This finding, which is the result of Polycom's The Changing World of Work research, shows that this percentage is going to increase exponentially over the next 12-18 months. Following on from the survey, Polycom has released their top five drivers that are set to impact business collaboration in the year ahead. The cloud space has transformed, not just our office and workspaces but the way we work. If we look at the next generation of technology – it's modular, it's adaptive, it's solutions based and it is cloud based. As cloud continues to move into the mainstream, the conversation is no longer about that sub 50 office space, the one-to-three user space is back on the table.

This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convolutional and fully-connected layers often cannot capture. In this paper, we explore the foundations for such an architecture: we show how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. In one notable example, we show that the method is capable of learning to play mini-Sudoku (4x4) given just input and output games, with no a priori information about the rules of the game; this highlights the ability of our architecture to learn hard constraints better than other neural architectures.

We consider the task of weakly supervised one-shot detection. In this task, we attempt to perform a detection task over a set of unseen classes, when training only using weak binary labels that indicate the existence of a class instance in a given example. The model is conditioned on a single exemplar of an unseen class and a target example that may or may not contain an instance of the same class as the exemplar. A similarity map is computed by using a Siamese neural network to map the exemplar and regions of the target example to a latent representation space and then computing cosine similarity scores between representations. An attention mechanism weights different regions in the target example, and enables learning of the one-shot detection task using the weaker labels alone. The model can be applied to detection tasks from different domains, including computer vision object detection. We evaluate our attention Siamese networks on a one-shot detection task from the audio domain, where it detects audio keywords in spoken utterances. Our model considerably outperforms a baseline approach and yields a 42.6% average precision for detection across 10 unseen classes. Moreover, architectural developments from computer vision object detection models such as a region proposal network can be incorporated into the model architecture, and results show that performance is expected to improve by doing so.

Interest for the search term'data science,' as measured by Google, over the last five years. In the tech industry, new skills and roles emerge faster than traditional education can keep up with. A recent example is the field of data science and the associated profession, Data Scientist. The simplest definition of the data science field is the practice of collecting, analyzing, and interpreting data -- aided by technology. Most Computer Science degrees do not yet offer Data Science as a major and, as such, many Data Scientists are self-taught. For this reason, it is possible to become a Data Scientist without a formal degree This article will explore what it's like to be a Data Scientist, the skillset required, and how to acquire these skills using mostly free or cheap online resources.

SYDNEY: Though artificial intelligence is evolving quickly, consumers remain wary of the technology and prefer chatbots to stay simple with guided options, according to an Australian expert. Douglas Nicol, founder of On Message – Australia's first messaging agency – explained that the public at large is yet to catch up with the enthusiasm of marketers for futuristic chat solutions. In fact, consumers expect simpler chatbot formats which directly address their issues, rather than showcase the latest and greatest AI technology. "There is an innate fear amongst Australian consumers of machines and what machines can do to us in the future," Nicol told the Mumbrella MSIX conference in Sydney. "So the question is how do you navigate this world, because the world of artificial intelligence is changing everything."

Climate Council Australia and digital agency AKQA today announced a collaboration to launch the Climate Council's first ever chatbot designed to help better engage its followers with questions about climate change. The chatbot was designed to help engage the 25-35 year olds who already follow the Climate Council on its social channels but have low engagement. Housed on the Climate Council's Facebook page, the bot will help the audience access research and statistics across a range of climate-related topics, including extreme weather, heatwaves, bushfires and renewable energy and storage technology solutions. The AKQA Research and Development team worked closely with Climate Council to ensure all their findings and research could be transformed into data for the chatbot. AKQA's executive director of the R&D Lab, Tim Devine, said: "To ensure the bot was highly effective, the AKQA Research and Development Lab ran workshops with The Climate Council to gain an understanding of the challenges the organisation faced and how emerging technology such as bots can overcome these challenges. "In the development phase, the lab first tested the IBM Watson Knowledge Studio as a way to restructure content in a way that would train the bot that could answer any question on climate change.

January is a peak time for downloading health and fitness apps and putting those Christmas present fitness trackers to work. But do they actually help you stay motivated? After the Christmas self-indulgence comes the inevitable New Year's resolution to get fit, lose weight, and eat more healthily. But while 65% of us make resolutions, only 12% successfully keep to them, polling firm ComRes finds. When Sarah, 34, a law professor from Australia, wanted to lose weight last year, she took the unusual approach of placing bets that she would achieve her exercise goals.

Summary: This is the second in our multi-part series on Quantum computing. How Fast?" we laid out the case that Quantum computing is commercially available today and that companies are already beginning to use it in operations. We talked a little about who is out in front (D-Wave, IBM) and who is coming soon (Microsoft, Google, University of New South Wales). We also spoke briefly about how it might be applied to deep learning and have an impact on artificial intelligence. In commercial operation today there are two distinct types of Quantum computers and several entirely different types due within a year or two.

Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of iterations required by the method. Our novel approach learns the latent structure with Gibbs sampling and constructs batched queries using determinantal point processes. Experimental validations on both synthetic and real-world functions demonstrate that the proposed method outperforms the existing state-of-the-art approaches.

It is said – often by our mothers – that we are looking a bit'peaky' or under the weather even when we do not notice ourselves. Now researchers have discovered that humans have an ability to pick up the subtle signs that show someone is sick within minutes of them getting an infection. Some signs of sickness are obvious– such as a violent cough, or the spots on the face in measles. These are obvious enough to ensure the ill person gets a wide birth. But in an illustration of the amazing power of the human brain, a glance of a few seconds was enough for observers tell if people had just caught a nasty bug.