The past year has shown the power of artificial intelligence (AI) around the world. Globally, businesses and governments saw 2018 as the breakout year for AI, with venture capitalists pushing 47% more investment in 2018 into AI businesses in the UK alone. The increased use of AI had a seismic impact on consumers' daily lives in ways many do not even realise – take, for example, daily Spotify and Netflix recommendations. With many real-world applications beginning to use this technology in 2018, it may finally be the case that 2019 is the year that AI goes beyond the hype. Society's readiness to embrace these changes has allowed for advancement in this technology as the real-world benefits are finally being realised.

The billionaire philanthropist George Soros has delivered a stinging attack on China with a warning that Xi Jinping's regime is using breakthroughs in machine learning and artificial intelligence to repress its people. Soros used his annual dinner at the World Economic Forum to say Xi was the most dangerous opponent of open societies and to call on the west to crack down on Chinese tech companies that he said were being used as a means of authoritarian control. "I want to call attention to the mortal danger facing open societies from the instruments of control that machine learning and artificial intelligence can put in the hands of repressive regimes. I'll focus on China, where Xi Jinping wants a one-party state to reign supreme," Soros said. 'The reality is that we are in a cold war that threatens to turn into a hot one' In the past, Soros has used his keynote Davos address to attack Donald Trump, and he again criticised the US president for his handling of China.

Governments and companies worldwide are investing heavily in artificial intelligence in hopes of new profits, smarter gadgets, and better health care. Financier and philanthropist George Soros told the World Economic Forum in Davos Thursday that the technology may also undermine free societies and create a new era of authoritarianism. "I want to call attention to the mortal danger facing open societies from the instruments of control that machine learning and artificial intelligence can put in the hands of repressive regimes," Soros said. He made an example of China, repeatedly calling out the country's president, Xi Jinping. China's government issued a broad AI strategy in 2017, asserting that it would surpass US prowess in the technology by 2030. As in the US, much of the leading work on AI in China takes place inside a handful of large tech companies, such as search engine Baidu, and retailer and payments company Alibaba. Soros argued that AI-centric tech companies like those can become enablers of authoritarianism. He pointed to China's developing "social credit" system, aimed at tracking citizens' reputations by logging financial activity, online interactions and even energy use, among other things. The system is still taking shape, but depends on data and cooperation from companies like payments company Ant Financial, a spinout of Alibaba.

You don't need satellite photos to know that Sears is failing. Companies like Orbital Insight are typically tight-lipped when it comes to more important data -- as are their customers -- mainly because they see this information as a competitive advantage. But the line graph showed how Mr. Crawford and his start-up can target the performance of individual businesses. Orbital Insight tracks activity in more than 260,000 retail parking lots across the country, and it monitors the levels of more than 25,000 oil tanks around the world. Not surprisingly, Orbital Insight and SpaceKnow said, some of their customers use this satellite data to track the progress of their direct competitors, though those customers and their competitors are very reluctant to talk about it.

As the human population has grown, our demands on the ocean have increased rapidly. These demands have similarly increased the pressure we place on these systems, and we now cause considerable damage globally. If we want to maintain healthy ocean ecosystems into the future, we must learn to use ocean resources in a sustainable way and facilitate recovery in regions that have declined. Determining how to make these goals a reality, however, is no small challenge. Ingeman et al. review the challenge presented by attempting both to recover and to use ecosystems simultaneously and discuss several approaches for facilitating this essential dual goal. Ocean defaunation and loss of marine ecosystem services present an urgent need to recover degraded ocean ecosystems. Growing scientific awareness, strong regulations, and effective management have begun to fulfill the promise of recovery. Unfortunately, many efforts remain unsuccessful, in part because marine ecosystems and human societies are changing. Rapid shifts in environmental conditions are undermining previously effective recovery strategies. Moreover, divergent perceptions of recovery exist. Efforts toward reversing marine degradation must address the dynamic social-ecological landscape in which recoveries occur, or forever chase a moving target. Recovery efforts of tomorrow will require institutional and tactical flexibility to keep pace with a changing ocean, and an inclusive concept of recovery. Further, vital population-level efforts will be most successful when complemented by a broader ecosystem and social-ecological perspective. In this Review, we provide a synthesis of ocean-recovery goals as moving targets and highlight promising steps forward. While acknowledging the priority of basic conservation imperatives, successful recoveries can encompass a range of outcomes in the space between minimum ecological viability and maximum carrying capacity. Ongoing advances are improving our ability to predict the effects of environmental change on ocean productivity and to calibrate recovery targets to changing conditions. As a complement to predict-and-prescribe methods, research can also point the way toward robust approaches in the face of irreducible uncertainty.

