Did you know that in Rosario, Argentina -- the hometown of Lionel Messi -- a law has been passed preventing parents from naming their children after the Barcelonian superstar?? So, I tried a lot but couldn't find the exact data but still, I believe that there would be almost 25% to 30% of the population sharing similar words in their names. I have a small task for you to do: Check this link out and see whether your name or one of your friend's name is from https://nameberry.com/popular_names Also, you would know the trouble of finding out the correct phone number from a phone book. There are a lot of similar names in this world. If we are having such a problem.

Should artificial intelligence be regulated? And if so, what should those regulations look like? These are difficult questions to answer for any technology still in development stages – regulations, like those on the food, pharmaceutical, automobile and airline industries, are typically applied after something bad has happened, not in anticipation of a technology becoming dangerous. But AI has been evolving so quickly, and the impact of AI technology has the potential to be so great that many prefer not to wait and learn from mistakes, but to plan ahead and regulate proactively. In the near term, issues concerning job losses, autonomous vehicles, AI- and algorithmic-decision making, and "bots" driving social media require attention by policymakers, just as many new technologies do. In the longer term, though, possible AI impacts span the full spectrum of benefits and risks to humanity – from the possible development of a more utopic society to the potential extinction of human civilization.

Crowdsourcing has been successfully applied in many domains including astronomy, cryptography and biology. In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy consumption. In this experiment, the crowd generated hypotheses about factors that make one home different from another in terms of monthly energy usage. To implement this concept, we deployed a web-based system within which 627 residential electricity customers posed 632 questions that they thought predictive of energy usage. While this occurred, the same group provided 110,573 answers to these questions as they accumulated. Thus users both suggested the hypotheses that drive a predictive model and provided the data upon which the model is built. We used the resulting question and answer data to build a predictive model of monthly electric energy consumption, using random forest regression. Because of the sparse nature of the answer data, careful statistical work was needed to ensure that these models are valid. The results indicate that the crowd can generate useful hypotheses, despite the sparse nature of the dataset.

Artificial intelligence can accurately predict whether people are gay or straight based on photos of their faces, according to new research suggesting that machines can have significantly better "gaydar" than humans. The study from Stanford University – which found that a computer algorithm could correctly distinguish between gay and straight men 81% of the time, and 74% for women – has raised questions about the biological origins of sexual orientation, the ethics of facial-detection technology and the potential for this kind of software to violate people's privacy or be abused for anti-LGBT purposes. The machine intelligence tested in the research, which was published in the Journal of Personality and Social Psychology and first reported in the Economist, was based on a sample of more than 35,000 facial images that men and women publicly posted on a US dating website. The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and visa versa.

The International Space Station hovers a couple hundred miles above the Earth's surface, yet it appears it, or at least one of its patches, is not above commercial plugs. Officials have unveiled the new mission patch for the U.S. national laboratory aboard the ISS that will adorn the apparel of our astronauts. The patch, in the shape of the rebel spaceship Millennium Falcon, includes the silhouettes of the spherical spacecraft called the Death Star that the Galactic Empire and their evil dictator Darth Vader use to blow up entire planets full of innocent people; and three robots from the movie franchise, BB-8, K-2SO and Chopper, meant to symbolize technology. The bots were featured in the two most recently released Star Wars movies, "Star Wars: The Force Awakens" and "Rogue One: A Star Wars Story," chosen over the highly identifiable R2-D2 and C-3PO that people grew to love in the original three films from the late 1970s and early '80s. To cap things off, the bottom of the patch is emblazoned with "Star Wars" and "Lucasfilm."

Regulatory compliance has always been and will always be one of the top priorities and concerns of every financial institution (FI). Regulatory reforms following the global financial crisis of 2008 compelled FIs to make substantial investments in risk and compliance – both in terms of technology and headcount – to prevent and remediate regulatory issues. Despite their best efforts, FIs often find themselves falling short of regulatory obligations owing to highly manual processes and silo-based solutions which hinder transparency, efficiency and availability of fast and meaningful data. Non-compliance means being slapped with hefty penalties not to mention consequent reputational damage. Compliance processes today need to be backed up like never before by automation, artificial intelligence and big data – to name a few crucial technologies – to keep up with increasing regulation and stricter enforcement.

SAN FRANCISCO, Sept. 3 (Xinhua) -- Researchers from the U.S. Department of Energy's SLAC National Accelerator Laboratory and Stanford University have shown that neural networks, a form of artificial intelligence, can analyze the complex distortions in spacetime known as gravitational lenses 10 million times faster than traditional methods. The work, by a research team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, was detailed in a study published in Nature. The researchers used neural networks to analyze images of strong gravitational lensing, where the image of a faraway galaxy is multiplied and distorted into rings and arcs by the gravity of a massive object, such as a galaxy cluster. The distortions provide clues about how mass is distributed in space and how that distribution changes over time, which are linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that is accelerating the expansion of the universe. Until now, analyzing such images has been a tedious process that involves comparing actual images of lenses with a large number of computer simulations of mathematical lensing models, according to a news release from SLAC, originally named Stanford Linear Accelerator Center.

Only Bernie Sanders can win. "What the prosecutors should be looking at are Hillary Clinton's 33,000 deleted emails." One of the fellow staffers said some of the computers the Awans managed were being used to transfer data to an off-site server.

The future of the U.S. military may be focused on artificial intelligence (A.I.), an effort that could improve cybersecurity, precision weaponry and other military functions. A recent report from the Harvard's Belfer Center for Science and International Affairs states that advancements in the last five years have made it possible for the U.S. military to expand its use of A.I. in the near future-- but only if certain questions are addressed first. "Though the United States military and intelligence communities are planning for expanded use of A.I. across their portfolios, many of the most transformative applications of A.I. have not yet been addressed," the report, written by Greg Allen and Taniel Chan, states. "We propose three goals for developing future policy on A.I. and national security: preserving U.S. technological leadership, supporting peaceful and commercial use and mitigating catastrophic risk." The researchers examined nuclear, aerospace, cyber and biotech opportunities to develop recommendations for national security policy involving A.I .funding

A computer program can tell if someone is gay or not with a high level of accuracy by looking at a photograph, a study claims. But critics say that the software could be used to'out' men and women currently in the closet. To'train' their computer, the researchers downloaded 130,741 different images of 36,630 individual men's faces, and 170,360 images of 38,593 women from a US dating website. The users had all declared their sexuality on their profiles. Removing images which were not clear enough, they were left with even numbers of 35,326 pictures of 14,776 people, gay and straight, male and female.