These were then shown actual images of new gravitational lenses, and it was able to analyze the distortions 10 million times faster than traditional methods. This is significant because while the traditional method weeks or a month just to analyze one lens using computer simulations and mathematical models, the same can be done by AI in less than half a second. The SLAC study isn't the first time researchers have turned to AI to study gravitational lensing. Previous works included having a neural network identify if an image showed gravitational lensing or not.

Science fiction has an uncanny ability to predict the future of technology, from Star Trek's Padd, essentially an iPad, to the Jetsons' robot vacuum, basically a Roomba. Now that the voice assistant is here, that's another checklist off the sci-fi predictor, but while our Alexas, Siris, Cortanas and Google Assistants are pretty basic right now, if sci-fi continues its great prelude to the future, what will the computers of the future really be like? According to Amazon's head of devices, Dave Limp, the next phase in computing is less about the physical thing and more about how and where you access it. He says: "We think of it as ambient computing, which is computer access that's less dedicated personally to you but more ubiquitous. "Our vision is to create that Star Trek computer and work backwards from that.

Earthquakes are, by their nature, unpredictable. Although geologists understand why and how the tremors occur, forecasting them more than a few minutes ahead is very difficult. A team of scientists believes that machine learning could help solve this problem one day. A paper published Wednesday in the Geophysical Research Letters describes a method that relies on listening for acoustic signals from a laboratory simulation of failing fault lines. Stress is applied to two heavy steel blocks, causing them to slip and slide over one another like tectonic plates during an earthquake.

In lab tests involving simulated tabletop earthquakes, researchers at the Los Alamos National Laboratory in New Mexico demonstrated that machine-learning technology can play a role in predicting major tremors by analyzing acoustic signals to find failing fault lines. For the experiment, earthquakes were modeled by the researchers using two large blocks of steel, which were put under stress. This resulted in them rubbing against one another like tectonic plates on the Earth's surface. The movement released energy in the form of seismic waves -- which was then analyzed by the team's artificial intelligence. "We discovered that an artificial intelligence can learn to discern a very specific pattern in the sound emitted by the fault before it ruptures," Bertrand Rouet-LeDuc, one of the researchers on the project, told Digital Trends.

That Smart Manufacturing system advises engineers each morning about any trend it has detected during the previous day, he says. "As well as highlighting any issues, the system provides options and recommendations on the best temperatures, pressures and running speeds that can help get an extra 10 tonnes or even 100 tonnes of [refined material] out each day -- and that can be worth a lot of money," says Walker. Another of the AI technology platforms in action at Shell Downstream is Salesforce.com's It is able to make predictions and recommendations by digesting operational data as it passes through the systems, for example, prioritizing the order in which maintenance tasks should be tackled. "It's helping us make faster and better decisions," says Walker.

A computer program could hold the key to predicting when the next tremors will occur in an earthquake zone. Researchers at the Los Alamos National Laboratory developed an algorithm that demonstrated a high level of accuracy in determining the failure times of quakes simulated in a lab. Here's how the experiment worked. The team analyzed data from a laboratory fault system that contains fault gouge, the ground-up material created by the stone blocks sliding past one another. When a frictional failure occurred in the lab quake, the shearing block moves or displaces, while the gouge material simultaneously dilates and strengthens based on measurably increasing shear stress and friction.

Apple's eagerly anticipated new iPhone may not be called iPhone 8 after all, it has been claimed. However, Apple will also unveil an iPhone 8 and 8 plus at the event - but these will be lower specced models that were previously expected to be called the 7s and 7s Plus. The firm sent invites to key media for the event, which is also expected to see the launch of an iPhone 7s and 7s Plus, a new version of the Apple Watch and a new 4K Apple TV. It is believed the will lack the edge to edge OLED screen of the edition model, and instead be an upgraded model that looks similar to the existing design. 'One casemaker has updated their internal SKUs based on the information and is actively printing packaging which I was able to see in the form of preliminary artwork,' 9to5mac's Seth Weintraub claims.

Artificial intelligence isn't just good for customer service chatbots and personal assistants on your mobile, advances in the field are also helping to revolutionise scientific research. Scientists from the Department of Energy's SLAC National Accelerator Laboratory and Stanford University have shown that a form of AI known as neural networks can accurately analyse complex distortions in spacetime a whopping ten million times faster than traditional methods. "Analyses that typically take weeks to months to complete, that require the input of experts and that are computationally demanding, can be done by neural nets within a fraction of a second, in a fully automated way and, in principle, on a cell phone's computer chip," said postdoctoral fellow Laurence Perreault Levasseur, a co-author of a study published in Nature. KIPAC scientists have for the first time used artificial neural networks to analyze complex distortions in spacetime, called gravitational lenses, demonstrating that the method is 10 million times faster than traditional analyses. The team at the Kavli Institute for Particle Astrophysics and Cosmology, a joint institute of SLAC and Stanford, used the neural networks to look at images of strong gravitational lensing, where a picture of a far-flung galaxy is multiplied and distorted by the gravity of a massive object that's closer to us, such as a galaxy cluster.

Researchers from the Department of Energy's SLAC National Accelerator Laboratory and Stanford University have for the first time shown that neural networks – a form of artificial intelligence – can accurately analyze the complex distortions in spacetime known as gravitational lenses 10 million times faster than traditional methods. "Analyses that typically take weeks to months to complete, that require the input of experts and that are computationally demanding, can be done by neural nets within a fraction of a second, in a fully automated way and, in principle, on a cell phone's computer chip," said postdoctoral fellow Laurence Perreault Levasseur, a co-author of a study published today in Nature. KIPAC scientists have for the first time used artificial neural networks to analyze complex distortions in spacetime, called gravitational lenses, demonstrating that the method is 10 million times faster than traditional analyses. The team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, used neural networks to analyze images of strong gravitational lensing, where the image of a faraway galaxy is multiplied and distorted into rings and arcs by the gravity of a massive object, such as a galaxy cluster, that's closer to us. The distortions provide important clues about how mass is distributed in space and how that distribution changes over time – properties linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that's accelerating the expansion of the universe.

Researchers from the Department of Energy's SLAC National Accelerator Laboratory and Stanford University have for the first time shown that neural networks - a form of artificial intelligence - can accurately analyze the complex distortions in spacetime known as gravitational lenses 10 million times faster than traditional methods. "Analyses that typically take weeks to months to complete, that require the input of experts and that are computationally demanding, can be done by neural nets within a fraction of a second, in a fully automated way and, in principle, on a cell phone's computer chip," said postdoctoral fellow Laurence Perreault Levasseur, a co-author of a study published today in Nature. The team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, used neural networks to analyze images of strong gravitational lensing, where the image of a faraway galaxy is multiplied and distorted into rings and arcs by the gravity of a massive object, such as a galaxy cluster, that's closer to us. The distortions provide important clues about how mass is distributed in space and how that distribution changes over time - properties linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that's accelerating the expansion of the universe. Until now this type of analysis has been a tedious process that involves comparing actual images of lenses with a large number of computer simulations of mathematical lensing models.