Artificial Intelligence Could Be a $14 Trillion Boon to the Global Economy--If It Can Overcome These Obstacles – Fortune


Trade wars are hammering manufacturers, from Shanghai to Stuttgart to Seattle. But, awful as today's economic outlook appears, Industry 4.0 is alive and well, its most ardent backers say. Industry 4.0 is the catch-all term for the implementation by businesses of big data, improved robotics and artificial intelligence systems. And it's still expected to be a major driver in global growth over the next decade, and beyond. By 2035, this A.I.-powered push will provide a $14 trillion boost to the global economy, consulting giant Accenture predicts.

Toward digital power over states - Atlantic Council


Security officers keep watch in front of an AI (Artificial Intelligence) sign at the annual Huawei Connect event in Shanghai, China September 18, 2019. Rapid advances in digital technologies amplify the potential for data acquisition from and influence over other states. One state aggressively pursuing digital advantage globally is China, especially in its leveraging of artificial intelligence (AI). This memo presents recent data from multiple sources and initial analysis to set the stage for discussion about the profound implications of imminent digital power by China. A leading concern for those states not presently engaged in AI and related technologies is that they will fall behind those that are already heavily innovating and investing.

Hottest job in China's hinterlands: Teaching AI to tell a truck from a turtle


Their company, located in a city near their parents' village in Henan province, provides an essential early service in the AI process, labeling images and videos to help make computers smarter. Before a self-driving car can learn to avoid hitting people or trees, it must learn what people and trees look like -- by digesting thousands of images labeled by thousands of humans. Demand for labeling is exploding in China as large tech companies, banks and others attempt to use AI to improve their products and services. Many of these companies are clustered in big cities like Beijing and Shanghai, but the lower-tech labeling business is spreading some of the new-tech money out to smaller towns, providing jobs beyond agriculture and manufacturing. The science is mired in controversy in China, where the ruling Communist Party is using AI to help it identify and track people in mass-surveillance programs, most prominently in the largely Muslim province of Xinjiang, according to Human Rights Watch.

Argus Prototype and Lung Cancer SAP Labs China SAP News


Faster and more accurate diagnosis of lung cancer is helping save lives in China. Dr. Yang Yang is a very busy man. Although he agreed to a video interview, he doesn't really have time to talk. Cameras and lights are set up in a small brick-walled room at the end of a narrow hallway in the largest pulmonary hospital in Shanghai, but we are unsure when he will show up to discuss how it came about that he is using machine learning to diagnose lung cancer in his patients. Click the button below to load the content from Youtube.

A.I. could give eye charts a personalized overhaul - Futurity


You are free to share this article under the Attribution 4.0 International license. Artificial intelligence could help make eye charts a whole lot better, Zhong-Lin Lu says. Eye charts date back to the middle of the 19th century, and, over the ensuing decades, have changed relatively little. Many optometrists and ophthalmologists still gauge patients' vision by having them read rows of letters or numbers. Lu, associate provost and chief scientist at New York University Shanghai and a professor in the university's Center for Neural Science and psychology department, thinks these charts continue to have value, but are too imprecise for measuring vision loss or other changes in vision.

China's new 500-megapixel 'super camera' can instantly recognize you in a crowd


China is already home to extensive facial recognition technology, using it to identify criminals, monitor students' attention, and even let citizens purchase train tickets. Now, in an attempt to enrich its surveillance arsenal, reseachers from the country have developed a 500 megapixel facial recognition camera capable of capturing "thousands of faces at a stadium in perfect detail and generate their facial data for the cloud while locating a particular target in an instant." The AI-based cloud camera service was developed in collaboration between Shanghai-based Fudan University and Changchun Institute of Optics, Fine Mechanics and Physics of Chinese Academy of Sciences in Changchun, according to Global Times. The "super camera" is also said to have the ability to shoot panoramic photos with a clear image of every single human face, something that can be put to use in extremely crowded public spots. The facial recognition system has been designed keeping national defense, military and public security in mind, the report said, adding it could "serve as a watchdog at military bases, satellite launch bases and national borders to prevent suspicious people and objects from entering or exiting."

