Despite China’s considerable strides, industry analysts expect America to retain its current AI lead for another decade at least. But this is cold comfort: China is already developing powerful new surveillance tools, and exporting them to dozens of the world’s actual and would-be autocracies. Over the next few years, those technologies will be refined and integrated into all-encompassing surveillance systems that dictators can plug and play.
Xi Jinping is using artificial intelligence to enhance his government’s totalitarian control—and he’s exporting this technology to regimes around the globe.
Consumers are ending up increasingly responsive about sharing their data, as data integrity and security has turned into a developing concern. In any case, with the advent of nations teching up with facial recognition, even explorers need to truly begin thinking about what sort of data they could be reluctantly offering to nations, individuals and places.
Facial recognition innovation is a framework that is fit for identifying or confirming an individual from an advanced picture or a video frame. It works by comparing chosen facial highlights and faces inside a database. The technology is utilized in security frameworks and can be compared with different biometrics, for example, fingerprint or iris recognition frameworks. As of late, it has been grabbed and utilized as a business identification and advertising tool. The vast majority have a cell phone camera fit for recognizing features to perform exercises, for example, opening said a cell phone or making payments.
The worldwide market for facial recognition cameras and programming will be worth of an expected $7.8 billion, predicts Markets and Markets. Never again consigned to sci-fi films and books, the technology is being used in various vertical markets, from helping banks recognize clients to empowering governments to look out for criminals. Let’s look at some of the top countries adopting facial recognition technology.
Gravity is no obstacle for this climbing robot. It scales vertical walls—even those made of smooth materials like glass. Jeff Krahn, an engineer from Simon Fraser University in British Columbia, created this gecko-inspired tank of a robot, which he detailed in a paper in the journal Smart Materials and Structures this week.
Like a gecko, which can hang on to sheer glass with just one toe, the climbing bot uses what physicists call Van der Waals forces to stick to the wall. Its tanklike tracks are covered in a dry adhesive, a polymer resembling silicon that allows adhesion without chemicals or added energy. The molecules that make up this substance are temporary dipoles; they have a positively charged side and a negatively charged side. The charged sides of the molecules are attracted to their corresponding opposites on the wall the robot is climbing: negative to positive, positive to negative. Given enough surface area for these attractions to take place, Van der Waals forces can keep a pretty substantial weight stuck to a vertical wall. The climbing bot, for example, weighs in at half a pound.
To boost the climbing bot’s stickiness, Krahn needed to increase the surface area of its tracks, which allows more molecular interactions. So the tracks are covered with small bumps shaped like mushroom caps, each about the size of a human red blood cell. These bumps also allow the bot to cling to microscopic bumps and cracks in the surface of whatever it’s climbing. However, Krahn’s creation can’t scale a surface that’s too rough; the texture of concrete, for example, wouldn’t provide enough surface area for its tracks to get the proper grip, Krahn says.
Researchers from the Institute of Industrial Science at The University of Tokyo designed and built specialized computer hardware consisting of stacks of memory modules arranged in a 3D-spiral for artificial intelligence (AI) applications. This research may open the way for the next generation of energy-efficient AI devices.
Machine learning is a type of AI that allows computers to be trained by example data to make predictions for new instances. For example, a smart speaker algorithm like Alexa can learn to understand your voice commands, so it can understand you even when you ask for something for the first time. However, AI tends to require a great deal of electrical energy to train, which raises concerns about adding to climate change.
Now, scientists from the Institute of Industrial Science at The University of Tokyo have developed a novel design for stacking resistive random-access memory modules with oxide semiconductor (IGZO) access transistor in a three-dimensional spiral. Having on-chip nonvolatile memory placed close to the processors makes the machine learning training process much faster and more energy-efficient. This is because electrical signals have a much shorter distance to travel compared with conventional computer hardware. Stacking multiple layers of circuits is a natural step, since training the algorithm often requires many operations to be run in parallel at the same time.
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Machine Learning offers important new capabilities for solving today’s complex problems, but it’s not a panacea. To get beyond the hype, engineers and scientists must discern how and where machine learning tools are the best option — and where they are not.
A drone has successfully inspected a 19.4 meter high oil tank onboard a Floating Production, Storage and Offloading vessel. The video shot by the drone was interpreted in real-time by an algorithm to detect cracks in the structure.
Scout Drone Inspection and class society DNV GL have been working together to develop an autonomous drone system to overcome the common challenges of tank inspections. For the customer, costs can run into hundreds of thousands of dollars as the tank is taken out of service for days to ventilate and construct scaffolding. The tanks are also tough work environments, with surveyors often having to climb or raft into hard to reach corners. Using a drone in combination with an algorithm to gather and analyse video footage can significantly reduce survey times and staging costs, while at the same time improving surveyor safety.
“We’ve been working with drone surveys since 2015,” said Geir Fuglerud, director of ofshore classification at DNV GL – Maritime. “This latest test showcases the next step in automation, using AI to analyse live video. As class we are always working to take advantage of advances in technology to make our surveys more efficient and safer for surveyors, delivering the same quality while minimising our operational downtime for our customers.”
February 27, 2020 — Accuray Incorporated announced that Mercy Hospital St. Louis continues to demonstrate its commitment to improving patient outcomes with the installation of the first CyberKnife M6 System in Missouri at their state-of-the-art David C. Pratt Cancer Center. The next-generation CyberKnife System has been shown to deliver precise stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments with ease, making it possible for the clinical team to expand access to one of the most advanced methods for administering radiation to more cancer patients.
SRS and SBRT are non-invasive forms of radiation therapy that use high doses of very targeted radiation to destroy tumors, in just a few treatment sessions (1 to 5). SRS is commonly used to treat conditions within the brain and spine, while SBRT is used for those tumors located outside these areas. The CyberKnife M6 System is equipped with sophisticated functionality that will streamline the creation of personalized treatment plans and reduce the time to deliver radiation treatments, enabling the Mercy St. Louis team to offer precision SRS and SBRT treatments to more patients each day.
The Mercy Hospital St. Louis team uses the most advanced radiotherapy technology to design and deliver an individualized treatment plan designed to help cancer patients take control of their disease and resume their lives. The hospital is part of the Mercy system, named one of the top five large U.S. health systems from 2016 to 2019 by IBM Watson Health. Mercy announced in 2018 that it intended to work with Accuray to enhance cancer care through advanced life-saving technology, including the CyberKnife System that was recently installed as well as Accuray Radixact Systems that will be installed at other Mercy hospitals.
DeepMind this week released Acme, a framework intended to simplify the development of reinforcement learning algorithms by enabling AI-driven agents to run at various scales of execution. According to the engineers and researchers behind Acme, who coauthored a technical paper on the work, it can be used to create agents with greater parallelization than in previous approaches.
Reinforcement learning involves agents that interact with an environment to generate their own training data, and it’s led to breakthroughs in fields from video games and robotics to self-driving robo-taxis. Recent advances are partly attributable to increases in the amount of training data used, which has motivated the design of systems where agents interact with instances of an environment to quickly accumulate experience. This scaling from single-process prototypes of algorithms to distributed systems often requires a reimplementation of the agents in question, DeepMind asserts, which is where the Acme framework comes in.
If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Three years? Five years? How about the cost? $30 million? $100 million?
Well, here’s the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an average of 10 years to reach the market and cost anywhere from $2.5 billion to $12 billion to get there.
But what if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.
