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Terms like deep learning, artificial intelligence (AI), and machine learning are often used interchangeably. They’re concepts we probably have heard, but they are also concepts we might not fully understand—especially when it comes down to the differences among them.
While AI, machine learning, and deep learning are all overlapping concepts and tend to be mentioned within each other’s realms, they’re actually not the exact same thing.
Understanding AI, machine learning, deep learning, and other buzzwords that tend to pop up within these conversations can be tough, but, by using real examples, we can get down to the nitty-gritty of what differentiates these concepts.
Before we dive into the smaller details of each unique element, let’s begin with some overall, generalized definitions that can help you better understand the basics of each.
Artificial intelligence (AI) is defined simply as intelligence demonstrated by machines, or, rather, using technology to get a computer to mimic human behavior in some way. All in all, it’s about incorporating human intelligence into machines.
Machine learning,on the other hand, is a subset of AI that uses techniques that allow a computer or other smart device to figure things out from data and then deliver AI applications.
Deep learning, a subset of machine learning, enables computers to solve more complex problems.
Now that we’ve covered the generalities, it’s time to dive into a deeper understanding of what these concepts are, how they’re applied, and the similarities and differences among them.
Artificial intelligence is, undoubtedly, a topic that’s come up in your circle—even if you don’t work in tech, internet, or manufacturing industries. Why? Because AI is practically everywhere, and it’s becoming a more nuanced part of our daily lives.
While the purest forms of AI, which are multi-faceted and based on machines that can learn on their own (like Google’s DeepMind network), aren’t yet part of our normal routines, some AI with underlying and fundamental technologies such as behavioral algorithms (i.e. voice-powered personal assistants like Siri), suggestive searches, and predictive capabilities (like self-driving cars) are now commonly woven into our normal routines.
Unlike machine learning and deep learning, AI refers to the specific output of a computer—that the computer is doing something intelligent; it’s completing tasks and using rules to solve problems with intelligent behavior. But the term AI itself doesn’t necessarily say anything about how those problems are solved. That’s where machine learning and deep learning come into play.
Now that you have an understanding of AI, you can form a stronger understanding of machine learning. Why? Because machine learning is artificial intelligence—it’s just referring to a subset of AI that is a specific technique for realizing AI.
Machine learning, at its core, can be defined as the intention to enable machines to learn by themselves using the provided data to make accurate predictions. Loosely, machine learning can mean empowering a specific computer system to learn on its own from a set of data and designing an algorithm to process that data.
Whereas AI—without machine learning concepts—can be used to mimic human behavior, machine learning is more about mimicking how humans learn so that machines can continue learning too. Machine learning is all about a system processing data and continually learning from that data.
The most accessible example of machine learning is something most of us use every day—Netflix (or similar streaming services). Machine learning is how Netflix knows which types of shows we might want to watch next, how Facebook understands whose face is in a photo, and how customer service reps can figure out if you’re satisfied with your service before you fill out a survey.
To avoid any confusion, let’s start this off with a very simple statement—deep learning is machine learning. Just like machine learning is a subset of AI, deep learning is a subset of machine learning—it’s just referring to a specific type of machine learning.
Deep learning is an evolution of machine learning. It uses a programmable neural network that enables machines to make decisions without assistance from humans. Do you see how the term “evolution” really comes into play here?
The easiest way to think about deep learning is to consider that deep learning algorithms are inspired by patterns found in the human brain. Just like our brains identify patterns and classify diverse information, deep learning techniques can be taught to accomplish the same type of tasks for machines. When our brains are tasked with receiving new information, our brains compare that information to a known set of information in order to make better sense of it. That’s the core conceptual function of deep learning algorithms as well.
Deep learning is all about eliminating the need for human interference when it comes to making decisions. Whereas machine learning might require manual interference from humans, the goal of deep learning is to eliminate that need, with AIs independently discovering how to process complex information.
There are plenty of examples of deep learning within a variety of industries—medical research, aerospace and defense, etc.—but one of the most commonly used examples is automated hearing and speech transition in electronic devices. Deep learning also has industrial automation applications. For example, deep learning is used to improve worker safety by detecting when workers get dangerously closed to machinery.
Though AI, deep learning, and machine learning might intersect, they’re all unique and have distinct applications.
Deep learning is a subset of machine learning which is a subset of artificial intelligence. Machine learning and deep learning are ultimately additional layers of complexity and nuance added onto the broad technology of artificial intelligence.
This entry was posted on July 6th, 2020 and is filed under Technology. Both comments and pings are currently closed.
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