Is Machine* Learning the New AI Rabbit Hole ?

Machine-learning is a hot topic, and right smack in the early adopter phase. MIT Technology Review quotes Jeff Dean of Google for it's popularity:

"The enrollment in computer science program machine-learning classes is shooting through the roof."

But, how applicable is machine-learning to most problems ? Is it a powerful, but niche, technology ? Prolog is a good historic parallel, as it promised to apply AI processing that would revolutionize computing analytics. Turns out, Prolog excels at a very specific range of problems, but isn't the general purpose AI tool that everyone had hoped.

David Linthicum applies some common sense to the machine-learning debate in his article on "
Machine Learning is a Poor Fit for Most Businesses:"

"Machine learning is valuable only for use cases that benefit from dynamic learning — and there are not many of those. Examples of machine learning use cases include financial systems that deal with risk, medical diagnosis, or recommendation systems like those at Amazon.com. 

But the online transaction processing (OLTP) style of applications that run most businesses are not a good fit for machine learning."

My guess is that as more and more big data projects are developed, machine-learning will find a fertile ground for more applications (but, it'll be a slower trend than anticipated).

*Ironically, there was a misspelling in the original post. I think that is pretty funny, as it demonstrates my dependency on automated spell-checking. Thanks Bruce for the nudge.

Forget Neflix Binging, DataUSA.io Is All I Want

MIT Media lab has built a GUI in front of the vast data store of US public records. It's the biggest time-swamp I've stepped into since the 2nd season of Daredevil. It's an amazing aggregation of data into visualization sets. It is simply, "the most comprehensive website and visualization engine of public US Government data." Look into a location and it'll break down per capita demographics from housing to health and safety.

Even more interesting, to me, than the impressive interface, and underlying search logic, is that the code is open source. All of the content on the site is presented under a GNU Affero General Public License v3.0 (GPLv3). Developers can hack together their own interface from JSON calls into the four core categories of data: geographies, occupations, industries and educational studies.

And looking back at superheroes, DataUSA includes several interesting topical narratives, as with "ARE WE HAVING FUN YET? WHO WORKS IN THE ARTS, ENTERTAINMENT, AND RECREATION INDUSTRY."

Now, not everybody who works in the industry is a rock star—or an athlete. But a surprising share of employees in the industry do, in fact, have something to do with athletics.

Well, that justifies my earlier hours with the Marvel hero of Hell's Kitchen. I wonder what else I'll discover . . .