LONDON (Reuters) - Britain announced on Thursday a 1 billion pound ($1.4 billion) joint investment in the artificial intelligence (AI) industry to capitalize on what the government sees as a competitive advantage in the sector as it prepares for life after Brexit. The deal is the latest in a series of targeted public-private investment pacts in the government's industrial strategy that aims to modernize Britain's economy and address decades of regional and industrial decline. "It's evident that Britain is a place that people want to come to for AI," business minister Greg Clark told Reuters during a visit to a facility in London that nurtures early-stage tech businesses from across the world. "We have a position of strength that we want to capitalize on because if we don't build on it the other countries around the world would steal a march." Governments worldwide are plowing cash AI to keep up with international rivals and seeking to harness its power to transform industries from transport to agriculture.

Those funds will be matched by more than 300 million pounds of public funding, on top of an existing 400 million pound budget. The funds will be spent on teacher training, research and developing regional technology hubs to explore how AI can be used in industries such as law and insurance.

Random Fourier features is one of the most popular techniques for scaling up kernel methods, such as kernel ridge regression. However, despite impressive empirical results, the statistical properties of random Fourier features are still not well understood. In this paper we take steps toward filling this gap. Specifically, we approach random Fourier features from a spectral matrix approximation point of view, give tight bounds on the number of Fourier features required to achieve a spectral approximation, and show how spectral matrix approximation bounds imply statistical guarantees for kernel ridge regression. Qualitatively, our results are twofold: on the one hand, we show that random Fourier feature approximation can provably speed up kernel ridge regression under reasonable assumptions. At the same time, we show that the method is suboptimal, and sampling from a modified distribution in Fourier space, given by the leverage function of the kernel, yields provably better performance. We study this optimal sampling distribution for the Gaussian kernel, achieving a nearly complete characterization for the case of low-dimensional bounded datasets. Based on this characterization, we propose an efficient sampling scheme with guarantees superior to random Fourier features in this regime.

For several years, the state of Utah was collecting statistics and feedback on public opinion, but the state didn't really have a plan for what to do with the data. Recently, it decided to use machine learning tools to analyze health, transportation, air quality and geo-based Twitter information to perform sentiment analysis before, during and after Utah's winter inversions and air quality spikes. Utah CIO Michael Hussey explained how the state went about it at the 2018 National Association of State Chief Information Officers (NASCIO) Midyear Conference in Baltimore on Tuesday. Winter inversions in Utah occur when the usual atmospheric conditions become inverted. A dense layer of cold air becomes trapped under a layer of warm air, essentially sealing pollutants closer to the ground.

A nuclear war that threatens to wipe out humanity could be brought about by artificial intelligence (AI) as early as 2040, according to security experts. The Rand corporation, a not for profit security think tank based in the US, warned that computers by "mistake or malice" could lead to mankind's untimely end. It argues in a report released yesterday that technological advances could lead to "doomsday AI" - machines that could encourage world leaders to take major risks with their nuclear arsenal. During the cold war the value of "mutually assured destruction (mad)" stopped world powers firing nuclear weapons, as to do so would lead to a devastating retaliation. But according to RAND, improved AI as well as more sensor and open source data could convince countries that their opponents' nuclear capabilities are vulnerable, leading them to take drastic action.

You might be tempted to put these pieces together and assume that AI might autonomously start a nuclear war. This is the subject of a new paper and article published today by the RAND Corporation, a nonprofit thinktank that researches national security as part of its Security 2040 initiative. But AI won't necessarily cause a nuclear war; no matter what AI fear-mongerer Elon Musk tweets out, artificial intelligence will only trigger a nuclear war if we decide to build artificial intelligence that can start nuclear wars. The RAND Corporation hosted a series of panels with mysterious, unnamed experts in the realms of national security, nuclear weaponry, and artificial intelligence to speculate and theorize on how AI might advance in the coming years and what that means for nuclear war. Much of the article talks about hyper-intelligent computers that would transform when a nation decides to launch its nuclear missiles.

The head of the U.S. military's Special Operations Command said Wednesday that Air Force gunships, needed to provide close air support for American commandos and U.S.-backed rebel fighters in Syria, were being "jammed" by "adversaries." Calling the electronic warfare environment in Syria "the most aggressive" on earth, Air Force Gen. Tony Thomas told an intelligence conference in Tampa that adversaries "are testing us every day, knocking our communications down, disabling our AC-130s, etc." Thomas' remarks, which were first reported by the website The Drive, come on the heels of reports that Russian forces are jamming U.S. surveillance drones flying over the war-torn nation. An Air Force AC-130 gunship was among the U.S. military aircraft used to kill dozens of Russian mercenaries in Syria in early February. The Pentagon said the mercenaries attacked an outpost manned by American commandos and U.S.-backed fighters of the Syrian Democratic Forces (SDF), comprising Syrian Kurdish and Arab fighters. Wednesday was not the first time General Thomas has been so forthcoming about Syria in a public setting.

Insider threats aren't going away, but the introduction of machine learning and AI are proving to be powerful tools in the fight. See Also: Live Webinar Phishing Like the Bad Guys: Social Engineering's Biggest Success Trzeciak, director of the CERT Insider Threat Center at Carnegie Mellon University, works at the Software Engineering Institute, where his team researches threats trusted insiders pose to the U.S. government, industry and academia.

Facebook beat Wall Street revenue projections and announced that its user numbers were in line with estimates in the wake of a user data privacy scandal. Up to 87m users saw their data end up in the possession of political consulting firm Cambridge Analytica, which worked for Donald Trump's presidential campaign. Facebook has since been scrambling to mollify angry politicians and reassure users that it will safeguard their personal information. Amid that turmoil, observers were keenly watching the company's user figures to assess the potential damage and see if the scandal would suppress Facebook's long-term growth. Its North American user numbers were already flagging at the end of 2017, and since then a number of users have vowed to quit the platform, among them some prominent technology executives, as the #DeleteFacebook movement gained steam.

Two weeks ago I logged into good old Facebook dot com to discover I was one of the 311,127 Australians – and one of about 87 million people worldwide – who had their personal data harvested by Cambridge Analytica sometime around 2013-15. I was a small and unwitting cog in a vast, beguiling narrative of unfurling geopolitical upheaval encompassing the Trump presidency, Russian interference and Brexit. Here's what Facebook told me. I was not one of the 270,000-odd people who signed up to the now infamous This is Your Digital Life survey app but one of my friends was. As a result, Facebook "probably" shared my public profile, page likes, my date of birth and the city I lived in.