Category Archives: Knowledge Representation

Most knowledge is not verbal

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2022 is the Year of Prolog

In the summer of 1972, Alain Colmerauer and his team in Marseille developed and implemented the first version of the logic programming language Prolog. Together with Robert Kowalski and his colleagues in Edinburgh, this work laid the practical and theoretical … Continue reading

Posted in Constraint Programming, Events, Knowledge Representation, Languages, Logic and AI | Leave a comment

Keynotes at DecisionCAMP and Declarative AI

Posted in DecisionCAMP, Digital Decisioning, Events, Knowledge Representation, Logic and AI | Leave a comment

AI in Decision Domain 

An interesting discussion has started by this LinkedIn post: Typically, we analyze data and models from the operational domain. However, no amount of crunching on this data will reveal opportunities for decision advantage – because there is no information about … Continue reading

Posted in Artificial Intelligence, Decision Making, Decision Modeling, Human-Machine Interaction, Insurance Industry, Knowledge Representation | Leave a comment

Using Episodic Memories to Predict Upcoming Events

This paper addresses an important problem in control of episodic memory to be used to predict upcoming states in an environment where past situations sometimes reoccur. One of the key benefits is reducing the risk of retrieving irrelevant memories. Read more

Posted in Decision Making, Decision Modeling, Event-driven, Knowledge Representation, Machine Learning | Leave a comment

Again about AI understanding

On Dec-2021 Melanie Mitchell published “What Does It Mean for AI to Understand“: “Remember IBM’s Watson, the AI Jeopardy! champion? A 2010 promotion proclaimed, “Watson understands natural language with all its ambiguity and complexity.” However, as we saw when Watson subsequently failed spectacularly in … Continue reading

Posted in Artificial Intelligence, Human-Machine Interaction, Knowledge Representation, Natural Language Processing | Leave a comment

Semantic Rules & Machine Learning

Dr. Walid Saba discusses the limitations of the data-driven, statistical and machine learning (ML) approaches that are the currently dominant paradigm in the use of natural language processing (NLP) in text analytics. Using very simple examples, he argues that these … Continue reading

Posted in Knowledge Representation, Logic and AI, Machine Learning, Natural Language Processing | Leave a comment

Machine Learning vs. Knowledge Acquisition

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GPT-3, the latest evolution in language technology – What is the big deal?

Over the summer 2020, the latest language model from OpenAI, called GPT-3, created a lot of buzz around the internet. Both within the AI community and outside people shared links to numerous examples on what GPT-3 could do, ranging from … Continue reading

Posted in Artificial Intelligence, Human-Machine Interaction, Knowledge Representation, Languages, Natural Language Processing | Leave a comment

Intuit Tax Knowledge Engine

Intuit just published a technical overview of their new Tax Knowledge Engine, the key innovation to make TurboTax smarter and more personalized for 37M+ consumers. First they listed key limitations for the traditional approach that are common for many rules-based … Continue reading

Posted in Artificial Intelligence, Business Rules, Explanations, Human-Machine Interaction, Knowledge Representation, Logic and AI | Leave a comment