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The European Mobility Startup Landscape

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It's an exciting time to be investing in mobility startups. Below are the current trends in the mobility industry and an overview of the startup ecosystem in Europe. The mobility industry is undergoing rapid change these days. While they bring opportunities for newcomers, they create challenges for the incumbents. Let's have a look at the four trends in more detail: While Mercedes Benz had already started to experiment with self-driving technology three decades ago, it wasn't until recently that autonomous driving (AD) efforts really speed up.


Artificial Intelligence used to Protect Passport Data at Singapore Travel Firm

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Darktrace, the world's leading cyber AI company, announced today that travel company, Global Travel, a Singapore Top 500 Enterprise, has deployed artificial intelligence to protect confidential traveler information, including passport data. With more than 40 years of experience in corporate and leisure travel, Global Travel's reputation in Singapore is well-established. The company takes cybersecurity seriously in light of the numerous cyber-attacks wielded on organisations all over the world, where cyber-criminals look to steal or compromise personal information. While the company complies with Singaporean data privacy regulations under the Personal Data Protection Act (PDPA), Global Travel selected Darktrace to dramatically strengthen its security posture. It relies on Darktrace's world-leading cyber AI to not only monitor its digital systems 24/7, but also to act on its behalf when the AI spots malicious activity occurring.


Artificial Intelligence could change the face of Healthcare - Bugle24

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Artificial Intelligence, the science of inducing the simulation of human intelligence in machines, especially computer systems, is the future of all industries. There have been many instances of AI taking over manual work to increase efficiency and decrease work load in the industrial sector. The technology is also expected to have a boom in the medical sector because of the constant need of improvement of the machinery and medical equipment. This advancement in science could save a million lives by helping the doctors in diagnosing, treating, preventing, and rescuing the diseases by the push of a button. How it works is, basically a company which is trying to develop an AI for a particular hospital or even for the government, has to take in a ton of data from a ton of people.


Yamagata University team finds 143 ancient geoglyphs in Peru's Nazca grasslands

The Japan Times

YAMAGATA – Yamagata University has announced the discovery of 143 geoglyphs on the Nazca Pampa and surrounding areas in Peru, including one found in a study using artificial intelligence technology. The university's team, led by professor Masato Sakai, found 142 geoglyphs, including ones depicting humans, snakes and birds, through analysis of high-resolution images of the areas and fieldwork there between 2016 and 2018. The research was based on a hypothesis that many geoglyphs were created along small paths in the western region of the Nazca Pampa, according to the university's announcement Friday. The team conducted the AI-based study with cooperation from IBM Japan Ltd. between 2018 and 2019. The world's first such study analyzed aerial photographs using deep-learning techniques to look for what are likely to be geoglyphs.


Where AI and ethics meet

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Given a swell of dire warnings about the future of artificial intelligence over the last few years, the field of AI ethics has become a hive of activity. These warnings come from a variety of experts such as Oxford University's Nick Bostrom, but also from more public figures such as Elon Musk and the late Stephen Hawking. The picture they paint is bleak. In response, many have dreamed up sets of principles to guide AI researchers and help them negotiate the maze of human morality and ethics. Now, a paper in Nature Machine Intelligence throws a spanner in the works by claiming that such high principles, while laudable, will not give us the ethical AI society we need.


From black box to white box: Reclaiming human power in AI

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It's hard to imagine what life was like before the peak of AI hype in which we currently find ourselves. But it was just a few years ago, in 2012, that Apple gave the world the first integrated version of Siri on the iPhone 4S, which people used to impress their friends by asking it banal questions. Google was just beginning to test its self-driving cars in Nevada. And the McKinsey Global Institute had recently released "Big data: The next frontier for innovation, competition, and productivity." On the starting blocks of the race to release the next big AI-powered thing, no one was talking about explainable AI.


