The BBC says Jodie Whittaker is the next star of the long-running science fiction series "Doctor Who" -- the first woman to take the title role. Whittaker, who starred in detective drama "Broadchurch," will replace Scottish actor Peter Capaldi. Whittaker is the 13th incarnation of the Doctor, a galaxy-hopping Time Lord who travels in the Tardis, a time machine shaped like an old-fashioned British police telephone booth. The 54-year-old program has a firm place in Britain's cultural life, and the announcement was made during a live television broadcast Sunday after the Wimbledon men's tennis final. "Doctor Who" was first broadcast in 1963.

An electric glove which can convert sign language into text messages has been unveiled by scientists. The $100 (£77) device will will allow deaf people to instantly send messages to those who don't understand sign language, according to its inventors. Researchers fitted a standard sports glove with nine flexible strain sensors which react when a user bends their fingers to create the new device. The device consists of a sports glove which has been fitted with nine stretchable sensors positioned over the knuckles. When a user bends their fingers or thumb to sign a letter, the sensors stretch, which causes an electrical signal to be produced.

The age of the robot has been predicted for decades. First used as a term to mean automated labor back in the 1920s, and popularised in the classic 1927 slient movie Metropolis, they have been a regular feature of science fiction ever since. The fact that robots and science fiction go hand in hand – and that predictions that we will soon have robot helpers being regular features of future-gazing since the 1930s – has meant that the idea of robots becoming a central part of our lives have become so familiar that we've come to ignore them. Yet robots have been a reality in manufacturing for decades, having become cost-effective production-line solutions by the 1970s. The Roomba – an automated vacuum cleaner that is perhaps the most famous domestic-helper robot – was launched back in 2002, and has sold more than 10 million units worldwide.

Jim Robinson of RRE Ventures said it best last month at the Silicon Dragon Conference when comparing Silicon Valley to New York, "There are two kinds of centers that have a lot of startups and technology, there are technology centers and commerce centers." New York falls into the later category, while the Valley is the former. Sitting next to Jim, I reflected that Singapore might be in both groups, an Asian commerce hub and a leader in mechatronics. As an advocate for automation, I am often disheartened that the United States significantly lags behind its industrial counterparts in manufacturing autonomous machines. The key to a pro-job policy could be gleaning from the successes of countries like Singapore to implement America's own'Robot First Plan.'

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so.

An interdisciplinary workshop on self-organization and swarm intelligence in cyber physical systems was held at Lakeside Labs this week. Experts presented their work and discussed open issues in this exciting field. "Our crazyswarm is the largest indoor drone swarm that I'm aware of," Nora Ayanian states. The assistant professor from the Viterbi School of Engineering at the University of Southern California (USC) in Los Angeles was recently described by MIT Technology Review to be one of "35 innovators under 35." She came to Klagenfurt to expound her latest results on multirobot coordination.

Lots of algorithms go bad unintentionally. Some of them, however, are made to be criminal. Algorithms are formal rules, usually written in computer code, that make predictions on future events based on historical patterns. To train an algorithm you need to provide historical data as well as a definition of success. We've seen finance get taken over by algorithms in the past few decades. Trading algorithms use historical data to predict movements in the market. Success for that algorithm is a predictable market move, and the algorithm is vigilant for patterns that have historically happened just before that move.

We are currently going through one of those periodic phases of "automation anxiety" when we become convinced that the robots are coming for our jobs. These fears are routinely pooh-poohed by historians and economists. The historians point out that machines have been taking away jobs since the days of Elizabeth I – who refused to grant William Lee a patent on his stocking frame on the grounds that it would take work away from those who knitted by hand. And while the economists concede that machines do indeed destroy some jobs, they point out that the increased productivity that they enable has generally created more new jobs (and industries) than they displaced. Faced with this professional scepticism, tech evangelists and doom-mongers fall back on the same generic responses: that historical scepticism is based on the complacent assumption that the past is a reliable guide to the future; and that "this time is different".

We are currently going through one of those periodic phases of "automation anxiety" when we become convinced that the robots are coming for our jobs. These fears are routinely pooh-poohed by historians and economists. The historians point out that machines have been taking away jobs since the days of Elizabeth I – who refused to grant William Lee a patent on his stocking frame on the grounds that it would take work away from those who knitted by hand. And while the economists concede that machines do indeed destroy some jobs, they point out that the increased productivity that they enable has generally created more new jobs (and industries) than they displaced. Faced with this professional scepticism, tech evangelists and doom-mongers fall back on the same generic responses: that historical scepticism is based on the complacent assumption that the past is a reliable guide to the future; and that "this time is different".

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