In November 2018, a text-generating "bot" named Tobi produced an incredible 40,000 news stories about Switzerland elections in just five minutes for Tamedia. According to a paper presented at the Computation Journalism conference in Miami, Tobi wrote on vote results of Switzerland's 2,222 municipalities in both French and German. Just three months after the incredible feat was reported by world media, robo-journalism is now being touted to change media landscape in the coming years. Now, artificial intelligence programs, which have been available for more than a decade, are being used by in news organisations to produce stories, personalise new delivery and even anchor news bulletins, reported AFP. "We've seen a greater acceptance of the potential for artificial intelligence, or robo-journalism, in newsrooms around the world," Damian Radcliffe, a University of Oregon professor who follows business models for journalism, told the news agency.

Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning. Although Mini-batch SGD is one of the most popular stochastic optimization methods in training deep networks, it shows a slow convergence rate due to the large noise in gradient approximation. In this paper, we attempt to remedy this problem by building more efficient batch selection method based on typicality sampling, which reduces the error of gradient estimation in conventional Minibatch SGD. We analyze the convergence rate of the resulting typical batch SGD algorithm and compare convergence properties between Minibatch SGD and the algorithm. Experimental results demonstrate that our batch selection scheme works well and more complex Minibatch SGD variants can benefit from the proposed batch selection strategy.

The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. A social trust and preference segmentation-based matrix factorization (SPMF) recommendation system is proposed to solve the above-mentioned problems. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly higher than that of some state-of-the-art recommendation algorithms. The proposed SPMF algorithm is a more accurate and effective recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

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The QWERTY typewriter was introduced in 1872, and since then tapping on a keyboard or screen has become the standard way to interact with digital technology. But this isn't always convenient or safe, so new "touchless" ways to control machines are being developed. Imagine being out for a jog, headphones on, and wanting to turn up the volume without breaking your stride. Or receiving a "new message" alert on your phone while driving and wanting to activate the text-to-speech function without taking your eye off the road. These are scenarios where touchless control would come in handy.

Healthcare sectors are increasingly adopting artificial intelligence to improve patient care and improve process efficiencies.

This app uses artificial intelligence algorithms to understand the meaning of your words and to spot the mistakes.

In fact, machines may even have to be paid for their services as artificial intelligence (AI) becomes more advanced, the expert claims.