This guide will help you get a handle on the difference between "machine learning" and "deep learning."
Artificial intelligence is having its moment. Business leaders can't stop talking about it. New tech products invariably include it. And news headlines incessantly chronicle the buzz around it. But for many people, artificial intelligence remains a mystery.
To help, we've created a guide that explains some of the key terms associated with the technology, an increasingly useful tool for businesses that improves as it crunches more data.
Read more, from 2016: Why Deep Learning Is Suddenly Changing Your Life
This A.I. technique is like training a dog with treats. The software learns by successfully executing a task and, on the flip side, from failure. This fusion of reinforcement learning with deep learning has led to tremendous breakthroughs, like computers beating humans at complicated video and board games. -Example: Facebook's -targeted notifications.
A.I.'s rise can be traced to software developed decades ago that was intended to approximate how the human brain learns. Inside a neural network are layers of interconnected nodes where calculations take place that help computers sift though data in minute detail. By doing so, the software can learn to recognize patterns that even the most intelligent humans may overlook. Example: Baidu search.
Mixing neural networks with machine learning makes for deep learning, a powerful technology that can crunch enormous amounts of data, like vast archives of audio clips. A.I.'s biggest breakthroughs--such as recognizing snow leopards in photos--can be traced to the technology. -Example: Nvidia's 3D A.I.-generated faces.
You can thank machine learning for recommending how to respond to your boss when she emails asking whether an important document is in order ("Looks good to me") or whether you can meet at noon ("Let's do it!"). This is just a taste of how algorithms help computers "learn." The chief attraction: Companies don't need humans to program the technology for each specific task it handles. Example: Google Gmail.
Devices using computer vision are able to see and understand their surroundings almost like a human. Think of facial-recognition technology that can automatically unlock your iPhone or the systems that help navigate self-driving cars without crashing them into trees. The problem seems easy to solve. But in reality, it's very -difficult. Example: Waymo's -autonomous vehicles.
This technology makes it possible for -computers to understand and react to human speech and language. Voice-controlled digital -assistants, which take dictation or power Internet--connected home speakers, would be impossible without it. The technology is still imperfect, but it's improving quickly. Example: Amazon Alexa digital assistant.
A version of this article appears in the June 2019 issue of Fortune with the headline “Artificial Intelligence 101.”
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