In this picture taken on December 13, 2017, patients wait in a hospital in Baoding. With their mobile payment and online shopping services, China's big internet companies have transformed consumption habits in the country. Now they're extending their reach into China's healthcare sector, which has long been searching for ways to help overburdened doctors. Unlike in the U.S., where tech giants such as Apple or Amazon have only just made their healthcare ambitions known by building clinics for employees, China's tech companies have been experimenting with online healthcare for the past few years. From smaller startups to behemoths like Alibaba and Tencent, firms are trying to improve hospital efficiency by creating online appointment and diagnostic apps.

Even by the standards of Bitcoin, things are crazy in China. As the boom in cryptocurrencies has become the biggest speculative bubble in recorded history, a single company in Beijing's Haidian District has been selling the chips that generate as much as 80 percent of the world's cryptocoins. "We feel lucky," says Jihan Wu, the co-chief executive of Bitmain Technologies Ltd., which was more or less unknown two years ago and, according to Wu, booked revenue of $3.5 billion in 2017. Cryptocurrency networks run on number-crunching, electricity-hogging "mining" technology, and to play in that game with any seriousness, you pretty much need Bitmain's chips. And because it's China, the whole thing could fall apart at any minute.

Visitors enjoy an artificial intelligence robot's performance during the 2018 Exposition on China's Indigenous Brands at Shanghai Exhibition Center on May 10, 2018 in Shanghai. China, thanks to its 1.37 billion residents, leads the world on a number of counts. The smartphone user population totaled 663 million last year, more than anywhere else, and its internet penetration stood at 751 million. Now China ranks first in the world in the number of patent applications from two heavily watched, fast-growing areas of high tech: cryptocurrency and artificial intelligence, claims American startup ecosystem research organization Startup Genome in a 2018 report. Chalk that up to active government support, domestic demand for new technology and, per one study, a more relaxed patent regime.

Autonomous car makers are running more and more real-world tests, and Intel is now joining the fray, bringing its self-driving cars to the roads of Jerusalem. The company's Mobileye subsidiary, which develops self-driving technology, calls the city home. And since, according to Mobileye CEO Amnon Shashua, Jerusalem has a reputation for aggressive driving, it doesn't seem it will have to go very far to test the limits of the cars' artificial intelligence. Testing in the challenging Jerusalem conditions, Shashua wrote in a blog post, should showcase the cars' ability to make quick decisions. They must react to other drivers and pedestrians who don't use crosswalks without causing slowdowns or accidents. The initial test cars are using camera data alone to process their surroundings, and Mobileye will soon complement that with radar and lidar.

In this paper, we aim at introducing a new machine learning model, namely reconciled polynomial machine, which can provide a unified representation of existing shallow and deep machine learning models. Reconciled polynomial machine predicts the output by computing the inner product of the feature kernel function and variable reconciling function. Analysis of several concrete models, including Linear Models, FM, MVM, Perceptron, MLP and Deep Neural Networks, will be provided in this paper, which can all be reduced to the reconciled polynomial machine representations. Detailed analysis of the learning error by these models will also be illustrated in this paper based on their reduced representations from the function approximation perspective.

Model counting is the problem of computing the number of satisfying assignments of a given propositional formula. Although exact model counters can be naturally furnished by most of the knowledge compilation (KC) methods, in practice, they fail to generate the compiled results for the exact counting of models for certain formulas due to the explosion in sizes. Decision-DNNF is an important KC language that captures most of the practical compilers. We propose a generalized Decision-DNNF (referred to as partial Decision-DNNF) via introducing a class of new leaf vertices (called unknown vertices), and then propose an algorithm called PartialKC to generate randomly partial Decision-DNNF formulas from the given formulas. An unbiased estimate of the model number can be computed via a randomly partial Decision-DNNF formula. Each calling of PartialKC consists of multiple callings of MicroKC, while each of the latter callings is a process of importance sampling equipped with KC technologies. The experimental results show that PartialKC is more accurate than both SampleSearch and SearchTreeSampler, PartialKC scales better than SearchTreeSampler, and the KC technologies can obviously accelerate sampling.

One of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the possible high support of the inside noise events, such free-rider episodes may have abnormally high support that they cannot be filtered by frequency based framework. An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently. In this paper, we take more complex subepisodes into consideration and develop a novel partition model named EDP for free-rider episode filtering from a given set of episodes. It combines (1) a dual partition strategy which divides an episode to an underlying real pattern and potential noises; (2) a novel definition of the expected support of a free-rider episode based on the proposed partition strategy. We can deem the episode interesting if the observed support is substantially higher than the expected support estimated by our model. The experiments on synthetic and real-world datasets demonstrate EDP can effectively filter free-rider episodes compared with existing state-of-the-arts.

Bangalore: An in-depth study on talent in the Machine Learning (ML) space by Zinnov, a global management consulting firm, revealed that while a few startups have a had success stories in their AI (artificial intelligence)/ML journeys, there still exists a deep chasm, and most startups and global enterprises haven't been able to succeed and/or scale, their ML initiatives. The AI/ML spend is predicted to touch $400 billion by 2020, according to industry estimates. Given this, it is more important for organizations to invest in the talent that will capitalize on this niche technology. However, acquiring and retaining, the right kind of ML talent continues to remain a significant challenge for organizations. Zinnov's study explained that a large contributor to this challenge is the skewed concentration of the niche ML talent.

Legislation follows innovation like hangovers follow red wine, and the impact can be extensive. In January this year, there were only rumblings of new cryptocurrency legislation in South Korea, and the value of Bitcoin fell by more than 15%, a drop from which it has not recovered. The 2018 General Data Protection Regulation (GDPR) come as a long overdue update of the 1998 Data Protection Act and the Privacy and Electronic Communications Regulations (PECR). The updates include conditions like the right to be removed from a given database, and the right to be notified of data sales to third parties. Now we are in the midst of the Cambridge Analytica data scandal, and the amount of personal data being collected has become a central matter of public debate.

JERUSALEM (Reuters) - Mobileye, Intel Corp's Israel-based autonomous driving unit, has signed a contract to supply eight million cars at a European automaker with its self-driving technologies, a company official told Reuters. Financial terms of the deal and the identity of the automaker were not disclosed. The deal, one of the largest yet for Mobileye, is a sign of how carmakers and suppliers are accelerating the introduction of features that automate certain driving tasks – such as highway driving and emergency braking – to generate revenue while technology to enable fully automated driving in all conditions is still years away from mass-market deployment. The deal for the advanced driver assisted systems will begin in 2021, when Intel's EyeQ5 chip, which is designed for fully autonomous driving, is launched as an upgrade to the EyeQ4 that will be rolled out in the coming weeks, said Erez Dagan, senior vice president for advanced development and strategy at Mobileye. Intel and Mobileye are competing with several rival chip and machine vision system manufacturers, including Nvidia Corp., to provide the brains and eyes of automated cars.