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Category Archives: Machine Learning
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
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
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
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
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
Posted in Decision Modeling, Machine Learning
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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
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
Posted in Machine Learning
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Can Your Machine Learning Do the Lambada?
Ulrich Wiesner from FICO wrote: “Machine learning and data analytics are powerful methods, but typically the benefits do not come without effort, and careful considerations are required to make these tools efficient. When you purchase a tool or service, make … Continue reading
Posted in Decision Modeling, Machine Learning
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Why a YouTube Chat About Chess Got Flagged for Hate Speech
This WIRED article talks about shortcomings in AI programs designed to automatically detect hate speech, abuse, and misinformation online. When WIRED fed some of statements gathered by the CMU researchers into two hate-speech classifiers, the statement “White’s attack on black is … Continue reading
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