It is important to know why machine learning matters so that you know the intrinsic value of the field and of methods and open questions in the field.
Like knowing your why, knowing the value of the field can be used as a powerful filter of information and help you focus on those methods that actually deliver on the promise that the field makes.
In this post you will learn that machine learning matters because it provides methods that can create solutions to complex problems. You will discover that there are problems for which it is not feasible to manually specify how a program solves a problem.
The promise that machine learning makes is that it provides tools to generate a solution to complex problems, faster, more accurately and more scalable than we could program a solution manually.
Writing programs in a computer can be summarized as automating procedures on input data to create output artifacts. Almost always, they are linear, procedural and logical. A traditional program is written in a programming language to some specification, and it has properties like:
- You know or can control the inputs to the program
- You can specify how the program will achieve its goal
- You can map out what decisions the program will make and under what conditions it makes them
- You can test your program and be confident that because the inputs and outputs are known and all conditions have been exercised the program will achieve its goal
There are some problems that you can represent in a computer that you cannot write a traditional program to solve. They resist a procedural and logical solution. They have properties such as:
- The scope of all possible inputs is not known beforehand
- You cannot specify how to achieve the goal of the program, only what that goal is
- You cannot map out all the decisions the program will need to make to achieve its goal
- You can collect sample input data for the program
Problems like this resist traditional programmed solutions because manually specifying a solution would require a disproportionate amount of resources.
You are probably a programmer, and you might be an experienced programmer. This might sound very odd, even unbelievable. As programmers, we believe as long as we can define what a program needs to do, we are confident we can define how a program can achieve that goal. This is not always the case.
Spam Filter Example
An example of an every-day decision problem that resists a manually defined solution is the discrimination of spam email from non-spam email.
How would you write a program to filter emails as they come into your email account and decide whether to put them in the spam folder or the inbox folder?
Some of my thoughts on how to do this are:
- I’d collect examples of emails I knew to be spam or not-spam
- I’d read the emails I had collected and write down any patterns I saw in either group
- I’d think about abstracting those patterns into more general rules I could program
- I’d look for emails that I could safely and quickly categorize as either spam or non-spam
- I’d write tests for my program to ensure it was making accurate decisions
- I’d monitor the deployed system and keep an eye on the decisions it was making
I could write a program to do this, and so could you. It would take a lot of time. A lot of emails would have to be read. The problem would need to be thought about very deeply. It would take a lot of development and testing time before the system could be trusted enough to be put into operations. Once in operations, there would be so many hard coded rules that were specific to the email I had read that it would be a maintenance nightmare.
The process above also describes a machine learning solution to the problem of discriminating spam email from non-spam email. The punch line is that machine learning methods can automate the process for you.
Pro Tip: Approaching complex problems in this way is an incredibly valuable skill that will serve you well later on in preparing data and selecting the right machine learning method. Thinking through the process of “how would I manually write a program to solve this” is a master skill that is often overlooked and forgotten by professionals.
Machine Learning Matters
The field of machine learning provides tools to automatically make decisions from data in order to achieve some goal or requirement. The research questions focus on how to do this better and what the results mean.
Let us focus on the practical problem-solving capabilities of the tools and practices of machine learning. These tools and practices of machine learning matter to the world. Four reasons that they matter are:
- Automatically: Machine learning methods are automated processes (algorithms) that create algorithms. The methods run on data and produce a model that specifies how to achieve the program’s goal.
- Fast: Machine learning methods save you time. The methods can analyze sample input data and deliver a program faster than you could manually write one.
- Accurate: Machine learning methods can do a better job than you. As automated methods, they can run longer on more data than you in order to make more accurate decisions.
- Scale: Machine learning methods can provide solutions to problems that you cannot solve. The methods can scale and be interconnected to achieve solutions to problems that previously could not be considered or even conceived.
In this post you learned that machine learning matters because it provides methods that can create solutions to complex problems. Specifically, these are problems that resist a manually specified solution.
You learned that the promise of machine learning is that it can solve these types of problems automatically, faster and more accurately than a manually specified solution and at a larger scale.
What are some complex problems that you think resist a manually programmed solution? Leave a comment.