AI and “X”

Walid Saba started an interesting discussion at LinkedIn: It is amazing how the commercialism and the misguided hype have invented AI topics that have nothing to do with AI, only because AI is now a hot commodity. So now you have talks and seminars and NGOs and non-profit orgs (that later secure big funding!!!) under labels such as “Democratizing AI”, “AI and the Environment”, “AI and human rights”, “AI and Ethics”, “AI and Fairness”, … Shouldn’t we have (some) AI before we discuss “AI and X”? Link

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The Decision-Making Side of Machine Learning

Michael I. Jordan is a professor at Berkeley and one of the most influential people in machine learning, statistics, and artificial intelligence. Watch his recent presentation: “Much of the recent focus in machine learning has been on the pattern-recognition side of the field. I will focus instead on the decision-making side, where many fundamental challenges remain. Some are statistical in nature, including the challenges associated with multiple decision-making, and some are algorithmic, including the challenge of coordinated decision-making on distributed platforms.” See also his discussion with Lex Fridman. Continue reading

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Intelligent Event Broker

Jason English, a Principal Analyst from Intellyx, wrote a whitepaper called Event Mesh: Event-Driven Architecture for the Real Time Enterprise”:  “We live and work in an event-driven world. Tightly coupled integrations, and conventional data transfer and processing techniques won’t be able to keep up with the accelerating pace of events in the new economy. Customer demand and cost pressures on IT will require enterprises to change the way they think about integration and messaging. In your next design or change exercise, rather than calling for more point-to-point integrations – start finding ways to incorporate pub-sub calls that support an event-driven approach.” Link

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GPT-3, the latest evolution in language technology – What is the big deal?

Over the summer 2020, the latest language model from OpenAI, called GPT-3, created a lot of buzz around the internet. Both within the AI community and outside people shared links to numerous examples on what GPT-3 could do, ranging from writing poetry and generating HTML code to answering philosophical questions on the meaning of life. Link

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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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