Machine Learning: A job killer?
VentureBeat reports that Clover, a startup company in Mountain View, CA, has received $5.5 million of funding to focus on machine learning technology. Machine learning is still in its infancy, but it has the potential to be a disruptive technology as it progresses.
Machine learning is one of the primary technologies that powers IBM’s Watson computer. Watson was able to achieve championship-level proficiency at Jeopardy! by analyzing thousands of previous Jeopardy! questions. One of the things I’ve tried to point out here, and also in my book The Lights in the Tunnel, is that any jobs that are routine and repetitive in nature—regardless of the skill and education required to perform the job—are going to be increasingly susceptible to automation. Now, most people would probably not characterize playing Jeopardy! at a championship level as a “routine and repetitive” activity. Yet, a machine was able to prevail.
Machine learning essentially allows a computer to analyze past situations (together with outcomes) and develop optimal, statistical-based rules that can be applied in the future. In other words, machine learning is basically a way to take a seemingly non-routine task or job and turn it into something that can be handled by a computer.
Here’s why that’s important: in today’s business world nearly everything gets recorded. This often shows up as a privacy issue: web surfers and shoppers are disturbed to learn that their activities and preferences are recorded and used for profit. What receives less attention is the fact that everything happening internal to organizations is also probably being recorded.
All transactions are, of course, recorded. Customer interactions (sales, support, service), together with their ultimate resolution are recorded. Emails get recorded. Many tasks and decisions, together with outcomes, made by knowledge workers are likely recorded.
I would argue that all that data is probably the equivalent of the sample Jeopardy! questions that Watson used to analyze and become proficient. In other words, in large organizations there is an enormous amount of data (activities coupled with outcomes) that is waiting for a machine learning algorithm to come along and churn though it. That may ultimately result in software automation applications of unprecedented sophistication. Anyone who sits in a cubicle performing a knowledge-based job may have cause for concern.