Google CEO Sundar Pichai testifies during a House Judiciary Committee hearing on Capitol Hill. Google CEO Sundar Pichai testifies during a House Judiciary Committee hearing on Capitol Hill. Google CEO Sundar Pichai made his public debut before Congress on Tuesday, spending much of his testimony countering Republicans' allegations of anti-conservative bias in the company's search results. He also faced scrutiny of how much data Google collects on users and on the company's work on a censored search tool for China. Time and again, Republican lawmakers pressed Pichai on allegations of political bias in search results on Google and its video subsidiary YouTube.

Visitors play the video game "Call of Duty" at the Tokyo Game Show on September 21, 2018. Last year, the global film industry made $40 billion. The global gaming industry made $122 billion. And the world of video games is just as broad as the film industry. There are indie games and big budget games -- games that feel like a graphic novel and games that play through like a blockbuster movie.

Google has'no plans' to relaunch a search engine in China though it is continuing to study the idea, Chief Executive Sundar Pichai told a U.S. congressional panel on Tuesday amid increased scrutiny of big tech firms. Lawmakers and Google employees have raised concerns the company would comply with China's internet censorship and surveillance policies if it re-enters the Asian nation's search engine market. Google's main search platform has been blocked in China since 2010, but the Alphabet Inc unit has been attempting to make new inroads into the country, which has the world's largest number of smartphone users. Chief Executive Sundar Pichai told a U.S. congressional panel Google had over 100 people working on the project at one point. 'Right now, there are no plans to launch search in China,' Pichai told the U.S. House of Representatives Judiciary Committee.

The round was led by Cambridge angel Simon Thorpe who has already demonstrated the Midas touch. He was an investor in Swiftkey and Vocal IQ – Cambridge-founded businesses bought by Google and Apple, respectively, for a combined $350 million. The new round was also backed by Angel CoFund, a VC fund that co-invests alongside angel investors. John Spearman, GW Asia Capital Ltd and Adrian Lloyd also participated, and all three non-exec directors from round one have re-invested. Quick access to expertise on demand is essential for growth and progress in everything from rare disease research to the development of sustainable fuels.

China is due to become the world's AI research superpower as researchers in Europe are poached up by US technology giants, research has suggested. A study by Elsevier, the information analytics company, found that Chinese research output in artificial intelligence had increased by around two thirds between 2013 and 2017. The study's authors said that "China is bound to overtake Europe in publication output in AI in the near future, having already overtaken the United States in 2004". Beijing has made no secret of its plans to become a world leader in AI, despite fears that its research is being used to monitor its population and crack down on dissent. Elsevier's study found that Chinese research...

Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigate the role the logic matrix and its structure plays in determining the final opinions, including existence of the limiting opinions, of a strongly connected network of individuals. We provide a set of results that, given a set of individuals' belief systems, allow a systematic determination of which topics will reach a consensus, and which topics will disagreement in arise. For irreducible logic matrices, each topic reaches a consensus. For reducible logic matrices, which indicates a cascade interdependence relationship, conditions are given on whether a topic will reach a consensus or not. It turns out that heterogeneity among the individuals' logic matrices, including especially differences in the signs of the off-diagonal entries, can be a key determining factor. This paper thus attributes, for the first time, a strong diversity of limiting opinions to heterogeneity of belief systems in influence networks, in addition to the more typical explanation that strong diversity arises from individual stubbornness.

Islamophobic hate speech on social media inflicts considerable harm on both targeted individuals and wider society, and also risks reputational damage for the host platforms. Accordingly, there is a pressing need for robust tools to detect and classify Islamophobic hate speech at scale. Previous research has largely approached the detection of Islamophobic hate speech on social media as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically-informed social science and effectively monitoring social media. Drawing on in-depth conceptual work we build a multi-class classifier which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. We apply the classifier to a dataset of 109,488 tweets produced by far right Twitter accounts during 2017. Whilst most tweets are not Islamophobic, weak Islamophobia is considerably more prevalent (36,963 tweets) than strong (14,895 tweets). Our main input feature is a gloVe word embeddings model trained on a newly collected corpus of 140 million tweets. It outperforms a generic word embeddings model by 5.9 percentage points, demonstrating the importan4ce of context. Unexpectedly, we also find that a one-against-one multi class SVM outperforms a deep learning algorithm.

Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need an illumination control on color, intensity, and luminance, etc., which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise non-trivial challenges both in modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.

We consider the problem of asynchronous online testing, aimed at providing control of the false discovery rate (FDR) during a continual stream of data collection and testing, where each test may be a sequential test that can start and stop at arbitrary times. This setting increasingly characterizes real-world applications in science and industry, where teams of researchers across large organizations may conduct tests of hypotheses in a decentralized manner. The overlap in time and space also tends to induce dependencies among test statistics, a challenge for classical methodology, which either assumes (overly optimistically) independence or (overly pessimistically) arbitrary dependence between test statistics. We present a general framework that addresses both of these issues via a unified computational abstraction that we refer to as "conflict sets." We show how this framework yields algorithms with formal FDR guarantees under a more intermediate, local notion of dependence. We illustrate these algorithms in simulation experiments, comparing to existing algorithms for online FDR control.

In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usually too complex to model in the whole sample space; some fundamental assumptions are required to simplify the global model, for example, the class conditional independence assumption for naive Bayesian classification. In this paper, with the insight that the distribution in a local sample space should be simpler than that in the whole sample space, a local probabilistic model established for a local region is expected much simpler and can relax the fundamental assumptions that may not be true in the whole sample space. Based on these advantages we propose establishing local probabilistic models for Bayesian classification. In addition, a Bayesian classifier adopting a local probabilistic model can even be viewed as a generalized local classification model; by tuning the size of the local region and the corresponding local model assumption, a fitting model can be established for a particular classification problem. The experimental results on several real-world datasets demonstrate the effectiveness of local probabilistic models for Bayesian classification.