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Meta's latest AI can translate 200 languages in real time

Engadget

More than 7,000 languages are currently spoken on this planet and Meta seemingly wants to understand them all. Six months ago, the company launched its ambitious No Language Left Behind (NLLB) project, training AI to translate seamlessly between numerous languages without having to go through English first. On Wednesday, the company announced its first big success, dubbed NLLB-200. It's an AI model that can speak in 200 tongues, including a number of less-widely spoken languages from across Asia and Africa, like Lao and Kamba. According to a Wednesday blog post from the company, NLLB-200 can translate 55 African languages with "high-quality results."


How To Use Real-Time Data? Key Examples And Use Cases

#artificialintelligence

Which is more important – understanding what happened to your business last week or understanding what's happening right now? Well, both can provide useful insights that you might be able to use to improve your customer experience, make better products and services, or create efficiencies in your business processes. But there's a strong argument to be made that nothing is as vital as understanding what's going on in the here-and-now. Real-time analytics is about capturing and acting on information as it happens – or as close as it's possible to get. This involves streaming data, which could come from cameras or sensors, or it could come from sales transactions, visitors to your website, GPS, beacons, the machines and devices that operate your business, or your social media audience.


2021 insurtech challenge winner revealed

#artificialintelligence

Intelligent AI provides a 360-degree view of risk, with more than 300 datasets, including AI, IoT, Satellite, NatCat, and Open Data. "Intelligent AI offers an exceptionally relevant use case for the industry right now," said Chris Newman, global managing director at ACORD. "Their presentation demonstrated the art of the possible in what can be done with data in the insurance commercial lines space." Apart from the cash prize, the firm will be featured in an ACORD-promoted webinar to present its innovation. "I'd like to thank ACORD and the AIIC judges for this great opportunity," said Anthony Peake, chief executive officer at Intelligent AI. "We have had a fantastic first year at Intelligent AI and are excited for the future. We think real-time data and digital twins are going to be an industry game-changer for both insurers and commercial customers."


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


National Digital Transformation and Smarter Cities: Eight Forces That Will Shape the Future

#artificialintelligence

The world's economy is at a tipping point as digital technologies continue to be embedded into both working and personal lives at an unprecedented rate. By 2023, digitally transformed enterprises will account for more than half of global Gross Domestic Product (GDP). Two overarching factors will drive this trend: the proliferation of digital devices and the rising prominence of the millennial and zoomer (Generation Z) user base. These digital-savvy generations account for 75% of the population in the Middle East today. By 2025, the number of connected devices globally is predicted to reach 100 billion, more than 12 times the number of people in this world.


