Today's deep neural networks move vast amounts of data through many layers of hardware and software, each layer coming up with its own representation of the data and passing what it "learned" to the next layer. Artificial intelligence attempts "to make a machine that thinks like a human. Deep neural networks try to solve pretty narrow tasks," says Schabenberger. Relinquishing the quest for human-like intelligence, deep learning has succeeded in vastly expanding the range of narrow tasks machines can learn and perform. "We noticed a couple of years ago," says Peter Maynard, Senior Vice President of Global Analytics at Equifax, "that we were not getting enough statistical lift from our traditional credit scoring methodology." The conventional wisdom in the credit scoring industry at the time was that they must continue to use traditional machine learning approaches such as logistical regression because the results were interpretable, i.e., in compliance with regulation.

Artificial intelligence, machine learning and neural networks-based deep learning are concepts that have recently come to dominate venture capital funding, startup formation, promotion and exits and policy discussions. The highly-publicized triumphs over humans in Go and Poker, rapid progress in speech recognition, image identification, and language translation, and the proliferation of talking and texting virtual assistants and chatbots, have helped inflate the market cap of Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these companies dominate the headlines--and the war for the relevant talent--other companies that have been analyzing data or providing tools for analysis for years are also capitalizing on recent AI advances. A case in point are Equifax and SAS: The former developing deep learning tools to improve credit scoring and the latter adding new deep learning functionality to its data mining tools and offering a deep learning API. Neural network created in SAS Visual Data Mining and Machine Learning 8.1 Both companies have a lot of experience in what they do.

Artificial intelligence, machine learning, and neural networks-based deep learning are concepts that have recently come to dominate venture capital funding, startup formation, promotion and exits, and policy discussions. The highly-publicized triumphs over humans in Go and Poker, rapid progress in speech recognition, image identification, and language translation, and the proliferation of talking and texting virtual assistants and chatbots, have helped inflate the market cap of Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these companies dominate the headlines--and the war for the relevant talent--other companies that have been analyzing data or providing tools for analysis for years are also capitalizing on recent AI advances. A case in point are Equifax and SAS: The former developing deep learning tools to improve credit scoring and the latter adding new deep learning functionality to its data mining tools and offering a deep learning API. Neural network created in SAS Visual Data Mining and Machine Learning 8.1 Both companies have a lot of experience in what they do.