Are our Rule Engines Smart Enough?

On Sep 27, 2022 we had several discussions related to this question during the ongoing DecisionCAMP. Are we happy with where we are or should we raise the bar trying to come closer to our long-standing “big” objective when a subject matter expert defines a business problem and our smart engine comes up with a decision? In my presentation I presented a good enough solution of a real-world problem which still forced a business user to define nested loops to specify how to find a decision. I asked if it was really necessary? Then we discussed a more general question during our expert panel: watch this discussion and provide your comments.

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DecisionCAMP-2022 Starts on September 26

DecisionCAMP is around the corner. It will feature very interesting presentations: informative, based on real-world experience, with new perspectives, and leading to controversial discussions about where we are and where we want to be. You may click on the links “Slides” to check the presentations ahead of time. Here is the complete Schedule. If you miss some presentations, they will be available next day on YouTube channel. You still can register for free to receive an invite. Link

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Treating Your Employees Like Customers

The onboarding process should be a streamlined integrated experience that asks for and offers the right information at the right time. How to do it? Sandy Kemsley in her latest vlog recommends treating your employees like customers. “I spend most of my time with clients looking at core line of business processes that form part of the customer journey in some way. But there are also significant benefits to making your internal processes just as good as your customer facing ones.Link

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Extracting Decision Models from Text

KU Leuven scientists published the paper “Extracting Decision Model and Notation models from text using deep learning techniques“. It is the first attempt to use deep learning to extract DMN models from text. They classify sentences describing logic or dependencies, then they extract decision dependencies from sentences; and finally labeled and tagged dataset is made available for decision model extraction. It would be interesting to see how this technique is applied to a real-world use case. Link

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Christopher Columbus, global warming and mathematical optimization

In this highly popular article IBM’s Alex Fleischer wrote: “One of the levers is the mathematical optimization of business decision making, made possible by data and the deployment of decision models associated with constraint programming. Or, put more simply: an algorithm that relies on data generated by the company to suggest precise, logical and – as long as it is – efficient decisions.” Link

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On the Paradox of Learning to Reason from Data

Cornell University published this article on May-2022: “Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning. We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has in fact learned statistical features that inherently exist in logical reasoning problems. Link

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Most knowledge is not verbal

https://www.noemamag.com/ai-and-the-limits-of-language/
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Operations Research is the most underrated AI field

Berk Orbay published a nice article An Ode to Operations Research and the Future: “Operations Research (OR) is the most underrated “Artificial Intelligence” field. Is OR actually AI? It depends how you classify AI. If it is about decision making by a machine given a set of context and limitations, then OR is right in the field of AI. OR provides a mathematical framework and algorithms to efficiently find the best solutions.” Link

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10 Common Mistakes in Automation

Jean Pommier from IBM wrote: “I’m often suspicious when I see best practices not issued by practitioners who spend their lives in the trenches but this contains quite some field-tested wisdom, well done Gartner! And since too few people listen to experienced people in the cloud age, let’s amplify…Link

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Algorithms for Decision Making

MIT press published an excellent book “Algorithms for decision making” with free download that provides a broad introduction to algorithms for decision making under uncertainty. The book takes an agent based approach when physical entities like humans or robots act based on observations of their environment. The interaction between the agent and the environment follows an observe-act cycle. Link

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