Shifting ground

Science

Fleets of radar satellites are measuring movements on Earth like never before. East Africa has been called the cradle of humanity. But the geologically active region has also given birth to dozens of volcanoes. Few have been monitored for warnings of a potential eruption, and until recently, most were believed to be dormant. Then, Juliet Biggs decided to take a closer look—or rather, a farther look. Biggs, a geophysicist at the University of Bristol, uses a technique called interferometric synthetic aperture radar (InSAR) to detect tiny movements of Earth's surface from space. In a series of studies, she and her co-authors analyzed satellite data on the East African volcanoes. According to their latest results, which were published last month, 14 have been imperceptibly growing or shrinking in the past 5 years—a clue that magma or water is moving underground and that the volcanoes are not completely asleep. “It's really changed the way these volcanoes are viewed, from something that's kind of dormant to really very active systems,” Biggs says. After data showed that the Corbetti volcano, which abuts the fast-growing city of Hawassa, Ethiopia, is inflating steadily at a rate of 6.6 centimeters per year, Biggs's Ethiopian colleagues included it in the country's geological hazard monitoring network. No other technology could produce such a comprehensive survey. Individual GPS stations can track surface movements of less than 1 millimeter, but InSAR can measure changes almost as subtle across a swath hundreds of kilometers wide. That has made it a vital tool for earth scientists studying the heaves and sighs of our restive planet. “We tend to think of the ground as this solid platform,” Biggs says, “and actually, it's really not.” With InSAR, scientists are tracking how ice streams flow, how faults slip in earthquakes, and how the ground moves as fluids are pumped in or out. “Everywhere you look on Earth, you see something new,” says Paul Rosen, an InSAR pioneer at NASA's Jet Propulsion Laboratory (JPL). “It's a little bit like kids in a candy store.” And the flood of InSAR data is growing fast. Since 2018, the number of civil and commercial SAR satellites in orbit has more than doubled. And at least a dozen more are set to launch this year, which would bring the total to more than 60. With the help of computing advances that make data processing easier, the satellite fleets may soon be able to detect daily or even hourly surface changes at just about every patch of ground on Earth. As the technology grows more powerful and ubiquitous, InSAR is spreading beyond the geosciences. With InSAR data, railroads are monitoring the condition of their tracks and cities are monitoring shifts in buildings caused by construction. “It's popping up everywhere,” says Dáire Boyle, who follows trends in the space industry for Evenflow, a consulting firm in Brussels. Analysts value the SAR market at roughly $4 billion, and expect that figure to nearly double over the next 5 years. Many believe InSAR will eventually underpin our daily lives. From measuring the water stored in mountain snowpacks to enabling quick responses to natural disasters, InSAR data will prove invaluable to governments and industries, says Cathleen Jones, a science team leader for NISAR, an upcoming joint SAR mission from NASA and the Indian Space Research Organisation (ISRO). “I want it to become so socially relevant that they can't go back to not having this data.” SYNTHETIC APERTURE RADAR , the “SAR” on which InSAR depends, originated in the 1950s as a tool for airborne military reconnaissance. Like traditional radar, SAR instruments captured images of the planet by sending out microwave pulses and recording the echoes. And like a traditional radar, the instruments could penetrate clouds and worked equally well at night. A key difference was the “synthetic” aspect of SAR. Larger radar antennas, like larger apertures on a camera, collect more of the echoes and enable sharper pictures. But building a single antenna large enough to take a high-resolution image isn't practical. Researchers realized they could instead create an artificially large aperture by combining the signals received on a much smaller antenna as it moved through space. Today, SAR satellites with antennas just a few meters across can produce images with pixel resolutions as sharp as half a meter—better than many satellite-borne cameras. SAR images, on their own, suffice for many types of surveillance, from counterterrorism to tracking oil spills in the ocean. But InSAR goes further, by looking for differences between multiple SAR images. The technique takes advantage of phase information in the returning microwaves—in other words, where a signal is in its sinusoidal path when it hits the antenna. Any phase difference in the signal between SAR images taken from the same position at different times means the round-trip distance has changed, and can reveal surface movements down to a few millimeters. “There's nothing else that compares to it,” says Michelle Sneed, a hydrologist at the U.S. Geological Survey. “I'm still amazed by it after a couple of decades.” The 1978 launch of Seasat, NASA's first ocean-observing satellite, provided data for early InSAR efforts. Seasat operated for just 105 days before a power failure brought the mission to an untimely end. But in that time, it collected repeat images of California's Imperial Valley taken over the course of 12 days. Scientists at JPL later compared those images using InSAR to show the subtle swelling of fields as they soaked up irrigation water. “It is not hard to think of numerous applications for the type of instrument demonstrated,” the authors wrote in a 1989 paper. And they were right. