Why The Golden Age Of Machine Learning is Just Beginning

Even though the buzz around neural networks, artificial intelligence, and machine learning has been relatively recent, as many know, there is nothing new about any of these methods. If so many of the core algorithms and approaches have been around for decades, why is it just now that they are getting their day in the sun? To answer that question, we can take a look at what has happened over the last five years or so with the attention and tooling around data. And we can also point to the dramatic increase in scalable compute power, or to be more specific about it, performance per watt and bit. These two factors combined have fed the development fury, growing data analysis well beyond the standard database and calculation approaches that have themselves been around for decades. The point is, we are at peak "data hype"--there was a rush to develop a host of new tools and frameworks (Hadoop, as but one example) to support larger, more complex datasets, then a secondary effort to push the performance of the data analysis on new or enhanced frameworks.