Chinese AI firm Megvii Technology, backed by Alibaba, has filed in Hong Kong to conduct an IPO targeting proceeds of at least $500 million, two people said, just as the city faces political unrest and its first recession in a decade. Beijing-based Megvii, widely known for facial recognition platform Face, may raise as much as $1 billion in the initial public offering, said one of the people, who expect the share sale in the fourth quarter of the year. The filing comes as companies postpone or slow down listing plans in a recession-bound city blighted with nearly three months of anti-government protests, and where the benchmark Hang Seng share price index fell to seven-month lows this month. Reuters reported last week that China's biggest ecommerce firm, Alibaba Group, had delayed its up to $15 billion Hong Kong listing. Megvii has decided to press ahead with its IPO plans because it has little business in Hong Kong and expects the unrest to ease later this year, said a third person.

As Western powers continue to grapple with if or how to fit Huawei's 5G networks into their societies, reports have revealed the Chinese telecom giant is already well into researching 6G mobile technology. Presently, that network is slowly being rolled out in cities around the globe, and in Australia, access to the service has been slow, with coverage so far being provided by just Telstra and Optus. However, this week, tech website The Logic reported that Huawei was the latest company to join a small list of companies and universities commencing 6G's research and development. Huawei's research will happen at the company's Canadian lab, and Song Zhang, Huawei Canada's vice-president of research strategy and partnerships, told Logic the company was "in talks with Canadian university researchers" about the network's development. Yang Chaobin, the president of Huawei's 5G products, said that 6G would not be viable until 2030.

We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept "disentangled representations" used in supervised and unsupervised methods along three dimensions-informativeness, separability and interpretability - which can be expressed and quantified explicitly using information-theoretic constructs. This helps explain the behaviors of several well-known disentanglement learning models. We then propose robust metrics for measuring informativeness, separability and interpretability. Through a comprehensive suite of experiments, we show that our metrics correctly characterize the representations learned by different methods and are consistent with qualitative (visual) results. Thus, the metrics allow disentanglement learning methods to be compared on a fair ground. We also empirically uncovered new interesting properties of VAE-based methods and interpreted them with our formulation. These findings are promising and hopefully will encourage the design of more theoretically driven models for learning disentangled representations.

Due to the popularity of context-awareness in the Internet of Things (IoT) and the recent advanced features in the most popular IoT device, i.e., smartphone, modeling and predicting personalized usage behavior based on relevant contexts can be highly useful in assisting them to carry out daily routines and activities. Usage patterns of different categories smartphone apps such as social networking, communication, entertainment, or daily life services related apps usually vary greatly between individuals. People use these apps differently in different contexts, such as temporal context, spatial context, individual mood and preference, work status, Internet connectivity like Wifi? status, or device related status like phone profile, battery level etc. Thus, we consider individuals' apps usage as a multi-class context-aware problem for personalized modeling and prediction. Random Forest learning is one of the most popular machine learning techniques to build a multi-class prediction model. Therefore, in this paper, we present an effective context-aware smartphone apps prediction model, and name it "AppsPred" using random forest machine learning technique that takes into account optimal number of trees based on such multi-dimensional contexts to build the resultant forest. The effectiveness of this model is examined by conducting experiments on smartphone apps usage datasets collected from individual users. The experimental results show that our AppsPred significantly outperforms other popular machine learning classification approaches like ZeroR, Naive Bayes, Decision Tree, Support Vector Machines, Logistic Regression while predicting smartphone apps in various context-aware test cases.

Stationarity is an important concept in time series analysis. For a concise (but thorough) introduction to the topic, and the reasons that make it important, take a look at my previous blog post on the topic. As such, the ability to determine if a time series is stationary is important. Rather than deciding between two strict options, this usually means being able to ascertain, with high probability, that a series is generated by a stationary process. In this brief post, I will cover several ways to do just that.

When it comes to the diagnosis of pigmented skin lesions, artificial intelligence is superior to humans. In a study conducted under the supervision of the MedUni Vienna human experts "competed" against computer algorithms. The algorithms achieved clearly better results, yet their current abilities cannot replace humans. The results were published in the journal The Lancet Oncology. The International Skin Imaging Collaboration (ISIC) and the MedUni Vienna organized an international challenge to compare the diagnostic skills of 511 physicians with 139 computer algorithms (from 77 different machine learnings labs).

When it comes to the diagnosis of pigmented skin lesions, artificial intelligence is superior to humans. In a study conducted under the supervision of the MedUni Vienna human experts "competed" against computer algorithms. The algorithms achieved clearly better results, yet their current abilities cannot replace humans. The results were published in the journal The Lancet Oncology. The International Skin Imaging Collaboration (ISIC) and the MedUni Vienna organized an international challenge to compare the diagnostic skills of 511 physicians with 139 computer algorithms (from 77 different machine learnings labs).

I swear by the Hypatia, by lovelace, by Turing, by Fisher (and/or Bayes), and by all the statisticians and data scientists, making them my witness, that i will carry out, according to my ability and judgement, this oath and this indenture. Could this be the first line of a "Hippocratic Oath" for mathematicians and data scientists? Hannah Fry, Associate Professor in the mathematics of cities at University College London, argues that mathematicians and data scientists need such an oath, just like medical doctors who swear to act only in their patients' best interests. "In medicine, you learn about ethics from day one. It has to be there from day one and at the forefront of your mind in every step you take," Fry argued. But is a tech version of the Hippocratic Oath really required?

Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

Consumers International has released a new study on consumer experiences of artificial intelligence (AI) and the ways in which AI enabled services shape their consumer journeys and experiences, and consumer outcomes. The report'Artificial Intelligence: consumer experiences in new technology' contains a summary of new findings about the consumer experience of AI from IPSOS Global's participatory research with families and individuals in India, Australia and Japan, and insights from interviews with expert stakeholders from the region. It also incorporates the results of the multi-stakeholder roundtable held in Singapore in March 2019, where consumer organisations, businesses, academics and regulators discussed how AI enabled technology can deliver the best possible outcomes for consumers, whilst recognising and mitigating against potential challenges and risks.