by Angela Guess
Gurjeet Singh writes in Data-Informed, “Machine learning is generating a tremendous amount of attention these days from the press as well as the practitioners. And rightly so – machine learning is a transformative technology. But despite the references to the topic, the money raised from venture capitalists, and the spotlight that Google is bringing to the subject, machine learning is still poorly understood outside of a core group of highly technical leaders. This has the effect of underestimating how transformative machine learning is going to be. It also has the effect of shielding business leaders from what they need to do to prepare for the era of machine learning. Let’s discuss both sides of the sword – the promise and the pitfalls, starting with a definition.”
Singh goes on, “Machine learning is a class of algorithms that can learn from and make predictions on data. Generally speaking, the more data, the better the outcome for machine learning techniques. Machine learning doesn’t require explicit rules to govern performance. It does not require manual construction of “if this, then that.” It will make that determination on its own, based on the data. The transformative effect of machine learning, and why it is so important now, is a function of that fact that we are hitting trigger points across data, compute, and algorithmic sophistication. This confluence of advances with each of these elements makes the machine learning appear to be a sudden success. That’s a bit of mirage – what is happening today has been in the works for quite some time.”
photo credit: Flickr/ utentedei