Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. It’s like if you had a flashlight that turned on whenever you said, “It’s dark ” it would recognize different phrases containing the word “dark.” When we say something is capable of “machine learning,” it means it performs a function with the data given to it and gets progressively better over time. Machine learning involves a lot of complex math and coding that, at the end of the day, serves the same mechanical function that a flashlight, car, or computer screen does. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have similar musical tastes. Machine learning definition: An application of artificial intelligence that includes algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions.Īn easy example of a machine learning algorithm is an on-demand music streaming service. It uses a programmable neural network that enables machines to make accurate decisions without help from humans.īut for starters, let’s first define machine learning. More specifically, deep learning is considered an evolution of machine learning. The first step in understanding the difference between machine learning and deep learning is to recognize that deep learning is machine learning. So, what exactly are these two concepts that dominate conversations about AI, and how are they different? Read on to find out. Machine learning and deep learning often seem like interchangeable buzzwords, but there are differences between them.

It’s what makes self-driving cars a reality, how Netflix knows which show you’ll want to watch next, and how Facebook recognizes whose face is in a photo. Understanding the latest advancements in artificial intelligence (AI) can seem overwhelming, but if it’s learning the basics that you’re interested in, you can boil many AI innovations down to two concepts: machine learning and deep learning.Įxamples of machine learning and deep learning are everywhere.

Deep learning