What Is Machine Learning Anyway?

One of the consistent characteristics of the tech industry is an endless labelling of technology and approaches. Some of it is foundational resulting from some entirely new. Much of it is re-categorizing something, either because it is suddenly trendy again or because a set of ideas have been organized in a new way. When I was in my 20s, I found this exciting. Now that I’m in my 50s and am used to this, I find it relaxing, as it makes me feel at home.

An example of this is artificial intelligence (or AI). If you teleported here from another planet yesterday, you’d think we just discovered this thing called AI and were creating bots to exercise it while others were writing philosophical treatises to try to figure out how to prevent it from exterminating the human race. If the following names – John McCarthy, Marvin Minsky, Allen Newell, Arthur Samuel and Herbert Simon – don’t mean anything to you and you think you know something about AI, I encourage you to go buy a copy of The Society of the Mind and to set your DMC-12 with a flux capacitor to 1956. If you still don’t know what I’m talking about, that’s cool – just ignore me.

Another example is big data which became all the rage around 2012. I keynoted an Xconomy Conference on Big Data with the opening line “Big Data is Bullshit.” My real quotable comment was “Twenty years from now, the thing we call ‘big data’ will be tiny data. It’ll be microscopic data. The volume that we’re talking about today, in 20 years, is a speck.” Nonetheless, hundreds of big data companies were created and funded.

Within the past two years, the phrase machine learning has taken over as the label de jour. Any reader of science fiction knows that the phrase – and the activity – has been around for a long time. If you have a Tesla, you are probably telling all your friends about how it uses machine learning. There’s even a Stanford course on Coursera about Machine Learning. But, what does it actually mean?

I ran into two awesome blog posts the other day titled Machine Learning is Fun! and Machine Learning is Fun! Part 2. Adam Geitgey, who I don’t know, did a wonderful job of writing about this in an accessible way while evolving examples that includes Super Mario Brothers (from 1985) that goes very deep by way of demonstration.

If you’ve got other great introductory resources for Machine Learning, I encourage you to put links in the comments.


Also published on Medium.