bpmNEXT-2017 on Apr 18-20 in Santa Barbara

bpmnextNow in its fifth year, bpmNEXT is the definitive showcase of the next generation of Business Process Management software – including intelligent operations, the Internet of Things, mobile case management, business decision management, automated process discovery, and goal-directed processes. This is no typical “how to” conference aimed at BPM newcomers. It is designed specifically for those already chestdeep in BPM and wanting to get in early on the next generation of process innovation technology.

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Measuring Customer Loyalty Index and Using It to Make Smarter Sell/Buy Decisions

customerloyaltyI thought DMCommunity readers will be interested to listen this informal presentation given by a real-world Data Scientist without pseudo-scientific references. One of my long-time colleagues, Igor Elbert today is VP of Data Science at Tapad. Previously he applied predictive and prescriptive analytics at Gilt.com and Barnes & Noble tracking movements of millions of items across the supply chain for major brands. Igor is pushing innovative data analysis to new frontiers. – JF

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Drools Now Implements DMN

Following the announcement from last year, the popular open source Drools rules engine added full run-time support for DMN at compliance level 3 into its upcoming version 7.0. As of January/2017, the implementation is feature complete and the team is working on additional nice to have capabilities and the eventual bug fixes in preparation for the release. See Drools/DMN solution of our Oct-2016 Challenge “Flight Rebooking

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The “First High-Profile Deserter” from the World of AI

For 40 years, the Stanford professor Terry Winograd has steered the increasingly complex and meaningful interactions between humans and computers. As a graduate student at MIT in the late 1960s, Winograd wrote one of the seminal programs in the emerging discipline of artificial intelligence. Later on, Winograd reached a conclusion that intelligence wasn’t simply a matter of pattern recognition and processing data; it involved being and existing. And as a result, computers alone couldn’t possess true intelligence. The proper role of artificial intelligence was therefore to help humans live more fully human lives, not to replace them. He became, in the words of one historian, “the first high-profile deserter from the world of AI.” He also was an adviser to Google cofounder Larry Page and LinkedIn founder Reid Hoffman. Read more at “Siri, Who s Terry Winograd?

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Bruce Silver: Will DMN achieve BPMN adoption?

Bruce Silver, a BPMN guru and a member of the OMG DMN task force, just published his view of the current state and future of the DMN standard. “Whether DMN can achieve adoption similar to BPMN remains an open question. Of course, the emergence of an enterprise-class DMN runtime engine would be a truly disruptive force in the market.  It would change things.” Read more and share your opinion

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White-collar workers being replaced by AI?

whitecoloraiOne Japanese insurance company, Fukoku Mutual Life Insurance, is reportedly replacing 34 human insurance claim workers with “IBM Watson Explorer,” starting by January 2017. The AI will scan hospital records and other documents to determine insurance payouts, according to a company press release, factoring injuries, patient medical histories, and procedures administered. Automation of these research and data gathering tasks will help the remaining human workers process the final payout faster, the release says. Read more

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Nuts and Bolts of Deep Learning

deeplearningDeep Learning has been the core topic in the Machine Learning community the last couple of years. You may read “The major advancements in Deep Learning in 2016” to go through the advancements contributed the most (or have the potential) to move the field forward. Then you may watch a recent Andrew Ng’s presentation “Nuts and Bolts of Applying Deep Learning” and/or read this interesting overview. And then look at Machine Learning from the Decision Management perspective like Tom Davenport and Rich Masi did in the article “Printing-Money” with Operational Machine Learning.

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