Why Machine Learning Matters?

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.

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.