Category: Collaborative Work

Talk: Socially Embedded Search

This week I attended a full house talk by Dr. Meredith Ringel Morris on Socially Embedded Search Engines. Dr. Morris put together a lot of material in her presentation and we (audience) could appreciate how she presented all of it, with great clarity, in just one hour. But I think it would tricky for me to summarize everything in a short post. Do check out Dr. Morris’ website to find out more information on the subject.

Social Search is term for when you pose a question to your friends by using one of the social networking tools (like Facebook, Twitter). There is good chance that you might have already been using “Social Search” without knowing the term for it. So, why would you want to do that instead of using regular search engines that you have access to? It may be simpler to ask your friends at times and they could also provide direct, reliable and personalized answers. Moreover, this is something that could work along with the traditional search engines. Dr. Morris’ work gives some insight into the areas where the search engineers have opportunities in combining traditional algorithmic approaches with social search. She tells us about what kind of questions are asked more in a social search and which types of them are more likely to succeed in getting a useful answer. She goes on further into how the topics for these questions vary with people from different cultures.

I really liked the part about “Search buddies” during the talk. In their paper, Dr. Morris and her colleagues have proposed implanting automated agents that post relevant replies to your social search queries. One type of such an agent tries to figure out the topic for the question and recommends friends who seem to be interested in that area by looking at their profiles. While another one would try to use an algorithmic approach and post a link to a web-page that is likely to contain an answer to the question. It was interesting to know more about how other people reacted to the involvement of these automated agents. While some of the people in the experiment appreciated being referred to for an answer, a lot of them found them obnoxious when they didn’t perform well in identifying the contexts. In her more recent work, Dr. Morris has tried to solve these problems by recruiting real people from Mechanical Turk to answer questions on Twitter. Such an approach could respond to people’s questions in a smarter way by collecting information from a several people. It could then respond to these questions in the form of a polling result and quote the number of people recommending a particular answer. It can also work by taking into account any other replies that the participant would have already received from one of his followers. The automated agent would then present that answer for a opinion poll from the Turkers. Although such a system could provide more intelligent replies than ‘dumb’ algorithms but it may still fail in comparison to responses from your friends which would certainly be more personalized and placed better contextually. During the QnA session, one of audience members raised a question (with a follow-up question by Prof. Kraut)  about comparing these methods with question-and-answer websites such as Quora. While these sites may not provide as personalized results but will certainly do better in drawing the attention of people interested in similar topics. It may not be always possible to find somebody amongst your friends, to answer question on a specialized  topic.

Dr. Morris’ talk provided some really good backing for some of the recent steps taken by search engines like Bing (having ties with both Twitter and Facebook), Google (and the Google plus shebang) and also Facebook (with Graph Search) in this direction. It would be interesting to see how social computing research shapes the future of internet search.

Further Reading

You can find Dr. Morris’ publications on this topic here: http://research.microsoft.com/en-us/um/people/merrie/publications.html

How about collaboration?

My previous post on Computers and Chess, serves as a good prologue to this.

watson
That’s me geeking out at the Jeopardy stage setup.

A little more than two years ago, the IBM Watson played against and defeated the previous champions of Jeopardy!, the TV game show in which the contestants are tested on their general knowledge with quiz-style questions.[1] I remember being so excited while watching this episode that I ended up playing it over and over again, only to have the Jeopardy jingle loop in my head for a couple of days! Now, this is a much harder challenge for the computer scientists to solve than making a machine play chess.

Computers have accomplished so many things that we thought that only humans could do (play chess and jeopardy, drive a car all by itself …). While these examples are by no means small problems that we have solved, we still have a long way to go. While it can solve problems that we as humans often find difficult (such as playing chess, calculating 1234567890 raised to the power 42 etc.), it cannot* do a lot of things that you and I take for granted. For example, it can’t comprehend this post as well as you do (Watson may not be able to answer everything), read it out naturally & fluently (Siri still sounds robotic) and make sense of the visuals on this page (and so on). *At least not yet.

Computers were designed as tools to help us with calculations or computations. By this very definition, are computers are inherently better at handling certain types of problems while in others they fail? Well, we have no answer [2] to this question now and I at least hope that it isn’t in affirmative so that someday we can replicate human intelligence. As we have seen in the past, we certainly can not say that “X” is something that computers will never be able to do. But we can sure point out the areas in which the researchers are working hard and hoping to improve.

Here’s a video that talks about the topic that I am hinting at. While I promise not to post many TED talks in future, you can be sure of finding this central idea (the first half of the talk) as a common theme on this blog. Also, I prefer the word “Collaboration” over “Cooperation” [3] :

TLDR Let’s not try to solve big problems solely with computers. Make computers do the boring repetitive work and involve humans for providing creative inputs or heuristics for the machines. Try to improve interfaces that make this possible.

Although this was an idea envisioned in "Man-Computer Symbiosis" (Licklider J. C. R., 1960) more than half-a-century ago, researchers seem to have not given due importance to it when [4] the computers failed to perform as well as expected. Of course, more the number of “X”s that the computers are able to do by themselves, the more it frees us to do whatever we do best. When we do look around and observe the devices that we use and how we interact with the machines everyday, we seem to have knowingly or unknowingly progressed in the direction shown by Licklider. With the furthering of research in areas such as Human Computing, Social Computing, and (the new buzzword) Crowd-sourcing, the interest shown in such ideas has never been greater.

References

  1. Licklider J. C. R. (1960), Man-Computer Symbiosis. IEEE. Available: http://groups.csail.mit.edu/medg/people/psz/Licklider.html.

Footnotes

  1. More about Watson from IBM here. See also, Jeopardy vs. Chess. ^
  2. Amazon’s Mechanical Turk does talk about “HITs” or Human Intelligence Tasks ^
  3. In AI terms, it would indeed be multi-agent co-operation but then again we are not treating humans just as agents in this case. ^
  4. AI Winter: http://en.wikipedia.org/wiki/AI_winter ^