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This form of machine learning continues to grow rapidly. Here's what you need to know as you consider whether to implement deep learning in your organization.
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Last year, InformationWeek published a high-level introduction to deep learning that was meant to explain the basics of the technology to CIOs and IT managers. Since then, interest in deep learning has skyrocketed, so now seems like a good time to revisit the topic with a deeper dive into the technology.
Enterprises have been spending a lot of money on deep learning and related technologies — and they are about to spend much more. According to IDC, spending on artificial intelligence (AI), which includes deep learning, will likely grow from an estimated $24.0 billion in 2018 to $77.6 billion in 2022. In other words, AI investments will triple in just four years.
"Worldwide Cognitive/Artificial Intelligence Systems spend has moved beyond the early adopters to mainstream industry-wide use case implementation," said Marianne Daquila, an IDC research manager. "Early adopters in banking, retail and manufacturing have successfully leveraged cognitive/AI systems as part of their digital transformation strategies. . . . There is no doubt that the predicted double-digit year-over-year growth will be driven by even more decision makers, across all industries, who do not want to be left behind."
The consultants at PWC forecast that the impact of AI and deep learning could be much greater than just enterprise spending. The firm said that AI "could contribute up to $15.7 trillion to the global economy by 2030." It added, "AI adoption, which has happened in fits and starts, will accelerate in 2019."
Vendors have been quick to jump on the AI bandwagon, adding machine learning and deep learning capabilities to their products. In fact, so many companies offer these types of solutions that Amazon Web Services recently rolled out a Machine Learning and Artificial Intelligence Marketplace.
But the future isn't all rosy for deep learning.
Gartner placed deep neural nets (another term for deep learning) at the very top of its most recent Hype Cycle for Data Science and Machine Learning. If deep learning follows the usual path of emerging technologies, initial interest has likely peaked, and the next stage will be disillusionment as enterprises struggle to turn the technology into something useful.
A big part of the problem is that IT and business leaders don't understand how the technology works. They don't fully grasp its potential benefits — or, more importantly, its shortcomings.
With that reality in mind, here are nine more things that you should know about deep learning.
Cynthia Harvey is a freelance writer and editor based in the Detroit area. She has been covering the technology industry for more than fifteen years. View Full Bio
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