AI tools work by being given enormous amounts of data, analyzing that for patterns and correlations, and using those patterns to make predictions or decisions. Most AI tools use machine learning for processing data.
Traditional computer programming involves creating a computer program, giving that program input or data and then receiving output. One subset of AI, called machine learning, is an alternative to traditional programming which is used to create, among other things, GenAI tools. Machine learning works by giving a computer a large set of data, telling it what you want the result to be, and letting the computer figure out the best way to give you that result.
Traditional Programming
In traditional programming, a programmer writes explicit instructions for the computer to follow, like a list of things you must do to apply for college. Submit grades, test scores, an essay, fill out an application form, pay an application fee and your application is complete (unless you apply to a service academy). If you wanted to create a program to sort a list of numbers from highest to lowest, you would write out the exact steps the computer should follow to compare and arrange the numbers in order.
Machine Learning
Machine learning, on the other hand, involves teaching the computer to learn from data rather than giving it explicit instructions. Instead of writing out the steps, you provide the computer with a large amount of data and let it find patterns and make decisions based on that data. These patterns the computer finds for itself are called algorithms. For instance, if you wanted a program to recognize whether an image contains a cat or a dog, you would feed it thousands of labeled images of cats and dogs. The computer would then learn to identify the features that distinguish cats from dogs.
Explore the graphic below to see the difference between computer programming and machine learning in making a playlist of songs.
The key distinction between traditional programing and machine learning lies in the approach: machine learning employs vast datasets to discern patterns and devise algorithms for analyzing additional data, whereas traditional programming applies predefined rules to data to produce an output.
