Month: May 2008

The blogosphere is all atwitter with Powerset’s public launch last night. Over at Techcrunch, Michael Arrington refers to their approach as a lofty goal.

But I’d like us to dream bigger. In the science fiction stories that inspired me to study computer and information science, the human-computer interface is not just natural language input. It’s dialogue. The authors do not treat machine understanding of unambiguous requests as a wonder, but instead take it for granted as an artifact of technical progress. Indeed, the human-computer interface only becomes relevant to the plot when communication breaks down (aka “that does not compute”).

Ever since I hacked a BASIC version of ELIZA on a Commodore 64, I’ve felt the visceral appeal of natural language input as an interface. Conversely, the progress of speech synthesis attests to our desire to humanize the machine’s output. It is as if we want to reduce the Turing Test to a look-and-feel.

But the essence of dialogue lies beneath the surface. The conversations we have with machines are driven by our information needs, and should be optimized to that end. Even we human drop natural language among ourselves when circumstances call for more efficient communication. Consider an example as mundane as Starbucks baristas eliciting and delegating a latte order.

In short, let’s remember that we want to talk with our computers, not just at them. Today’s natural language input may be a step towards that end, or it may be just a detour.

As a member of the Powerset private beta, I just received this announcement:

Greetings Powerlabbers,

Later today, Powerset is going to launch the first publicly available version of our product. Since you’ve been active in the Powerlabs community, we wanted to give you a special heads-up to look for our release. Your suggestions, help, feedback, bug reports, and conversation have helped us immensely in creating an innovative and useful product. We hope that you’ll continue to be active in Powerlabs and make more great suggestions.

More information will be posted on Powerset’s blog later today, so keep your eye out for updates. Also, consider following us on Twitter or becoming a fan of Powerset on Facebook.

If you have a blog, we’d especially appreciate it if you’d write a blog post about your experience with this first Powerset product. Since you’ve been on the journey with us, your insight will be helpful in showing other people all of the amazing features in this release.

Again, we want to extend special thanks to you for sticking with us. We hope you feel almost as invested in this release as we are.

Thanks!

The Powerset Team

As loyal readers know, I’ve posted my impressions in the past. Now that the beta will be publicly available, I’m curious to hear impressions from you all.

My colleague Max Wilson at the University of Southampton recently called my attention to a pair of special issues of Information Processing & Management. The first is on Evaluation of Interactive Information Retrieval Systems; the second is on Evaluating Exploratory Search Systems. Both are available online at ScienceDirect. The interactive IR papers can be downloaded for free; the exploratory search papers are available for purchase to folks who don’t have access through their institutions.

I’m behind on my reading, but the titles look promising. Stay tuned!

This week, Forrester released a report entitled “Search + BI = Unified Information Access”. The authors assert the convergence of search and business intelligence, a case that Forrester has been developing for quite some time.

The executive summary:

Search and business intelligence (BI) really are two sides of the same coin. Enterprise search enables people to access unstructured content like documents, blog and wiki entries, and emails stored in repositories across their organizations. BI surfaces structured data in reports and dashboards. As both technologies mature, the boundary between them is beginning to blur. Search platforms are beginning to perform BI functions like data visualization and reporting, and BI vendors have begun to incorporate simple to use search experiences into their products. Information and knowledge management professionals should take advantage of this convergence, which will have the same effect from both sides: to give businesspeople better context and information for the decisions they make every day.

It’s hard to find any fault here. In fact, the convergence of search and BI is a corollary to the fact that people (yes, businesspeople are people too) use these systems, and that the same people have no desire to distinguish between “structured” and “unstructured” content as they pursue their information needs.

That said, I do have some quibbles with how the authors expect the convergence to play out. The authors make two assertions that I have a hard time accepting at face value:

• People will be able to execute data queries via a search box using natural language.

Sure, but will they want to? Natural language is fraught with communication challenges, and I’m no more persuaded by natural language queries for BI than I am by natural language queries for search.

• Visual data representations will increase understanding of linkages among concepts.

We’ve all heard the cliché that a picture is worth a thousand words. I know this better than most, as I earned my PhD by producing visual representations of networks. But I worry that people overestimate the value of these visualizations. Data visualization is simply a way to represent data analytics. I see more value in making analytics interactive (e.g., supporting and guiding incremental refinement) than in emphasizing visual representations.

But I quibble. I strongly agree with most of their points, including:

• BI interfaces will encourage discovery of additional data dimensions.
• BI and search tools will provide proactive suggestions.
• BI and search will continue to borrow techniques from each other.

And it doesn’t hurt that the authors express a very favorable view of Endeca. I can only hope they won’t change their minds after reading this post!

An interesting implication of blogging and other social media is that conversations once conducted privately have become public. The most common examples are conversations that take place through the comment areas for posts, rather than through private email.

My initial reaction to this phenomenon was to bemoan the loss of boundaries. But, in keeping with my recent musings about privacy, I increasingly see the virtues of public conversations. After all, a synonym for privacy, albeit with a somewhat different connotation, is secrecy. Near-antonyms include transparency and openness.

