they rarely misplace an ad. What I’m wondering is exactly how they changed their formula. I don’t think it was as simple as finding the word “pet” in the same document as the word “rat,” since I generally only refer to my rats as the rats, not as “my pet rats.” But maybe their algorithm was picking up on words like “ratties” or “cute” in my emails to further refine their ad targeting.

It’s still not on the same level as Svenonius-esque categorization, but at least it’s getting a little closer…

At the risk of fanning political flames, this jpg was just sent to me via email. If you move past the humor and politics of the photo, it seems salient to today’s topic of tagging. Specifically, using the characteristics we collectively/culturally ascribe to trains of varying types to tag each of the presidential/vice-presidential candidates. It was done visually instead of with words (modern, green, fast, powerful, coal powered, archaic, plastic, child’s toy). Are these “good” tags? I think guys named Nick who went to Amherst (the h is silent) would say yes.

After I stopped laughing, this made me wonder if there were already a system tagging things with pictures out there. I did not find any with a quick google search. Just a number of whitepapers.

However, I did find Tonchidot.

While not specifically related to using images to tag other images or ideas, they are developing an iPhone app that adds tags to the images the camera sees in real time. They take community tags and make them mobile in a very compelling way. Want to know what type of flower that is? Tree? Year a building you are looking at was made, who designed it? Which store at the mall has the thing you want to buy? How many stars the restaurant you are looking at has on yelp? When the next bart is arriving at your station? Find a lower price for something in a different store. Purchase something via the phone. Leave a message for a friend to pick up by walking by a specific place.

Tagging a specific location is also possible. This reminds me of William Gibson’s book Spook Country. One aspect of the storyline was the development of location based digital art installations. In order to see a specific digitally created piece you needed specially made hardware (eyeglass digital display) and a computer. You also needed to be in a specific geo-spatial location. Now, you’ll just need your iPhone.

One of the things an artist in the book said reminds me of the potential of Tonchidot’s technology. Imaging traveling across the country and seeing a whole 2nd landscape that covers, interacts, and integrates with the physical world. Offering different things to see, information about what you’re seeing, directions to get there, prices for goods/services (who would not love to know the cheapest place to get gas?). And of course a whole new opportunity for advertising and spam.

Maybe that’s the problem with spam. No ontological control.

The video is about 18mins long and worth watching. There is a particularly interesting practical question around the 14:15 min mark.

There has been an inrush of information on how humans deal with feeling a loss of control over the past few weeks, and how the internet has a positive (or negative) effect on our experience of chaotic times. This article from the New York Times postulates that, while people’s tendency is to seek out more and more information in troubled times (and that need is more than handily satisfied by the internet), it actually leads to more anxiety as one tries to keep up with the endless tide. The article follows on the heels of a study that relates feelings of one’s life being in chaos and the adoption of superstition and conspiracy theories.
So what does this have to do with tagging? In the past 8 years, there’s been a huge increase in the importance of internet news and user-generated content, along with the invention of tagging as we know it today and other Web 2.0 technologies. This explosion of content and information consumption coincides with and is to some extent driven by a society concerned with the ever-changing state of war and economic downturn.
I submit that tagging in praticular has become ubiquitous because tagging allows people to exercise control over their own content, and, even better, other people’s content. So much content now exists that the concept of having personal control is particularly attractive, rather than conforming to categories imposed by an outside source.
In terms of an externalized benefit to society, I agree that good machine tags are probably more useful, but I think it’s particularly true that human-generated tags are far more psychologically important to individual humans.

Last month the CS department hosted an excellent talk by Bill Schilit, a researcher at Google, about how Google has done analysis of every word in every one of its digitized books (yes, it’s N^2, that’s how much processing power Google has) and found every time one book is quoted in another book.

This seems particularly relevant to Gruber’s “Collective Knowledge Systems” piece, which recommends pulling semantic information out of the participatory architecture of the social web.  Schilit and Google are effectively extracting the most important passage from each book — effectively learning what the book was about — by looking through all the massive information that users (authors of various other books) have written.  

This obviously reaches Gruber’s criteria for collected knowledge systems: it takes advantage of user-generated content (the books themselves) and human-machine synergy (human-written books and computerized analysis of them), and it gets all the more powerful at scale.  But I think the Google quotations system even reaches the point of Gruber’s “emergent knowledge“: the system can make non-trivial conclusions about the core subject matter of the book that even skilled cataloguers might find difficult, can link books in ways we might otherwise have missed and, (though I’m not sure we see this in practice yet, but it certainly seems possible) can reason over the topics and connections between books to reach completely new conclusions.

I don’t believe that EECS recorded the talk, but it looks like Schilit gave a very similar talk at PARC which was recorded (there’s a good abstract there too).

This may or may not be worthy of a blog post, but I thought it would be better than sending out an email.  As I was doing our current homework assignment I was stuck on defining a particular document type.  The reason I was stuck is because I wasn’t sure whether or not to call it a Survey or a Poll.  I had never realized their relative ambiguity in my mind until this point.  I consulted a dictionary to see if that would clear things up but it just muddled things further.  It was at this point that I thought I should take a poll to see if others could make a distinction in their meanings, but then I realized that a survey might provide more insight into my problem…

Here are two (of many) definitions from dictionary.com

Poll – noun

“a sampling or collection of opinions on a subject, taken from either a selected or a random group of persons, as for the purpose of analysis.”

Survey – noun

“a sampling, or partial collection, of facts, figures, or opinions taken and used to approximate or indicate what a complete collection and analysis might reveal”

I’d like to quote Bob and say “These problems are hard people!”  Feel free to respond if you have a good way of making a distinction between the two.