A new research collaboration focused on developing a solution that leverages artificial intelligence (AI) to detect and monitor brain aneurysms on scans faster and more efficiently was recently announced. As reported, Australia's Macquarie University will work with an ICT company, a medical tech company, and a medical imaging company to improve brain aneurysm diagnoses. The project has already received a Cooperative Research Centres Projects (CRC-P) grant of AU$ 2.1M from the Department of Industry, Innovation and Science. Brain aneurysms are a common disorder caused by a weakness in the wall of a brain artery. Aneurysms are present in between 2% and 8% of adults, with multiple aneurysms in more than 10% of these people.

One prominent approach for data collection has been to automatically generate pseudo-language paired with logical forms, and paraphrase the pseudo-language to natural language through crowdsourcing (Wang et al., 2015). However, this data collection procedure often leads to low performance on real data, due to a mismatch between the true distribution of examples and the distribution induced by the data collection procedure. In this paper, we thoroughly analyze two sources of mismatch in this process: the mismatch in logical form distribution and the mismatch in language distribution between the true and induced distributions. We quantify the effects of these mismatches, and propose a new data collection approach that mitigates them. Assuming access to unlabeled utterances from the true distribution, we combine crowdsourcing with a paraphrase model to detect correct logical forms for the unlabeled utterances. On two datasets, our method leads to 70.6 accuracy on average on the true distribution, compared to 51.3 in paraphrasing-based data collection. 1 Introduction Conversing with a virtual assistant in natural language is one of the most exciting current applications of semantic parsing, the task of mapping natural language utterances to executable logical forms (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Liang et al., 2011). Semantic parsing models rely on supervised training data that pairs natural language utterances with logical forms. Alas, such data does not occur naturally, especially in virtual assistants that are meant to support thousands of different applications and use-cases. Thus, efficient data collection is per-Figure 1: An overview of G RA NNO, a method for annotating unlabeled utterances with their logical forms.

In previous generations, robots have fallen over while being unveiled, failed to do the simplest of tasks or been great for one use only. However, the latest models, both automatons and software robots, are flexible and here to serve businesses today. The concept of the robot has changed greatly in recent years. Accepted for decades in factories and production lines, robots already roam the streets delivering takeaway food, flying drones deliver parcels and a modern robot is a flexible and smart multi-purpose friend to many. Software robots act as influencers on social media, chatbots engage millions of customers every day and will soon take over a lot of municipal, medical or similar interactions to help better manage growing populations.

So far, we've had to learn to interact with computers on their terms and limitations. To make as precise online search as possible we are required to know the optimal keywords, and still, we get millions of search results that we have to choose from. However, now with the emergence of conversational search and AI chatbots, things are changing. The demand for more human-like chatbots has resulted to significantly improved natural language processing and machine learning. We are now teaching computers to interact with us on our terms and limitations.

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).