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Machine learning methods provide new insights into organic-inorganic interfaces

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Oliver Hofmann and his research group at the Institute of Solid State Physics at TU Graz are working on the optimization of modern electronics. A key role in their research is played by interface properties of hybrid materials consisting of organic and inorganic components, which are used, for example, in OLED displays or organic solar cells. The team simulates these interface properties with machine-learning-based methods. The results are used in the development of new materials to improve the efficiency of electronic components. The researchers have now taken up the phenomenon of long-range charge transfer.


Artificial Intelligence: Trends & Applications To Watch In 2020 - Simpliv Blog

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For movie buffs, the work that the factory machines do in Charlie Chaplin's 1936 classic, Modern Times, may have seemed too futuristic for its time. Fast forward eight decades, and the colossal changes that Artificial Intelligence is catalyzing around us will most likely give the same impression to our future generations. There is one crucial difference though: while those advancements were in movies, what we are seeing today are real. A question that seems to be on everyone's mind is, What is Artificial Intelligence? The pace at which AI is moving, as well as the breadth and scope of the areas it encompasses, ensure that it is going to change our lives beyond the normal.


Tulane to use artificial intelligence to study how nation's schools are responding to coronavirus

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The U.S. Department of Education's Institute of Education Sciences has awarded the National Center for Research on Education Access and Choice (REACH) at Tulane University a $100,000 contract to collect data from approximately 150,000 school websites across the country to see how the nation's education system is responding to the coronavirus pandemic. The project, which will track traditional public schools, charter schools and private schools, aims to quickly answer questions that are critical for understanding how students are learning when school buildings are closed. Key questions include: how many schools are providing any kind of instructional support; which are delivering online instruction; what resources are they offering to students and how do students stay in contact with teachers? "This data will also help answer important questions about equity in the school system, showing how responses differ according to characteristics like spending levels, student demographics, internet access, and if there are differences based on whether it is a private, charter or traditional public school," said REACH National Director Douglas N. Harris, Schlieder Foundation Chair in Public Education and chair of economics at Tulane University School of Liberal Arts. REACH will work in cooperation with Nicholas Mattei, assistant professor of computer science at Tulane University School of Science and Engineering, to create a computer program that will collect data from every school and district website in the country.


Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System

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Recently, there has been increasing interest in transparency and interpretability in Deep Reinforcement Learning (DRL) systems. Verbal explanations, as the most natural way of communication in our daily life, deserve more attention, since they allow users to gain a better understanding of the system which ultimately could lead to a high level of trust and smooth collaboration. This paper reports a novel work in generating verbal explanations for DRL behaviors agent. A rule-based model is designed to construct explanations using a series of rules which are predefined with prior knowledge. A learning model is then proposed to expand the implicit logic of generating verbal explanation to general situations by employing rule-based explanations as training data.


Conversational AI: Intelligent Virtual Assistants and the road ahead.

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In a fast-moving world, customers require efficiency and promptness when talking to any company. Here is where chatbots and Intelligent Virtual Assistants (IVAs) come into play. Thanks to their ability to engage into more advanced conversations, unlike rule-based chatbots, AI-powered systems are equipped with a multitude of features to assist and even entertain the users in their day-to-day activities. In addition to their customizable features, their self-learning ability and scalability have lead virtual assistants to gain popularity across various global enterprises. According to Grand View Research, the global intelligent virtual assistant market size was valued at USD 3.7 billion in 2019, growing at a Compound Annual Growth Rate (CAGR) of 34.0% over the forecast period.


The Future of Machine Learning is Tiny and Bright

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Posted by Josh Gordon, Developer Advocate A new HarvardX TinyML course on edX.orgProf. Vijay Janapa Reddi of Harvard, the TensorFlow Lite Micro team, and the edX online learning platform are sharing a series of short TinyML courses this fall that you can observe for free, or sign up to take and receive a certificate. In this article, I'll share a bit about TinyML, what you can do with it, and the …


Logistic Regression using SAS - Indepth Predictive Modeling

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What is this course all about? This course is all about credit scoring / logistic regression model building using SAS. There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. How to clarify objective and ensure data sufficiency?


Conservation machine learning

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Ensemble techniques--wherein a model is composed of multiple (possibly) weaker models--are prevalent nowadays within the field of machine learning (ML). Well-known methods such as bagging [1], boosting [2], and stacking [3] are ML mainstays, widely (and fruitfully) deployed on a daily basis. Generally speaking, there are two types of ensemble methods, the first generating models in sequence--e.g., AdaBoost [2]--the latter in a parallel manner--e.g., random forests [4] and evolutionary algorithms [5]. AdaBoost (Adaptive Boosting) is an ML meta-algorithm that is used in conjunction with other types of learning algorithms to improve performance. The output of so-called "weak learners" is combined into a weighted sum that represents the final output of the boosted classifier.


Developing Artificial Intelligence in Russia: Objectives and Reality

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Russia's leaders have been paying close attention to artificial intelligence (AI) technologies for several years now. President Vladimir Putin has said on numerous occasions that the leader in the field of AI would become "the master of the world." Until recently, however, Russia remained virtually the only large country without its own AI development strategy. That changed in October 2019, when the country adopted a long-discussed National Strategy for the Development of Artificial Intelligence Through 2030. One of the driving forces behind the strategy was Sberbank president German Gref. The state-owned bank has also developed a road map for developing AI in Russia and coordinated the creation of Russia's AI development strategy, which is largely corporate, involving the internet giants Yandex and Mail.ru


Simultaneous clustering and representation learning

AIHub

The success of deep learning over the last decade, particularly in computer vision, has depended greatly on large training data sets. Even though progress in this area boosted the performance of many tasks such as object detection, recognition, and segmentation, the main bottleneck for future improvement is more labeled data. Self-supervised learning is among the best alternatives for learning useful representations from the data. In this article, we will briefly review the self-supervised learning methods in the literature and discuss the findings of a recent self-supervised learning paper from ICLR 2020 [14]. We may assume that most learning problems can be tackled by having clean labeling and more data obtained in an unsupervised way.