Category Archives: Machine Learning

On the Paradox of Learning to Reason from Data

Cornell University published this article on May-2022: “Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question … Continue reading

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Forrester announces AI 2.0

As more businesses leverage artificial intelligence to drive transformative customer experiences and real-time business decisions, Forrester announces “a new era of AI development – one that addresses accuracy, speed, and security.” Link 

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Intelligent Business Automation (IBA)

The hype surrounding intelligent business automation is at all-time high. “Intelligent business process automation is the next evolution of BPM. BPM is a way to automate processes, which allows people and companies to be more efficient and effective when getting … Continue reading

Posted in Business Processes, Case Management, Machine Learning | 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

Learning Jointly from Rules and Data

Today’s post in Google AI Blog “Controlling Neural Networks with Rule Representations” introduces a novel approach that does not require machine learning models retraining to adapt the rule strength. In real-world domains where incorporating rules is critical – such as physics … Continue reading

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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

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ML has a proof-of-concept-to-production gap

Andrew Ng: “All of AI, not just healthcare, has a proof-of-concept-to-production gap. The full cycle of a machine learning project is not just modeling. It is finding the right data, deploying it, monitoring it, feeding data back [into the model], … Continue reading

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Machine Learning vs. Knowledge Acquisition

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Everything old is new again

Prof. Gene Freuder writes about Human-Centered AI: “human-centered”, “human-aware“, “human-AI collaboration” are, rightly, very prominent nowadays. But “everything old is new again”: I ran across an interesting twenty-year-old paper from the European Journal of Operational Research on Human centered processes and decision support … Continue reading

Posted in Business Rules, Constraint Programming, Decision Making, Human-Machine Interaction, Machine Learning | Leave a comment

Improving code vs improving data quality

Andrew Ng: “Traditional software is powered by code, whereas AI systems are built using both code (models + algorithms) and data. When a system isn’t performing well, many teams instinctually try to improve the code. But for many practical applications, … Continue reading

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