|Photo: Smita Adhikary|
If you are in a career or looking to get into one that has anything to do with deriving insights out of data, you probably know what I am talking about.
The year 2016 has seen over three dozen machine learning startups being acquired by tech giants; another several dozen machine learning startups raked up a aggregate funding to the tune of $4 Billion worldwide. Is it a blip or a bubble? Definitely not. In times when automation is key, it was but imperative that we figure out methods of data analysis & model building that automates data analysis & model building. Sounds tautological? It is. And in a way that is what machine learning is … err … rather does. It picks up right where traditional statistical models stop. It’s all about building algorithms that learn iteratively from data. The more data you feed it, the better results it churns out.
While conceptually machine learning has been around for more than 80 years (recent history dates it back to World War II and Turing), the recent frenzy around it can be attributed to the overall advances and affordability in computing power. While manually getting these models to improve themselves through numerous iterations may seem tedious, if not impossible, a modern computer fed with the algorithm can get these models to learn, grow, change, and develop by themselves in a matter of seconds … and we are already talking “real-time!” What more, they can look for insights without being told exactly where to look for insights a.k.a. dealing with unstructured data (think social media, web-searches). It iterates, learns new stuff, and adapts, and iterates and continues the whole process all over again learning from new data every time. It really embodies the adage that practice makes perfect.
Now if you put this in the context of the self-driving cars, or the recommender engines in Netflix or Amazon – you can see why such algorithms that generate decisions out of data real time without human intervention, would be key to where we are headed both in terms of technology and user experience. It is machine learning that has turned the “hype” around the importance of “big data” into a reality. When availability of more data could have caused concerns around it’s usability for deriving meaningful insights, it was machine learning that came to the rescue. Let’s just say that compared to traditional statistical methods which dealt with static models, machine learning is more in tune with the current times and it’s needs.
The discussion becomes a bit more exciting and a little more tangible when we start considering some problems where machine learning is a clear improvement over traditional statistical methods (although a strong caveat here would be … a lot of machine learning techniques are really enhancements or extensions of their “statistical” counterparts).