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.
Many airports hope to start using biometric scanners in lieu of passports to identify travelers. Buzz60's Tony Spitz has the details. The next time you go to the airport you might notice something different as part of the security process: A machine scanning your face to verify your identity. U.S. Customs and Border Protection (CBP) has been working with airlines to implement biometric face scanners in domestic airports to better streamline security. But how does the process work?
As one of the world's busiest airports, (ranked No. 3 in 2018 according to Airports Council International's world traffic report), Dubai International Airport is also a leader in using artificial intelligence (AI). In fact, the United Arab Emirates (UAE) leads the Arab world with its adoption of artificial intelligence in other sectors and areas of life and has a government that prioritizes artificial intelligence including an AI strategy and Ministry of Artificial Intelligence with a mandate to invest in technologies and AI tools. The Emirates Ministry of the Interior said that by 2020, immigration officers would no longer be needed in the UAE. They will be replaced by artificial intelligence. The plan is to have people just walk through an AI-powered security system to be scanned without taking off shoes or belts or emptying pockets.
TransLink says the accuracy of its bus departure times will be improved with a "new machine learning algorithm." The system-wide implementation follows a pilot program which saw 13 bus routes utilize this technology. "We're proud to have developed the new algorithm in-house, with collaboration from technology companies Microsoft and T4G," said TransLink CEO Kevin Desmond, in a news release. "This method is going to result in better information for customers, who can make more informed decisions throughout their journey. During the pilot phase the difference between predicted and actual bus departure times improved by 74 per cent."
It may not help you get through the airport faster, but artificial intelligence (AI) can now give you information on wait times at Pittsburgh International Airport security checkpoints in real time. The airport recently announced a partnership with Zensors, a Pittsburgh-based company that applies AI to feeds from airport security cameras to estimate wait times at the airport's three Transportation Security Administration (TSA) checkpoints on a minute-by-minute basis. Wait-time information is then posted on the airport's information screens and website, including predictions about whether wait times will increase or decrease. Zensors' AI observes passenger volume and also includes factors like time of day and TSA staffing levels to make its estimates. "We know security can be a frustration for travellers and having accurate wait estimates can help set expectations and aid in planning trips," said Pittsburgh International Airport CEO Christina Cassotis.
St. Cloud, Minnesota, USA –New Flyer of America Inc., a subsidiary of NFI Group Inc., the largest bus manufacturer in North America, recently announced an exclusive partnership with Robotic Research, LLC to advance autonomous bus technology through developing and deploying advanced driver-assistance systems (ADAS) in heavy-duty transit bus applications. Last week, New Flyer had more big news as the company announced a new contract from New York's Capital District Transportation Authority for four 40-foot, zero-emission, battery-electric Xcelsior CHARGE heavy-duty transit buses. New Flyer invested more than two years assessing world-leading technology providers for sophisticated autonomous vehicle development. It ultimately selected Robotic Research based on the company's proven, industry-leading, artificial intelligence-based technology, coupled with its extensive experience delivering successful Level 5 autonomous vehicle applications for customers within the defense and intelligence community, including the U.S. Department of Defense. "New Flyer has a proud history of leading innovation, industry firsts, and technology advancement in public transportation," said New Flyer President Chris Stoddart, in a press release "Our ADAS vision supports the mobility needs of all Americans relying on public transit for safe and reliable transportation every day. Partnering with Robotic Research furthers our commitment to utilize the best expertise and technology available, while reaffirming our responsibility to work with regulators and stakeholders on standards and test protocols that integrate automated vehicles safely into the existing transportation system."
We present a new type of coordination mechanism among multiple agents for the allocation of a finite resource, such as the allocation of time slots for passing an intersection. We consider the setting where we associate one counter to each agent, which we call karma value, and where there is an established mechanism to decide resource allocation based on agents exchanging karma. The idea is that agents might be inclined to pass on using resources today, in exchange for karma, which will make it easier for them to claim the resource use in the future. To understand whether such a system might work robustly, we only design the protocol and not the agents' policies. We take a game-theoretic perspective and compute policies corresponding to Nash equilibria for the game. We find, surprisingly, that the Nash equilibria for a society of self-interested agents are very close in social welfare to a centralized cooperative solution. These results suggest that many resource allocation problems can have a simple, elegant, and robust solution, assuming the availability of a karma accounting mechanism.
Traffic signals serve to regulate the worst bottlenecks in highly populated areas but are not always very effective. Researchers at Penn State are hoping to use deep reinforcement learning to improve traffic signal efficiency in urban areas, thanks to a one-year, $22,443 Penn State Institute for CyberScience Seed Grant. Urban traffic congestion currently costs the U.S. economy $160 billion in lost productivity and causes 3.1 billion gallons of wasted fuel and 56 billion pounds of harmful CO2 emissions, according to the 2015 Urban Mobility Scorecard. Vikash Gayah, associate professor of civil engineering, and Zhenhui "Jessie" Li, associate professor of information sciences and technology, aim to tackle this issue by first identifying machine learning algorithms that will provide results consistent with traditional (theoretical) solutions for simple scenerios, and then building upon those algorithms by introducing complexities that cannot be readily addressed through traditional means. "Typically, we would go out and do traffic counts for an hour at certain peak times of day and that would determine signal timings for the next year, but not every day looks like that hour, and so we get inefficiency," Gayah said.
Toronto Pearson is one of North America's busiest airports. It handled 465,400 flights last year and processes over 45 percent of Canada's air cargo. In 2018, nearly 50 million passengers passed through its terminals. The demands of that vast amount of footfall, 24 hours a day, requires stringent organization. With airports serving as thriving commercial hubs, ensuring operations are smooth and customers remain satisfied is always one of the airport's core goals.