With all the hype over Artificial Intelligence, there is additionally a lot of disturbing buzz about the negative results of AI. More than one-quarter (27%) of all employees state they are stressed that the work they have now will be disposed of within the next five years because of new innovation, robots or artificial intelligence, as indicated by the quarterly CNBC/SurveyMonkey Workplace Happiness review. In certain industries where technology already has played a profoundly disruptive role, employees fear of automation likewise run higher than the normal: Workers in automotives, business logistics and support, marketing and advertising, and retail are proportionately more stressed over new technology replacing their jobs than those in different industries. The dread stems from the fact that the business is already witnessing it. Self-driving trucks already are compromising the jobs of truck drivers, and it is causing a huge frenzy in this job line.
"Current machine text-generation models can write an article that may be convincing to many humans, but they're basically mimicking what they have seen in the training phase," said [PhD student Yuchen] Lin. "Our goal in this paper is to study the problem of whether current state-of-the-art text-generation models can write sentences to describe natural scenarios in our everyday lives." Essentially, fake news bots can sound like the New York Times or marketing copy by generating mimics, after taking in thousands of natural examples. Specifically, Ren and Lin tested the models' ability to reason and showed there is a large gap between current text generation models and human performance. Given a set of common nouns and verbs, state-of-the-art NLP computer models were tasked with creating believable sentences describing an everyday scenario.
The clever canine has learned to differentiate between balls, Frisbees, rings, or ropes -- and can even categorise new toys into these groups. When the researchers first met Whisky, she already knew the name of 59 toys, but her owners say that she has now learnt around 31 more. Whisky may have a little way to go before she breaks the all-time record for the cleverest dog, however. Fellow border collie Chaser, of South Carolina -- who was owned by psychologist John Pilley -- is said to have learnt more than 1,000 words before she died last year. 'At first it was hard for me to believe that a dog learned the name of so many toys, but after several days of rigorous testing, I had to change my mind,' says Claudia Fugazza of the Eötvös Loránd University in Hungary.
Natural language processing (NLP) has taken great strides recently--but how much does AI understand of what it reads? Less than we thought, according to researchers at USC's Department of Computer Science. In a recent paper Assistant Professor Xiang Ren and Ph.D. student Yuchen Lin found that despite advances, AI still doesn't have the common sense needed to generate plausible sentences. "Current machine text-generation models can write an article that may be convincing to many humans, but they're basically mimicking what they have seen in the training phase," said Lin. "Our goal in this paper is to study the problem of whether current state-of-the-art text-generation models can write sentences to describe natural scenarios in our everyday lives." Specifically, Ren and Lin tested the models' ability to reason and showed there is a large gap between current text generation models and human performance.
A London-based startup has combined some of today's most disruptive technologies in a bid to change the way we'll build the future. By retrofitting industrial robots with 3D printing guns and artificial intelligence algorithms, Ai Build has constructed machines that can see, create, and even learn from their mistakes. When CEO and founder Daghan Cam was studying architecture, he noticed a disconnect between small-scale manufacturing and large-scale construction. "On one side we have a fully automated production pipeline," Cam explained at a recent conference in London. With the emergence of more efficient printing technologies, he thought there must be a better way.