3 Things You Need to Know About Machine Learning
You don't have to be able to build a machine learning program to benefit from them. You don't even need to know how to code to know the benefits machine learning has to offer. If machine learning is a buzz term you've seen around but you aren't familiar with what it means, here are three basic concepts to keep in mind.
Programs with machine learning make decisions based on experience, not hard rules.
Traditional programming might have a few 'if, then' loops. If you click 'X' button then 'A' action takes place, and if you click 'Y' button then 'B' action takes place. It can get more and more complicated as more rules or actions are built into the program. But if the program isn't sure about the input, you'd be left with no action taken or an error message.
But programs made through machine learning don't use hard rules. Instead, they build their own. One of the most common examples of machine learning is image identification. Identifying different types of road signs would take an impossibly large set of rules even under the most ideal circumstances, and there will always a picture that breaks those rules. Instead, machines build up a list of factors and details that help them classify a picture with greater and greater certainty. Every time they classify one picture, it adds more details and 'rules' the program can incorporate into its next decision.
It can grow beyond what the original programmer built.
The program doesn't stay stagnant. Instead, it builds new guidelines based on more and more minute details as it gains more experience. That means the program gradually transforms. Sometimes it's just that its ability to classify data becomes more complex: instead of differentiating between a square and a triangle on white backgrounds, it can start to find squares and triangles in real-world photographs. Sometimes it can learn entirely new actions: many AIs built to win at one strategy game can teach itself to win at another.
You need good data to get started.
But machine learning programs need data to get started. The classifier, or the function box in machine learning, uses a pre-established set of classified photos to inform their own understanding of differences and similarities. The better this initial study packet is, the smarter your program is. That means you need accurately classified data, and a lot of it.
If your company wants to start incorporating machine learning into business operations, you need more than that first study packet. Go to folio1 for more information about machine learning and AI.