Much of the hype surrounding AI involves the recent developments in machine learning. The terms "AI" and "Machine Learning" are often incorrectly used, so let's redefine both. AI, as defined in a previous post, can be understood as computers performing tasks typically associated with human cognition. Machine learning is one of the six disciplines of AI. In other words, machine learning is one of multiple categories of AI, but not all AI involves machine learning.
To clear things up, think of machine learning as a ‘special’ kind of AI that detects patterns within datasets, and uses these to make recommendations or predictions. Instead of having human coded instructions, machine learning algorithms rely on data to make their insights. Hence the ‘learning’ part of machine learning.
For simplicity let's think of an algorithm we rely on every day: weather prediction. Let’s say, for example, we believe that the weather tomorrow is based on the weather today, the weather yesterday, and the weather on this date last year. We can draw up an imaginary equation.
WeatherTomorrow = B0 + B1Today + B2Yesterday + B3LastYear
Weather forecasters would then use our equation and feed the machine learning algorithm all of the different variables for as far back as we have data recorded. The most important thing to keep in mind is the data includes the WeatherTomorrow variable all the way up until today. Based on this historical data, which includes the historical ‘answers’ to the question the equations asks, our algorithm tries to predict what the weather will be tomorrow. This is called supervised machine learning. In supervised machine learning, the algorithm is trained with data that includes the output variable or the ‘answer’ we are looking for.
Another quick example: housing prices. You give the computer monthly input data (eg. interest rates,square footage , location of property) and the monthly output data (eg. sale price of home). The algorithm is then trained to make future predictions based on the inputs and outputs it has been given.
Supervised machine learning is best when you have a dataset with outputs that can be classified or collected by humans (this is called labelled data). Here are a few use cases for supervised machine learning:
The effectiveness of this type of algorithm lies in the quality of the data collected. Up until very recently, data collection was difficult, expensive, and time consuming. Nowadays, with smartphones, social media, and constant access to the internet, there is a plethora of data available to feed supervised machine learning algorithms. The challenge for companies is to gather accurate data, understand what insights it can provide, and be agile enough to shift their business strategy in response to consistent real-time insights.
Understanding the capabilities of the algorithms, the type of data needed to feed them, and the type of business problems that can be solved, is a good first step to making your company or business unit more data-driven.