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) ESA; WMO; GUNTER'S SPACE PAGE A classic InSAR study came in 1993, when a team of scientists in France used data from the SAR-enabled European Remote Sensing satellite to study a powerful earthquake that rocked Landers, California, the year before. By analyzing images taken before and after the quake, they calculated that the fault had slipped by up to 6 meters, which agreed with detailed field observations. The InSAR data also revealed how the ground buckled for kilometers around the fault—illustrating the full effects of the temblor at an unprecedented scale. The paper inspired scientists like Sneed, who went on to use InSAR to study how groundwater extraction causes the ground to sink. During a drought in California's San Joaquin Valley in the late 2000s, she and her colleagues discovered that the surface was subsiding as fast as 27 centimeters per year in places where farmers pumped the most groundwater. Irrigation canals were sagging as a result of uneven sinking, impeding water flow. “It's a really expensive problem,” Sneed says. (Another recent InSAR study linked specific water-intensive crops—notably corn, cotton, and soy—to increased subsidence.) Glaciologists adopted the technology, too. As a young researcher at JPL in the 1990s, Ian Joughin used InSAR—which tracks both vertical and horizontal movements—to measure the speed of polar ice streams. Some scientists thought flow rates would be relatively immune to climate change. But, sadly for the world, InSAR studies by Joughin and others proved those predictions wrong. “Especially in the early 2000s, we just saw all kinds of glaciers double their speed,” says Joughin, who now studies the fate of polar ice sheets and their contribution to sea-level rise at the University of Washington, Seattle. By the 2000s, many earth scientists were using InSAR—and grappling with its limitations. There were few SAR satellites in orbit, and they tended to switch between instruments or imaging modes to accommodate different users' needs, making the data hard to use for InSAR. The early missions collected the repeat images needed for InSAR only about once a month, and researchers often had to correct for their wobbly orbits. That meant that although scientists could study an event after it happened, they could rarely watch it unfold in real time. Leaders at the European Space Agency (ESA) were convinced there was a better way. MALCOLM DAVIDSON REMEMBERS the excitement and anxiety he felt on 3 April 2014, the day the first Sentinel-1 satellite launched. “All your life goes into a few minutes,” says Davidson, mission scientist for ESA's flagship SAR program. He also remembers the relief when the satellite safely reached orbit, and the awe that came over him when he saw its first image, of ocean swells. “It was very convincing that the mission was going to do great things,” he says. With Sentinel-1, the plan was simple: “We cut out all the experiments, and we said, ‘Look, this is a mapping machine.’” He and his colleagues chose a primary imaging mode to use over land—surveying a 250-kilometer swath at a resolution of 5 meters by 20 meters—that they hoped would satisfy most researchers, and made sure the orbits would overlap precisely, so all the data would be suitable for InSAR. The first satellite, Sentinel-1a, retraced its path every 12 days. Then, in 2016, ESA launched a clone that made repeat images available about every 6 days for many places on Earth. SAR missions like Italy's COSMO-SkyMed and Germany's TerraSAR-X also support InSAR and can achieve even higher resolutions. But they do not distribute data freely like Sentinel, which many credit for driving a transition from opportunistic experiments to what Davidson sees as “a more operational view of the world.” With Sentinel-1 data, Norway created a national deformation map that has helped identify rockslide hazards and revealed that parts of Oslo's main train station were sinking. Water managers in California rely on the data to track groundwater use and subsidence. And in Belgium, it is used to monitor the structural integrity of bridges. “It can all be done remotely now, saving time, saving money,” Boyle says. The large and growing body of InSAR data has also revealed small surface movements that were previously hidden by noise. As radar signals pass through the atmosphere, they slow down by an amount that depends on the weather, producing variability that can swamp tiny but important displacements. Thanks to long-term records from missions like Sentinel, researchers can now tease information from the noise, for example, helping them track movements of just a few millimeters per year in Earth's crust—enough to strain faults and eventually cause earthquakes. Such efforts would not have been possible without huge gains in computing power. In the 1990s, stacking a single pair of SAR images could take days, Sneed says, and interpreting the results could take much longer. Now, researchers can process hundreds of images overnight, and they increasingly rely on artificial intelligence (AI) algorithms to make sense of the data. In one recent test, an AI algorithm was tasked with identifying small fault movements known as slow earthquakes. It correctly found simulated and historical events, including ones that had eluded human InSAR experts, says Bertrand