AHA: Artificial Intelligence Examining ECGs Predicts Irregular Heartbeat, Death Risk

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Artificial intelligence can examine electrocardiogram (ECG) test results, a common medical test, to pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia) or of dying within the next year, according to two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019 -- November 16-18 in Philadelphia. The Association's Scientific Sessions is an annual, premier global exchange of the latest advances in cardiovascular science for researchers and clinicians. Researchers used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey's Geisinger Health System to train deep neural networks -- advanced, multi-layered computational structures. Both studies, from the same group of researchers, are among the first to use artificial intelligence to predict future events from an ECG rather than to detect current health problems, the scientists noted. "This is exciting and provides more evidence that we are on the verge of a revolution in medicine where computers will be working alongside physicians to improve patient care," said Brandon Fornwalt, M.D., Ph.D., senior author on both studies and associate professor and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania.


SC19: AI and Machine Learning Sessions Pepper Conference Agenda

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AI and HPC are increasingly intertwined – machine learning workloads demand ever increasing compute power – so it's no surprise the annual supercomputing industry shindig, SC19 at the Colorado Convention Center in Denver next week, has taken on a strong AI cast. As we noted recently ("Machine Learning Fuels a Booming HPC Market") based on findings by industry watcher Intersect360 Research, "enterprise infrastructure investments for training machine learning models have grown more than 50 percent annually over the past two years, and are expected to shortly surpass $10 billion, according to a new market forecast," and much of that training calls for HPC-class systems. With that in mind, here's a rundown of AI-related sessions and activities coming up at SC19 (all event locations are in the Convention Center unless otherwise specified): Deep Learning on Supercomputers, 9am-5:30pm, room 502-503-504: This workshop will be led by Zhao Zhang of the University of Texas, Valeriu Codreanu of SURFsara and Ian Foster of Argonne National Laboratory and the University of Chicago and is designed to be a forum for practitioners working on all aspects of DL for science and engineering in HPC and to present their latest research results and development, deployment, and application experiences. Tools and Best Practices for Distributed Deep Learning on Supercomputers, 1:30-5pm, room 201: This tutorial will be led by Xu Weijia and Zhao Zhang of the Texas Advanced Computing Center and David Walling of the University of Texas and is intended to be a practical guide on how to run distributed deep learning over multiple compute nodes. Deep Learning at Scale, 8:30am-5pm, room 207: Led by seven experts from Lawrence Berkeley National Lab, Intel and Cray, this tutorial will focus on the impact of deep learning is having on the way science and industry use data to solve problems and the need for scalable methods and software to train DL models.


Hikvision Markets Uyghur Ethnicity Analytics, Now Covers Up

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Hikvision has marketed an AI camera that automatically identifies Uyghurs, on its China website, only covering it up days ago after IPVM questioned them on it. This AI technology allows the PRC to automatically track Uyghur people, one of the world's most persecuted minorities. Hikvision's product description states this camera supports Uyghur recognition (screenshot via Google Translate): Capable of analysis on target personnel's sex (male, female), ethnicity (such as Uyghurs, Han) and color of skin (such as white, yellow, or black), whether the target person wears glasses, masks, caps, or whether he has beard, with an accuracy rate of no less than 90%. By April 2019, Hikvision was well-aware of the human rights issues surrounding Xinjiang; that same month, they disclosed in their ESG report that they had "recently commissioned an internal review" on the matter. The PRC officially recognizes 56 ethnic groups, which the Chinese ambassador recently described as being'part of big family of Chinese nation'.


Guide to autonomous vehicles: What business leaders need to know ZDNet

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This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Few technologies have been more anticipated heading into the 2020s than autonomous vehicles. Tantalizingly close and yet still perhaps decades from market adoption in some use cases, the technology is as promising as it is misunderstood. You've heard the consumer hype, but what gets less ink are the transformative changes that autonomous vehicles will bring -- in some cases already are bringing -- to the enterprise. Affecting sectors as disparate as shipping and logistics, energy, agriculture, transportation, construction, and infrastructure -- to name just a few -- it's hard to overstate the impact of the diverse and versatile set of technologies lumped into the decidedly broad category of'autonomous vehicles'. This guide will help you sort the hype from the business reality and tell you all you need to know about the autonomous vehicle revolution on the ground, in the air, and even at sea. In 1939, General Motors predicted we'd have an autonomous vehicle highway system up and running by the dawn of the 1960s. As with a lot of autonomous vehicle hype, that prediction was a tad premature, but it demonstrates the long history of autonomous vehicle development.