Yesterday, Waymo announced it would "open" a large dataset of self-driving training data. This gathered attention because Waymo has, by a huge margin, the largest number of self-driving miles under its belt, and thus one of the most envied collections of tagged data that can be used to train and test neural networks, one of the key tools used in building robots and self-driving cars. People setting up to build a self-driving car almost universally use machine learning techniques. With machine learning for computer vision, you provide the computer with images that a human being has already put labels on, saying what in the image is a car, or pedestrian, or road surface. Give the computer enough, and your machine learning technique -- today, most commonly a convolutional neural network -- will use advanced statistical techniques to come to a more general understanding of what distinguishes the various components.
Thirteen years ago, in Silicon Valley, a company was born. One simple goal of this company was to prove that electric cars could be better in every way over the traditional fuel-powered cars. Since then, Tesla led by Elon Musk has become a household name in the automotive industry, especially when it comes to manufacturing electric cars. They are said to be the pioneer in manufacturing electric cars, but they were not the first one to make electric cars. What they did first was to create a large consumer base who would want to try out new technology, and Tesla did not let their car owners down.
FedEx's robot has a top speed of 10 mph and can carry about 100 pounds. A company spokesman said its typical speed would vary depending on the route. The robot relies on sensors typically used on self-driving cars to identify and avoid pedestrians. The SameDay Bot is capable of climbing steps, but customers will need to be home to accept packages -- it won't leave a package on a doorstep or open a front gate. The robot is being developed by DEKA, the Manchester, New Hampshire engineering company owned by Segway inventor Dean Kamen.
Artificial intelligence (AI) is becoming big business, with all kinds of fascinating opportunities. Growth has been extraordinary: in 2015, global AI revenues were $126 billion, and last year revenues were $482 billion. The prediction for 2024 is that revenues will top $3.061 trillion. Advances in AI are making it possible for computers to take on more tasks that were formally done by humans. While this trend is creating greater efficiencies, it is also increasing the degree to which people feel that they are talking to a wall.
Tyrata, Inc., a tire sensor, data management and analytics company, has expanded its Scientific Advisory Board (SAB) with two experts in complex data handling, machine learning and data analytics. The new board members, both Duke University professors, will focus on optimizing data collection and analytics for the IntelliTread technology platform as Tyrata continues to transform how the tire and transportation industries sense and use tread wear data to improve tire safety, reduce costs, and optimize tire design and maintenance. Tyrata CTO and Scientific Advisory Board leader Aaron Franklin is pleased to welcome Dr. Miroslav Pajic and Dr. Leslie Collins to the committee. Dr. Pajic is an Electrical and Computer Engineering Assistant Professor at Duke University and has a deep understanding of data handling and management in the digital world of automobiles and other complex environments. Dr. Pajic will contribute his expertise to the tread wear data stream and handling solutions for the IntelliTread technology platform.
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.