In what is possibly the largest funding round ever in an artificial intelligence company, Shenzhen-based intelligent humanoid robots maker UBTECH Robotics has announced the closing of a Series C investment of an $820-million round led by Tencent Holdings. The round, which values UBTECH at $5 billion, was also joined by the company's lead investor in its Series B round, CDH Investments, as well as a host of other new backers including Telstra, China Film and TV Capital, Green Pine Capital Partners and Whale Capital, according to an official release. UBTECH develops consumer humanoid robots and claims to be pioneering a future populated by affordable and intuitive robots. The company says its products are currently sold in more than 40 countries and regions, including China, North America, Europe, and Southeast Asia, at over 7,000 retail outlets around the globe, including nearly 500 Apple stores worldwide. The proceeds from the new funding round, UBTECH founder and CEO James Zhou said, will mainly be used to improve research and development by increasing investment in tech innovations, and to expand the company's market and brand by accelerating its deployment and branding in the global market.

Robots and smart devices are the face of some new hotels in Japan, where owners are using hi-tech ways to offer a friendly welcome to guests who don't speak the language. A major travel agency opened a so-called smart hotel in April in Hamamatsucho, central Tokyo, where humanoid robots greet guests at the front desk in English, Chinese, Korean, or Japanese. Robots also perform cleaning duties. Hotel officials say technology is helping to ease the strain of Japan's labour shortage. At another hotel in Akihabara, central Tokyo, guests can control lighting, air conditioning and curtains with a smartphone or smart speaker.

Doomsday conspiracies of the human race being ruled by robots aside, Artificial Intelligence (AI) is here to stay. From changing the job landscape, to becoming a buzzword for almost every company, AI is now an integral part of most organisations. Whether it is Google, Facebook, or even companies like Oracle, Microsoft and SAP, they are all working to design software that can learn, and make decisions – in short, Artificial Intelligence. It isn't only private organisations that are keen on the technology, but government organisations like Niti Aayog are also looking closely at it. In fact, the Indian Government has allocated Rs 3,073 crore to spearhead work on fifth generation technology startups like Artificial Intelligence, Machine Learning (ML), Internet of Things (IoT), 3D printing, and Blockchain.

Westworld watchers, we knew this moment was coming. The second season's third episode, "Virtù e Fortuna," opens not in Westworld but in an India-themed park. Where Westworld is an emblem of the colonization of Native American land, this park represents Britain's takeover of the subcontinent, and the racial-social hierarchy is clearly encoded: Women in saris and men in turbans--the hosts--walk amidst people dressed in turn-of-the-20th-century British garb. A white man, Nicholas (Neil Jackson), approaches a woman seated at a lawn table and flirts with her. But she's a seasoned guest, and she's done having flings with hosts--she wants to know that he's a real human with real desire, not a fleshbot programmed to seduce her. She announces that she'll have to shoot him to know for sure.

Billions are being invested in AI startups across every imaginable industry and business function. Google, Amazon, Microsoft and IBM are in a heavyweight fight investing over $20 billion in AI in 2016. Corporates are scrambling to ensure they realise the productivity benefits of AI ahead of their competitors while looking over their shoulders at the startups. China is putting its considerable weight behind AI and the European Union is talking about a $22 billion AI investment as it fears losing ground to China and the US. From the 3.5 billion daily searches on Google to the new Apple iPhone X that uses facial recognition to Amazon Alexa that cutely answers our questions.

First, we're going to take a look at the tensor object type. Then we'll have a graphical understanding of TensorFlow to define computations. Finally, we'll run the graphs with sessions, showing how to substitute intermediate values. In TensorFlow, data isn't stored as integers, floats, or strings. These values are encapsulated in an object called a tensor, a fancy term for multidimensional arrays.

Written and performed by Pao-Chang Tsai, Solo Date tells the story of an international businessman from Taiwan, Ho-Nien, whose long-time lover, Alan, is killed in an air crash. Ho-Nien's grief leads him to attempt to reunite with Alan, first through an ancient Taoist ritual and then utilizing artificial intelligence. The journey Ho-Nien takes through the virtual underworld changes his perception of reality and uncovers past secrets that could diminish his potential reunion with Alan. Solo Date poses the profound question, "Is this quest for love a surreal duet, or a solitary monologue?" With themes of loneliness, death, artificial intelligence and longing, it also moves audiences to think about online privacy, the ethics of bringing someone back to life through digital means and how we as humans process loss.

It's not easy to promote closer US-China ties these days. The countries are moving toward a trade war; a US delegation left Beijing Friday reporting little progress on resolving disputes. US executives accuse China of stealing their intellectual property. The US government is imposing ever tighter restrictions on Chinese telecommunications firms. That made an uncomfortable backdrop for the annual conference of the Committee of 100, a group of influential Chinese-Americans, in Silicon Valley over the weekend.

Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be insufficiently effective more and more frequently, which raises risks of dangerous and emergency conditions in power systems. The paper is aimed at compensating for the shortcomings of man (a cognitive barrier, exposure to stresses and so on) and automatic devices by combining their strong points, i.e. the dispatcher's intelligence and the speed of automatic devices by virtue of development of the intelligent system "Artificial dispatcher" on the basis of deep machine learning technology. For realization of the system "Artificial dispatcher" in addition to deep learning it is planned to attract the game theory approaches to formalize work of the up-to-date power system as a game problem. The "gain" for "Artificial dispatcher" will consist in bringing in a power system in the normal steady-state or post-emergency conditions by means of the required control actions.

Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental science to evaluate the dangerous impact of particulate matters on public health. Besides that, deterministic models of air pollutant behavior are also generated; however, this is both complex and often inaccurate. On the contrary, deep recurrent neural network reveals potent potential on forecasting out-comes of time-series data and has become more prevalent. This paper uses Recurrent Neural Network (RNN) with Long Short-Term Memory units as a framework for leveraging knowledge from time-series data of air pollution and meteorological information in Daegu, Seoul, Beijing, and Shenyang. Additionally, we use encoder-decoder model, which is similar to machine comprehension problems, as a crucial part of our prediction machine. Finally, we investigate the prediction accuracy of various configurations. Our experiments prevent the efficiency of integrating multiple layers of RNN on prediction model when forecasting far timesteps ahead. This research is a significant motivation for not only continuing researching on urban air quality but also help the government leverage that insight to enact beneficial policies