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.
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?
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
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“
“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
Adam DeJans Jr. wrote: “Modeling the world isn’t the same as making decisions in it! A common trap is to spend months building a perfect simulator… a beautiful digital twin with clean architecture, smooth animations, and every physical nuance modeled. And yet… when it comes time to actually make decisions?
No policy. No framework. Just dashboards and “what-if” buttons.
Always separate the two:
🔹 Modeling the system (the physics, flows, stochastic processes) gives you a sandbox to play in.
🔹 Designing the policy (decision model) means deciding how you’ll act over time, based on what you observe in the system.” Link
“The U.S. insurance industry is at a critical juncture, grappling with a crisis in consumer trust, mounting climate-related risks, and the transformative power of advanced technologies. As insurers navigate these challenges, they must balance innovation with regulatory compliance, prioritize customer-centric approaches, and upskill their workforce to stay ahead.” Discover the key takeaways, trends, and solutions from the new report: The Future of Insurance: Trust, Tech Smart & Talent Focused.
The annual MiniZinc Challenge celebrates the state of the art in constraint modelling and solving. Across the different problems, this year’s result once again highlighted the diversity of approaches, pushing the boundaries of what’s possible in modelling and solving complex problems. Here are this year’s results:
“Sometimes innovation is just panic dressed as strategy.
Sometimes marketing is just noise to distract from a lack of meaning.
Sometimes chasing growth, valuations, market share etc. can make you forget why you started in the first place.”
https://lnkd.in/eDXuiNvS
Decision Management Community 
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