Astronomers took centuries to figure it out. But now, a machine-learning algorithm inspired by the brain has worked out that it should place the Sun at the centre of the Solar System, based on how movements of the Sun and Mars appear from Earth. The feat is one the first tests of a technique that researchers hope they can use to discover new laws of physics, and perhaps to reformulate quantum mechanics, by finding patterns in large data sets. The results are due to appear in Physical Review Letters 1. Physicist Renato Renner at the Swiss Federal Institute of Technology (ETH) in Zurich and his collaborators wanted to design an algorithm that could distill large data sets down into a few basic formulae, mimicking the way that physicists come up with concise equations like E mc 2. To do this, the researchers had to design a new type of neural network, a machine-learning system inspired by the structure of the brain. Conventional neural networks learn to recognize objects -- such as images or sounds -- by training on huge data sets.
Lightning regularly kills people and animals, starts fires, damages power lines and keeps aircraft grounded. Until now it has been virtually impossible to predict lightning, with no simple technology for predicting when and where it will strike the earth. Engineers at the Ecole Polytechnique Federale de Lausanne's (EPFL) School of Engineering developed a simple and inexpensive system to predict when lightning will strike. The research led by Farhad Rachidi, resulted in a method of predicting lightning between 10 and 30 minutes before it strikes, within a 30km radius. Using a combination of Artificial Intelligence and meteorological data, researchers are now planning to use this technology in the European Laser Lightning Rod project, a project designed to draw lightning away from areas that are susceptible to lightning damage, the project is shown in the video bellow.
Given how deadly and destructive lightning can be, it would certainly be good to know in advance where and when it was going to strike. A new artificial intelligence-based system could help, utilizing nothing but standard weather-station data. Developed by a team from the Electromagnetic Compatibility Laboratory at Switzerland's EPFL research institute, the system was "trained" using a database of readings of four basic weather parameters: atmospheric pressure, air temperature, relative humidity and wind speed. Gathered over a 10-year period from 12 Swiss weather stations in urban and mountainous regions, these readings were cross-referenced with recordings from lightning detection and location systems. This allowed the AI algorithms to learn which weather conditions were associated with lightning strikes in given areas.
Scientists on Friday said they have developed a simple and inexpensive artificial intelligence (AI) system that can predict when lightning will strike any place within a 30-kilometre radius, up to 30 minutes in advance. Lightning -- one of the most unpredictable phenomena in nature -- regularly kills people and animals and sets fire to homes and forests. It keeps aircraft grounded and damages power lines, wind turbines and solar-panel installations. However, little is known about what triggers lightning, and there is no simple technology for predicting when and where lightning will strike the ground, noted the researchers from Ecole polytechnique federale de Lausanne in Switzerland. The new system, described in the journal Climate and Atmospheric Science, uses a combination of standard meteorological data and artificial intelligence.
Between 6,000 and as many as 24,000 people are killed by lightning every year. Switzerland's leading university has found a way to predict when and where lightning will strike to the nearest 10 to 30 minutes and within a radius of 30 kilometres. The method developed by researchers at the federal technology institute ETH Zurich combines standard data drawn from weather stations and artificial intelligence. Lightning is one of the most unpredictable and lethal phenomena in nature. It regulary kills people and animals, sets fire to homes and forests, keeps aircraft grounded and damages power installations.
Weather prediction has gotten substantially better over the course of the past decade, with five-day forecasts now being about 90% accurate. However, one aspect of weather that has long eluded attempts to predict it is lightning. Because lightning is so unpredictable, it's very difficult to minimize the damage it can do to human lives, property, and nature. Thanks to the work of a research team from the EPFL (Ecole Polytechnique Fédérale de Lausanne) School of Engineering, lightning strikes may be much more predictable in the near future. As reported by SciTechDaily, a team of researchers from EPFL' s School of Engineering – Electromagnetic Compatibility Laboratory, recently created an AI program capable of accurately predicting a lightning strike within a period of 10 to 30 minutes away and over a 30-kilometer radius.
Lightning has been deemed'the most unpredictable phenomena in nature' - until now. The technology is set to work as an early warning system to prevent effects of lightning strikes to critical infrastructure, sensitive equipment and outdoor facilities. The system, developed by students at École polytechnique fédérale de Lausanne (EPFL School of Technology), is capable of predicting when and where lighting will strike to the nearest 10 to 30 minutes, within an 18 mile radius. Lightning has been deemed'the most unpredictable phenomena in nature' -until now. Amirhossein Mostajabi, the Ph.D. student who came up with the technique, said, 'Current systems are slow and very complex, and they require expensive external data acquired by radar or satellite.'
We recently had the opportunity to catch up with Attila Toth, CEO, zesty.ai, the Silver Winner of the 2019 Zurich Innovation World Championship, to discuss how Artificial Intelligence is impacting the Property & Casualty Insurance market across personal and commercial lines. Click the link to have a listen. You can also read the full transcript of the conversation below. With me today is Attila Toth, CEO of zesty.ai Today we have an interesting show planned for you where we're going to talk about the global insurance industry as it undergoes a digital transformation. As insurance companies find themselves trying to make sense of all these new technologies – artificial intelligence, natural language processing, machine learning, computer vision, – understanding the business case for each can be extremely confusing and daunting. With insurance companies being held to higher customer expectations, the time is now to embrace new technologies to leapfrog the competition. Being status quo is no longer an option. Technology is driving diversity across many industries – insurance included – as it reshapes the value chain. Age-old processes are being disrupted, while new market entrants and changing business models are bringing new threats, as well as opportunities for those who act on them. Some of the questions we'll cover today include: What is the value that AI is delivering to the insurance industry, and how are insurance providers reacting to these seismic changes?
ETH researchers use artificial intelligence to improve quality of images recorded by a relatively new biomedical imaging method. This paves the way towards more accurate diagnosis and cost-effective devices. Scientists at ETH Zurich and the University of Zurich have used machine learning methods to improve optoacoustic imaging. This relatively young medical imaging technique can be used for applications such as visualizing blood vessels, studying brain activity, characterizing skin lesions and diagnosing breast cancer. However, quality of the rendered images is very dependent on the number and distribution of sensors used by the device: the more of them, the better the image quality.
Physicists have designed artificial intelligence that thinks like the astronomer Nicolaus Copernicus by realizing the Sun must be at the centre of the Solar System.Credit: NASA/JPL/SPL Astronomers took centuries to figure it out. But now, a machine-learning algorithm inspired by the brain has worked out that it should place the Sun at the centre of the Solar System, based on how movements of the Sun and Mars appear from Earth. The feat is one the first tests of a technique that researchers hope they can use to discover new laws of physics, and perhaps to reformulate quantum mechanics, by finding patterns in large data sets. The results are due to appear in Physical Review Letters1. Physicist Renato Renner at the Swiss Federal Institute of Technology (ETH) in Zurich and his collaborators wanted to design an algorithm that could distill large data sets down into a few basic formulae, mimicking the way that physicists come up with concise equations like E mc2.