Tag Archives: Science 2.0

Web(MD) 2.0

Just when I thought that the uses for recommendation systems were already exhausted…

CureTogether is a site that lets you enter your medical conditions (strictly anonymous, only aggregated data are public), and get recommended for… other “co-morbid” conditions you may have. In other words, “people who have your disease usually also have that one too, perhaps you have it too?

Beyond the obvious jokes, this truly has potential. You don’t only get “recommended” for conditions, but rather also for treatments and causes. We all know that sometimes we have our own personal treatment that works only for us. What if it works for people in our profile, and sharing that profile, anonymously, will help similar people as well? so far this direction is not explicit enough in how the site works, possibly for lack of sufficient data, but you can infer it as you go through the questionnaires.

The data mining aspect of having a resource such as CureTogether’s database is naturally extremely valuable. CureTogether’s founders share some of their findings on their blog. The power of applying computer science analytics and experimentation methodologies – sharpened by web-derived needs – to social sciences and others, reminded me of Ben Schneiderman’s talk on “Science 2.0. The idea that computer science can contribute methodologies that stretch beyond the confines of computing machines is a mind-boggling one, at least for me.

But would you trust collaborative filtering with your health? it’s no wonder that the main popular conditions on the site are far from life threatening, and the popular ones are such with unclear causes and treatments, such as migraines, back pains and allergies. Still, the benefit on these alone will probably be sufficient for most users to justify signing up.

IBM IR Seminar Highlights (part 2)

The seminar’s third highlight for me (in addition to IBM’s social software and Mor’s talk), was the keynote speech by Human-Computer Interaction (HCI) veteran Professor Ben Schneiderman of UMD. Ben’s presentation was quite an experience, but not in a sophisticated Lessig way (which Dick Hardt adopted so well for identity 2.0), rather by sheer amounts of positive energy and passion streaming out of this 60-year-old.

[Warning – this post turned out longer and heavier than I thought…]

Ben Shneiderman in front of Usenet Treemap - flickr/Marc_SmithBen is one of the founding fathers of HCI, and the main part of his talk focused on how visualization tools can serve as human analysis enhancers, just like the web as a tool enhances our information.

He presented tools such as ManyEyes (IBM’s),  SpotFire (which was his own hitech exit), TreeMap (with many examples of trend and outlier spotting using it) and others. The main point was in what the human eye can do using those tools, that no predefined automated analysis can, especially in fields such as Genomics and Finance.

Then the issue moved to how to put such an approach to work in Search, which like those tools, is also a power multiplier for humans. Ben described today’s search technology as adequate mainly in “known item finding”. The more difficult tasks that can’t be answered well in today’s search, are usually for a task that is not “one-minute job”, such as:

  • Comprehensive search (e.g. Legal or Patent search)
  • Proving negation (Patent search)
  • Finding exceptions (outliers)
  • Finding bridges (connecting two subsets)

The clusters of current and suggested strategies to address such tasks are:

  • Enriching query formulation – non-textual, structured queries, results preview, limiting of result type…
  • Expanding result management – better snippets, clustering, visualization, summarization…
  • Enabling long-term effort – saving/bookmarking, annotation, notebooking/history-keeping, comparing…
  • Enhancing collaboration – sharing, publishing, commenting, blogging, feedback to search provider…

So far, pretty standard HCI ideas, but then Ben started taking this into the second part of the talk. A lot of the experimentation employed in these efforts by web players has built an entire methodology, that is quite different from established research paradigms. Controlled usability tests in the labs are no longer the tool of choice, rather A/B testing on user masses with careful choice of system changes. This is how Google/Yahoo/Live modify their ranking algorithms, how Amazon/NetFlix recommend products, how the Wikipedia collective “decides” on article content.

This is where the term “Science 2.0” pops up. Ben’s thesis is that some of society’s great challenges today have more to learn from Computer Science, rather than traditional Social Science. “On-site” and “interventionist” approaches should take over controlled laboratory approaches when dealing with large social challenges such as security, emergency, health and others. You (government? NGOs? web communities?) could make actual careful changes to how specific social systems work, in real life,  then measure the impact, and repeat.

This may indeed sound like a lot of fluff, as some think, but the collaboration and decentralization demonstrated on the web can be put to real life uses. One example on HCIL is the 911.gov project for emergency response, as emergency is a classic case when centralized systems collapse. Decentralizing the report and response circles can leverage the power of the masses also beyond the twitter journalism effect.