Rouet-Leduc, a geophysicist at Los Alamos National Laboratory who presented preliminary results in December 2020 at the annual meeting of the American Geophysical Union. Rouet-Leduc and his team now plan to monitor faults around the world using the same approach. He says it's mostly a matter of exploiting the vast quantity of data that “sits on servers without being looked at,” because it's simply too much for scientists to tackle. The researchers hope they will be able to answer questions like when and why slow earthquakes happen, and whether they can trigger big, damaging events by increasing stress on other parts of a fault. Commercial users often lack the expertise to process InSAR data, so hundreds of companies have sprung up to help. One, Dares Technology, monitors the ground for the construction, mining, and oil and gas industries. By tracking surface changes as fluids are injected or extracted from an oil reservoir, for example, Dares can help companies estimate pumping efficiency and prevent dangerous well failures. In the beginning, convincing clients that InSAR data were useful and trustworthy was difficult, says Dares CEO Javier Duro. Now, he says, “Everybody wants to include InSAR in their operations.” Duro is particularly interested in detecting precursors to accidents, for example, by looking for signs of instability in the walls of open-pit mines or in the dams used to store mine tailings. The company usually sends out several alerts per month to clients, who can take actions to avoid disasters. “Typically, InSAR data have been used for back analysis,” Duro says. “Our mission is to focus on the present and the future, and try to predict what could happen.” THE SURGE IN SATELLITES promises to bring yet another InSAR revolution. Italy, Japan, Argentina, and China all plan to launch additional SAR satellites soon, and NISAR, the NASA-ISRO mission, will take flight in late 2022 or early 2023. NISAR will image Earth's full land surface every 6 days, on average, says Rosen, the mission's project scientist. Its two radar sensors will help researchers track many things, including crop growth and changes in woody biomass—crucial for understanding the climate system. With a better view of Antarctica than other missions, NISAR can also monitor changes in ice. Taken together, Sentinel-1, NISAR, and the other civil satellites will image most places on Earth at least every 12 hours, Rosen says. But the temporal resolution of InSAR will remain constrained by the revisit rate of the individual missions, because the technique can't be done with imagery from different missions. However, private companies with large constellations of microsatellites hope to vault the field into yet another realm, by radically increasing revisit frequencies. On 24 January, a SpaceX Falcon 9 rocket blasted off from Cape Canaveral, Florida, carrying three satellites, each about the size of a minifridge and weighing less than 100 kilograms, from Iceye. The Finnish SAR startup has raised more than $150 million toward its audacious goal of imaging every square meter of Earth every hour. The launch brought Iceye's commercial constellation to six, giving it an early lead over rival companies such as Capella Space—which had two satellites on the same rocket—and Umbra, both based in California. Iceye plans to add at least eight more satellites this year, allowing it to revisit most of the globe once a day. “That is groundbreaking,” says Pekka Laurila, who co-founded Iceye as an undergraduate at Aalto University and now serves as the company's chief strategy officer. Ultimately, Iceye hopes to assemble a constellation of as many as 100 satellites as it approaches its hourly monitoring objective. That would open up new applications, like tracking how buildings and dams expand during the heat of the day and contract at night—a clue to their structural integrity. Already, Iceye data have been used to guide ships through Arctic sea ice and to track illegal fishing vessels. “If you can work closer to real time, you can actually do something about it,” Laurila says. So far, though, Iceye has focused on flood monitoring, which can guide disaster response efforts. In fact, the company provided some of the first images of Grand Bahama after Hurricane Dorian devastated the island in 2019, Laurila says. Precise flood data are also valuable to insurers, who can use them to trigger automatic insurance payouts after an event instead of processing claims and sending out inspectors. Until now, Iceye has tracked floods using regular SAR data, but it hopes to start to apply InSAR as it increases its revisit frequencies, because the technique can measure the height and extent of inundation much more precisely. And that's just the beginning of what Laurila hopes Iceye will do. His ultimate goal is to build a “new layer of digital infrastructure” that will provide a “real-time, always-available, objective view on the world,” he says. He believes that, like modern GPS, reliable SAR and InSAR data will support myriad applications, many of which have yet to be imagined. “Nobody thought of your Uber and pizza delivery when they thought of GPS,” Laurila says. If Iceye and its peers succeed, they will expose the shifts and shudders of the planet, day in and day out. They will spy tilting buildings and slumping slopes, and they will witness the growth of crops and the flow of commodities around the world. If space-based imagery often portrays Earth as quiet and still, InSAR reveals the true restlessness of our living planet. [1]: pending:yes


