The upstart indie games publisher is taking steps to directly help developers that were planning to attend the 2017 Game Developer's Conference but can't, because of the Trump administration's controversial immigration ban affecting seven predominantly Muslim nations. SEE ALSO: The video games industry responds to Trump's immigration ban It works like this: if you're a game developer from one of the seven affected nations -- Iran, Iraq, Libya, Somalia, Sudan, Syria and Yemen -- and you had a game to show at GDC, get in touch with Devolver. You'll find all the info for submitting your game right here. Here's the message Devolver blasted out on Twitter: If you canceled plans to demo a game at GDC due to the US immigration ban, we'd like to demo it on your behalf. The upcoming GDC has already taken a very anti-Trump stance against the immigration ban.

In fact, in many countries, the internet, the very thing that was supposed to smash down the walls of authoritarianism like a sledgehammer of liberty, has been instead been co-opted by those very regimes in order to push their own agendas while crushing dissent and opposition. And with the emergence of conversational AI -- the technology at the heart of services like Google's Allo and Jigsaw or Intel's Hack Harassment initiative -- these governments could have a new tool to further censor their citizens. Turkey, Brazil, Egypt, India and Uganda have all shut off internet access when politically beneficial to their ruling parties. Nations like Singapore, Russia and China all exert outsize control over the structure and function of their national networks, often relying on a mix of political, technical and social schemes to control the flow of information within their digital borders. The effects of these policies are self-evident.

Boss Sundar Pichai asked employees to fly back to the US ASAP. The President's executive order that bans almost all citizens from Iran, Sudan, Syria, Iraq, Libya, Somalia and Yemen from entering the country is making tech execs move fast. Google's Sundar Pichai told overseas employees to fly back to the US as soon as possible, or risk not being able to get back at all. The immigration order also saw companies like Apple and Microsoft rush to help employees stranded following the restrictions. Surgeons were able to perform a difficult eye procedure with some robotic help.

Machine learning as a subset of Artificial Intelligence is an emerging trend in South Africa, with demand for data scientists rising sharply and university programmes incorporating the discipline in study programmes. The machine learning sector is really beginning to take form in South Africa with various start-ups taking off and entering the international scene. At DataProphet, we specialise in the application of machine learning algorithms to provide actionable solutions for a variety of industries. Having built a presence in the U.S. we have experienced the difference in industry trends first hand. South Africa's diverse range of spoken languages makes it difficult to use existing personal assistants, chatbots and speech recognition tools which were designed solely for the English language.

Driving into Johannesburg, the skyline is often encased in a dome of smog. As Africa urbanises and industrialises, its economic growth is powered by coal, its vehicles by dirty fuels, and many still cook over paraffin and three-stone fires, burning their rubbish in the street. Researchers believe that more than 700,000 Africans die each year as a result of air pollution – and the number continues to grow. Few African cities even measure air pollution, but those that do routinely rank among the dirtiest on the planet. Nigeria is home to four of the worst, including Onitsha, which holds the dubious distinction of the Earth's most toxic air.

With every passing day, we're reminded that the future is here. Yeah, that's sort of a redundant thing to say. What I really mean is that new innovations and disruptions are popping up every day, and they're materializing at a rate never seen before. I mean, think about the fact that the modern computer, which was created in either 1942 or 1946, depending on who you ask, used to cost a fortune and fill up an entire room.[1] It was almost 50 years before that computer would be reduced to the size of an affordable desktop in 1995.

President Donald Trump is widely expected to sign his latest executive order Wednesday seriously restricting immigration and refugees from seven countries in the Middle East and Africa, five of which the U.S. bombed under the previous administration. Throughout his presidential campaign, Trump accused President Barack Obama of supporting liberal immigration and refugee policies that the Republican billionaire-turned-politician said threatened national security. Trump directed his anti-immigration rhetoric toward Mexico and the Middle East, particularly singling Muslims as targets for tough, new visa policies. While he at one time suggested implementing a ban on all Muslims, Trump has since said he would focus on several countries whose population he felt presented the greatest risk. The list of countries was confirmed by congressional aides and immigration experts who the new administration had briefed on the upcoming order, according to Reuters.

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We study the statistical limits of both detecting and estimating a rank-one deformation of a symmetric random Gaussian tensor. We establish upper and lower bounds on the critical signal-to-noise ratio, under a variety of priors for the planted vector: (i) a uniformly sampled unit vector, (ii) i.i.d. $\pm 1$ entries, and (iii) a sparse vector where a constant fraction $\rho$ of entries are i.i.d. $\pm 1$ and the rest are zero. For each of these cases, our upper and lower bounds match up to a $1+o(1)$ factor as the order $d$ of the tensor becomes large. For sparse signals (iii), our bounds are also asymptotically tight in the sparse limit $\rho \to 0$ for any fixed $d$ (including the $d=2$ case of sparse PCA). Our upper bounds for (i) demonstrate a phenomenon reminiscent of the work of Baik, Ben Arous and P\'ech\'e: an `eigenvalue' of a perturbed tensor emerges from the bulk at a strictly lower signal-to-noise ratio than when the perturbation itself exceeds the bulk; we quantify the size of this effect. We also provide some general results for larger classes of priors. In particular, the large $d$ asymptotics of the threshold location differs between problems with discrete priors versus continuous priors. Finally, for priors (i) and (ii) we carry out the replica prediction from statistical physics, which is conjectured to give the exact information-theoretic threshold for any fixed $d$. Of independent interest, we introduce a new improvement to the second moment method for contiguity, on which our lower bounds are based. Our technique conditions away from rare `bad' events that depend on interactions between the signal and noise. This enables us to close $\sqrt{2}$-factor gaps present in several previous works.

DAVOS, Switzerland In this Swiss Alps town where the global elite have gathered to debate the world's problems, there seems to be little room on the radar for Africa, a continent of over a billion people where a commodity-fueled growth boom has soured with a vengeance.