Machine Learning - Introduction

Machine Learning - Introduction

After my latest blog on "Why Robots are the way of the future" I realized I have not really written about one of my greater passions...Machine Learning/Artificial Intelligence.

Machine Learning and Artificial Intelligence are not quite the same thing. Usually Machine Learning falls under the category of Computational Science, Mathematics, Computer Science and Statistics. Artificial Intelligence usually belongs to the Computer Science, Cognitive Science, Psychology and perhaps Mathematical Philosophy departments.

Machine Learning is generally the field of study related to the question: How can we teach a machine to perform a certain task as well as it can be performed?

However, Artificial Intelligence is generally an answer to the question: How can we simulate (or reproduce) the cognitive functions of humans/animals/intelligent beings? A lot of Artificial Intelligence seeks to model the human mind, which doesn't always do things optimally.

Both fields of study are important for developing algorithms and systems that are able to interact, aid, and enhance our lives. For instance, Machine Learning was used to create software to control a refinery. Up until the 1980's most refineries and their myriad pipes, valves, and gauges were controlled by humans. Any potential catastrophes had to be averted by alert workers. However, ML systems were developed that could optimally monitor and control refineries...even better than humans can! Furthermore, as conditions in the refinery change over time the software adapts and retrains itself without having to be reprogrammed over and over.

Interestingly, many of the Machine Learning algorithms are fairly simple in their approach. For instance, a basic classification algorithm is as follows:

Given a set of descriptions x and their associated objects/predictions y which are part of a set of classification categories {y_1, y_2, ... , y_n},

Build a model (a brain)

- For each unique classification y:

-take mean(x) for all x associated with the classification categories y_n

Make Predictions

- take an unclassified description x and evaluate the distances to each of the category means

- the unclassified description x will be classified according to the closest mean

Enhance the model with new examples

-given additional information x and y, recalculate the mean(x)'s with the new information...the model is now enhanced.

For example: Bobby stands by the road and for every vehicle that passes by quickly measures the length of the vehicle. Bobby also writes down whether the vehicle was an "18-wheeler" or "other". Using the above algorithm he calculates the mean "18-wheeler" length to be 20.4 feet, and the mean "other" vehicle length to be 10.9 feet. A blind girl named Jane comes along and says that she has just measured a vehicle that is 16.9 feet long, but can't tell if it's an "18-wheeler" or not. Bobby says that since the unobserved vehicle is closer in length to an "18-wheeler" it must be one!

This may not seem like a very intelligent algorithm (and it's not) but it does demonstrate one key feature of an intelligent algorithm, the ability to form it's internal computing algorithm via external data.

In the next few articles on Machine Learning I'd like to discuss some other more intelligent algorithms such as Support Vector Machines, Neural Networks and Random Forests...all three of which are some of the coolest and most effective AI/ML algorithms. Until then, see if you can think up your own intelligent algorithm...or use for an algorithm. I'd love to hear about it!