IBM Watson Runs a Food Truck?!

What to do after winning Jeopardy against the world’s best players?  Open a food truck, of course!  Yes, that Watson is now running a food truck, and apparently it creates some truly delicious dishes!  Confused?  Don’t be.  The intelligence behind the Watson engine that successfully answered hundreds of randomized Jeopardy questions is now the creative engine behind a gourmet food truck.  IBM is endeavoring to show that predictive analytics have uses in the most unusual of places!

As with most predictive analytics, there is still an important role for humans to play in providing balance, judgment and expertise.  Watson does  the data-crunching heavy lifting to find interesting and appealing flavor combinations, faster (better?) than human beings can do on their own, and then trained chefs implement the Watson directions.

This hybrid approach should have a familiar ring to it.  Let the software do the hard, data-intensive number-crunching and then marry that output with human skill and finesse.  It’s a winning combination and one that we employ here at Valora every day.  We utilize our analytics, indexing, and rules platform, PowerHouse, to organize, catalog and find relationships in content for us and then we add the human skill, the expertise, to refine the output and do custom things for specific projects.  

Here’s an example:  We run 50,000 emails and attachments through PowerHouse, which quickly finds well over 150 attributes about each item.  Then we ask PH to find important relationships and insights, such as trend data or topic clusters.  From there, we adapt the rules programming to customize the output so it yields middle initials, or zip + 4, or the top 3 issues per document, or whatever it is that any particular customer needs.  Load it up to BlackCat for easy, online review (often by the client’s workforce) and we’re done.  Predictive analytics mastery!

Now, if you’ll excuse me, I think pork belly moussaka sounds amazing!

What Do Self-Driving Cars and Documents Have in Common?

In case you missed it, Google had an announcement earlier this week about the rapidly improving reliability of their self-driving cars.  The cars now automatically recognize, pedestrians, trucks, and construction areas and even when a cyclist suddenly veers in front of the car.  Having logged more than 700,000 accident-free miles, it’s an impressive demonstration of a potentially society-altering capability.

So, why mention it here, other than its super-coolness factor?  Because of how it works.  Google’s self-driving cars function because they are taught to recognize patterns.  Patterns of behavior, actions, appearance, movement and trends.  Once a pattern is recognized, the cars’ on-board computer systems run a series of rapid-fire statistical algorithms to determine what is happening (context), and thus what actions the car should take.  Sound familiar?  It should.  This is the same kind of technology behind IBM’s Watson and Valora’s PowerHouse.

Much like Google teaching its cars to recognize a stroller in a crosswalk, Valora teaches PowerHouse to recognize a patent application in Chinese or a break in privilege from an email string.  Google’s vehicles accurately assess and predict traffic behavior patterns almost 100% of the time, much better than human beings.  Valora’s PowerHouse sees similar marks for accuracy and prediction. 

In addition to one day allowing us to text messages or read an e-book while we “drive,” the autonomous vehicles have another enormous advantage:  they better utilize roads, gas and electricity.  These types of benefits have broad-reaching impact beyond whether any one person is using or not using the self-driving car.  The same holds true for autonomous data mining.  Once the document house is in order, everyone benefits from easy, organized search, to intuitive data visualization to automated notifications of significant events.

Too bad I couldn't write this blog entry while on my way to work this morning…