An artificial intelligence (AI)-trained facial recognition system (FRS) has been installed at the Puratchi Thalaivar Dr. MGR Central railway station for detecting known culprits passing through the gates and alerting authorities. "For the first time, we have introduced the CCTV camera device backed by artificial intelligence. In the existing system, we capture the picture and video of any suspect. But we have to manually analyse the footage to detect their movement. The new system will automatically alert us about known culprits," said a senior police officer of the Government Railway Police (GRP).

Preventing unfair treatment of individuals on the basis of race, gender or ethnicity, for example, been a long-standing concern of civilized societies. However, detecting such discrimination resulting from decisions, whether by human decision makers or automated AI systems, can be extremely challenging. This challenge is further exacerbated by the wide adoption of AI systems to automate decisions in many domains -- including policing, consumer finance, higher education and business. "Artificial intelligence systems -- such as those involved in selecting candidates for a job or for admission to a university -- are trained on large amounts of data," said Vasant Honavar, Professor and Edward Frymoyer Chair of Information Sciences and Technology, Penn State. "But if these data are biased, they can affect the recommendations of AI systems."

New update will be the first AI-based cybersecurity solution for the Chrome Operating System, available immediately. With global Chromebook unit shipments slated to nearly double this year to 10.1 million since their release in 2011 (and expected to reach 17 million units in sales by 2023), the Chrome OS is more prevalent in the market than ever before. The new offering makes Deep Instinct the first deep learning based solution with multi-layer protection across endpoints, servers and mobile devices for Windows, macOS, iOS, Android or Chrome OS from the convenience of a single platform. It also protects against a range of attack vectors that have been known to breach Android systems. As the first company to apply end-to-end deep learning to cybersecurity, Deep Instinct provides complete protection against attacks, taking a prediction and prevention first approach, followed by detection and response against known and unknown cyber threats.

As soon as the words "AI" and "music" are used in the same sentence, one comes across skepticism. If robots are making call centre jobs useless, one is scared to think of what would happen to all the musicans who are anyway underpaid. "In the world of personalisation and on-demand services, music is one of the very few remaining static artefacts," says Ken Lythgoe, head of business development at creative AI technology company MXX, based in London, England. The company has created the world's first AI tech that allows individual users to instantly edit music to fit their own video footage, complete with rises and fades. According to Lythgoe, AI doesn't need to be the enemy of music, and instead of replacing us, AI can empower us. MXX's AI tech listens to music and creates a metadata based on its understanding of it.

Computer pioneer Margaret Hamilton was critical to landing astronauts on the moon for the first time on 20 July 1969 and returning them safely a few days later. The young Massachusetts Institute of Technology (MIT) computer programmer and working mother led the team that created the onboard flight software for the Apollo missions, including Apollo 11. The computer system was the most sophisticated of its day. Her rigorous approach was so successful that no software bugs were ever known to have occurred during any crewed Apollo missions. "She symbolises that generation of unsung women who helped send humankind into space," said President Barack Obama in 2016 when he awarded Hamilton the Presidential Medal of Freedom, the United States' highest civilian award.

OXFORD, England, April 11 (Thomson Reuters Foundation) - When Nepal suffered devastating twin earthquakes in 2015 that killed nearly 9,000 people, the government provided help for families whose homes had collapsed to rebuild. But tens of thousands of others with damaged homes that were still standing faced a tougher decision: Was it safe to make repairs? Or were they better off building a new, often smaller home at their own cost? Artificial intelligence (AI), it turned out, could help, said Elizabeth Hausler, a U.S.-based engineer and builder who works on creating affordable, disaster-resilient housing. In Nepal, many homes are variations on a standard design - rectangular, multi-storey and with similar windows, she said.

Elon Musk's Neuralink, the venture working to bridging the gap between potential superhuman artificial intelligence (AI) and the human brain itself, is finally providing an update on the research it's been doing since its formation in 2016. The event will take place on July 16th in San Francisco for a select audience, and a live stream will be provided as well, according to the company's official Twitter account. "We're having an event next Tuesday in San Francisco to share a bit about what we've been working on the last two years, and we've reserved a few seats for the internet," the company posted. "It will also be livestreamed, for those who don't have the chance to come!" An update on Neuralink's research activity was teased in September last year by Musk during a live interview.

One of the biggest problems health plans face is dirty data, says Jordan Bazinsky, executive vice president and administrative officer at Atlanta-based Cotiviti. The healthcare solutions and analytics company reports having "several hundred" health insurance companies among its clientele, including 24 of the top 25 plans, he says. "Dirty data is one of the key problems that blocks health plans from finding insights from data," Bazinsky says. "You might be able to push data real-time, but if you can't trust the underlying kernel of data--all the other things can't be trusted." According to Bazinsky, Cotiviti uses data analytics to help payers achieve financial health through payment accuracy that is appropriate to the care delivered.

To take advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges. Some leading-edge organizations are beginning to do just that, focusing on machine learning, a subset of artificial intelligence (AI) that encompasses statistical methods in which computer systems recognize patterns or correlations in data by ingesting large sets of training data. They improve their performance, or "learn," over time as they incorporate new data; revising their approach as needed without human programmers updating the rules. In the healthcare industry, most machine learning applications are in the research stage. "There is not a ton of clinical use," according to Brian Edwards, independent validation consultant for AI vendors.

Among the many achievements of machine learning in recent years, some of the most striking are the victories of the machine against human players in games, such as Google's DeepMind group's conquest of Go in 2016. In such milestones, researchers are often guided by theoretical math that says there can be an optimal strategy to be found, given a good algorithm and enough compute. But what do you do when theory breaks down? Two researchers at Carnegie Mellon University and Facebook went back to the drawing board to solve "heads-up no-limit Texas hold'em," the most popular form of multiplayer poker in the world. Theory isn't computable for this form of the card game, so they designed some elegant search strategies for their computer program, "Pluribus," to beat the best human players in 10,000 hands of poker.