Mr. Kant is a former firefighter, paramedic and emergency manager with a proven history of saving lives with innovation, applying operational expertise, and offering hands-on guidance at significant events and disasters worldwide. He serves as the disaster portal coordinator for the International Association of Emergency Mangers and works as an innovation entrepreneur. He has been recognized by the Department of Defense, National Geospatial Intelligence Agency, NATO, DHS S&T and others for innovative thinking and applying expert analysis to complex cascading operational interdependencies. During his career, Mr. Kant has supported real-time command/control operations for multiple agencies around the world, including the Florida Night of Tornadoes, the World Trade Center Disaster, hurricanes Katrina and Sandy, and many other significant events and disasters over the past two decades.
The 2010 decade sure had its challenges, but one positive change was the leap in technology capabilities. This holds true not only for consumers, but also for marketers. For example, the proliferation of smartphones with powerful cameras, loads of apps and high-bandwidth mobile networks has changed how we communicate and share ideas. Marketers can promote events on the go, livestream sessions, share pictures and essentially keep people informed -- globally and in real time. In short, we now have a multimedia studio in our hands.
Countries around the world – including the US, South Korea and Taiwan – are using artificial intelligence (AI) to help slow the spread of COVID-19. The technology is being used to speed up the development of testing kits and treatments, to track the spread of the virus, and to provide citizens with real-time information. In South Korea, the government mobilised the private sector to begin developing coronavirus testing kits soon after reports of a new virus began to emerge from China. As part of this drive, Seoul-based molecular biotech company Seegene used AI to speed up the development of testing kits, enabling it to submit its solution to the Korea Centers for Disease Control and Prevention (KCDC) three weeks after scientists began working on it. The company's founder and chief executive, Chun Jong-yoon, told CNN that had AI not been used, the process would have taken two to three months.
The multi-limbed da Vinci can be utilized in a variety of procedures, including cardiovascular, colorectal, gynaecological, head and neck, thoracic and urologic medical procedures, however, only if they're minimally invasive. How large the market could be is as yet hazy, yet experts concur the potential still can't seem to be tapped. So more players are moving in, and rapidly. As the beginning of robotic surgery offers an approach to increasingly precise control and better patient results, early pioneers like Intuitive Surgical Inc. are seeing increased pressure from large organizations like Johnson and Johnson and Medtronic PLC, which have made major M&A investments to break into the market as of late. Intuitive's da Vinci system was first affirmed by the U.S. Food and Drug Administration in 2000 for urology.
University of Arizona researchers are collaborating on an autonomous technology project that could prove autonomous vehicles can improve traffic flow and decrease fuel consumption. The project aims to demonstrate for the first time in real traffic that using intelligent control of a small number of connected and automated vehicles can improve the energy efficiency of all the vehicles by reducing traffic congestion, said Electrical and Computer Engineering (ECE) Professor Jonathan Sprinkle. "More and more passenger vehicles come with features that automate some driving tasks," Sprinkle said. "New advancements in machine learning are showing how small changes to those features can work to address societal-scale challenges, such as the amount of fuel spent while sitting in stop-and-go traffic during a daily commute." The project is being funded through a $3.5 million U.S. Department of Energy cooperative research project.
Four years ago, mathematician Vlad Voroninski saw an opportunity to remove some of the bottlenecks in the development of autonomous vehicle technology thanks to breakthroughs in deep learning. Now, Helm.ai, the startup he co-founded in 2016 with Tudor Achim, is coming out of stealth with an announcement that it has raised $13 million in a seed round that includes investment from A.Capital Ventures, Amplo, Binnacle Partners, Sound Ventures, Fontinalis Partners and SV Angel. More than a dozen angel investors also participated, including Berggruen Holdings founder Nicolas Berggruen, Quora co-founders Charlie Cheever and Adam D'Angelo, professional NBA player Kevin Durant, Gen. David Petraeus, Matician co-founder and CEO Navneet Dalal, Quiet Capital managing partner Lee Linden and Robinhood co-founder Vladimir Tenev, among others. Helm.ai will put the $13 million in seed funding toward advanced engineering and R&D and hiring more employees, as well as locking in and fulfilling deals with customers. Helm.ai is focused solely on the software.
Microsoft is pulling out of an investment in an Israeli facial recognition technology developer as part of a broader policy shift to halt any minority investments in facial recognition startups, the company announced late last week. The decision to withdraw its investment from AnyVision, an Israeli company developing facial recognition software, came as a result of an investigation into reports that AnyVision's technology was being used by the Israeli government to surveil residents in the West Bank. The investigation, conducted by former U.S. Attorney General Eric Holder and his team at Covington & Burling, confirmed that AnyVision's technology was used to monitor border crossings between the West Bank and Israel, but did not "power a mass surveillance program in the West Bank." Microsoft's venture capital arm, M12 Ventures, backed AnyVision as part of the company's $74 million financing round which closed in June 2019. Investors who continue to back the company include DFJ Growth and OG Technology Partners, LightSpeed Venture Partners, Robert Bosch GmbH, Qualcomm Ventures, and Eldridge Industries.
"Gone are the days of data engineers manually copying data around again and again, delivering datasets weeks after a data scientist requests it"-these are Steven Mih's words about the revolution that artificial intelligence is bringing about, in the scary world of big data. By the time the term "big data" was coined, data had already accumulated massively with no means of handling it properly. In 1880, the US Census Bureau estimated that it would take eight years to process the data it received in that year's census. The government body also predicted that it would take more than 10 years to process the data it would receive in the following decade. Fortunately, in 1881, Herman Hollerith created the Hollerith Tabulating Machine, inspired by a train conductor's punch card.
MADRID, SPAIN - MARCH 28: Health personnel are seen outside the emergency entrance of the Severo ... [ ] Ochoa Hospital on March 28, 2020 in Madrid, Spain. Spain plans to continue its quarantine measures at least through April 11. The Coronavirus (COVID-19) pandemic has spread to many countries across the world, claiming over 20,000 lives and infecting hundreds of thousands more. AI (Artificial Intelligence) has a long history, going back to the 1950s when the computer industry started. It's interesting to note that much of the innovation came from government programs, not private industry.
During the past decade, deep learning has seen groundbreaking developments in the field of AI (Artificial Intelligence). But what is this technology? And why is it so important? Well, let's first get a definition of deep learning. Here's how Kalyan Kumar, who is the Corporate Vice President & Chief Technology Officer of IT Services at HCL Technologies, describes it: "Have you ever wondered how our brain can recognize the face of a friend whom you had met years ago or can recognize the voice of your mother among so many other voices in a crowded marketplace or how our brain can learn, plan and execute complex day-to-day activities? The human brain has around 100 billion cells called neurons. These build massively parallel and distributed networks, through which we learn and carry out complex activities. Inspired from these biological neural networks, scientists started building artificial neural networks so that computers could eventually learn and exhibit intelligence like humans."