Trump suffers Minneapolis meltdown as president flinches at ICE shooting and aides panic: 'He doesn't like it' Heiress reveals cruel outbursts her hedge funder husband made after dumping her for younger woman while sheltering from Covid at $7.5m Martha's Vineyard compound America's biggest banks line up to bash Trump over his plot to cut credit card bills as they outline obvious problem Amy Schumer sent me these late-night texts when I last wrote about her. Sorry sweetie, truth hurts... here's what I think of you now! KENNEDY I've uncovered a seedy new dating red flag. So many men are at it... I can't believe I didn't spot the warning signs sooner: JANA HOCKING Major 6.0 magnitude earthquake strikes West Coast with shockwaves felt across major cities Taylor Swift and'defeated' Travis Kelce are facing'first real test' in their relationship... as insiders say'things are changing' What Trump's desire to buy Greenland may reveal about him... psychologist unpacks the president's personality Kate leaves fans stunned with her down-to-earth nature as she's spotted driving herself to glitzy reception at Windsor Castle Kylie Kelce makes raunchy reference to her and Jason's sex life after his post-NFL glow up Gene Hackman's Santa Fe mansion listed for over $6M one year after he died in the home with his wife Students were raped after being forced to live in complex alongside 125 refugees to'aid integration': Terrified Dutch youngsters'were subjected to years of sex assaults and violence' Bitter, past his best and seething with jealousy: Horrifying'insight' into mind of surgeon accused of killing ex-wife and her dentist spouse NFL team could be forced to move over wild conspiracy theory as players' fears mount Dementia is NOT inevitable: scientists reveal how MILLIONS of cases can be prevented as they unveil a'roadmap' to beat the disease Trump threatens tariffs over Greenland if allies don't play ball: 'We need it' Titanic star with a link to Clint Eastwood emerges on rare outing in LA can you guess who she is? Karoline Leavitt takes vicious swipe at Supreme Court over bombshell hearing on trans athletes in women's sport I freaked out when my toddler dropped her baby sister... it ended up saving her life Renee Good's last moments revealed as woman suffered FOUR gunshot wounds during deadly clash with ICE Baseball is'DEAD', claim livid fans as free-spending Dodgers sign new star player to take payroll over $2 BILLION Major 6.0 magnitude earthquake strikes West Coast with shockwaves felt across major cities A major earthquake has struck off the coast of Oregon, sending shockwaves felt in multiple cities along the US West Coast with more quakes expected to follow.

Forecasting large aftershocks is a challenge of great importance for human security. Today we dispose of statistical predictive models called Epidemic Type Aftershock Sequence (ETAS) tuned on the earthquake catalogue of the past seismicity. This catalogues contains basic information such as the location, the time and the magnitude of an earthquake. However we dispose of much richer data set about the crust dynamics, such as the daily displacement of the ground surface, that is nowadays measured by numerous GPS stations, devices that send their absolute position everyday to sattellites, thus telling us about how the ground deforms. In this study, we propose to forecast the Japanese aftershocks by means of a machine learning study of the GPS data alone. Our results show that this method is very promising and relies on the quality and the quantity of the available data.

Less than two weeks after the tragic earthquake that has killed more than 40,000 people in Turkey and Syria, another shake has rocked New Zealand. Wednesday's'widely felt' tremor, around magnitude 6, jolted both New Zealand's islands, although thankfully there's been no immediate reports of damage or injury. Earthquakes are happening all the time, from the ones too small to even be noticed to the devastating high magnitude quakes that lead to thousands of fatalities. But its occurrence so soon after the disaster in Turkey and Syria begs the question - could they be linked? Here, MailOnline takes a closer look at today's event and whether it's related to the catastrophic tremor in the Middle East last week.

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs.

Self-exciting Hawkes processes are used to model events which cluster in time and space, and have been widely studied in seismology under the name of the Epidemic Type Aftershock Sequence (ETAS) model. In the ETAS framework, the occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process, and aftershock events are then modelled via a separate triggering kernel. Most previous studies of the ETAS model have relied on point estimates of the model parameters due to the complexity of the likelihood function, and the difficulty in estimating an appropriate mainshock distribution. In order to take estimation uncertainty into account, we instead propose a fully Bayesian formulation of the ETAS model which uses a nonparametric Dirichlet process mixture prior to capture the spatial mainshock process. Direct inference for the resulting model is problematic due to the strong correlation of the parameters for the mainshock and triggering processes, so we instead use an auxiliary latent variable routine to perform efficient inference.

The aftershocks that follow an earthquake can be even more dangerous and damaging than the main temblor, for example by collapsing already structurally weakened buildings. With deep learning emerging as something of a panacea in the world of science, AI researchers and seismologists alike are leveraging the tech in pursuit of better aftershock forecast solutions. A major breakthrough seemed to occur in 2018 when a Harvard University and Google research team published the paper Deep learning of aftershock patterns following large earthquakes in Nature. The paper proposed a deep learning model that significantly improved aftershock location forecasts compared to previous methods. It went viral on social media and garnered global mainstream media coverage.

In 2018, artificial intelligence took on new tasks, with these smarty-pants algorithms acing everything from disease diagnosis to crater counting. In April, the U.S. Food and Drug Administration permitted marketing of the first artificial intelligence that diagnoses health problems at primary care clinics without specialist supervision (SN: 3/31/18, p. 15). The program, which inspects eye images for signs of diabetes-related vision loss, could be a boon for people in remote or low-resource areas where ophthalmologists are scarce. Other eye-inspecting AI programs are learning to recognize everything from age-related vision loss to heart problems. One artificial intelligence is a celestial cartographer after Galileo's own heart.

Recently, the regions around the Dead Sea in Jordan were flooded, causing the death of 21 children who were on a school trip, and injuring 35 more. Such disasters affect millions of people every year and cause property damage worth hundreds of billions. In 2017 alone, almost 335 natural disasters have affected more than 95.6 million people, and killed 9,697, costing around US $335 billion. But, the impact of these phenomena can be reduced if we were able to predict their occurrence. AI-powered systems can already predict the prices of stocks, which involve the analysis of numerous variables.

In the weeks and months following a major earthquake, the surrounding area is often wracked by powerful aftershocks that can leave an already damaged community reeling and can significantly hamper recovery efforts. While scientists have developed empirical laws to describe the likely size and timing of those aftershocks, such as Bath's Law and Ohmori's Law, forecasting their location has been harder. But sparked by a suggestion from researchers at Google, Brendan Meade, professor of earth and planetary sciences, and Phoebe DeVries, a postdoctoral fellow working in his lab, are using artificial intelligence technology to try to get a handle on the problem. Using deep-learning algorithms, the pair analyzed a database of earthquakes from around the world to try to predict where aftershocks might occur, and developed a system that, while still imprecise, was able make significantly better forecasts than random assignment. The work is described in an Aug. 30 paper published in the journal Nature.

A machine-learning study that analysed hundreds of thousands of earthquakes beat the standard method at predicting the location of aftershocks. Scientists say that the work provides a fresh way of exploring how changes in ground stress, such as those that occur during a big earthquake, trigger the quakes that follow. It could also help researchers to develop new methods for assessing seismic risk. "We've really just scratched the surface of what machine learning may be able to do for aftershock forecasting," says Phoebe DeVries, a seismologist at Harvard University in Cambridge, Massachusetts. She and her colleagues report their findings on 29 August in Nature.