I can’t promise to always serve personally as an open, transparent information access provider. But I’ll do so where possible. Here at The Noisy Channel, the conversation is public.

I was fortunate to attend the Tri-State CIO Forum these last couple of days, and I thought I’d change the pace a bit by posting some reflections about it.

In his keynote speech last night, George Colony, Chairman and CEO of Forrester Research, called on the business community to drop the name “information technology” (IT) in favor of “business technology” (BT). His reasoning, in a nutshell, was that such nomenclature would reflect the centrality of technology’s role for businesses.

Following similar reasoning but reaching a different conclusion, Julia King, an Executive Editor for Computerworld and one of of today’s speakers, noted that IT titles are being “techno-scrubbed”, and that there is a shift from managing technology to managing information.

While I can’t get excited about a naming debate, I do feel there’s an important point overlooked in this discussion. Even though we’ve achieved consensus on the importance of technology, we need a sharper focus on information. It is a cliché that we live in an information age, but expertise about information is scarce. Information scientists struggle to influence technology development, and information theory is mostly confined to areas like cryptography and compression.

We have no lack of information technology. Search engines, databases, and applications built on top of them are ubiquitous. But we still just learning how to work with information.

There is no Nobel Prize in computer science, despite computer science having done more than any other discipline in the past fifty years to change the world. Instead, there is the Turing Award, which serves as a Nobel Prize of computing.

But the Turing Award has never been given to anyone in information retrieval. Instead, there is the Gerard Salton Award, which serves as a Turing Award of information retrieval. Its recipients represent an A-list of information retrieval researchers.

Last week, I had the opportunity to talk with Salton Award recipient Tefko Saracevic. If you are not familiar with Saracevic, I suggest you take an hour to watch his 2007 lecture on “Relevance in information science”.

I won’t try to capture an hour of conversation in a blog post, but here are a few highlights:

• We learn from philosophers, particularly Alfred Schütz, that we cannot reduce relevance to a single concept, but rather have to consider a system of interdependent relevancies, such as topical relevance, interpretational relevance, and motivational relevance.
• When we talk about relevance measures, such as precision and recall, we evaluate results from the perspective of a user. But information retrieval approaches necessarily take a systems perspective, making assumptions about what people will want and encoding those assumptions in models and algorithms.
• A major challenge in the information retrieval is that users–particularly web search users–often formulate queries that are ineffective, particularly because they are too short. Studies have shown that reference interviews can lead to improved retrieval effectiveness (typically through longer, more informative queries). He said that automated systems could help too, but he wasn’t aware of any that had achieved traction.
• A variety of factors affect interactive information retrieval, including task context, intent, expertise. Moreover, people react to certain relevance clues more than others, and more within some populations than others.

As I expected, I walked away with more questions than answers. But I did walk away reassured that my colleagues and I at Endeca , along with others in the HCIR community, are attacking the right problem: helping users formulate better queries.

I’d like to close with an anecdote that Saracevic recounts in his 2007 lecture. Bruce Croft had just delivered an information retrieval talk, and Nick Belkin raised the objection that users need to be incorporated into the study. Croft’s conversation-ending response: “Tell us what to do, and we will do it.”

We’re halfway there. We’ve built interactive information retrieval systems, and we see from deployment after deployment that they work. Not that there isn’t plenty of room for improvement, but the unmet challenge, as Ellen Voorhees makes clear, is evaluation. We need to address Nick Belkin’s grand challenge and establish a paradigm suitable for evaluation of interactive IR systems.

I’m still waiting for the ECIR organizers to post the slides from the Industry Day. I particularly liked Nick Craswell’s presentation on A Brief Tour of “Query Space”. Until his slides are up, I recommend this SIGIR ’07 paper to give you an idea of his approach.

Slides are here as a PowerPoint show for anyone interested, or use the embedded SlideShare show below.

http://static.slideshare.net/swf/ssplayer2.swf?doc=guidedsummarization-1213239205653056-9

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Dan Keldsen has posted a list of findability-related solutions at BizTechTalk. The 80 or so solutions that he lists are certainly an attempt to err on the side of recall, by including search, taxonomies, interfaces, and visualization as aspects of findability. Definitely a useful resource for anyone interested in enterprise information access.

I had lunch today with Harr Chen, a graduate student at MIT, and we were talking about the consequences of information efficiency for privacy.

A nice example is the company pages on LinkedIn. No company, to my knowledge, publishes statistics on:

• the schools their employees attended.
• the companies where their employees previously worked.
• the companies where their ex-employees work next.

If a company maintains these statistics, it surely considers them to be sensitive and confidential. Nonetheless, by aggregating information from member profiles, LinkedIn computes best guesses at these statistics and makes them public.

Arguably, information like this was never truly private, but was simply so difficult to aggregate that nobody bothered. As Harr aptly put it, they practiced “privacy through difficulty”–a privacy analog to security through obscurity.

Some people are terrified by the increasing efficiency of the information market and look for legal remedies as a last ditch attempt to protect their privacy. I am inclined towards the other extreme (see my previous post on privacy and information theory): let’s assume that information flow is efficient and confront the consequences honestly. Then we can have an informed conversation about information privacy.