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Machine learning picks out hidden vibrations from earthquake data

#artificialintelligence

Over the last century, scientists have developed methods to map the structures within the Earth's crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface. There is a narrow range of seismic waves -- those that occur at low frequencies of around 1 hertz -- that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth's noisy seismic hum, and are therefore difficult to pick up with current detectors.


Industry 4.0 Is Leading IoT Adoption in 2020, Boosting Demand for Integrated Data

#artificialintelligence

Manufacturing and processing plants might not be at the front of anyone's minds when it comes to tech adoption, but as illustrated by a recent IDC report, The Worldwide Internet of Things Spending Guide, the manufacturing industry is transforming into industry 4.0 and spearheading the adoption of IoT. Industry 4.0 is the newest industrial revolution, bringing automation, big data and AI into plants and factories around the world. One of the building blocks of industry 4.0 is the internet of things, or IoT. A recent report forecast that spending on IoT platforms would see a 40% CAGR between 2019 and 2024, resulting in spending that exceeds $12.4 billion. In 2019, leading industry corporations were expected to invest almost $200 billion in IoT solutions.


FitByte Uses Sensors on Eyeglasses To Automatically Monitor Diet

CMU School of Computer Science

Food plays a big role in our health, and for that reason many people trying to improve their diet often track what they eat. A new wearable from researchers in Carnegie Mellon University's School of Computer Science helps wearers track their food habits with high fidelity. FitByte, a noninvasive, wearable sensing system, combines the detection of sound, vibration and movement to increase accuracy and decrease false positives. It could help users reach their health goals by tracking behavioral patterns, and gives practitioners a tool to understand the relationship between diet and disease and to monitor the efficacy of treatment. The device tracks all stages of food intake.


Machine learning picks out hidden vibrations from earthquake data

#artificialintelligence

Over the last century, scientists have developed methods to map the structures within the Earth's crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface. There is a narrow range of seismic waves--those that occur at low frequencies of around 1 hertz--that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth's noisy seismic hum, and are therefore difficult to pick up with current detectors.


Machine Learning picks Hidden Vibrations from Earthquake Data - AnalyticsWeek

#artificialintelligence

Empirical observations of earthquakes were rare until the 1750s. In fact, until the 18th century, very few factual descriptions of earthquakes were recorded, and the natural cause of earthquakes was entirely misunderstood. However, the 20th and the 21st-century saw an increasing interest in the scientific study of earthquakes. The post Machine Learning picks Hidden Vibrations from Earthquake Data appeared first on GreatLearning.


Machine Learning picks Hidden Vibrations from Earthquake Data - AnalyticsWeek

#artificialintelligence

Empirical observations of earthquakes were rare until the 1750s. In fact, until the 18th century, very few factual descriptions of earthquakes were recorded, and the natural cause of earthquakes was entirely misunderstood. However, the 20th and the 21st-century saw an increasing interest in the scientific study of earthquakes. The post Machine Learning picks Hidden Vibrations from Earthquake Data appeared first on GreatLearning.


People can now be identified at a distance by their heartbeat

#artificialintelligence

BEFORE PULLING the trigger, a sniper planning to assassinate an enemy operative must be sure the right person is in the cross-hairs. Western forces commonly use software that compares a suspect's facial features or gait with those recorded in libraries of biometric data compiled by police and intelligence agencies. Such technology can, however, be foiled by a disguise, head-covering or even an affected limp. For this reason America's Special Operations Command (SOC), which oversees the units responsible for such operations in the various arms of America's forces, has long wanted extra ways to confirm a potential target's identity. Responding to a request from SOC, the Combating Terrorism Technical Support Office (CTTSO), an agency of the defence department, has now developed a new tool for the job.


Intelligent tow tank automatically carries out 100,000 experiments in just one year

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A team of researchers working in a MIT lab has built an intelligent tow tank (ITT) that is capable of carrying out fluid dynamics experiments, and have used it to carry out 100,000 such experiments in just one year. In their paper published in the journal Science Robotics, the team describes the ITT, its capabilities and what it has been working on for the past year. When engineers design ships, they want the resulting vehicle to move through the water as efficiently as possible. This involves applying fluid dynamics research. But as the experiments by the team at MIT demonstrate, there is still more to learn.


We're thinking about A.I. wrong. Quantum computing can change that

#artificialintelligence

There's a lot of convention behind the term "artificial intelligence," and potentially that is the problem. Conventional models for AI, which are based on how the human brain might work, are not effective as we still don't have a definitive understanding of how the brain works, says Eberhard Schoeneburg, founder of Alternative AI. He believes a new way of thinking must be adapted for AI. "Even if you have a very simplified model of the brain, it wouldn't solve all these issues or all these problems. The key aspect of Alternative AI is to come up with explaining intelligence without referring to brains," says Schoeneburg. But quantum processes in nature can be studied for insights to create AI with actual intelligence, known as Artificial General Intelligence (AGI). That may soon become a reality. As Google claims "quantum supremacy" in the developing field of quantum computing, some experts suggest the breakthrough could be a boon to the field of artificial intelligence (AI) and vice-versa. In a recent interview with MIT Technology Review, Google CEO Sundar Pichai gave credence to AI as it "can accelerate quantum computing and quantum computing can accelerate AI." See related article: How blockchain can save A.I. Deep learning methods used in AI currently have narrow use cases which rely on static pattern recognition, while a quantum-based system may be more suited for real life applications, says Schoeneburg. Nonetheless, other analysts are less bullish on the prospect of quantum computing applications in the short term. Schoeneburg explains how artificial intelligence should adapt to quantum technology and more. This Forkast.News exclusive brings together two leading voices in artificial intelligence today: Susan Oh, founder of Muckr AI and who also serves as cochair of AI, Blockchain for Impact for the United Nations General Assembly, sits down with the "Godfather of Alternative AI" Eberhard Schoeneburg and calls out "deep learning" as being too specific to be "intelligent." To understand the future of AI, one must understand the roots of its past. Susan Oh: I have the great honor of sitting down with Eberhard Schoeneburg, who is the godfather of Alternative AI. He's also the man that gave us [one of the first] chatbots, though he says that he thinks it's a gimmick and bullsh*t now. So Eberhard, thank you so much for sitting down with me. I think we both agree that AI has failed to live up to the hype and the promise. I don't think what people realize is that this is the fifth wave of AI, that people have been working on intelligent computing systems since the 1950s.


Choose the Right Accelerometer for Predictive Maintenance

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Maintenance, traditionally preventive or corrective, usually represents a significant portion of production costs. Now, having the IIoT monitoring a machine's health status helps enable predictive maintenance, which allows industries to anticipate breakdowns and realize substantial operational savings. Industry 4.0, made possible by the generalization of digitization and connectivity for industrial equipment, is on track to revolutionize production tools. This game changer makes the production chain more flexible and allows for the manufacture of customized products, while maintaining earnings. Maintenance, too, can benefit from the advantages of digitization and connectivity of the IIoT.