Decision Optimization and Generative AI

There are two new articles devoted to this topic:

Optimization and Generative AI” by Alex Fleischer

LLM-ify me – Optimization edition” by Richard Oberdieck

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AI in Commercial Context

Harley Davis: “At least in industrial/commercial contexts, we are usually interested in solving a series of more specific problems rather than having one big AI that can do anything. And those specific problems always involve interfacing with some existing processes, data, and systems. So using AI in a business context means understanding the business domain, the regulations and policies that govern how the business works for the problems we are solving, and software engineering to interface with the existing context. Discussions of AGI are really beside the point but discussions of which combination of technologies to use, and how to engineer systems for correctness and manageability are critical. This is the driver behind Hybrid AI.Link

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Challenge Dec-2024: Pricing Policy

Dec-2024 Challenge deals with an order pricing policy that depends on a number of different units, product types (Standard or Premium), country of origin, etc. A seller uses a mixed bracket volume and cost policy for orders with multiple units:

  • 1-99 units: $10 per unit (Standard), $15 per unit (Premium)
  • 100-499 units: $9 per unit (Standard), $14 per unit (Premium)
  • 500+ units: $8 per unit (Standard), $13 per unit (Premium)

For products that originated not in the US or Canada, there is a handling fee of $50 per order. If the order includes hazardous material, the total price should be increased by 30%. Link

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Agentic AI with Self-Reflection

Armand Ruiz, VP of Product – AI Platform @IBM, posted “The future of AI is Agentic” with this architectural schema that looks promising for business decision-making with ongoing improvements:

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W2 job vs Your own company

There is an interesting discussion on LinkedIn: “Exactly 1 year ago today I left my high-paying W2 job to start my own company. Here are 9 reasons why you should NOT do the same thing.” Link

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Focus on Learning Not On Knowledge

Peter Voss lists “Focus on Knowledge, Not Learning” as one of The 7 Deadly Sins of AGI Design: “There’s a common misconception that knowledge is a good measure of intelligence. This is not so. An encyclopedia has a lot of knowledge but no intelligence. We also all know people who are quite knowledgeable but seem to lack in intelligence. On the other hand, we know of Bushmen in Africa who grew up not knowing books, electricity, or cars and yet able to learn to succeed in a large modern city within a short period of time.

AI development over the past several decades has focused far too much on having knowledge and skills rather than the ability to dynamically acquire, integrate, and generalize new knowledge and skills effectively. Large Language Models in particular are quite unable to update their core model in real time.” Link

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What is Decision Intelligence (DI)?

Gartner defined decision intelligence (DI) as “a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.” Sparkling Logic describes how the term “Decision Intelligence” evolved from the mid-2000s until today. It addresses the following questions: What is Decision Intelligence? What is the Difference Between Decision Intelligence and Business Intelligence? What is a Decision Intelligence Platform? Link

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The evolution of machine learning experts

Published by Andriy Burkov, author of The Hundred-Page Machine Learning Book: Link

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Agentic DI as GPS for Decisions

Joe Dery published the article “Why Decision Intelligence Is the Future of Analytics – And How It Delivers Real Outcomes“. He offers us the challenge: “Take a problem your team is tackling right now and reframe it through the lens of Decision Intelligence (DI). Map out the decision-making processes tied to that challenge. Where could you enhance decisions? What tasks could be automated? What behaviors could you ethically influence? And most importantly, where would analytical insights make the biggest impact if embedded directly into the process?” He states: “With DI, it’s not just about analyzing data – it’s about building GPS ecosystems for decisions. These systems are dynamic, adaptive, and designed to guide us toward better outcomes, step by step.” Link

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The Business Complexity of a DMN Model

This article with this title was published by Stefaan Lambrecht, a frequent DecisionCAMP‘s presenter. “You’re a DMN decision modeler, about to embark on a new project. You’re excited, a little nervous, and ready to face the challenges that lie ahead. But there’s one thing you can’t control – the complexity of the business or business unit you’re about to tackle... Most likely DMN decision models will never form the basis for the logic of self-driving cars, but we can’t deny the fact that business complexity is here to stay, and it’s only going to get more intricate. Let’s face it, we live in a world where the demands on products, services, and compliance with rules and legislation are constantly increasing.” Link

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