Category Archives: Knowledge Representation

Combining Symbolic and Generative AI

From an expert working on cutting edge neuro symbolic AI solutions, integrating LLM and decision management systems to bring real AI value to the enterprises: “Enterprise should not consider Gen AI as the solution to implement their interactive, customer facing, conversation … Continue reading

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LLMs and Attributed Provenance

George Keller Hart: “Large language models are potentially our civilization’s self blinding. How? By undermining our 5,000+ year heritage of written language – specifically because LLMs substitute memetics for attribution, breaking the first rule of shared, collective knowledge. Our vast … Continue reading

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The seven levels of artificial intelligence by Warren B. Powell

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Posted in Artificial Intelligence, Business Analytics, Business Rules, Decision Optimization, Knowledge Representation, Machine Learning, Optimization | Leave a comment

Knowledge Graph in 100 Lines of Code

Knowledge graphs are getting lots of attention at the moment, as they are the natural Yin to the Yang of LLMs, providing structured data to chat interfaces, and powering Retrieval Augmented Generation. In this article Dan Selman demonstrates how to create your own custom Knowledge … Continue reading

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Novel Decision Modeling Techniques (DC2023 Notes Part 2)

There were two presentations at DecisionCAMP-2023 devoted to new ideas that extend traditional Decision Modeling and DMN-like tools in new directions: 1) Probabilistic reasoning when a decision model deals with uncertain facts about which we know only their probability; 2) Declarative … Continue reading

Posted in Constraint Programming, Decision Intelligence, Decision Modeling, Decision Optimization, DecisionCAMP, Knowledge Representation, Reasoning, Rule Violations | Leave a comment

In Memory of Doug Lenat

“Intelligence is ten million rules.” Doug Lenat Douglas Lenat, who Tried to Make A.I. More Human, died at 72. He spent decades working on artificial intelligence, striving to create computers that could replicate common sense. In 2012 Doug was our … Continue reading

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Knowing-How and Knowing-That

Dr. Saba discusses our use of the phrase ‘I know’ in our everyday linguistic communication and points to the critical difference in the two major uses of the phrase. His point is that ML, as it is practiced today, is … Continue reading

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Generative AI on the Peak of the 2023 Hype Cycle

On Aug 16, 2023 Gartner published a press release Gartner Places Generative AI on the Peak of Inflated Expectations on the 2023 Hype Cycle for Emerging Technologies: “While all eyes are on AI right now, CIOs and CTOs must also … Continue reading

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Avoiding AI Hallucinations

Large language models (LLMs) trained on stale, incomplete information are prone to “hallucinations”—incorrect results, from slightly off-base to totally incoherent. Hallucinations include incorrect answers to questions and false information about people and events. This article “Why knowledge management is foundational to … Continue reading

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Semantics, Ontology and Explanation

While ChatGPT dominates social forums, scientists quietly continue to work on real understanding of our surrounding using 100-years-old concepts of Logic, Semantics, and more recently Ontologies. Computer science people build symbolic models to represent their assumptions about a certain domain … Continue reading

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