Deep Learning Is Becoming an Enterprise AI Essential

Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

Deep learning, a form of machine learning that mimics the way humans process information by using neural networks, is quickly becoming a go-to tool for businesses interested in artificial intelligence (AI) systems that improve efficiency and enable innovative new business models.

In fact, 80 percent of data scientists will include deep learning as part of their AI toolkits by 2018, predicted Gartner. And by 2019, deep learning will be delivering demand, fraud and failure predictions with “best-in-class performance,” said the analyst firm in a Sept. 20 announcement.

“Deep learning is here to stay and expands ML [machine learning] by allowing intermediate representations of the data,” said Alexander Linden, research vice president at Gartner, in prepared remarks. “It ultimately solves complex, data-rich business problems. Deep learning can, for example, give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognize and understand a specific person’s speech.”

Already, deep learning is having a measurable effect on how business gets done.

Gartner noted that Amazon is using deep learning to help power its product recommendations. Paypal is using the technology to curb fraud and humans are already being outclassed by Baidu’s speech-to-text services.

The downside to deep learning is that talent may be in short supply.

Currently, most enterprises fall short on machine learning skills, not to mention deep learning expertise. Linden suggested that IT leaders get some data scientist onboard who can “can extract a wide range of knowledge from data, can see an overview of the end-to-end process, and can solve data science problems.”

In the meantime, the market for AI technologies is growing by leaps and bounds.

Tractica recently predicted that the market for AI technologies will reach $43.5 billion by 2024. Enterprise demand for AI hardware, software and services will account for $11.1 billion of that total, from $202.5 million this year.

Another forecast, this one from Jupiter Research, stated that banks and healthcare organizations will save $8 billion per year by 2022 with the help of AI chatbots. Banks will save 70 cents per each chatbot interaction, on average, and boost their chat bot success rates to 93 percent in 2022 from 20 percent in 2017.

Pedro Hernandez is a contributing editor at Datamation. Follow him on Twitter @ecoINSITE.