LLM humanizes Optimization Solver’s results

Tiago de Morais Montanher altered the data for the classic transportation problem to render the problem infeasible. Then he called the conflict refiner from CPLEX to obtain a list with 5 points like the one below:

_TConflictConstraint(name=’capacity_seattle’, element=docplex.mp.LinearConstraint[capacity_seattle](quantity_seattle_new_york+quantity_seattle_chicago+quantity_seattle_topeka,LE,350), status=<ConflictStatus.Member: 3>)

Finally, he asked Perplexity to clarify these conflicts for decision-makers and propose actions to rectify the formulation. The results were impressive: Link. Also see Adam DeJons’s post

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When good intentions backfire

Cobra Effect

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“We replaced AI workers with human interns..”

Link

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Gartner Stock Down 49%

Forbes: Gartner Stock Down 49%. Learn Why, What CEO Can Do, And Whether To Buy $IT

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On the way to Cognitive AI

Peter Voss will present “Beyond LLMs: INSA – Integrated Neuro-Symbolic Architecture” at DecisionCAMP-2025 on Sep 23. Meanwhile, you may read his article “The Jury is out: Scaling LLMs will not get us to Real Intelligence. What will?” about the importance of real-time incremental, lifelong learning.

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MIT: Shadow AI in Business 2025

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Integrating Relational Databases and Decision Intelligence Platforms

This topic has been of interest to many rule engine vendors and practitioners for years. There was an interesting discussion about an integrated use of Business Rules and DB a year ago: https://lnkd.in/ecych49r

What do you think today about the relationships between databases and the modern DI platforms?

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The next much more meaningful chapter will be about human–AI symbiosis, not parroting chatbots

Martin Milani commented on Thomas Kehler’s article “Is windetr on the horizon for Generative AI?“: “The next much more meaningful chapter will be about human–AI symbiosis, not parroting chatbots. Like the dot-com bust, this bubble will burst, and AI will come back down to reality — stronger, if we rebuild it on the right foundations that today are being drowned out by noise and hype.” Link

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The Intuition Behind How Large Language Models Work

Prof. Mark Riedl posted intuitive explanations of how LLMs, LLM-chatbots, and Agentic AI work:
Part I “LLMs and chatbots
Part II “RAG, Chain of Thought, Agents

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Announcing IBM Decision Intelligence

“IBM Decision Intelligence addresses the gap between policy intent and operational execution. It represents a paradigm shift from static rule engines to AI-native decision making. The generative AI engine interprets natural language policy descriptions, automatically synthesizes business rules, and deploys intelligent models that will learn and improve from every outcome. Business users are empowered to become decision architects, collaborating with AI to create decision logic that combines human expertise with machine intelligence to improve business outcomes.” Link

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