A Farewell to In-Person Conferences? Anti-notes from DecisionCAMP-2020

This year almost all major conferences went virtual. Our 12th DecisionCAMP-2020 on June 29-July 1 in Oslo also went virtual and became a kind of success. No wonder: the registration count was 3-4 times larger than usual, people did not have to travel, the Zoom sessions ran rather smoothly and the use of Slack for QnA was very helpful. Along with interesting technical sessions, we even had a virtual cocktail-hour with BYOD. Sandy Kemsley did a great job as our moderator. All sessions were synchronously streamed live at DecisionCAMP’s YouTube channel and all recordings were made publicly available on this channel almost in no time. The majority of attendees liked to event. So, as many other conferences, we managed to convert a virtual necessity to actual success. But why do I, the chair of this successful conference, not feel satisfied? Continue reading

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Real-Time Decision-Making

How does your Decisioning Platform support real-time decision-making? Coolfire: “In order to make real-time decision-making a core organizational capability, it’s time for organizations of all kinds to invest in a platform designed to make their data actionable. That means utilizing a technology designed precisely for real-time operational success. Throughout industries, it’s the companies already investing in this capability that maintain an edge on the competition. When the right data is delivered at the right time — to the people that need it — the results can be transformative.”  Link

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Challenge Sep-2020: Compressing Decision Tables

Using common sense people can replace larger decision tables with smaller ones. However, when a decision table includes more attributes (columns), the manual compression of the decision table becomes difficult or impossible even if you allow a certain level of unsuccessful results. In most cases, you need some special tools provided by digital decisioning products. This challenge gives you an opportunity to try. Link

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How to Evaluate Performance of Machine Learning Models

A new post in KDnuggets explains “How to Evaluate the Performance of Your ML Model“: “You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.Link

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How AI Will Automate 70% Of Software Development

On Sep 24 at noon EST Mike Gualtieri, VP/Principal Analyst at Forrester and our Keynote Speaker at DecisionCAMP-2019, will run the webinar “The Future of Software Development“. Mike asserts that “70% of business software is non-creative and doesn’t require computer-genius skills. Far from it. It involves using frameworks, wiring APIs, if-thens, and loops. That’s why #AI will be able to automate much of appdev in 5 years.Link

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Self-Learning Decision Models

The next monthly DecisionCAMP session with this topic is scheduled to run on Sep 4 at 12:00 PM EST – get a Zoom URL at our Slack channel. Here is the abstract: “A new open source RuleLearner.com is oriented to business analysts who have sets of already classified data instances and want to find business rules that can successfully classify similar new data instances. Without forcing business analysts to become experts in data science or programming, Rule Learner discovers business rules by naturally incorporating machine learning algorithms into Business Decision Models giving them self-learning capabilities.” Link

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Understanding the business problem is the biggest challenge

The new TechCrunch’s post states: “The most important question in data science is not which machine learning algorithm to choose or even how to clean your data. It is the questions you need to ask before even one line of code is written: What data do you choose and what questions do you choose to ask of that data? What is missing (or wishfully assumed) from the popular imagination is the ingenuity, creativity and business understanding that goes into those tasks.Link

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Too many AI researchers think real-world problems are not relevant

MIT Technology Review published an article with this title. “Many machine learning papers that describe new applications present both novel concepts and high-impact results. But even a hint of the word “application” seems to spoil the paper for reviewers. As a result, such research is marginalized at major conferences… When studies on real-world applications of machine learning are excluded from the mainstream, it’s difficult for researchers to see the impact of their biased models, making it far less likely that they will work to solve these problems.” Link

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Intuit Tax Knowledge Engine

Intuit just published a technical overview of their new Tax Knowledge Engine, the key innovation to make TurboTax smarter and more personalized for 37M+ consumers. First they listed key limitations for the traditional approach that are common for many rules-based systems. Then they describe a fundamental paradigm shift in representing complicated tax compliance calculations and rules at scale via knowledge graphs and connect associated user data together, instead of hard-coding tax logic in procedural programming code. Link Continue reading

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Common Misconceptions About Analytics

In her Aug 14 post, Cassie Kozyrkov, the Head of Decision Intelligence at Google, analyzes three common misconceptions about analytics:

  1. Analytics is data journalism / marketing / storytelling. (No.) 
  2. Analytics is decision-making. (No!)

and then describes the 3 excellences in data science.  Link

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