KUALA LUMPUR – Japan has granted a Malaysian school $68,985 in financial aid for teacher training to ease a shortage of Japanese-language instructors in the nation. The funding via the Embassy of Japan in Malaysia will be used to upgrade laboratories and training rooms as well as to purchase new computers and books at the International Languages Teacher Training Institute in Kuala Lumpur, according to Hiroyuki Orikasa, deputy chief of mission at the embassy. "Many high schools and universities in Malaysia provide Japanese-language classes, but they are facing a shortage of instructors," he said last week at a handover ceremony for the grant. In the predominantly Muslim country where Malay, Chinese and English are spoken, the number of Japanese-language learners totaled 33,234 in 2015, up from 22,858 in 2009, according to an embassy survey. "We see no sign of slowing down," Orikasa said.

No discussion in information technology today is complete without reference to artificial intelligence or AI, in quickspeak. Needless to say, experts in AI are in great demand. Among them, Andrew Ng is often referred to as a go-to guru on AI. He is the co-founder of Coursera, which offers online courses. He is also an adjunct professor at the Stanford University and was formerly the head of Baidu AI Group, and Google Brain.

Google's "Learn with Google AI" is a new course introduced by the company, which will bring machine learning skills and concepts to all users who sign up. The course is free and available to all who are interested. Google says the set of educational resources in this course have been developed by machine learning experts at the company. Google says the idea with this machine learning course is to encourage people to learn about machine learning concepts, develop skills in the area, and apply artificial intelligence to real-world problems. The new Machine Learning Crash Course will give a quick introduction to practical ML concepts using high-level TensorFlow (TF) APIs.

Artificial Intelligence (AI) and machine learning (ML) are currently some of the trending topics in the tech industry. Google wants to make AI and ML more accessible to more people by providing lessons, tutorials and hands-on exercises at all experience levels. Therefore, Google India on Thursday (March 1) introduced a new website called "Learn with Google AI" that encourages everyone to understand how AI works, learn about core ML concepts, develop skills and apply AI to solve real-world challenging problems. These educational resources are developed by ML experts at the company and caters to everyone, from beginners to researchers looking for advanced tutorials. "We believe it's important that the development of AI reflects as diverse a range of human perspectives and needs as possible. So, Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content," Zuri Kemp, Programme Manager for Google's machine learning education, said in a statement.

Google is to make its artificial intelligence and machine learning courses - previously only for insiders - available to everyone. The new'Learn with Google' AI portal will let anyone with an interest, at almost any level, to learn how to make the most of AI and neural networking. Previously, the courses were designed purely as an internal for the purpose of training Google drones. The course was designed as part of the company's move to become "AI first" company. "To help everyone understand how AI can solve challenging problems, we've created a resource called Learn with Google AI. This site provides ways to learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems," wrote Google's technical program manager Zuri Kemp in a blog posting explaining the decision.

We've heard plenty about AI – how AI is the future of work, how AI adoption raises new questions about ethics, how AI can transform your business – and it's no question that there is a lot of hype. It's time to reduce the confusion around how AI truly moves the needle for businesses and offer guidance on how to get started. Businesses need to understand their own capabilities, look to relevant use cases that match their situation, and look at any available data. Let's dive in more closely on each of these three steps: Do you have an in-house AI team? Like teaching a child to walk, businesses need to first evaluate their capabilities; would you characterize your company at the "crawl," "walk," or "run" stage in your Deep Learning development?

In this paper, we construct a reference score for online peer assessments based on HodgeRank [5]. Peer assessment is a process in which students grade their peers assignments [2, 6]. A peer assignment system is used to enhance students learning process, especially in higher education. Through such a system, students are given the opportunity to not only learn knowledge from textbooks and instructors, but also from the process of making judgements on assignments completed by their peers. This process helps them understand the weaknesses and strengths in the work of others, and then to review their own. However, there are some practical issues associated with a peer assignment system.

The original Cloud services include three different segments. In the recent times, we have seen other types of services, like Visibility as a Service (VaaS), where the customers are provided visibility to the type of traffic accessing the cloud servers and details about the traffic. This is very critical because of the inherent inability in the cloud infrastructure to provide the visibility. Now, the wave of Machine Learning and Artificial Intelligence has taken Cloud computing too. Many Cloud service providers are launching new services in Machine Learning as a Service.

Deep reinforcement learning has rapidly become one of the hottest research areas in the deep learning ecosystem. The fascination with reinforcement learning is related to the fact that, from all the deep learning modalities, is the one that resemble the most how humans learn. In the last few years, no company in the world has done more to advance the stage of deep reinforcement learning than Alphabet's subsidiary DeepMind. Since the launch of its famous AlphaGo agent, DeepMind has been at the forefront of reinforcement learning research. A few days ago, they published a new research that attempts to tackle one of the most challenging aspects of reinforcement learning solutions: multi-tasking. Since we are infants, multi-tasking becomes an intrinsic element of our cognition.

Developed by a team of MIT graduate students, TensorFire can run TensorFlow-style machine learning models on any GPU, without requiring the GPU-specific middleware typically needed by machine learning libraries such as Keras-js. TensorFire is another step towards making machine learning available to the broadest possible audience, using hardware and software people are already likely to possess, and via advances in how accurate model predictions can be served with a fraction of the resources previously needed. TensorFire works using the WebGL standard, a cross-platform system for rendering GPU-accelerated graphics in browsers. WebGL supports GLSL, a C-like language used to write shaders, which are short programs used to transform data directly on the GPU. Shaders are typically used in the WebGL pipeline to transform how graphics are rendered--for example, to render shadows or other visual effects.