As it looks to bring a massive number of foreign blue-collar workers into the country from April, the Cabinet on Tuesday adopted a package of policy measures that it says will provide greater support for those hoping to benefit from the new visa categories. The 126 measures, backed by a collective budget of ¥22.4 billion for the next fiscal year, are supplementary to the immigration control law that was revised earlier this month. The measures include the establishment of about 100 consultation centers nationwide offering support in 11 languages: Japanese, English, Chinese, Vietnamese, Korean, Spanish, Portuguese, Nepalese, Indonesian, Thai and Tagalog. The central government also pledged to introduce stricter screening processes to crack down on rogue brokers that exploit migrant foreign workers through debt-bondage. The government is also allocating ¥600 million for a Japanese-language education program for non-Japanese that will include a standardized curriculum and textbooks.

In her nearly 13 years at Match Group Inc., where she became chief executive in January, Ms. Ginsberg has watched the stigma of online dating fade almost entirely. Today, many people even proudly pursue a multiapp dating strategy. Match owns well-known dating apps including Tinder, Hinge and OkCupid, along with lesser-known brands such as PetPeopleMeet.com, The Dallas-based company is expanding in Latin America, Japan, South Korea and India to tap what it estimates is a market of 600 million singles. Her first year at the helm has been an eventful one. After unsuccessfully trying to acquire the dating app Bumble, Match sued its rival last spring for infringing patents for "swiping" and other features that have made Tinder popular.

Apple approved more than 25,000 government requests to access customer data in the first half of 2018, according to its own figures. The iPhone maker's latest Transparency Report reveals it received 32,342 demands from governments around the world to access 163,823 devices, with 80 per cent of the requests granted. The latest figures represent a 9 per cent rise in the number of demands from the previous six-month reporting period, when 79 per cent of 23,477 requests were granted. More than 42 per cent of all applications in the first half of this year came from German authorities – the government with the most requests. Apple said in its report that the high volume of device requests from Germany was "predominantly due to stolen devices investigations".

In this Oct. 31, 2018, photo, Watrix employees demonstrate their firm's gait recognition software at their company's offices in Beijing. A Chinese technology startup hopes to begin selling software that recognizes people by their body shape and how they walk, enabling identification when faces are hidden from cameras. By hook or by crook, Europe needs to differentiate itself, in its approach to artificial intelligence, from mighty competitors such as the U.S. and China. To be fair, competitors might not be the right word given that, for the time being at least, there's no real competition. According to some of the latest data available, AI investment in Europe totaled $3 to $4 billion in 2016, compared with $8 to $12 billion in Asia and $15 to $23 billion in North America.

Stephen Hawking has said, "The development of full artificial intelligence could spell the end of the human race." Elon Musk claims that AI is humanity's "biggest existential threat." That might have people asking: Wait, what? But these grand worries are rooted in research. Along with Hawking and Musk, prominent figures at Oxford and UC Berkeley and many of the researchers working in AI today believe that advanced AI systems, if deployed carelessly, could end all life on earth. This concern has been raised since the dawn of computing. But it has come into particular focus in recent years, as advances in machine-learning techniques have given us a more concrete understanding of what we can do with AI, what AI can do for (and to) us, and how much we still don't know. Some of them think advanced AI is so distant that there's no point in thinking about it now. Others are worried that excessive hype about the power of their field might kill it prematurely. And even among the people who broadly agree that AI poses unique dangers, there are varying takes on what steps make the most sense today.

If the United States wants to maintain its leadership stance in the world for development and use of artificial intelligence, it's time to adopt a national strategy. That's the conclusion of a report developed by the Center for Data Innovation, a self-described thinktank that studies the "intersection of data, technology and public policy." According to Senior Policy Analyst Joshua New, finding success with AI requires more than a bunch of companies investing in it. It also needs the federal government to support the development and adoption of AI in areas such as research, skills development and data usage. According to New, many other countries, including China, France and the United Kingdom, are setting up "significant initiatives" to grab global market share in AI.

World of Warcraft allows players to dive into a vast fantasy realm populated with players from around the world. Together they battle to survive alongside dragons, trolls and warlocks - even on Christmas Day. Video games have long proved a formidable force in capturing the hearts, imaginations and wallets of people all over the world. The most immersive gaming experience, according to its fans, is World of Warcraft and in Azeroth, where the game is set, even Christmas Day is celebrated with turkey feasts, snowball fights and presents under a tree. In a time where many of us spend more time online, what does it mean to celebrate 25 December in a virtual world?

This paper studies whether rationality can be computed. Rationality is defined as the use of complete information, which is processed with a perfect biological or physical brain, in an optimized fashion. To compute rationality one needs to quantify how complete is the information, how perfect is the physical or biological brain and how optimized is the entire decision making system. The rationality of a model (i.e. physical or biological brain) is measured by the expected accuracy of the model. The rationality of the optimization procedure is measured as the ratio of the achieved objective (i.e. utility) to the global objective. The overall rationality of a decision is measured as the product of the rationality of the model and the rationality of the optimization procedure. The conclusion reached is that rationality can be computed for convex optimization problems.

We design a dispatch system to improve the peak service quality of video on demand (VOD). Our system predicts the hot videos during the peak hours of the next day based on the historical requests, and dispatches to the content delivery networks (CDNs) at the previous off-peak time. In order to scale to billions of videos, we build the system with two neural networks, one for video clustering and the other for dispatch policy developing. The clustering network employs autoencoder layers and reduces the video number to a fixed value. The policy network employs fully connected layers and ranks the clustered videos with dispatch probabilities. The two networks are coupled with weight-sharing temporal layers, which analyze the video request sequences with convolutional and recurrent modules. Therefore, the clustering and dispatch tasks are trained in an end-to-end mechanism. The real-world results show that our approach achieves an average prediction accuracy of 17%, compared with 3% from the present baseline method, for the same amount of dispatches.

Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years. Hierarchical mixture of experts is a hierarchically gated model that defines a soft decision tree where leaves correspond to experts and decision nodes correspond to gating models that softly choose between its children, and as such, the model defines a soft hierarchical partitioning of the input space. In this work, we propose a variant of dropout for hierarchical mixture of experts that is faithful to the tree hierarchy defined by the model, as opposed to having a flat, unitwise independent application of dropout as one has with multi-layer perceptrons. We show that on a synthetic regression data and on MNIST and CIFAR-10 datasets, our proposed dropout mechanism prevents overfitting on trees with many levels improving generalization and providing smoother fits.