Google demonstrated a jaw-dropping new capability in Google Assistant earlier this month, allowing the Assistant to make calls on your behalf. While Google Duplex generated controversy and discussion around artificial intelligence, Microsoft has been testing similar technology with millions of people in China. At an AI event in London today, Microsoft CEO Satya Nadella showed off the company's Xiaoice (pronounced "SHAO-ICE") social chat bot. Microsoft has been testing Xiaoice in China, and Nadella revealed the bot has 500 million "friends" and more than 16 channels for Chinese users to interact with it through WeChat and other popular messaging services. Microsoft has turned Xiaoice, which is Chinese for "little Bing," into a friendly bot that has convinced some of its users that the bot is a friend or a human being.

Ahead of the event in May, we are taking a look at some the companies and people who are taking part in the massive unconference–an open space event with organization powered by participants. TechNode is organizing the Explore Expo, an exhibition area for young tech startups looking for exposure. The education industry is generally viewed as traditional, dogmatic, and oppressive in many Asian countries, especially in China. As China's edtech sector takes off and begins to attract a deluge of investment, tech companies are exploring more ways to spice up the learning experience. "The compulsory education system is rigid," Chu Liang, CTO of Ellabook, told TechNode, "but over the past decade, technology has been transforming many industries and sectors. Ellabook (咿啦看书) is an ebook reading platform, like Kindle, but for kids from 3 to 12 years-old. The app is animated and interactive, which encompasses a wide range of learning categories like reading skills, English, mathematics, and art.

English is spoken by hundreds of millions of people worldwide, but do the development of translation technology and "hybrid" languages threaten its status? Which country boasts the most English speakers, or people learning to speak English? According to a study published by Cambridge University Press, up to 350 million people there have at least some knowledge of English - and at least another 100 million in India. There are probably more people in China who speak English as a second language than there are Americans who speak it as their first. But for how much longer will English qualify as the "world's favourite language"?

The world is teeming with intelligence, from little wormy grubs in the garden to physicists poring over equations in university offices. In the past few years we've also come to view our virtual assistants as possessing some kind of intelligence--imperfect and sometimes downright creepy, but intelligence nonetheless. A.I. has come a long way since Microsoft's Clippy. Whether we're talking to Siri like a friend or asking our dogs for advice, humans love to imagine other animals' intelligence. As we enter into the infancy of A.I., it's fun to speculate how some existing lifeforms stack up to our best A.I so far.

After Steven Yang left his coveted job at Google, he asked his mother whether he should take venture-capital money to fund his business idea. If his online consumer-electronics enterprise was a risky bet, she told him, go with the venture capitalists. But if building the business into something great was his destiny, he instead should use her money from a pharmaceutical career in China. So Yang combined his Google money with his mom's, and with less than $1 million in seed capital he moved from California to Shenzhen, a hub in southern China for technology companies. Seven years later, Anker Innovations Technology Co. sells products ranging from smartphone chargers to portable power banks on Amazon.com.

We predict both microscopic and macroscopic variables of a chaotic fluid flow using reservoir computing. In our procedure of the prediction, we assume no prior knowledge of physical model describing a fluid flow except that its behavior is complex but deterministic. We present two ways of prediction of the complex behavior; the first called partial-prediction requires continued knowledge of partial time-series data during the prediction as well as past time-series data, while the second called full-prediction requires only past time-series data as training data. For the first case, we are able to predict long-time motion of microscopic fluid variables. For the second case, we show that the reservoir dynamics constructed from only past data of energy functions can predict the future behavior of energy functions and reproduce the energy spectrum. This implies that the obtained reservoir system constructed without the knowledge of microscopic data is equivalent to the dynamical system describing macroscopic behavior of energy functions.

In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either 1) incorporate content information into collaborative filtering to perform hybrid recommendation, or 2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leverage both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.

Hierarchical clustering (HC) is a widely used data analysis tool, ubiquitous in information retrieval, data mining, and machine learning (see a survey by Berkhin [2006]). This clustering technique represents a given dataset as a binary tree; each leaf represents an individual data point and each internal node represents a cluster on the leaves of its descendants. HC has become the most popular method for gene expression data analysis Eisen et al. [1998], and also has been used in the analysis of social networks Leskovec et al. [2014], Mann et al. [2008], bioinformatics Diez et al. [2015], image and text classification Steinbach et al. [2000], and even in analysis of financial markets Tumminello et al. [2010]. It is attractive because it provides richer information at all levels of granularity simultaneously, compared to more traditional flat clustering approaches like k-means or k-median. Recently, Dasgupta [2016] formulated HC as a combinatorial optimization problem, giving a principled way to compare the performance of different HC algorithms. This optimization viewpoint has since received a lot of attention Roy and Pokutta [2016], Charikar and Chatziafratis [2017], Cohen-Addad et al. [2017], Moseley and Wang [2017], Cohen-Addad et al. [2018] that has led not only to the development of new algorithms but also to theoretical justifications for the observed success of popular HC algorithms (e.g.

Recurrent Neural Networks (RNNs) play a major role in the field of sequential learning, and have outperformed traditional algorithms on many benchmarks. Training deep RNNs still remains a challenge, and most of the state-of-the-art models are structured with a transition depth of 2-4 layers. Recurrent Highway Networks (RHNs) were introduced in order to tackle this issue. These have achieved state-of-the-art performance on a few benchmarks using a depth of 10 layers. However, the performance of this architecture suffers from a bottleneck, and ceases to improve when an attempt is made to add more layers. In this work, we analyze the causes for this, and postulate that the main source is the way that the information flows through time. We introduce a novel and simple variation for the RHN cell, called Highway State Gating (HSG), which allows adding more layers, while continuing to improve performance. By using a gating mechanism for the state, we allow the net to "choose" whether to pass information directly through time, or to gate it. This mechanism also allows the gradient to back-propagate directly through time and, therefore, results in a slightly faster convergence. We use the Penn Treebank (PTB) dataset as a platform for empirical proof of concept. Empirical results show that the improvement due to Highway State Gating is for all depths, and as the depth increases, the improvement also increases.

Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms (e.g. Taobao). Generally speaking, two essential modules named "Click-Through Rate Prediction" (CTR) and "Conversion Rate Prediction" (CVR) are included, where CVR module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multi-Level Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (GBDT) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding GBDT as the input feature representation for next level GBDT, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level GBDT corresponds to the combination of certain traversals in the preceding GBDT. The deep cascade structure and the combination rule enable the proposed ldcTree to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble ldcTree (E-ldcTree) to encourage the model's diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on EldcTree (F-EldcTree) for taking adequate use of weak and strong correlation features identified by pre-trained GBDT models. Experimental results on off-line dataset and online deployment demonstrate the effectiveness of the proposed methods.