A city that knows your every move: Saudi Arabia's new smart city might be a glimpse of the future

ZDNet

"The Line" is a 170 kilometer-long city on the Red Sea in northwestern Saudi Arabia that is currently being built from the ground up in the desert. Picture this: you land from your flight, walk through the airport undisturbed, then jump on a high-speed underground transit line that within less than 20 minutes takes you to the city center. As you hop off, forget about pulling your phone out to search your way from the station to the hotel: a small autonomous shuttle is awaiting you at the exit, and it already knows where you're going. After a short ride – nothing here is further than a few hundred meters away – through a city that has traded cars and roads for open piazzas and luxuriant green spaces, the shuttle drops you off at your hotel. Don't bother checking in; a facial recognition system has already pinned you down. You walk directly to your room, press your fingertips next to the handle to authenticate, and sigh comfortably as the doors open.


Obsolete Personal Information Update System for the Prevention of Falls among Elderly Patients

arXiv.org Artificial Intelligence

Falls are a common problem affecting the older adults and a major public health issue. Centers for Disease Control and Prevention, and World Health Organization report that one in three adults over the age of 65 and half of the adults over 80 fall each year. In recent years, an ever-increasing range of applications have been developed to help deliver more effective falls prevention interventions. All these applications rely on a huge elderly personal database collected from hospitals, mutual health, and other organizations in caring for elderly. The information describing an elderly is continually evolving and may become obsolete at a given moment and contradict what we already know on the same person. So, it needs to be continuously checked and updated in order to restore the database consistency and then provide better service. This paper provides an outline of an Obsolete personal Information Update System (OIUS) designed in the context of the elderly-fall prevention project. Our OIUS aims to control and update in real-time the information acquired about each older adult, provide on-demand consistent information and supply tailored interventions to caregivers and fall-risk patients. The approach outlined for this purpose is based on a polynomial-time algorithm build on top of a causal Bayesian network representing the elderly data. The result is given as a recommendation tree with some accuracy level. We conduct a thorough empirical study for such a model on an elderly personal information base. Experiments confirm the viability and effectiveness of our OIUS.


An Artificial Intelligence based approach to estimating time of arrival and bus occupancy for public transport systems in Africa

arXiv.org Artificial Intelligence

This document entails a progressive report on the design and implementation of a bus tracking and monitoring system . This report has its contents within the limits of five chapters with each concisely exploring their various objectives. Chapter one is the introductory chapter. It entails a brief description of a bus tracking and monitoring system ,the need and the aims and objectives of this project. Chapter two consists the literature review of this project. This entails the critical analysis of previous related research and projects undertaken by other people. The merits and demerits of the various implementations.Chapter three consists of theory and design considerations of the proposed system for Kwame Nkrumah University campus. Chapter four talks about the methods used to collect data and the approach and technology stack adopted to build the proposed system.Chapter five concludes the thesis and discusses the results of test and deployment of the proposed system on Kwame Nkrumah University of Science and Technology campus


Artificial Intelligence in mining - are we there yet?

#artificialintelligence

While Artificial Intelligence (AI) is a much touted technology in mining, it would seem that the sector is yet to fully embrace this advance technology. Why is this and how can we insure that AI can be beneficial to mining in Africa. According to Prof. Frederick Cawood, Director of Wits Mining Institute at the University of the Witwatersrand, it will take a policy change to ensure that it can benefit mining in Africa. Cawood was a panellist on a recent Mining Review Africa webinar titled Mining 2025: A 5-year vision for AI in mining. Cawood was joined on the panel by Eric Croeser, MD for Africa at Accenture Industry X and Jean-Jacques Verhaeghe, programme manager for real-time information management systems at Mandela Mining Precinct.