When athletes and organizers descend on Tokyo for the 2020 Olympic Games, they'll be ferried around in autonomous cars, while torch relay runners will be accompanied by AI-equipped cars. Robots will ferry javelins and hammers. All told, Toyota Motor Corp. will provide 3,700 vehicles, including dozens of self-driving cars, about 500 fuel-cell vehicles and 850 battery-electric cars to the international sports competition. As a top sponsor of the Tokyo Olympics and an automaker facing a murky future when gasoline-powered engines will fade away, Toyota is doing everything it can to market its transition into an eventual provider of on-demand transportation for consumers and businesses, instead of being merely an industrial manufacturer. "We want to use the Olympics and Paralympics that happen every two years as a milestone," Masaaki Ito, general manager of Toyota's Olympic and Paralympic Division, said in an interview.
Tesla showed the computer at the Hot Chips conference. Designing your own chips is hard. But Tesla, one of the most aggressive developers of autonomous vehicle technology, thinks it's worth it. The company shared details Tuesday about how it fine-tuned the design of its AI chips so two of them are smart enough to power its cars' upcoming "full self-driving" abilities. Tesla Chief Executive Elon Musk and his colleagues revealed the company's third-generation computing hardware in April.
The project, which began in July 2016, aimed to integrate a data-capturing device into maintenance vehicles for railway lines and feed a database with a corresponding system of geographical information. That way, it can update an automatic or semi-automatic inventory of railway assets, at the same time as it reviews and evaluates parts of the railway, thereby optimizing maintenance operations. Its development has drawn on technology related to Industry 4.0, including big data, machine learning, artificial intelligence and artificial vision, models and simulations, European GNSS, and satellites. The prototype's validation tests, which were done in April 2018 on the high-speed Amussafes – Javea line and then in November of the same year on the high-speed Madrid – Chamartín y Torrejón de Velasco line which is currently under construction, consisted in integrating Lidar systems (which obtain 3D maps and images of the railway though dynamic scans by using technology called mobile mapping systems), GPS, cameras, and high-precision recorders, lighting… All this allowed digitalizing this new infrastructure at a traveling speed of 80 km/h on a maintenance vehicle equipped for the tests.
Tel Aviv is the city with the highest number of startups per capita in the world, according to the 2018 Global Startup Ecosystem report -- more than 6,000, of which 18 are unicorns. The city's tech cluster, dubbed Silicon Wadi, is home to more than 100 venture capital funds, plus hundreds of accelerators and co-working places. "Tel Aviv is transitioning from startup nation to scale-up nation," says Eyal Gura, co-founder of Zebra Medical Vision. Amit Gilon, an investor at Kaedan Capital VC fund, agrees – adding that Israel is not just about successful B2B companies anymore, such as Checkpoint, Nice and Amdocs, but also about "big B2C success stories like Playtika, Wix, Fiverr and others". Founded in 2015, Arbe has built a 4D ultra-high-resolution imaging radar for cars.
These are exciting times for computational sciences with the digital revolution permeating a variety of areas and radically transforming business, science, and our daily lives. The Internet and the World Wide Web, GPS, satellite communications, remote sensing, and smartphones are dramatically accelerating the pace of discovery, engendering globally connected networks of people and devices. The rise of practically relevant artificial intelligence (AI) is also playing an increasing part in this revolution, fostering e-commerce, social networks, personalized medicine, IBM Watson and AlphaGo, self-driving cars, and other groundbreaking transformations. Unfortunately, humanity is also facing tremendous challenges. Nearly a billion people still live below the international poverty line and human activities and climate change are threatening our planet and the livelihood of current and future generations. Moreover, the impact of computing and information technology has been uneven, mainly benefiting profitable sectors, with fewer societal and environmental benefits, further exacerbating inequalities and the destruction of our planet. Our vision is that computer scientists can and should play a key role in helping address societal and environmental challenges in pursuit of a sustainable future, while also advancing computer science as a discipline. For over a decade, we have been deeply engaged in computational research to address societal and environmental challenges, while nurturing the new field of Computational Sustainability.
According to the Automotive Council UK (ACUK) "… in the East Midlands and Yorkshire… Over a third of automotive manufacturers produce components. Read more: Mark Casci: Can cannabis save the high street? A quarter produce commercial vehicles, one fifth are aftermarket suppliers." In June 2017, the ACUK's report "Growing the Automotive Supply Chain: Local Vehicle Content Analysis" found "…cars manufactured in Britain are becoming more British…" A main reason quoted in the report was "the parts sourced by UK car manufacturers from UK first-tier suppliers has increased from 36 per cent in 2011, to 44 per cent in 2017." This is of course great news for the UK – but we would be foolish to ignore the advancements in technology, including artificial intelligence (AI) and how it has infiltrated a large part of our lives, domestically, commercially and politically.
At almost every point in our day, we interact with digital technologies which collect our data. From the moment our smart phones wake us up, to our watch tracking our morning run, every time we use public transport, every coffee we purchase with a bank card, every song skipped or liked, until we return to bed and let our sleep apps monitor our dreaming habits – all of these technologies are collecting data. This data is used by tech companies to develop their products and provide more services. While film and music recommendations might be useful, the same systems are also being used to decide where to build infrastructure, for facial recognition systems used by the police, or even whether you should get a job interview, or who should die in a crash with an autonomous vehicle. Despite huge databases of personal information, tech companies rarely have enough to make properly informed decisions, and this leads to products and technologies that can enhance social biases and inequality, rather than address them.
Disease Diagnosis & Medication: Data privacy and regulatory barriers will cause a delay in disrupting this segment. If the patient is able to access their own data, they should be able to use AI for diagnosis of their X-rays or MRI scans as a second opinion. A soldier in war zones can get the AR/VR experience with instructions to help treat themselves and remove a bullet. DNA based personalized medicine to extend the life of humans. Robots to remind you to take medicine pills (e.g.
The race to fully autonomous vehicles is on. In April, Elon Musk declared that Tesla should have over a million level 5 autonomous vehicles manufactured by 2020. To clarify, that means over a million cars equipped with the necessary hardware capable of driving with no help from a driver. In addition, government approvals will be necessary (read: mandatory) long before self-driving Teslas will be commonplace. In addition, Musk also sparked some lively debate when he commented that Tesla will not be relying on lidar, the laser sensor technology that self-driving cars from many other companies (most notably Google's Waymo) currently depend on for "seeing" lines on the road, pedestrians, and more.
If we're going to map the world, we're not going to do it with ever-greater volumes of elbow grease. There's just too much work to do. AI and computer vision are helpful assistants in this task, however, as a Facebook effort has shown, laying down hundreds of thousands of miles of previously unmapped roads in Thailand and other less well-covered countries. The problem is simply that there's a whole lot of Earth and only a handful of people actually making maps of it. Sure, Google and Apple have dueling products -- but their focus is on businesses in cities and accurate navigation, not including every dirt path and gravel road.