Machine Learing - A Guide for the Perplexed

Machine learning may seem like true machine intelligence. That is, learning is dependent upon the ability to learn. The scale of our ability to learn is dependent upon intelligence. Thus, if something can learn it follows that it must be intelligent. This may leave us perplexed however.

What is Machine Learning?

After all, machine learning is a specific sub-field within artificial intelligence. What's more, popular culture perceives learning machines to possess a type of uncanny consciousness whereby these systems can create knowledge for themselves.

Take AlphaGo for [example]((https://www.wired.com/2016/03/googles-ai-viewed-move-no-human-understand/)- the way in which AlphaGo learned appears to be hidden from us. This is a common perception of machine learning, that the actual learning is opaque despite being explicitly programmed. Again, this might be perplexing.

AlphaGo may be a bad example of machine learning, though. Let's illustrate some more widespread cases then. Because, lest we think otherwise, machine learning is already present.

First, if you've used location services on your smartphone, you've experienced machine learning. Second, if you've used Uber or Lyft, you've experienced machine learning. Lastly, if you've used a plagiarism checker (e.g., Turnitin) or never received an invitation to financial fortune from a Nigerian prince, you've experienced machine learning.

All of these technologies rely upon machine learning. Machine learning is used to optimize solutions to practical problems. Broadly, we can describe the practical issues as shortest path to a destination, modifying the path dynamically given environmental factors, and differentiating valid email from nefarious scams.

Dispel the Confusion

To be certain, there are even more machine learning examples and the rate is exponentially increasing. We have to wonder though...if the use of machine learning is spreading, and how machines learning is not understood, do we have a growing problem?

Accordingly, I am aiming this series as follows. First, I want to illustrate the simplistic beauty of machine learning. Second, I want to dispel the mystery of how machine learning works. While I want the conversation to be accessible, I also feel that some pseudo-code examples will increase our understanding. We'll dig into a little mathematics too.

Don't worry. You won't need advanced degrees in computer science or math however. The opposite is my intention.

I want to bring you to a place from which you can (a) understand machine learning generally and perhaps (b) pursue more advanced study if you wish knowing you have a basic foundation already.

Does Learning Mean Intelligent?

My contention is machine learning is not machine intelligence. Don't misunderstand my intention though- I don't say such to discredit machine learning in some manner. Quite the contrary, I merely want to place machine learning into an approachable context. Further, machine learning is not some esoteric craft.

Rather, machine learning is simply the use of statistics to analyze data. There is a strong undercurrent of science in machine learning as well. Thus, we will spend some time exploring hypothesis formation and testing.