Data Sanity Check for Deep Learning Systems via Learnt Assertions Machine Learning

Data Sanity Check for Deep Learning Systems via Learnt Assertions Haochuan Lu †, Huanlin Xu †, Nana Liu †, Y angfan Zhou †, Xin Wang † School of Computer Science, Fudan University, Shanghai, China † Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China Abstract --Deep learning (DL) techniques have demonstrated satisfactory performance in many tasks, even in safety-critical applications. Reliability is hence a critical consideration to DLbased systems. However, the statistical nature of DL makes it quite vulnerable to invalid inputs, i.e., those cases that are not considered in the training phase of a DL model. This paper proposes to perform data sanity check to identify invalid inputs, so as to enhance the reliability of DLbased systems. T o this end, we design and implement a tool to detect behavior deviation of a DL model when processing an input case, and considers it the symptom of invalid input cases. Via a light, automatic instrumentation to the target DL model, this tool extracts the data flow footprints and conducts an assertion-based validation mechanism. The assertions are built automatically, which are specifically-tailored for DL model data flow analysis. Our experiments conducted with real-world scenarios demonstrate that such an assertion-based data sanity check mechanism is effective in identifying invalid input cases. Moreover, SaneDL is lightweight, easy-to-construct, and non-intrusive to the target DL model. I NTRODUCTION In recent years, deep learning (DL) techniques have shown great effectiveness in various aspects. A huge amount of DLbased applications and systems have been proposed in favor of peoples daily life and industrial production [1]-[3], even in safety-critical applications. Image recognition module for auto-driving vehicles [1], for instance, determines what operation should be taken according to the real-time images captured by cameras. In such safety-critical scenarios, any unreliable system misbehavior may cause severe incidents. Reliability is hence of great significance for practical DLbased systems. It is widely-accepted that every software system has its valid input domain [4]-[7]. Inputs staying in such a domain, namely, valid inputs, are expected to receive proper execution results. Unfortunately, in real circumstances, there is no guarantee the inputs are always valid.

Autonomous vehicles to carry passengers in Shanghai


Local authorities in Shanghai last week have issued licenses – the first in China – for operational tests of smart and connected cars with passengers in them, that would pave the way for commercial robotaxis in the future. The licenses were given to car-hailing ride service Didi Chuxing as well as to car manufacturer SAIC Motor and BMW that allow them to conduct autonomous driving projects in real urban scenarios in Shanghai's Jiading district, local government officials announced at last week's World Autonomous Vehicle Ecosystem Conference. Each of the three companies are permitted to run 50 vehicles for pilot programs including robotaxis, unmanned deliveries and other autonomous driving services. The license holders can increase the number of test vehicles after six months if there are no traffic violations. The city issued China's first licenses on autonomous vehicle (AV) tests to SAIC and EV maker Nio in March 2018, with only company employees allowed to ride in the vehicles during tests.

HUAWEI CLOUD Gains Ground in Global Markets through Cloud AI 5G IoT


SHANGHAI, Sept. 20, 2019 /CNW/ -- HUAWEI CLOUD has announced a series of 2019 strategic investments to expand its global footprint in response to growing demand for its intelligent, secure and stable cloud platform. HUAWEI CLOUD is seeing strong adoption of its services, and today operates 23 cloud regions and 45 availability zones across Africa, Asia Pacific, Europe, and Latin America, empowering digital transformation for organizations globally. At HUAWEI CONNECT 2019, Edward Deng, President of HUAWEI CLOUD Global Market, said: "The convergence of Cloud AI 5G IoT creates a dramatically new value proposition. We define this technology combination and its accompanying changes as the'New Confluence.' This new fusion creates new experiences, applications, and industries, allowing all the things that were not good enough, impossible to realize, or unimaginable in the past to be realized, therefore delivering revolutionary social value."

Inspur Open-Sources TF2, a Full-Stack FPGA-Based Deep Learning Inference Engine


Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.