In true dork fashion, last week instead of watching the  match-up between “Watson” the supercomputer and Jeopardy’ s top two players, I watched a Nova episode on it. After getting to the underpinnings of just how it’s software works, a few thoughts came to mind. Bear with me.

• The quality of data that Watson uses is hugely important. If it’s premises are false then so will be it’s answers.
• Apparently, IBM programed the computer to learn patterns. But, what kind of patterns does it look for? Patterns aren’t as simple as we would like to think, because they’re bound by the rules of what they are – conceptual, physical, or otherwise.
• In stances where Watson can’t decide, “he” relies on probability. The software can’t override this function.

So, what does this mean?

Essentially, it appears that IBM has created a super search engine that integrates voice-to-text technology with the ability to “learn.” It’s not intelligent, but it certainly pushes the boundaries of what that means. The question in my mind is, “what’s next?

Intuitively, the next step is problem solving. Yes, Watson relies on textual cues to solve them already, but he can’t answer them without this context. Moreover, he can’t create information from scratch.

While search and retrieval are important elements when answering questions, being able to answer questions often requires creating new information. Or, a taking non-textual data into account.

Case in point – Hubble and the expanding universe. Prior to Hubble’s use of redshift to determine that certain objects in the sky are far away, astronomers thought that the Milky Was was the only galaxy in existence.  But, Hubble’s ability to 1) collect raw, primary data led him to b) create unique information that used redshift to prove that distances between celestial bodies are often vast.

Watson is limited to using text. He has no ability to call into question the status quo, and without this, no new information can be produced.

I’ve gotta admit, though, I like the idea of a real-time speech driven, computational search engine. And, if the answers are given in terms of probability then it’ll let me do my job even better than now.

So, within transportation there’s an effort to provide more resources to libraries and information centers.

This isn’t something new, and it’s a continuation of past efforts. For example, in 2001 a group of transportation libraries – in conjunction with the National Transportation Library – formed the Midwest Transportation Knowledge Network (MTKN). Since then, two more “TKN’s” have formed to serve both the eastern and western portions of the country.

On top of this, the  American Association of State and Highway Transportation Officials (AASHTO), Standing Committee on Transportation Research (SCOR) formed the Research Advisory Committee Taskforce on Transportation Knowledge Networks (RAC TKN Task-force). They act as an advisor to the regional knowledge networks.

These are voluntary grassroots associations, by the way. They’re trying to make sure that transportation research, policy-making, and outcomes are well-informed. Why? Well, for starters, in 2007 transportation accounted for 11% of the economy.

This is where I come in. You see, the alphabet soup of technocratic groups that I mentioned earlier are all informal. They have no funding or statutory authority.

Recognizing this, in 2005 a group of states decided to pitch in some money. Taking advantage of the Transportation Pooled Fund Study Program (TPF) through the Federal Highway Administration (FHWA), the Wisconsin Department of Transportation started the TPF-5(105) Library Connectivity Pooled Fund. This study didn’t provide funding for non-libraries, but it did offer resources to a segment of the TKN community. As of 2011, Missouri – my home state – has taken the lead in directing it.

In essence, transportation is trying to bake from scratch using an bunch of acronyms.

Like any good cook knows, the final outcome is going to depend on the ingredients used. The problem is, considering how much libraries have changed over the past twenty years, nobody knows what they are anymore. Not only are we baking from scratch, but we’re using a completely new recipe.

• Electronic media has changed user expectations.
• Given government budgets, financial flexibility is not an option.
• Oil and commodity instability hints that we may see a transportation shift within our lifetimes.
• Lines between information producers, providers, and users are blurring.

This represents a great opportunity, but I have to confess that I’m not completely sure what the end result will be. If given a chance to build a modern library network, what would it look like? And, how would it be different from those constructed in the past? Any ideas? It’s important we get this right.

I have to admit that I’m a bit of a pragmatist. Vague or trendy topics rarely command my attention and “knowledge management” is one such thing. Anyway, last week I had a chance to see a few knowledge maps – they are essentially visualizations of communication practices – and I realized just how useful that can be. Most procedures at work are pretty difficult to follow, so I think I’m going to do the unthinkable. I’m going to concede that I’ve been wrong.

Sometime in the next couple of months I’m going to send out a survey to identify communication patterns between researchers and practicing engineers. Hopefully, the resulting data will make it easier to see just what I need to do to get information to the ground level. Expect a follow up post sometime in the future. Of course, I’m also wondering if there are any other practices I can implement…

Wired’s magazine’s recent article on Craig Newmark starts with the same stuff I’ve come to expect from hardcore techies. Optimistic to a fault and with their respective employers weathering the current downturn, I.T. professionals don’t seem to be planning for or discussing future technological eventualities. And why should they? Most are problem solvers, and every problem since 1980 has, apparently, succumbed to the relentless march of progress!

Check this out:

“The Internet’s great promise is to make the world’s information universally accessible and useful. So how come when you arrive at the most popular dating site in the US you find a stream of anonymous come-ons intermixed with insults, ads for prostitutes, naked pictures, and obvious scams? In a design straight from the earliest days of the Web, miscellaneous posts compete for attention on page after page of blue links, undifferentiated by tags or ratings or even usernames. Millions of people apparently believe that love awaits here, but it is well hidden. Is this really the best we can do?”

At first glance this introductory paragraph discussing Craig’s List seems innocent, but the closer you look the more you realize just how laden with assumptions it really is. Now, I’m not saying we can’t make places like Craig’s List better at providing information… but the interesting thing is that the problems explicitly noted aren’t just technical. They’re humanistic. And, no matter how much new technology we invent, no matter how “smart” it gets, people need to understand that nothing exists in a vacuum.

The bottom line? Technology that isn’t built around people’s behavior will always do a poorer job of conveying information than it should. Here’s why.

Information retrieval essentially can be broken down into querries and returns. When I search Craig’s List it offers  ways for me to “query” the system. It then “returns” data according to a specific technical workflow. Some approaches are more efficient than others but all return information. The catch is that every single one of these approaches have to translate a query from a person to the system and then return it back again. No problem, right? Wrong.

So long as users (people) play a role in formulating a system query designers have to take unpredictable behavior into account. Most users of technology are complex, rational (while also equally irrational), and uniformly hard to understand. Certainly, there are a lot of things we can know to plan ahead but there’s also a lot we don’t know.

As best I can tell there are two ways to respond. On one hand developers and information professionals  can use technology to work around human limitations and automate previously manual processes. On the other, we can accept that users have to be involved to some degree and plan around roadblocks. A huge range of options exist but the ultimate fact remains is that any heavily automated approach to information retrieval relies on the presumption that… we can predict or model human behavior. I’m not going to say we cant, but at the very least a public discussion needs to occur about *how* we can better develop the tools that find our information.  Cory Doctorow pointed out in the early 2000′s that for every bit of quality metadata out there there can also be metacrap; useless or misleading information. My grandparents computer habits prove that the things people do with computers defy all expection.

To really transform websites like Craig’s List it’s going to require more than just cool technology.

Or a sudden unexpected breakthough…

Finally! Proof that: 1. I have a job. 2. That it isn’t bad at all. 3. And, that Jefferson City can actually be a nice place to work during late Spring. Now, I just need to retire.