Peter Norvig Highlights Challenges of ML-based Development

Peter Norvig, Google Director of Research, presented “State-of-the-Art AI: Building Tomorrow’s Intelligent Systems” at the EmTech Digital conference. He compares traditional software programming to machine learning development and highlights the new challenges of debugging and verifying ML-based systems. Continue reading

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Three Levels of Problem Solving

This post by Laura McLay mentions three levels for modeling that applies to decision-making:

  • LEVEL 1: You solve the problem.
  • LEVEL 2: You solve the problem in a cost-effective manner (e.g., using heuristics to get a quick solution that is “good enough”)
  • LEVEL 3: You solve the problem in a cost-effective manner that a decision-maker will implement.

Continue reading

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DMN Interchange Software

As a community, we would like to maintain different software pieces that can be shared and reused by vendors and practitioners to promote Decision Management in general and the DMN standard in particular. So, we added a special section “Community Software” to the menu “Resources”. The first piece called “DMN Interchange Software” with all sources has been kindly contributed by OpenRules and is available for free downloads. Consider to expand this piece and to share your own DM related software.

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DMN 1.2 RTF Meeting at DecisionCAMP

The Revision Task Force (RTF) for DMN 1.2 will be meeting in at Stony Brook University, New York, USA, on Wednesday 6th July, the day before DecisionCamp 2016.  The meeting is open only to members of the RTF, but others are welcome to propose issues for discussion though the OMG website (http://issues.omg.org/issues/create-new-issue), and to meet members of the RTF at the conference on 7th and 8th.  Issues currently being discussed include improvements to the notation of DRDs and boxed logic, adding composability to DRDs, representing decisions over lists, and diagram interchange.

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Does Machine Learning Require Programming?

“Machine learning is often touted as a field of study that gives computers the ability to learn without being explicitly programmed. Despite this common claim, anyone who has worked in the field knows that designing effective machine learning systems is a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish. Thus, contrary to what machine learning enthusiasts would have us believe, machine learning still requires a considerable amount of explicit programming.” Read more at this Randal Olson’s post.

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The Triple Crown Of Process Improvement Standards

TripleCrownDenis Gagné from Trisotech will present “BPMN™, CMMN™, DMN™: An Intro To The Triple Crown Of Process Improvement Standards” at the free webinar on May 12, 2016

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Decision Modeling: Tacit Knowledge and Business Rules

RuleToFromIntuitionIn this post Jacob Feldman talks about two opposite learning directions taken by humans and decision automation:

  • Human Learning: from Rules to Tacit Knowledge
  • Decision Modeling: from Tacit Knowledge to Rules
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Is Analytics losing its competitive edge?

John Poppelaars just published a paper with this title. He refers to the review recently published by MIT Sloan Management “Beyond the Hype: The hard work behind analytics success. One of the key findings is that analytics seems to be losing its competitive edge. Continue reading

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“Agile” is Dead – Long Live Agility

It started with this 2014 post by Dave Thomas, one of the creators of the Manifesto for Agile Software Development. Dave proposed to retire the word “Agile”. To learn why, you may listen his recent presentation. Below are a few interesting quotes: Continue reading

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How Big of a Role Do You See Decision Management Playing in BPM?

bpmNEXT provoked an interesting discussion with this title among BPM experts at bpm.com.

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