There’s a lot of discussion about machine learning these days and pretty much every one (vendors, users) is talking about it.
I remember attending courses on Artificial Intelligence, Machine Learning and even Artificial Neural Networks back in 1998. So what’s new?
How have AI and ML evolved?
I think a big reason why everyone is talking about machine learning now is that it it’s much simpler to use machine learning now for everyday, business use cases. Earlier, machine learning was mostly used for really complicated scenarios – think enterprise search (with advanced capabilities for proximity, sounds etc) or content analytics to do sentiment analysis. All these were useful but required expensive software and resources.
Not anymore. It’s become far easier to use machine learning for simpler problems. In fact, for lot of scenarios which required complex rules, you can actually use machine learning to take decisions. Let’s take an example. You are building a website that allows users to sell their old mobile phones. The website should be able to suggest a price based on a series of questions that a user answers. So you could have a set of rules that “rule-fy” each question.
Question 1: Phone model
If phone == A, Price = p
If phone == B, Price = q
Question 2: Age of phone
If phone == A, and bought within last year, price = P
If phone == A and bought more than one year ago but less than 2 years ago, price = 0.9 P
Question 3: Color
If phone == A, and bought within last year and color == black, price = P
If phone == A, and bought within last year and color == silver, price = 0.95 P
If phone == A and bought more than one year ago but less than 2 years ago, and color == black, price = 0.9 P
And so on. You can add more rules depending on questions about age, colour, defects, screen quality and so forth. And your rules become increasingly complex. And then what happens if a user wants to enter a value that the rule doesn’t handle?
Of course, in real life, you wouldn’t write rules like this. You will probably have a rules engine that that combines multiple rules and so forth but you get the idea.
Machine Learning as an alternative to Rules-based processing
Here’a how machine learning can replace a complex rules based application.
Let’s say you have historical data about phone sales. Yeah, I admit this is a big assumption but if you are creating rules and deciding prices, then you probably have some historical data anyways. So assume you have data such as this (this is just a sample; the more you have it, the better it is):
Fig: Second hand phone sales data
Now your original problem can be stated as a machine learning problem as follows:
How do you predict the price of a phone, that is not already there in the sample (or training set) above based on features and data available as part of training set?
Essentially, instead of you or your application making decisions based on pre-defined rules, you are now relying on your application to make decisions based on historical data. There are many techniques that can help you achieve this.
One relatively simpler technique is to use Linear regression. Linear regression is basically a statistical technique to predict an outcome (or dependent variable) based on one or more independent variables. Based on example above, you can describe Price P as a function of variables model, age, colour etc. Or in linear regression, it can be expressed as:
P = b0 + b1*model + b2*age + b3*colour + b4*condition…..
Machine learning algorithm then calculates values of b0, b1, b2 etc based on historical data and then you use this equation to predict price for an item that was not there in the training set. So if a new user now comes and offers a phone for sale on your site, you can recommend a price to her based on past sales.
Okay, that was a rather simplistic machine learning example and you can use many other more sophisticated techniques. For example, you can do a factor analysis or Principal Component Analysis (PCA) to reduce large number of items (e.g., news articles’ attributes) into smaller set of variables. Or use logistic regression instead of linear regression.. or whatever. The key point is that it is now much easier to use machine learning for everyday use cases without spending a lot on expensive software or resources. Pretty much all programing languages and development platforms have machine learning libraries or APIs that you can use to implement these algorithms.
The main drawback of using this approach (as in this example) is that the results might not always be as good as you would get with rules based technique. The quality of result is highly dependent on training set and as the training set improves (in terms of quality as well as quantity), the results would improve.
Are you using machine learning for your applications? If yes, what techniques are you using?