Category Archives: LLM

Will LLMs replace optimization solvers?

“It’s a tempting story. After all, LLMs can write code, generate documentation, and even produce what looks like a mathematical model. But LLMs are pattern generators. They predict the next word, token, or code snippet based on what they’ve seen … Continue reading

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What Bertrand Russell would say today

Martin Milani: “Bertrand Russell didn’t trust language to express truth. He built a new system—formal logic—to make thought precise. In Principia Mathematica, Russell didn’t try to say things clearly. He tried to prove them. Today’s AI skips that step. It … Continue reading

Posted in Artificial Intelligence, Knowledge Representation, LLM, Logic and AI, Scientists | Leave a comment

Cognitive AI vs Statistical AI

Peter Voss was our presenter at DecisionCAMP-2025 in September. You may watch his presentation. Read his latest article, “Why Cognitive AI, and not LLMs, will get us to AGI”. Link

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Decision Agents Today

Right after DecisionCAMP-2025, where James Taylor was the moderator of the Expert Panel, he posted a nice presentation, “Building Decision Agents with LLMs & Machine Learning Models,” about Decision Agents within modern decision intelligence platforms. A brief summary:

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DecisionCAMP-2025 Poll Results

During DecisionCAMP-2025, we conducted the poll “Using LLM-based tools in the Decision Intelligence Context“. This poll pertained solely to Operational Repetitive Business Decisions. It contained 13 questions about the use of LLMs for the various decision automation tasks. Here are the … Continue reading

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Can LLMs create something truly new?

See Martin Milani‘s answer: “Novelty is not born from history. It arises from logic, imagination, and reasoning: asking “what if?”, testing counterfactuals, building on principles, and pushing beyond what data alone can reveal. Every major leap in science and technology … Continue reading

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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>) … Continue reading

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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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The Rise of Small Language Models (SLMs)

Specialized small language models (SLMs) can outperform large, generalist models. Want to learn how? Read Armand Ruiz’s post on LinkedIn: “Inference is cheaper. Iteration is faster. Fine-tuning takes hours, not weeks. SLMs can run locally, privately, and securely and no … Continue reading

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Anthropic Copyright Ruling May Spur More AI Licensing Deals

The first federal court decision on the fairness of taking copyrighted material to train generative artificial intelligence is a mixed outcome for tech companies and content creators that could prompt both parties to seek coexistence, according to attorneys, with the … Continue reading

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