Category Archives: LLM

2025 LLMs in Review by Peter Norvig

Peter Norvig wrote today: “I am now done comparing three LLMs to my own coding on the Advent of Code problems. The LLMs did great! They couldn’t have done it last year.” Here are his main conclusions after asking 3 … Continue reading

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2025 LLM Year in Review by Andrey Karpathy

Andrey Karpathy published a not-too-technical review of technical developments in generative AI this year: “2025 was an exciting and mildly surprising year of LLMs. LLMs are emerging as a new kind of intelligence, simultaneously a lot smarter than I expected … Continue reading

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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

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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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