Bob Friday, VP & CTO at Mist a Juniper Company, Charna Parkey, VP of Applied Sciences at Texito, & Rajen Sheth, VP of AI Product Management at Google talk with Jeff Frick at Juniper NXTWORK 2019 at Caesar's Palace in Las Vegas, NV. #Nxtwork #UnpackingAI #theCUBE https://siliconangle.com/2019/11/18/reflections-on-ai-are-humans-up-to-the-task-of-controlling-what-theyve-built-nxtwork/ Reflections on AI: Are humans up to the task of controlling what they’ve built? Thanks to artificial intelligence and facial-recognition technology, mirrors in retail stores are now making judgments about people reflected in the glass. Is that progress or a descent into digital apocalypse? This was a question recently pondered by Bob Friday (pictured, left), vice president and chief technology officer of Mist, a Juniper company, as he strolled through a shopping mall one recent afternoon. It was not idle musing on Friday’s part either, because he’s played a significant role over the past 30 years in the development of wireless technologies and the use of AI tools to support them. “This is very personal,” Friday said. “AI in a bunch of mirrors calling me old? That’s a little annoying.” Friday spoke with Jeff Frick, host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during an Unpacking AI discussion at the Juniper NXTWORK event in Las Vegas. He was joined by Charna Parkey (center), an applied scientist at Textio Inc., and Rajen Sheth (right), vice president of product management at Google Cloud AI. They discussed how AI is being applied for various use cases in the enterprise, the technology’s growing impact on edge computing, potential regulation in the industry and the far-reaching influence AI is expected to have (see the full interview with transcript here). Building the self-driving network Friday’s annoyance at being called out by a mirror is one of the many subplots surrounding AI and its growing impact on business and society. Concerns about ethics and bias and unease that machines will replace humans are just a few on the controversies swirling around AI today. Still, adoption of the technology is expanding rapidly with enterprise growth reported to be 270% over the past four years. And the use cases are multiplying. Friday’s company — Mist Systems — was acquired by Juniper Networks Inc. in March. Earlier this month, Mist announced the first AI-Driven Self-Driving Network for enterprise users, a platform designed to streamline operations and simplify troubleshooting across wired or wireless domains. Part of Mist’s focus has been on processing customer data through long short-term memory, or LSTM, an artificial recurrent neural network architecture that plays a key role in deep learning. “Great wine starts with great grapes, and great AI starts with great data,” Friday said. “We’re really using the last 30 days of data to train LSTM algorithms to get to the point with tsunami detection that we don’t have false positives. Neural networks are one of the transformational things that really moved AI into the reality column.” Improved writing and edge processing Textio offers a different use case for AI, one that is setting out to redefine augmented writing to the point where machines can predict what a user wants to say. Some of the startup’s customers are using Textio’s technology to improve the hiring process and entice the most ideal applicants. Using AI, Textio can help enterprises align words with company culture and convey the kind of impression that will yield desired results. “The thing I’m most excited about with AI right now is the addition of creativity to a lot of our jobs,” said Textio’s Parkey, who described the benefits gained by people who leveraged smart technology to express sentiments closest to what they feel. “They get to use language that has never been used before to communicate an idea.” One of the companies that has been pushing the AI envelope for quite some time is Google. Just in the past month, the search giant released a new feature to let users check pronunciation of words, disclosed research around AI’s ability to correctly identify smells and beat top human players in a popular video game. In 2017, Google introduced “Federated Learning” as a way for mobile phones to learn a shared prediction model while keeping data native to the device. “One exciting development is around Federated Learning, where you can train on the edge and combine some of those aspects into a cloud site model,” Sheth explained. “What we see a lot of customers do is training on the cloud but performing inference on the edge. That will actually make edge even more powerful.” ... Here’s the complete video interview. (* Disclosure: Juniper Networks Inc. sponsored this segment of theCUBE. Neither Juniper nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)