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…
