Bill Franks of the International Institute for Analytics recently opined, "In recent years, the use of the term Machine Learning has surged. What I struggle with is that many traditional data mining and statistical functions are being folded underneath the machine learning umbrella. There is no harm in this except that I don’t think that the general community understands that, in many cases, traditional algorithms are just getting a new label with a lot of hype and buzz appeal. Simply classifying algorithms in the machine learning category doesn’t mean that the algorithms have fundamentally changed in any way."
He continues, "Many startup companies, particularly in the cloud, are touting machine learning capabilities. In some cases, the algorithms are hidden behind a user interface so that users may not know what is happening under the hood. Users may believe that a new capability or algorithm that is closer to artificial intelligence is being used. However, would those same users be excited if they knew that they are buying a very early and immature version of yet another tool to create a decision tree?"
Franks goes on, "Perhaps I have an outdated view, but I have always thought of machine learning as being closer to artificial intelligence than data mining. I want a machine learning algorithm to adjust itself dynamically and learn how to apply new rules. This is distinct from an iterative algorithm like a k-means cluster analysis. It can be argued that a clustering algorithm 'learns' after each pass and adjusts dynamically. However, the rules are set in advance and don’t change."
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