Notes from DecisionCAMP-2018

This year DecisionCAMP was held in Luxembourg Sep 17-19 as a part of the Logic for AI Summit. It became an important event that attracted experts and practitioners in the business decision management from 14 countries. We had 54 official registrations and sometimes even more people were present in the auditorium. The representatives from almost all major BR&DM vendors and many well-known experts attended the event. In these notes I will try to share my view of what was happening during the CAMP. Continue reading

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The Myth of Data-Driven Natural Language Understanding

Walid Saba published a short presentation on the difference between Natural Language Processing (NLP) and Natural Language Understanding (NLU) and why the data-driven approach, while useful for some NLP tasks, is irrelevant to NLU. Link

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DMN 1.2 has been approved by the OMG Board

Alan Fish, a co-chair of OMG DMN Group, just posted on the LinkedIn: “#DMN 1.2 has now been approved by the OMG Board. Phew. Now on to 1.3…”

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Tim Berners-Lee: Game On!

With an ambitious decentralized platform, the father of the web  Sir Tim Berners-Lee will launch Inrupt, a startup that he has been building, in stealth mode, for the past nine months. Its mission is to turbocharge a broader movement afoot, among developers around the world, to decentralize the web and take back power from the forces that have profited from centralizing it. In other words, it’s game on for Facebook, Google, Amazon. For years now, Berners-Lee and other internet activists have been dreaming of a digital utopia where individuals control their own data and the internet remains free and open. But for Berners-Lee, the time for dreaming is over. Link Continue reading

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Amazon automates buying and stocking decisions

Machines are beating people at the critical inventory decisions that separate the winners and losers in retail. For the staffers deciding how many books, games or plastic pool toys to peddle, the tradeoff can be stark: Order too little and you miss out. Order too much and you’re forced into costly clearance sales. Amazon’s algorithms, refined through years of monitoring customer behavior, are getting the Seattle-based company out of the guessing game. Read more

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Lifetime Achievement Award in Decision Management goes to Dr. Charles Forgy

The first Lifetime Achievement Award in Decision Management goes to Dr. Charles Forgy, the father of rule engines! It was just announced at DecisionCAMP-2018 – see the presentation. Congratulations and a big THANK YOU to Dr. Forgy from all members of the  Decision Management Community.

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DecisionCAMP-2018 and LuxLogAI start on Sep. 17

DecisionCAMP-2018 starts at the Luxembourg on Sep. 17. The 3-days program is available here. It will feature well-known DM experts and practitioners.  The QnA panel “Real-world Business Decision Modeling” will be held on Sep. 18. The instructions for attendees can be found here. Together with other co-located events of the Logic for AI Summit there will be about 250 attendees. There will be several social and cultural events as well as a lot of opportunities for inter-conference networking.

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Sparkling Logic is our new sponsor

DMCommunity welcomes a new premium sponsor Sparkling Logic Inc., a California-based company leading technology innovation in Decision Management. The most recent Sparkling Logic SMARTS Rio release empowers business and data analysts to discover, test, simulate, deploy and continuously improve data-informed business decisions. As usability, scalability, and security ramped up in Rio, the company set a path for some exciting innovation in its next release – Santiago – coming in Fall 2018. You may reach out to Sparkling Logic at DecisionCamp-2018 for some early news about Santiago!

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Challenge Sep-2018 “Balanced Assignment”

This challenge deals with assignment of people to certain activities, for instance: assigning students to project groups or assigning professors to offices.  We are using the problem formulation and test data provided by Prof. Robert Fourer (AMPL). We expect that not only special optimization tools like AMPL but traditional business decision modeling tools can be used to define and solve the problem. See the challenge

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Google’s What-If Tool for Machine Learning Models

Building effective machine learning (ML) systems means asking a lot of questions. It’s not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better: How would changes to a datapoint affect my model’s prediction? Does it perform differently for various groups–for example, historically marginalized people? How diverse is the dataset I am testing my model on?
To help to address these questions Google launched the What-If Tool, a new feature of the open-source TensorBoard web application, which let users analyze and better understand an ML model without writing code.

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