A survey of business leaders has found Australian companies are the worst prepared for the arrival of artificial intelligence (AI) technologies among selected major economies, despite spending the second-largest amount of money on automation. Independent research agency Vanson Bourne was commissioned by IT company Infosys (which as a seller of an AI platform has a vested interest in promoting such technology) to poll 1,600 business leaders of companies with more than 1,000 staff and at least US$500m in annual revenue across Australia, China, the United States, Germany, France, India and the UK. According to the survey, released at the World Economic Forum last week, major Australian businesses invested an average of $7.9m last year in AI, behind only the US, but placed last in both the skills required for AI takeup and in plans to integrate AI. The Infosys Australia regional head, Andrew Groth, told the Guardian the survey demonstrates that Australia risks becoming uncompetitive. "The challenge is the skills situation," he said.

Part two of our series "A day in the life of a city" looks at the ways in which offices are changing and how cities are coping with the ever-growing problem of pollution. The morning rush hour is over and, if you live in a city in the developed world, you are likely to be settling down at your desk for the next eight or so hours. However, the office block and skyscraper, which have been part of our urban landscape since the end of the 19th Century, may also soon become surplus to requirements. Urban architect Anthony Townsend thinks cities need more creative approaches to how we work and is keen to reclaim the streets by creating pop-up workspaces in the parks and plazas of the financial district in New York. "Before the New York Stock Exchange, traders met under a tree on Wall Street to buy and sell shares. It is only in the last 50 years that we have taken that creative energy and sucked it up into office buildings and separated it from public space," he said.

A survey of business leaders has found Australian companies are the worst prepared for the arrival of artificial intelligence (AI) technologies among selected major economies, despite spending the second-largest amount of money on automation. Independent research agency Vanson Bourne was commissioned by IT company Infosys (which as a seller of an AI platform has a vested interest in promoting such technology) to poll 1,600 business leaders of companies with more than 1,000 staff and at least US$500m in annual revenue across Australia, China, the United States, Germany, France, India and the UK. According to the survey, released at the World Economic Forum last week, major Australian businesses invested an average of $7.9m last year in AI, behind only the US, but placed last in both the skills required for AI takeup and in plans to integrate AI. The Infosys Australia regional head, Andrew Groth, told the Guardian the survey demonstrates that Australia risks becoming uncompetitive. "The challenge is the skills situation," he said.

It's the news that will shock grumpy old men everywhere - but we actually get nicer as the years roll by. According to University of Cambridge brain scientists, we become less moody, more agreeable and more conscientious as times passes. The changes are a natural process as the organ matures and come after new technology allowed the scientists to map the brains of 500 volunteers and track any changes over time. The researchers found high levels of neuroticism are associated with increased thickness and reduced folding in some regions of the brain. With colleagues in the US and Italy, they focused on the anatomy of the cortex, or outer layer, where the higher functions that make us human are centred.

Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains. One of the most important aspect of network analysis is community detection or network clustering. Until recently, the major focus have been on discovering community structure in single (i.e., monoplex) networks. However, with the advent of relational data with multiple modalities, multiplex networks, i.e., networks composed of multiple layers representing different aspects of relations, have emerged. Consequently, community detection in multiplex network, i.e., detecting clusters of nodes shared by all layers, has become a new challenge. In this paper, we propose Network Fusion for Composite Community Extraction (NF-CCE), a new class of algorithms, based on four different non-negative matrix factorization models, capable of extracting composite communities in multiplex networks. Each algorithm works in two steps: first, it finds a non-negative, low-dimensional feature representation of each network layer; then, it fuses the feature representation of layers into a common non-negative, low-dimensional feature representation via collective factorization. The composite clusters are extracted from the common feature representation. We demonstrate the superior performance of our algorithms over the state-of-the-art methods on various types of multiplex networks, including biological, social, economic, citation, phone communication, and brain multiplex networks.

The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representations from longitudinal EHR data remains challenging. In this paper, we explore deep neural network models with learned medical feature embedding to deal with the problems of high dimensionality and temporality. Specifically, we use a multi-layer convolutional neural network (CNN) to parameterize the model and is thus able to capture complex non-linear longitudinal evolution of EHRs. Our model can effectively capture local/short temporal dependency in EHRs, which is beneficial for risk prediction. To account for high dimensionality, we use the embedding medical features in the CNN model which hold the natural medical concepts. Our initial experiments produce promising results and demonstrate the effectiveness of both the medical feature embedding and the proposed convolutional neural network in risk prediction on cohorts of congestive heart failure and diabetes patients compared with several strong baselines.

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.

Objective: Using traditional approaches, a Brain-Computer Interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g.~by transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder to achieve a reliable calibration-less decoding with a guarantee to recover the true class means. Method: We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We modified a visual ERP speller to meet the requirements of the LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with N=13 subjects performing a copy-spelling task. Results: Theoretical considerations show that LLP is guaranteed to minimize the loss function similarly to a corresponding supervised classifier. It performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. Significance: The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the true class means. This makes it an ideal solution to avoid a tedious calibration and to tackle non-stationarities in the data. Additionally, LLP works on complementary principles compared to existing unsupervised methods, allowing for their further enhancement when combined with LLP.

A Hilbert space embedding of a distribution---in short, a kernel mean embedding---has recently emerged as a powerful tool for machine learning and inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel methods can be extended to probability measures. It can be viewed as a generalization of the original "feature map" common to support vector machines (SVMs) and other kernel methods. While initially closely associated with the latter, it has meanwhile found application in fields ranging from kernel machines and probabilistic modeling to statistical inference, causal discovery, and deep learning. The goal of this survey is to give a comprehensive review of existing work and recent advances in this research area, and to discuss the most challenging issues and open problems that could lead to new research directions. The survey begins with a brief introduction to the RKHS and positive definite kernels which forms the backbone of this survey, followed by a thorough discussion of the Hilbert space embedding of marginal distributions, theoretical guarantees, and a review of its applications. The embedding of distributions enables us to apply RKHS methods to probability measures which prompts a wide range of applications such as kernel two-sample testing, independent testing, and learning on distributional data. Next, we discuss the Hilbert space embedding for conditional distributions, give theoretical insights, and review some applications. The conditional mean embedding enables us to perform sum, product, and Bayes' rules---which are ubiquitous in graphical model, probabilistic inference, and reinforcement learning---in a non-parametric way. We then discuss relationships between this framework and other related areas. Lastly, we give some suggestions on future research directions.

Amazon has upgraded its smart assistant - and answered the prayers of Star Trek fans around the world. Traditionally, owners of Amazon's Echo speakers can simply say'Alexa' to trigger it. Now, the firm has added a new feature letting them say'computer' instead, in a nod to the sci fi series. Users can now choose between Alexa, Echo, Amazon and Computer to wake up their smart speaker - letting the smart assistant operate in the same way as Star Trek's computers. Open the left navigation panel, and then select Settings.