As nearly every day brings additional news about how artificial intelligence (AI) will affect the way we live, a heated debate has broken out over what the United States should do about it. On the one hand, the likes of Elon Musk and Stephen Hawking argue that we must regulate now to slow down and develop general principles governing AI's development because of its potential to cause massive economic dislocation and even destroy human civilization. On the other hand, AI advocates argue that there is no consensus on what AI is, let alone what it can ultimately do. Regulating AI in such circumstances, these advocates claim, will simply stifle innovation and cede to other countries the technological initiative that has done so much to power the U.S. economy. The intense focus on these foundational questions threatens to obscure, however, a key point: AI is already subject to regulation in many ways, and, even while the broader debates about AI continue, additional regulations look sure to follow.

One of the most important jobs of any national leader is to quit. National liberation heroes, from George Washington to Nelson Mandela, who stepped down without being forced to, ought to be venerated for that as much as for any good they accomplished while in office. Generally, rulers do not give up power unless they have to. In Africa, peaceful transfers of power are rare enough that a billionaire has set up a generous annual prize to reward leaders who step down voluntarily; many years it goes unclaimed. Around the world, cases like Bashar al-Assad, willing to watch his country crumble rather than give up power over it, or Robert Mugabe, forced out by his own military after 47 years, are more common.

Artificial intelligence (A.I.) is making the "rise of machines" -- once the stuff of science fiction -- a reality. As 60 Minutes reported on October 9, "It might not be long before machines begin thinking for themselves -- creatively, independently, and sometimes with better judgment than a human." On February 28, 2018, you're invited to "Meet the Future" at DePauw University as the Ubben Lecture Series presents the world's first artificial intelligence-fueled android, Sophia, and her creator, David Hanson. In a 7:30 p.m. program in Kresge Auditorium, Dr. Hanson -- founder, CEO and chief designer of Hong Kong-based Hanson Robotics -- will be joined by his one-of-a-kind robot character. At the free event, which is open to all, the two will deliver a speech, take questions from the audience, and offer insights into the world of tomorrow that we're already entering today.

Embeddings in machine learning are low-dimensional representations of complex input patterns, with the property that simple geometric operations like Euclidean distances and dot products can be used for classification and comparison tasks. The proposed meta-embeddings are special embeddings that live in more general inner product spaces. They are designed to propagate uncertainty to the final output in speaker recognition and similar applications. The familiar Gaussian PLDA model (GPLDA) can be re-formulated as an extractor for Gaussian meta-embeddings (GMEs), such that likelihood ratio scores are given by Hilbert space inner products between Gaussian likelihood functions. GMEs extracted by the GPLDA model have fixed precisions and do not propagate uncertainty. We show that a generalization to heavy-tailed PLDA gives GMEs with variable precisions, which do propagate uncertainty. Experiments on NIST SRE 2010 and 2016 show that the proposed method applied to i-vectors without length normalization is up to 20% more accurate than GPLDA applied to length-normalized ivectors.

Classifying samples into categories becomes intractable when a single sample can have millions to billions of features, such as in genetics or imaging data. Principal Components Analysis (PCA) is widely used to identify a low-dimensional representation of such features for further analysis. However, PCA ignores class labels, such as whether or not a subject has cancer, thereby discarding information that could substantially improve downstream classification performance. We describe an approach, "Linear Optimal Low-rank" projection (LOL), which extends PCA by incorporating the class labels in a fashion that is advantageous over existing supervised dimensionality reduction techniques. We prove, and substantiate with synthetic experiments, that LOL leads to a better representation of the data for subsequent classification than other linear approaches, while adding negligible computational cost. We then demonstrate that LOL substantially outperforms PCA in differentiating cancer patients from healthy controls using genetic data, and in differentiating gender using magnetic resonance imaging data with $>$500 million features and 400 gigabytes of data. LOL therefore allows the solution of previous intractable problems, yet requires only a few minutes to run on a desktop computer.

Astronauts aboard the International Space Station (ISS) will welcome a new passenger later this year: a floating, talking robot. Cimon, a free-flying ball-shaped robot with a smiling face, an appreciation of music and a vocabulary of more than 1,000 sentences, is set to join the crew of the ISS. Short for Crew Interactive Mobile Companion, Cimon is designed to float and fly around the ISS offering technical help, warning of system failures and dangers, and providing a source of entertainment. Free-flying drone Cimon, pictured, is'taught' to recognize a face. He has an artificially intelligent brain, an eight inch display screen, and uses propeller-driven thrusters to move about in weightless conditions.

Frankfurt: Rapid advances in artificial intelligence are raising risks that malicious users will soon exploit the technology to mount automated hacking attacks, cause driverless car crashes or turn commercial drones into targeted weapons, a new report warns. The study, published on Wednesday by 25 technical and public policy researchers from Cambridge, Oxford and Yale universities along with privacy and military experts, sounded the alarm for the potential misuse of AI by rogue states, criminals and lone-wolf attackers. The researchers said the malicious use of AI poses imminent threats to digital, physical and political security by allowing for large-scale, finely targeted, highly efficient attacks. The study focuses on plausible developments within five years. "We all agree there are a lot of positive applications of AI," Miles Brundage, a research fellow at Oxford's Future of Humanity Institute.

Artists and criminals are often the first to push the boundaries of technology. Barrett Brown is a criminal who has actually helped inspire art -- the TV show Mr. Robot. Its protagonist is a hacktivist -- a hacker that breaks into computer systems to promote a cause. Brown served time for being part of Anonymous, a group that hacked a private security firm to reveal secrets. He had spent years in a prison cell thinking about what he might do when he got out.

Science fiction is becoming reality. The day were robots will outsmart humans is not that far. Robots already have been highly involved in our manufacturing settings but every year the robot advancements have been evolving quickly. Some of them will work in the tertiary sector and other will be war robots or animal robots. Some military robots are terrifying.

The FAA is part of a Trump administration effort to expand testing of drones to include flights over people, nighttime operations and flights out of sight of the operator. The White House and the FAA in October announced a test program to speed up the integration of unmanned aerial vehicles into the national airspace system and to test drone detection and tracking while waiving some limits on their use.