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Neural networks and deep learning find traction in enterprises
Deep learning and neural networks are widely acknowledged to be powerful tools, but many enterprises continue to look for the right use cases to implement these technologies in their lines of business.
When experts discuss the promise of neural network applications, it seems like the sky's the limit. But it's one thing to say the tools can do just about anything, yet quite another to find specific uses where they excel.
"One of the best ways to use AI is if you have an open mind and let it bring you information that you wouldn't have thought about," said Juan José López Murphy, technical director and data science practice lead at consultancy Globant. "AI is good at getting us to think about things that we wouldn't have noticed."
That may be true, but it's likely to mean very different things to different businesses. And it probably means something different to separate units within a single organization.
The good news is that, through experimentation and trial and error, some enterprises are finding productive uses for deep learning and neural network applications. This handbook examines some of those use cases, including a chip manufacturer deploying computer vision in its supply chain, a pharmaceutical company using adversarial networks for drug discovery and a graphics company building artificial worlds for video games. We also report on how open standards for neural networks could create ever more use-case opportunities for businesses.