The education ministry plans to set up a new subsidy system for prefectures and large cities that offer detailed support to foreign students attending public elementary and junior high schools and their parents through the use of multilingual translation systems. The subsidies will be offered to prefectural governments, ordinance-designated major cities and other core cities that use tablet computers with multilingual speech translation functions when teaching Japanese to students from abroad at school and providing school guidance to their parents. The ministry has set aside ¥20 million for the subsidy system, which is designed to cover one-third of related costs, under the government's fiscal 2019 budget. According to sources, 100 language support programs are likely to become eligible for the financial aid. The launch of the new subsidy system comes in line with the government's policy of allowing more foreign workers to enter the country.

NEW DELHI: The Central Board of Secondary Education (CBSE) is all set to introduce Artificial Intelligence(AI) course as an elective subject in Classes 8,9, and 10 from the next academic session. The decision was taken in the recently held meeting of Board's governing body. By launching AI as a skill subject, Board's vision is to make the students well-versed in technologies and enhance their technical know-how. According to officials, The subject AI will be one of the optional subjects on the vocational side. The idea of introducing AI as a school subject was originated in a session held at the NITI Ayog, from where the Board began to explore the concept.

A Comprehensive Learning Path for Deep Learning in 2019 January 2, 2019 Introduction If there is one area in data science which has lead to the growth of Machine Learning and Artificial Intelligence in the last few years, it is Deep Learning. From research labs in universities with low success in industry to powering every smart device on the planet – Deep Learning and Neural Networks have started a revolution. Deep learning is ubiquitous – whether it's Computer Vision applications or breakthroughs in the field of Natural Language Processing, we are living in a deep learning-fueled world. Thanks to the rapid advances in technology, more and more people are able to leverage the power of deep learning. At the same time, it is a complex field and can appear daunting for newcomers.

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools towards real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models and the validation results based on the benchmarking metrics are reported.

Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a single principle. However, this estimator often has high variance and requires a full model evaluation per data point, making this algorithm costly in large graphs. In this work, we address these problems by generalizing concepts from the reinforcement learning literature. We introduce the concepts of value functions, baselines and critics for arbitrary SCGs, and show how to use them to derive lower-variance gradient estimates from partial model evaluations, paving the way towards general and efficient credit assignment for gradient-based optimization. In doing so, we demonstrate how our results unify recent advances in the probabilistic inference and reinforcement learning literature.

If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that report results that are difficult to reconstruct or reproduce, results that mis-represent the performance of the system, or contain other biases that limit their validity. In this semi-humorous article, we discuss issues that arise in running and reporting results of machine learning experiments. The purpose of the article is to provide a list of watch out points for researchers to be aware of when developing machine learning models or writing and reviewing machine learning papers.

The world of education is going to be deeply affected by the introduction of new AI-based technologies, and that's a fact. However, it is hard to tell if those changes are really going to push toward a positive evolution of our society. Education, in general, has a tremendous impact on our entire society and is one of the cornerstones of human evolution. The science of learning and instruction has changed significantly over the course of the last century, and it may be argued that many of the current behavioral changes of the latest generations can be attributed to the evolution in education we have witnessed. Increased use of artificial intelligence in education certainly holds immense potential for improving learning and teaching, but are these improvements going to build a better society and a better world?

One of my professional goals this year was to learn more about artificial intelligence (AI). Over the course of the past year, there have been a lot of stories coming out about how schools are adding the concept of artificial intelligence into their curriculum or trying to weave it into different courses offered. The purpose is to help students better understand its capabilities and how it might impact the future of learning and the future of work. When I did some research earlier this year, I was amazed at some of the different uses of artificial intelligence that we interact with each day, and may not realize. A quick Google search of the term "artificial intelligence" turns up 518 million results in .17

The education ministry plans to set up a new subsidy system for prefectures and large cities that offer detailed support to foreign students attending public elementary and junior high schools and their parents by using multilingual translation systems. The subsidies will be offered to prefectural governments, ordinance-designated major cities and other core cities that use tablet computers with multilingual speech translation functions in teaching Japanese to students from abroad at school and providing school guidance to their parents. The ministry has set aside ¥20 million for the subsidy system, which is designed to cover one-third of related costs, under the government's fiscal 2019 budget, with 100 language support programs likely to become eligible for the financial aid, informed sources said. The launch of the new subsidy system comes in line with the government's policy of allowing more foreign workers to come here. The number of foreign students in Japan needing Japanese language education totaled 43,947 in fiscal 2016, up 70 percent from 26,281 in fiscal 2006.