Parameter estimation of unnormalized models is a challenging problem because normalizing constants are not calculated explicitly and maximum likelihood estimation is computationally infeasible. Although some consistent estimators have been proposed earlier, the problem of statistical efficiency does remain. In this study, we propose a unified, statistically efficient estimation framework for unnormalized models and several novel efficient estimators with reasonable computational time regardless of whether the sample space is discrete or continuous. The loss functions of the proposed estimators are derived by combining the following two methods: (1) density-ratio matching using Bregman divergence, and (2) plugging-in nonparametric estimators. We also analyze the properties of the proposed estimators when the unnormalized model is misspecified. Finally, the experimental results demonstrate the advantages of our method over existing approaches.

NEW YORK - Billionaire George Soros warned of the dangers of artificial intelligence in the hands of authoritarian regimes such as China and called President Xi Jinping "the most dangerous" opponent of open societies. "The instruments of control developed by artificial intelligence give an inherent advantage of totalitarian regimes over open societies," the former hedge fund manager said on Thursday at the World Economic Forum in Davos, Switzerland. Soros said China is developing a centralized database that will use algorithms to determine whether a person poses a threat to the one party system. Soros, 88, has become an lightning rod for his political views and philanthropic efforts. A longtime supporter and financial backer of progressive causes and Democratic politicians, he's become a target of right wing activists.

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.

Artificial intelligence within CRM still has to make inroads with the pharmaceutical industry. That's a surprising conclusion given how much this sector is embracing AI for other functions. Yet some market analysts are calling this a "white space" for the industry. Pharma companies are already incorporating AI in their drug delivery, digital diagnostics and precision medicine programs -- areas where its usage has become highly sophisticated. The industry clearly recognizes that AI can be a massive value add.

A number of vendors are seeking to provide next-generation gun detection capabilities to schools and other organizations. Responding to too-frequent reports of shootings in the U.S., technology firms are beginning to roll out systems designed to quickly detect firearms in public settings. Many of the systems offer an advantage over conventional metal detectors, given that they often include video surveillance that can spot a gun in plain sight in a crowd, surveillance a metal detector may not be able to duplicate. Plus, some of the systems can utilize facial recognition to identify known persons of interest, such as known sex offenders, gang members, and the like; another bit of surveillance currently beyond the reach of conventional metal detectors. The Canadian firm Patriot One Technologies, for example, offers a solution featuring a microwave radar scanner driven by artificial intelligence (AI) that can detect hidden weapons, along with a video surveillance component.

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.

Google is pouring an additional $3.1 million into Wikipedia, bringing its total contribution to the free encyclopedia over the past decade to more than $7.5 million, the company announced at the World Economic Forum Tuesday. A little over a third of those funds will go toward sustaining current efforts at the Wikimedia Foundation, the nonprofit that runs Wikipedia, and the remaining $2 million will focus on long-term viability through the organization's endowment. Google will also begin allowing Wikipedia editors to use several of its machine learning tools for free, the tech giant said. What's more, Wikimedia and Google will soon broaden Project Tiger, a joint initiative they launched in 2017 to increase the number of Wikipedia articles written in underrepresented languages in India, and to include 10 new languages in a handful of countries and regions. It will now be called GLOW, Growing Local Language Content on Wikipedia.

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.

File photo - An unmanned 11-meter rigid-hull inflatable boat from Naval Surface Warfare Center Carderock operates autonomously during an Office of Naval Research-sponsored demonstration of swarmboat technology on the James River in Newport News, Va.(U.S. Navy photo by John F. Williams/Released) The U.S. Navy will launch a swarm of interconnected small attack drone boats on mock-combat missions to refine command and control technology and prepare its "Ghost Fleet" of autonomous, yet networked surface craft for war. Developed by the Office of Naval Research and Naval Sea Systems Command, "Ghost Fleet" represents a Navy strategy to surveil, counter, overwhelm and attack enemies in a coordinated fashion - all while keeping sailors on host ships at safer distances. The small boats, many of them called Unmanned Surface Vessels, are designed to conduct ISR missions, find and destroy mines and launch a range of attacks including electronic warfare and even mounted guns. The concept is to use advanced computer algorithms bringing new levels of autonomy to surface warfare, enabling ships to coordinate information exchange, operate in tandem without colliding and launch combined assaults. "Ghost Fleet is really helping us in the Command and Control and coms arena. The demonstration will allow us to learn lessons about integrated payloads with USVs," Capt.