These being pandemic times, a recent visit to the Silicon Valley offices of drone startup Skydio involved slipping past dumpsters into the deserted yard behind the company's loading dock. Moments later, a black quadcopter eased out of the large open door sounding like a large and determined wasp. Skydio is best known for its "selfie drones," which use onboard artificial intelligence to automatically follow and film a person, whether they're running through a forest or backcountry skiing. The most recent model, released last fall, costs $999. The larger and more severe-looking machine that greeted WIRED has similar autonomous flying skills but aims to expand the startup's technology beyond selfies into business and government work, including the military.
Robotic process automation startup UiPath today announced it has closed a $225 million funding round, bringing its total raised to over $1.2 billion. While the new round is roughly half the $568 million UiPath raised last April, it catapults the New York-based company's post-money valuation to $10.2 billion, up from $7 billion in 2019 and $3 billion in 2018. CEO Daniel Dines says the funding will be used to scale UiPath's platform and deepen its investments in "AI-powered innovation" as it expands its cloud software-as-a-service (SaaS) offerings. The round will also likely lay the groundwork for future strategic deals, following UiPath's acquisition of startups StepShot and ProcessGold last October. RPA -- technology that automates monotonous, repetitive chores traditionally performed by human workers -- is big business.
Researchers at NASA have been hard at work on a pilot AI system intended to help future exploration missions find evidence of life on other planets in our solar system. Machine learning algorithms will help exploration devices analyze soil samples on Mars and return the most relevant data to NASA. The pilot program is currently slated for a test run during the ExoMars mission that will see its launch in mid-2022. As IEEE Spectrum reports, the decision to use machine learning and artificial intelligence to aid the search for life on other planets was driven largely by Erice Lyness, the head of the Goddard Planetary Environments Lab at NASA. Lyness needed to come up with ways of automating aspects of geochemical analyses of samples taken in other parts of our solar system.
AI has become the buzzword of the world, and an integral part of almost every company's digital transformation agenda. AI users have become producers of AI tools and services. Corporate leaders and even the White House have come with forward with a directive on promotion, promulgation, and advancement of artificial intelligence. On February 11, 2019, President Trump signed Executive Order 13859 announcing the American AI Initiative. Executive Order 13859 is the United States' national strategy on artificial intelligence.
Testing for pathogens is a critical component of maintaining public health and safety. Having a method to rapidly and reliably test for harmful germs is essential for diagnosing diseases, maintaining clean drinking water, regulating food safety, conducting scientific research, and other important functions of modern society. In recent research, scientists from University of California, Los Angeles (UCLA), have demonstrated that artificial intelligence (AI) can detect harmful bacteria from a water sample up to 12 hours faster than the current gold-standard Environmental Protection Agency (EPA) methods. In a new study published yesterday in Light: Science and Applications, the researchers created a time-lapse imaging platform that uses two separate deep neural networks (DNNs) for the detection and classification of bacteria. The team tested the high-throughput bacterial colony growth detection and classification system using water suspensions with added coliform bacteria of E. coli (including chlorine-stressed E. coli), K. pneumoniae and K. aerogenes, grown on chromogenic agar as the culture medium.
Let's get our James Bond swag on shall we? Defense departments worldwide are betting on AI to deliver the next generation advanced military technology, and the US is no different. In the US of A, this strategy is being orchestrated by the Joint Artificial Intelligence Center (JAIC), a department under the umbrella of the Department of Defense (DoD) led by Acting Director Nand Mulchandani. And he recently gave his first press conference. NLP will play a bigger role in the future of JAIC strategy .
NASA-funded researchers applied artificial intelligence to Facebook user location data captured as two fires wrecked northern California in 2018 and gained new insight into people's evacuation movements and behaviors when disaster strikes, which could strengthen future response. The Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute are collectively crafting datasets to teach AI tools to assess buildings and structures after natural crises occur, and ultimately augment and increase the accuracy of damage estimates. These are two of many examples detailed in a new report from the Partnership for Public Service and Microsoft that explores how the maturing technology can improve disaster resilience and response, and considerations and actions governments should pursue when adopting AI to boost preparedness, recovery and relief. The report suggests agencies improve data collection and access, make proactive instead of reactive moves, collaborate with other organizations--and more. "While some governments, companies and universities have already used AI in this field, most are still in the early stages of use," officials wrote in the report.
At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.
MIM Software Inc., a leading global provider of medical imaging software, announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints," said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.
The U.S. Air Force plans to have an operational combat drone by 2023. The service plans to build out a family of unmanned aircraft, known as Skyborg, capable of carrying weapons and actively participating in combat. The Air Force's goal is to build up a large fleet of armed, sort-of disposable jets that don't need conventional runways to take off and land. The Air Force, according to Aviation Week & Space Technology, expects to have the first operational Skyborg aircraft ready by 2023. Skyborg will be available with both subsonic and supersonic engines, indicating both attack and fighter jet versions.