AI IN THE ENTERPRISE BY VENKAT SRINIVASAN [HIVE TALK RECORDING]

February 14th, 2017 by Travis Miller

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How proven are modern-day enterprise AI solutions? What paths have to be taken to build such applications? What enterprise AI predictions are likely to be fulfilled in the immediate future?

There is no shortage of questions around the ever-expanding realm of enterprise AI solutions. Dr. Venkat Srinivasan, Founder and CEO of RAGE Frameworks, tackled these questions head-on at The Hive Think Tank talk “AI in the Enterprise” event. Dr. Srinivasan’s presentation can be viewed in full here, and his corresponding slides can be viewed here.

Dr. Srinivasan began the evening by providing a summarized understanding of enterprise AI solution traits and methods. His overview proved helpful for novice and expert alike: while relaying a well-taught, foundational framework for enterprise AI, it underlined the development paths through which RAGE Frameworks chooses to operate.

Core to this foundational framework were what Dr. Srinivasan labeled as the three main determinants of an enterprise AI effort’s success: context, language, and traceability. Understanding the enterprise context at hand, handling unstructured data and natural language, and moving beyond black box systems – these are the required capabilities, according to Dr. Srinivasan, of any serviceable enterprise AI solution.

Dr. Srinivasan expounded upon different AI problems – prediction, search clustering, and interpretation, to name a few – and the various methods through which they are being solved – classification trees, neural networks, and computational linguistics, among others. He continued by distinguishing between “Machine Intelligence” (the ability to extract, store, and process knowledge) and “Intelligent Machines” (machines that use that knowledge in end-to-end business functions). Dr. Srinivasan emphasized this maturation as the key to creating machines that execute business functions with no human help.

RAGE’s financial statement processing machine automates the extraction, classification, and interpretation of financial files of all different languages, formats, and file types: ultimately processing millions of bank clients’ statements monthly, with 90-95% accuracy. RAGE’s real-time market intelligence machine interprets and analyzes all publicly and privately available content pertaining to a client – hundreds of millions of items – to generate a completely traceable, systematic projection of company stock prices.

Dr. Srinivasan’s talk provided a helpful introduction to enterprise AI solutions, and a deep-dive into one company’s strategies in the space. Nevertheless, numerous problems and questions still loom within the world of enterprise AI, a quandary Dr. Srinivasan welcomes: “There’s lots of unsolved problems – so there’s lots of opportunity! That’s the exciting part.”

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