Why archaic and cumbersome RFP no longer makes sense

Posted on November 7, 2019 by decisionmanagementcommunity

Forrester’s Elaine Hutton just published an article “RIP, RFP — This Is Not Your 1990s Vendor Selection Process” that explains why the RFP (request for proposal) does not satisfy modern digital business needs of innovation and agility and is close to extinction. Link

Posted in Trends | Leave a comment

Sketch-Based Image Synthesis

Posted on November 6, 2019 by decisionmanagementcommunity

Recently, there has been an increase in popularity of artwork generated by computers. The goal of sketch-based image synthesis is to generate some image given the constraint of a sketched object. This allows non-artist users to turn simple black and white drawings into more abstract, detailed art. See how it can be done here. This new article describes an interactive sketch recommendation system which helps the user to draw and interactively generate realistic images. See video

Posted in Art, Artificial Intelligence | 1 Comment

Continuous Delivery for Machine Learning

Posted on November 3, 2019 by decisionmanagementcommunity

ThoughtWorks put out an article about Continuous Delivery for Machine Learning: “Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in three axis: the code itself, the model, and the data. Their behaviour is often complex and hard to predict, and they are harder to test, harder to explain, and harder to improve. Continuous Delivery for Machine Learning (CD4ML) is the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications.” Link

Posted in Architecture, Machine Learning | Leave a comment

DMN Can Execute Machine Learning Models

Posted on November 1, 2019 by decisionmanagementcommunity

Bruce Silver: “The ability to execute machine learning models is a standard feature of DMN.  From the start, the DMN spec said that in addition to the normal value expressions modeled in FEEL, decisions can execute machine learning models packaged using the Predictive Model Markup Language, or PMML. By providing a standard format for a wide range of machine learning model types, PMML enables interchange and interoperability across tools. In this post we’ll see how DMN can be used to make machine learning human-understandable” Link

Posted in Decision Modeling, DMN, Machine Learning, PMML, Standards | Leave a comment

Six AI Fears on Halloween

Posted on October 31, 2019 by decisionmanagementcommunity

It’s Halloween, and if you want to know how scared you should or shouldn’t be about the future of AI, you can read Andrew Ng’s take on 6 AI fears in the Halloween special edition of The Batch! Each fear contains 4 sections:

  • What could go wrong
  • Behind the worries
  • How scared should you be
  • What to do                                                                                                           Link
Posted in Artificial Intelligence | Leave a comment

Impact of AI on Intellectual Property Law and Policy

Posted on October 31, 2019 by decisionmanagementcommunity

The United States Patent and Trademark Office (“USPTO”) is gathering information about the impact of artificial intelligence (“AI”) technologies on the copyright, trademark, and other intellectual property rights. It asks 13 questions like this one: “Should a work produced by an AI algorithm or process, without the involvement of a natural person contributing expression to the resulting work, qualify as a work of authorship protectable under U.S. copyright law?” Link

Posted in Artificial Intelligence | Leave a comment

Gartner’s Magic Quadrant for Metadata Management Solutions

Posted on October 31, 2019 by decisionmanagementcommunity

On Oct 16 Gartner published a new “Magic Quadrant for Metadata Management Solutions“.  Metadata management is an aspect of an organization’s management of its data and information assets. The term “metadata” describes the various facets of an information asset that can improve its usability throughout its life cycle. While it does not directly mention Decision Management and its vendors, it includes:

  • Business glossary — A repository used to communicate and govern an enterprise’s business terms, along with the associated definitions and the relationships between those terms
  • Rules management — Automates the enforcement of business rules that are tied to data elements and associated metadata. This capability supports dedicated interfaces for the creation of, and the order of execution and links with, information stewardship for effective governance.
Posted in Business Processes, Business Rules | Leave a comment

AI-Driven Decision Making

Posted on October 30, 2019 by decisionmanagementcommunity

This HBR’s article “What AI-Driven Decision Making Looks Like” discusses the evolution of decision-making. Here are the stages:

  • A decision-making model based on human judgement
  • Data-supported decision making
  • A decision-making that utilizes AI
  • A decision-making that combines the power of AI and human judgement  Link
Posted in Artificial Intelligence, Decision Making, Human-Machine Interaction | Leave a comment

New Vendor’s Corner

Posted on October 29, 2019 by decisionmanagementcommunity

Following the initiative of DecisionCAMP-2019, we decided to introduce a Vendor’s Corner where all BR&DM vendors may share their latest news. If you represent a vendor of Business Rules and Decision Management tool, feel free to send your latest news to decisionmanagementcommunity@gmail.com. The news should include: a product/feature image, 1-2 sentences, and a link that describes your latest achievement.

Posted in Decision Modeling, Rule Engines and BRMS, Vendors | Leave a comment

Breakthrough in Google Search

Posted on October 28, 2019 by decisionmanagementcommunity

On Oct. 25 Pandu Nayak, Google Fellow and Vice President of Search, published an article “Understanding Searches Better Than Ever Before“.  “Search is about understanding language. The latest advancements from our research team in the science of language understanding–made possible by machine learning–we’re making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search.”  Link

Posted in Machine Learning, Natural Language Processing | Leave a comment