There's never any shortage of buzzwords in the IT world, but when it comes to AI, they can be hard to tell apart. There's artificial intelligence, but then there's also machine intelligence. There's machine learning, but there's also deep learning. What's the difference? Here are five things you need to understand.
1. AI is basically an umbrella term for it all
Artificial intelligence refers to "a broad set of methods, algorithms and technologies that make software 'smart' in a way that may seem human-like to an outside observer," said Lynne Parker, director of the division of Information and Intelligent Systems for the National Science Foundation.
Machine learning, computer vision, natural language processing, robotics and related topics are all part of AI, in other words.
2. Machine intelligence = AI
"Some people may come up with distinctions between the two, but there is not a universal view that the two terms mean anything different," Parker said.
There may actually be a regional preference at work behind the origins of the separate terms. "Machine intelligence” has a more "down-to-earth engineering sensibility" that's been popular in Europe, whereas "artificial intelligence" has a slightly more "science-fiction feel" that's made it more popular in the U.S., said Thomas Dietterich, a professor at Oregon State University and president of the Association for the Advancement of Artificial Intelligence. In Canada, it's often been called "computational intelligence,” he added.
3. Machine learning is also a blanket term covering multiple technologies
As a part of AI, machine learning refers to a wide variety of algorithms and methodologies that enable software to improve its performance over time as it obtains more data. That includes both neural networks and deep learning (see below).
"Fundamentally, all of machine learning is about recognizing trends from data or recognizing the categories that the data fit in so that when the software is presented with new data, it can make proper predictions," Parker explained.
As an example, think about the task of recognizing someone's face. "I have no idea how I recognize my wife’s face," Dietterich said. "This makes it very difficult to guess how to program a computer to do it."
By learning from examples, machine learning provides a way to do that. "This is 'programming by input-output examples' rather than by coding," Dietterich said.
Commonly used machine-learning techniques include neural networks, support vector machines, decision trees, Bayesian belief networks, k-nearest neighbors, self-organizing maps, case-based reasoning, instance-based learning, hidden Markov models and "lots of regression techniques," Parker said.
4. Neural networks are a type of machine learning, and deep learning refers to one particular kind
Neural networks -- also known as "artificial" neural networks -- are one type of machine learning that's loosely based on how neurons work in the brain, though "the actual similarity is very minor," Parker said.
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