Machine Learning: The Evolution From An Artificial Intelligence Subset To Its Own Domain Posted on : Sep 22 - 2017

Machine Learning (ML) has reached an inflection point – at least in terms of messaging. For my first article as part of the TIRIAS Research contributor group, I’ll take this space to level set on a description of ML for what’s to follow.

I studied artificial intelligence (AI) back in the 1980s. Then, machine learning was clearly a subset of AI, focused on how to train machines to learn about their environments and synthesize information based on that gained knowledge. While study on artificial neural networks (ANN), today’s hot topic, was occurring , the main focus was in expert systems. Those two ML techniques still exist, but the massive advanced in machine power have flipped the balance. Faster chips, networks and software that manage parallel computing and clustering, and a maturity in model development have meant that ANN is now the main reference point when most people discuss ML.

However, AI is no longer the entire ML discussion. I’ve spent my career in the arena of business software. The same advances in computing that have driven ANN helped grow advances in business intelligence (BI). Complex analytics are able to discover data, run far more complex and advanced mathematical analysis than in previous decades and provide information visualizations that are informative and stunning.

It took me a while to wrap my head around it, given my earlier AI biases, but I’ve concluded that machine learning is now its own discipline, intersecting with both AI and BI in a very overlapped Venn Diagram.

ML has blended techniques from the other two arenas and combined them into a new discipline. The difference comes in the fuzzy definition of the word “learning”. View More