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AI in the enterprise - how everyone from IBM to SAP is making systems smarter

Scott Carey | June 27, 2016
How the enterprise stopped worrying and learned to love AI

Richard Peers, director of financial services industry at Microsoft wrote: "It's like Siri on steroids and, as a bonus, nobody gets to hear you clumsily and repeatedly speaking out loud into your phone like you've gone clinically insane. But we need quality bots."

Speaking at Build 2016, Microsoft CEO Satya Nadella said: "As an industry, we are on the cusp of a new frontier that pairs the power of natural human language with advanced machine intelligence. At Microsoft, we call this Conversations as a Platform, and it builds on and extends the power of the Microsoft Azure, Office 365 and Windows platforms to empower developers everywhere."

Speaking with ComputerworldUK, Dave Elkington  at InsideSales says: "Qi Lu, the chief product guy at Microsoft is a friend of mine. He and I will once in a while make wagers over who can predict things more accurately. Running Cortana and his predictive stuff versus ours and once in a while, despite being a smaller company, we still give them a run."

Google

When Google spent £400 million on the little-known artificial intelligence startup Deep Mind the question of how it would enrich Google's business was at the forefront of many people's minds.

Co-founder Demis Hassabis told The Verge that Deep Mind's role is to "turbocharge" Google: "Of course, we actually work on a lot of internal Google product things, but they're all quite early stage, so they're not ready to be talked about. Certainly a smartphone assistant is something I think is very core - I think Sundar [Pichai] has talked a lot about that as very core to Google's future.

Speaking to Wired last year, head of applied AI at Deep Mind, Mustafa Suleyman, said: "I've got five teams, working on YouTube, search, health, natural-language understanding and some Google X projects. We're working at applying the core engine that sits behind the Atari games player across the company.

"One is in YouTube-recommendation personalisation. We try to learn from the types of videos that lots of users are watching in aggregate to better recommend at the right time, in the right place, what they'd like. Then search: think of search as a process of querying an engine, browsing through links generated, then refining your query in this iterative feedback cycle. Over time we can improve results."

Away from Deep Mind, Jeff Dean, senior fellow at Google, said at its Cloud Next conference earlier this year: "Our goal is to create applications that can see, hear and understand." At the same conference the search giant announced Cloud ML to help machine-learning engineers to build "sophisticated, large" models based on its recently open-sourced TensorFlow deep-learning library.

 

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