The Google logo is pictured atop an office building in Irvine, California, U.S., August 7, 2017. BEIJING (Reuters) – Alphabet Inc's Google said on Wednesday it is opening an artificial intelligence (AI) research center in China to target the country's local talent, even as the …

Google's search engine is blocked in China, but the company still has hundreds of staff in China which work on its international services. In reference to that workforce, Alphabet chairman Eric Schmidt has said the company "never left" China, and it makes sense that Google wouldn't want to ignore …

The electronics markets of Shenzhen are bewildering. These football-field-sized buildings seemingly sell almost anything, any bit of electronics – chip, component, connector – if you know where to look among the myriad stores in the ten-storey towers. To find find what you need in that riot of abundance you have to ask someone. But as an Australian-American lacking Chinese language skills, a question like "Do you know where I can find PIC16 microcontrollers?" doesn't have much chance of success. Pointing works when there's something to point at, but but lacks subtlety.

The AI algorithm, the name of which can be translated as either Dragon Eye or Dragonfly Eye, was developed by Shanghai-based tech firm Yitu. It works off of China's national database, which consists of all 1.3 billion residents of the Asian nation as well as 500 million more people who have entered the country at some point. Dragon Eye interfaces with the database to detect the faces of individuals. Yitu chief executive and co-founder Zhu Long told the South China Morning Post (SCMP) that the purpose of the algorithm is to fight crime and make the world a safer place. "Let's say that we live in Shanghai, a city of 24 million people. It's challenging for the government to police such a large population. And it would be impossible without technology. Even when we have many cameras installed, it's a hard task. You can't watch all the videos, and doing a search is very time-consuming and requires too many resources to get meaningful results from such a huge amount of data," Long said.

A Chinese tech firm has developed an artificial intelligence algorithm that can connect to millions of surveillance cameras in the country and instantly identify the people on screen. Yitu Technology's Dragonfly Eye has a collection of 1.8 billion photos on file through the communist government's central database, which it can access. It may also have access to photos of anyone with a Hong Kong identity card, as well as images of foreign visitors who are photographed at ports and airports. The program is meant to quickly identify and track down "criminals," which has become a little more loosely defined since Xi Jinping came to power. However, Yitu officials claim the AI has been highly successful in identifying people.

Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan (Dated: December 14, 2017) Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry. However, VB is known to suffer from the problem of local optima. In this Letter, we generalize VB by using quantum mechanics, and propose a new algorithm, which we call quantum annealing variational Bayes (QA VB) inference. We then show that QA VB drastically improve the performance of VB by applying them to a clustering problem described by a Gaussian mixture model. Finally, we discuss an intuitive understanding on how QA VB works well.

Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

Visitors to the 2020 Tokyo Olympics can expect to arrive at an airport with robots "scattered" about to help them, an official said Tuesday as he unveiled seven new machines to perform tasks from helping with luggage to language assistance. Among the seven robots on show was a fluffy cat mascot that can carry out simultaneous interpretation in four different languages. Visitors speak into a furry microphone, and translations appear instantly on a smart screen. Travelers may also be approached by a small white humanoid robot, Cinnamon, asking if they need its help. The sleek white robot can converse with visitors through its AI system and give directions.

Zhu Long, co-founder and CEO of Yitu Technology, has his identity checked at the company's headquarters in the Hongqiao business district in Shanghai. "Our machines can very easily recognise you among at least 2 billion people in a matter of seconds," says chief executive and Yitu co-founder Zhu Long, "which would have been unbelievable just three years ago." Its platform is also in service with more than 20 provincial public security departments, and is used as part of more than 150 municipal public security systems across the country, and Dragonfly Eye has already proved its worth. On its very first day of operation on the Shanghai Metro, in January, the system identified a wanted man when he entered a station. After matching his face against the database, Dragonfly Eye sent his photo to a policeman, who made an arrest.

Machine learning will be a defining technology of 2018, doing more to change how we live and work than any technology since the internet. To understand why, we first need to get past sensational headlines about robots'stealing our jobs'. Innovation and the use of tools to make life easier have been a marker of progress throughout history, through agricultural and industrial revolutions. We are now in a data revolution and as we progress, so some of the roles people do will change, but progress has consistently brought the creation of new jobs, new business models and whole new industries. Far from making us obsolete, machine learning will augment humanity and make us more effective.