The announcement came after Hu Jinglin, director of the National Healthcare Security Administration, made a pledge during the ongoing annual two sessions in Beijing that it will severely crack down on fraudulent practices affecting the country's healthcare insurance funds. The health insurance sector has long been plagued with various fraud cases, like fabricated medical services and documents and fake invoices. Last November, two hospitals in Shenyang, capital of northeast China's Liaoning Province, were accused of insurance scams, which prompted the regulators to audit other medical institutions. While much progress has been made since supervision was tightened, the overall situation remains "grim", so it will be a top priority for the authorities to carry on the fight against insurance fraud in 2019. The administration will partner with more third-party service providers like Ping An HealthKonnect to identify potential fraud risks and gain better control of medical insurance costs by leveraging their advanced technologies in areas of big data and artificial intelligence.

The Industry 4.0 market is poised to grow significantly in the coming years. The increasing adoption of the IoT in the digital transformation of manufacturing and related industries, the rise of industrial robotics and the proportionally higher spend in the Industrial Internet of Things are just some contributing factors. While we are still in the early days of Industry 4.0 and challenges remain on many fronts such as the integration of IT and OT, data capabilities, implementation challenges, guidelines and strategic capacities, skills, culture, standards and the maturity/readiness levels on the path from sheer optimization/automation to real transformation, Industry 4.0 is also driven by myriad challenges in the supply chain and customer expectations. In an ongoing quest to deliver the value of Industry 4.0, it is certainly also boosted by national and supra-national pushes in a changing geopolitical industrial ecosystem, further driven by some of the larger players and alliances in the industry. It's interesting to take a look at the key evolutions, drivers and areas of spending in the digital transformation of manufacturing, which Industry 4.0 ultimately is.

Sanskrit is not only limited to spiritualism and philosophy, but can also be used for machine learning and artificial intelligence, said Indian President Ram Nath Kovind. Speaking at the 17th convocation of the Shri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha in New Delhi, Kovind emphasised that Sanskrit is the language of science and spirituality. "It is not that the works in Sanskrit are limited to spiritualism, philosophy, devotion, ritualism or literature. It is also the language of knowledge and science. Important works of scientists and mathematicians such as Aryabhatt, Varaah Mihir, Bhaskar, Charak and Sushrut were created in Sanskrit," he told the gathered audience.

This ebook, based on the latest ZDNet / TechRepublic special feature, explores how infrastructure around the world is being linked together via sensors, machine learning and analytics. As growing numbers of internet-connected sensors are built into cars, planes, trains and buildings, businesses are amassing vast amounts of data. Tapping into that data to extract useful information is a challenge that's starting to be met using the pattern-matching abilities of machine learning (ML) -- a subset of the field of artificial intelligence (AI). Firms are increasingly feeding data collected by Internet of Things (IoT) sensors -- situated everywhere from farmers' fields to train tracks -- into machine-learning models and using the resulting information to improve their business processes, products and services. One of the most visible pioneers is Siemens, whose Internet of Trains project has enabled it to move from simply selling trains and infrastructure to offering a guarantee its trains will arrive on time.

The 2 days BootCamp tells an overview of getting started, let attendees build some small applications using Python and helps them in knowing the use of Python in fields like Data Science, Machine learning, Cloud, Cryptocurrencies and Artificial Intelligence. It also gives an overview of contributing to Open Source, communities, Git & GitHub and from where they can contribute to different projects running across Globe. By filling this you are signing up for being a member of LetsPy Delhi Organizing Team. If you have any query you can mail us on letspydelhi@gmail.com

The answer, according to digital therapeutics pioneer Kaia Health, is potentially. Kaia Health are launching a new feasibility study to find out. If successful, this study could inform healthcare policies worldwide. Kaia Health have been developing which uses innovative artificial intelligence based motion tracking technology to address various health challenges. The next step with such applications is to assess how its digital therapeutic treatment can assist with Chronic Obstructive Pulmonary Disease (COPD) in relation to Japan's ageing population.

Leading Chinese AI startup 4Paradigm has announced a strategic partnership with Shanghai's Ruijin Hospital on AI application in healthcare, especially chronic health conditions. The two today unveiled their first AI-backed diabetes prediction and management product, "Rui Ning Zhi Tang," which predicts diabetes and diabetic cardiovascular complications risk in the next three years and provides assessments and personalized solutions for disease prevention and control. Ruijin is one of the most highly reputed hospitals in China. Its Department of Endocrinology has topped national endocrinology rankings for eight consecutive years. Ruijin also leads the National Metabolic Clinical Research Center and the Key Laboratory of the Ministry of Health.

Last November Synced ran an interview with Yoshua Bengio, in which the deep learning maverick, Université de Montréal Professor and MILA Scientific Director discussed his research and commented on the current state of deep learning and AI. In this follow-up piece we look at the talk Bengio gave late last year at Tsinghua University in Beijing. Challenges for Deep Learning towards Human-Level AI addressed difficulties Bengio and his collaborators are facing and efforts they have made to improve deep learning for human-like AI development. Research over the last decade has given us a much improved understanding of AI, such as why certain methods are helpful for model optimization and why deep learning is so useful. Researchers are showing great interest in deep learning and its potential for application across many different fields.

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy. The codes, including the extension, are available at https://github.com/lancopku/meSimp

Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalization for OSV by examining different loss functions for Convolutional Neural Network (CNN). We adopt our new approach to OSV by asking two questions: 1. which classification loss provides more generalization for feature learning in OSV? , and 2. How integration of different losses into a unified multi-loss function lead to an improved learning framework? These questions are studied based on analysis of three loss functions, including cross entropy, Cauchy-Schwarz divergence, and hinge loss. According to complementary features of these losses, we combine them into a dynamic multi-loss function and propose a novel ensemble framework for simultaneous use of them in CNN. Our proposed Multi-Loss Snapshot Ensemble (MLSE) consists of several sequential trials. In each trial, a dominant loss function is selected from the multi-loss set, and the remaining losses act as a regularizer. Different trials learn diverse representations for each input based on signature identification task. This multi-representation set is then employed for the verification task. An ensemble of SVMs is trained on these representations, and their decisions are finally combined according to the selection of most generalizable SVM for each user. We conducted two sets of experiments based on two different protocols of OSV, i.e., writer-dependent and writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and UT-SIG. Based on the writer-dependent OSV protocol, we achieved substantial improvements over the best EERs in the literature. The results of the second set of experiments also confirmed the robustness to the arrival of new users enrolled in the OSV system.