Welcome to the Momenta Learning News on Machine Learning. This is issue 78, please feel free to share this post.
With an eye toward taking the healthcare supply chain to new levels, UPMC on July 8 introduced Pensiamo, a new company whose goal is to help hospitals improve supply chain performance, where costs are high and getting higher, second only to labor costs. Pensiamo is an Italian word. Translation: ‘We think.’
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Two recent accidents involving Tesla’s Autopilot system may raise questions about how computer systems based on learning should be validated and investigated when something goes wrong. A fatal Tesla accident in Florida last month occurred when a Model S controlled by Autopilot crashed into a truck that the automated system failed to spot.
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A few years ago, Microsoft launched Bing Predicts, a project that aims to correctly predict sporting events and elections by combining lots of data with machine learning algorithms. Until now, though, there wasn’t really a product that made Bing Predicts available to users. Today, however, the company launched its Cortana Intelligence with Bing Predicts service.
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“Ever since we started Moodstocks, our dream has been to give eyes to machines by turning cameras into smart sensors able to make sense of their surroundings,” the company said in a statement. “Our [new] focus will be to build great image recognition tools within Google.”
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There are so many Machine Learning algorithms and so many parameters for each one. Why can’t we just use a meta-algorithm (maybe even one that uses Machine Learning) to select the best algorithm and parameters for our dataset? – Every first year grad student who has taken a Machine Learning class It seems obvious, right?
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One of the consistent characteristics of the tech industry is an endless labelling of technology and approaches. Some of it is foundational resulting from some entirely new. Much of it is re-categorizing something, either because it is suddenly trendy again or because a set of ideas have been organized in a new way.
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What are the best tools to get started with Java machine learning? They’ve been around for a while, but these days it feels like everyone is talking about artificial intelligence and machine learning. It’s no longer a secret reserved to scientists and researchers, with implementations in nearly any new emerging technology.
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Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table Jani Huttunen 1,2, Harri Kokkola 1Finnish Meteorological Institute (FMI), Atmospheric Research Centre of Eastern Finland, Kuopio, Finland 2Department of Applied Physics, University of Eastern Finland, Kuopio, Finland 3Independent
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Researchers from the University of Southern California have developed a new machine learning tool capable of detecting certain speech-related diagnostic criteria in patients being evaluated for depression. Known as SimSensei, the tool listens to patient’s voices during diagnostic interviews for reductions in vowel expression characteristic of psychological and neurological disorders that may not be sufficiently clear to human interviewers.
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I saw a lot of excitement and happiness a week or so ago around some reports that the EU’s new General Data Protection Regulations (GDPR) might possibly include a “right to an explanation” for algorithmic decisions.
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