WASHINGTON (Reuters) - The Midwestern states hit hardest by job automation in recent decades, places that were pivotal to U.S. President Donald Trump's election, will be under the most pressure again as advances in artificial intelligence reshape the workplace, according to a new study by Brookings Institution researchers. The spread of computer-driven technology into middle-wage jobs like trucking, construction, and office work, and some lower-skilled occupations like food preparation and service, will also further divide the fast-growing cities where skilled workers are moving and other areas, and separate the high- skilled workers whose jobs are less prone to automation from everyone else regardless of location, the study found. But the pain may be most intense in a familiar group of manufacturing-heavy states like Wisconsin, Ohio and Iowa, whose support swung the U.S. electoral college for Trump, a Republican, and which have among the largest share of jobs, around 27 percent, at "high risk" of further automation in coming years. At the other end, solidly Democratic coastal states like New York and Maryland had only about a fifth of jobs in the high-risk category. The findings suggest the economic tensions that framed Trump's election may well persist, and may even be immune to his efforts to shift global trade policy in favor of U.S. manufacturers.

WASHINGTON (Reuters) - The Midwestern states hit hardest by job automation in recent decades, places that were pivotal to U.S. President Donald Trump's election, will be under the most pressure again as advances in artificial intelligence reshape the workplace, according to a new study by Brookings Institution researchers. The spread of computer-driven technology into middle-wage jobs like trucking, construction, and office work, and some lower-skilled occupations like food preparation and service, will also further divide the fast-growing cities where skilled workers are moving and other areas, and separate the high- skilled workers whose jobs are less prone to automation from everyone else regardless of location, the study found. But the pain may be most intense in a familiar group of manufacturing-heavy states like Wisconsin, Ohio and Iowa, whose support swung the U.S. electoral college for Trump, a Republican, and which have among the largest share of jobs, around 27 percent, at "high risk" of further automation in coming years. At the other end, solidly Democratic coastal states like New York and Maryland had only about a fifth of jobs in the high-risk category. The findings suggest the economic tensions that framed Trump's election may well persist, and may even be immune to his efforts to shift global trade policy in favor of U.S. manufacturers.

I get the feeling you don't dislike rats enough. Because your struggles with the rodents chewing through your house pale in comparison to the problems wrought by rodents chewing through entire island ecosystems. Release just one pregnant rat on an island and soon enough the invasive predators will have decimated that pristine environment like an atom bomb. Sure, rats on their own are pretty neat, but we've got a nasty habit of transporting them where they don't belong, at which point they transform into menaces. Such is the plight of the Galapagos Island of Seymour Norte, a speck of 455 acres off the coast of Ecuador. In 2007, conservationists succeeded in ridding the island of invasive rats, but a decade later, the fiends had returned, likely by swimming from the neighboring island of Baltra.

Artificial intelligence (AI) presents limitless opportunity, but not without potential pitfalls and risks. This paradox has become increasingly evident for government leaders. They want to give domestic companies an edge over the competition, but are also expected to protect their citizens and use AI for social good. They want to support innovation, while still maintaining some level of control over how new technologies impact society at large. With a huge payoff on the line -- by our own estimates, AI has the potential to increase worldwide GDP by 14 percent by 2030, an infusion of US$15.7 trillion into the global economy -- it should come as no surprise that governments are eager to claim their share.

While there have been several key developments in machine translation (MT) in recent years, MT has not yet reached the level where businesses might be confident to allow it to proceed unchecked by humans. There is a paradox insofar that we want to allow artificial intelligence (AI) and automation to take on more and more tasks to relieve pressure on the human workforce, but, in turn, this creates more work for humans in terms of supervising their digital colleagues. We need look no further than the restaurant in China called "Translate Server Error" or Hillary Clinton's gift to the Russian foreign minister that was inscribed with a message that was supposed to say "reset" in Russian but actually showed the word "overcharge." AI still commits fundamental errors that are embarrassing at best, and at worst, they can convey offensive and/or completely unintended meanings. This is where the importance of quality estimation comes to the fore. A good definition of quality estimation comes from eBay, an enthusiastic user of QE: "A method used to automatically provide a quality indication for machine translation output without depending on human reference translations.