The concept of inhuman intelligence goes back to the deep prehistory of mankind. At first the province of gods, demons, and spirits, it transferred seamlessly into the interlinked worlds of magic and technology. Ancient Greek mythos had numerous robots, made variously by gods or human inventors, while extant artefacts like the Antikythera calendrical computer show that even in 200 BCE we could build machinery that usefully mimicked human intellectual abilities. There has been no age or civilisation without a popular concept of artificial intelligence (AI). Ours, however, is the first where the genuine article--machinery that comfortably exceeds our own thinking skills--is not only possible but achievable.

The acquisition will help boost eBay's structured data efforts. Ebay announced July 11 that it is acquiring SalesPredict, an Israel-based company that uses advanced analytics to predict customer buying behavior and sales conversions. The SalesPredict team will join eBay's structured data center and support eBay's artificial intelligence, machine learning and data science efforts. Ebay said that the acquisition of SalesPredict builds on a recently completed acquisition of Expertmaker, which offers predictive analytics and personalization powered by artificial intelligence. The addition of SalesPredict, however, will help eBay predict factors that can affect the price of a product and enable its sellers to increase customer sales conversions.

Cisco Systems, Inc. (NASDAQ:CSCO) has announced a partnership with Gupshup and API.ai with the purpose of creating a fully integrated cloud-based collaborative service. The announcement was made during Cisco Live! 2016. Following this announcement, we expect to see thousands of bots quickly joining Cisco Spark and Cisco Tropo platforms. The collaboration will also turn up the intensity of competition between team communication chat apps, the likes of Slack and Microsoft Corporation (NASDAQ:MSFT)'s Skype. Before Gupshup became an enterprise messaging service company in 2010, it had created an SMS social network of more than 50 million users, mainly in India.

With a pocketful of savings, Melbourne entrepreenur Michelle Mannering and three fellow founders are busy building a platform utilising artificial intelligence to make the world a smarter and better place. As a hacker-in-residence at Carlton Connect and a participant in the Melbourne Accelerator Program, Mannering and her team are one of the first residents in Melbourne's new "melting pot" for innovation. "My co-founders and I all met at a bunch of different hackathons," Mannering tells StartupSmart. Just a year after meeting, the team has launched Black AI, a startup offering a platform that is developing machine learning computer vision. "Basically, we just teach computers how to understand and interpret the world," Mannering says.

In this Asia Vision Series interview on artificial intelligence, Dr Hon Hsiao-Wuen, Corporate Vice President, Microsoft Research Asia, talks about AI enabling the invisible revolution and driving a future of guardian angels.

A new startup factory focused on building artificial intelligence companies is launching in Hong Kong this November. Zeroth, as the accelerator is known, will offer 10 startups 20,000 each ( 15,000) in exchange for a 6% equity stake. Startups will also receive mentoring and office space. The accelerator is being launched by Tak Lo, who was previously a director at accelerator TechStars London. Speaking to Business Insider on Tuesday, Lo said that he plans to make Zeroth a success with the help of his AI connections in Europe.

In this July 12, 2016 photo, Nissan Motor Co. Deputy General Manager Atsushi Iwaki gets his hands off of the steering wheel of a self-driving new Serena minivan during a test drive at Nissan test course in Yokosuka, near Tokyo. The Serena minivan equipped with ProPilot technology relies on a single camera in the back of the driver's rearview mirror. The car can then follow the vehicle ahead, maintaining a safe distance that the driver sets.

OSAKA – Japanese opposition party Osaka Ishin no Kai decided Tuesday to change its name by removing "Osaka," as the party tries to gain wider support from across the country. The party, which has its base in Osaka Prefecture, made the decision at a meeting of its executives in the city of Osaka. It will formally adopt a new party name at an extraordinary party convention to be held in the city on Aug. 23. In Sunday's election for the House of Councilors, in which half of the chamber's 242 seats were contested, Osaka Ishin won only seven seats -- three in prefectural constituencies and four under the nationwide proportional representation system. The three constituency seats it won are in Osaka and neighboring Hyogo Prefecture.

AlphaGo's uncanny success at the game of Go was taken by many as a death knell for the dominance of the human intellect, but Google researcher David Silver doesn't see it that way. Instead, he sees a world of potential benefits. As one of the lead architects behind Google DeepMind's AlphaGo system, which defeated South Korean Go champion Lee Se-dol 4 games to 1 in March, Silver believes the technology's next role should be to help advance human health. "We'd like to use these technologies to have a positive impact in the real world," he told an audience of A.I. researchers Tuesday at the International Joint Conference on Artificial Intelligence in New York. With more possible board combinations than there are atoms in the universe, Go has long been considered the ultimate challenge for A.I. researchers.

Anh Tong and Jaesik Choi Ulsan National Institute of Science and Technology Ulsan, 44919 Korea { anhth,jaesik } @unist.ac.kr Abstract Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages make possible to build unified frameworks to compute interesting probabilities of various large, real-world problems. When the structure of model is given, constructing a probabilistic program is rather straightforward. Thus, main focus have been to learn the best model parameters and compute marginal probabilities. In this paper, we provide a new perspective to build expressive probabilistic program from continue time series data when the structure of model is not given. The intuition behind of our method is to find a descriptive covariance structure of time series data in nonparametric Gaussian process regression. We report that such descriptive covariance structure efficiently derives a probabilistic programming description accurately.