Category Archives: Machine Learning

Machine Learning Yearning

Andrew Ng is regularly updating a draft of his book “ML Yearning“. “Machine learning is the foundation of countless important applications, including web search, email anti-spam, speech recognition, product recommendations, and more. I assume that you or your team is working … Continue reading

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NYT Interview with Benjamin Grosof

New Your Times published an interview with Benjamin Grosof, a leading AI expert and a frequent presenter at RuleML+RR and DecisionCAMP.  While deep learning comes from the statistical side of AI, the interview brings attention to non-deep learning tools such as Prolog, a programming language … Continue reading

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Custom R charts coming to Excel

This week at the BUILD conference, Microsoft announced that Power BI custom visuals will soon be available as charts with Excel. You’ll be able to choose a range of data within an Excel workbook, and pass those data to one of the … Continue reading

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Let the AI Benchmarking Wars Begin

On May 2, 2018 a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – released MLPerf, a nascent benchmarking tool “for measuring the speed of machine learning software and hardware.” The hope is … Continue reading

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Detecting cancer in real-time with machine learning

Google Research this week revealed an AR microscope (ARM) capable of detecting cancerous cells in real-time with the help of machine learning. Locating cancer with a standard microscope is a difficult and time-consuming process, with a raft of information for doctors to … Continue reading

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Open the Machine Learning Black Box with Rule-based Decision Automation

Francis Friedlander from IBM just published an article with this title. In particular, it says: “Machine learning is best in class to derive customer insight from customer data. Rules consume customer insight and are best in class to make justified … Continue reading

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Assessing Cardiovascular Risk Factors with Computer Vision

Google Brain Team: “Recently, we’ve seen many examples of how deep learning techniques can help to increase the accuracy of diagnoses for medical imaging, especially for diabetic eye disease. In “Prediction of Cardiovascular (CV) Risk Factors from Retinal Fundus Photographs via Deep … Continue reading

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