GridGain Professional Edition 2.4 Introduces Integrated Machine Learning and Deep Learning in New Continuous Learning Framework, Adds Support for Apache Spark DataFrames - EconoTimes

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FOSTER CITY, Calif., March 27, 2018 -- GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite, today announced the immediate availability of GridGain Professional Edition 2.4, a fully supported version of Apache Ignite 2.4. GridGain Professional Edition 2.4 now includes a Continuous Learning Framework, which includes machine learning and a multilayer perceptron (MLP) neural network that enable companies to run machine and deep learning algorithms against their petabyte-scale operational datasets in real-time. Companies can now build and continuously update models at in-memory speeds and with massive horizontal scalability. GridGain Professional Edition 2.4 also enhances the performance of Apache Spark by introducing an API for Apache Spark DataFrames, adding to the existing support for Spark RDDs. GridGain Continuous Learning Framework GridGain Professional Edition 2.4 now includes the first fully supported release of the Apache Ignite integrated machine learning and multilayer perceptron features, making continuous learning using machine learning and deep learning available directly in GridGain.


Leading AI country will be 'ruler of the world,' says Putin

@machinelearnbot

Russian President Vladimir Putin warned Friday (Sept. AI development "raises colossal opportunities and threats that are difficult to predict now," Putin said in a lecture to students, warning that "it would be strongly undesirable if someone wins a monopolist position." Future wars will be fought by autonomous drones, Putin suggested, and "when one party's drones are destroyed by drones of another, it will have no other choice but to surrender." U.N. urged to address lethal autonomous weapons AI experts worldwide are also concerned. On August 20, 116 founders of robotics and artificial intelligence companies from 26 countries, including Elon Musk and Google DeepMind's Mustafa Suleyman, signed an open letter asking the United Nations to "urgently address the challenge of lethal autonomous weapons (often called'killer robots') and ban their use internationally."


'Explainable Artificial Intelligence': Cracking open the black box of AI

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At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant. "It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble," said AWS' chief architect in his day two keynote at the company's summit in Sydney. Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why.


'Explainable Artificial Intelligence': Cracking open the black box of AI

#artificialintelligence

At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant. "It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble," said AWS' chief architect in his day two keynote at the company's summit in Sydney. Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why.


Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint

arXiv.org Machine Learning

Deep Belief Networks (DBN) have been successfully applied on popular machine learning tasks. Specifically, when applied on hand-written digit recognition, DBNs have achieved approximate accuracy rates of 98.8%. In an effort to optimize the data representation achieved by the DBN and maximize their descriptive power, recent advances have focused on inducing sparse constraints at each layer of the DBN. In this paper we present a theoretical approach for sparse constraints in the DBN using the mixed norm for both non-overlapping and overlapping groups. We explore how these constraints affect the classification accuracy for digit recognition in three different datasets (MNIST, USPS, RIMES) and provide initial estimations of their usefulness by altering different parameters such as the group size and overlap percentage.