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.

Ross Ihaka and Robert Gentleman at University of Auckland, New Zealand were the creators of R, which is now a widely used language for Machine learning. They created it for the application of S programming language in 1993 and the open source project was set up in 1997. These two men started it as an experiment to bring into play a statistical test bed in Lisp using a programming language providing in S. They eventually realized that they had created something that exceeded S. R turned out to be the best technology for statistical programming and applied machine learning. Despite these flaws one cannot ignore the strong benefits that R holds.

Many, especially science fiction writers, play somewhere in the middle. They write about technologies that don't yet exist. Or they assume the possibilities of technologies far ahead of their time. Melbourne, AUSTRALIA: Kellie Shaw (R) inspects one of Leonardo da Vinci's most famous designs which is recognised as the ancestor to the modern helicopter and is part of an exhibition featuring 50 models of 15th century inventions by Da Vinci, in Melbourne 04 July 2006. This craft is made of linen, reeds and iron thread and would have been operated by four men rotating a shaft. The exhibition focuses on four themes: mechanical, military, hydraulic and flying machines with each model built according to Da Vinci's drawing and are crafted from materials available in 15th century Italy.

Sex robots could be hijacked by hackers and used to cause harm or even kill people, a cybersecurity expert has warned. Artificial intelligence researchers have consistently warned of the security risks posed by internet-connected robots, with hundreds recently calling on governments to ban weaponized robots. The latest warning comes from a cybersecurity expert who made the prophecy to several U.K. newspapers. "Hackers can hack into a robot or a robotic device and have full control of the connections, arms, legs and other attached tools like in some cases knives or welding devices," Nicholas Patterson, a cybersecurity lecturer at Deakin University in Melbourne, Australia, told the Star. "Often these robots can be upwards of 200 pounds and very strong. Once a robot is hacked, the hacker has full control and can issue instructions to the robot. The last thing you want is for a hacker to have control over one of these robots. Once hacked they could absolutely be used to perform physical actions for an advantageous scenario or to cause damage."

Through the use of artificial intelligence, we will let billions of people have a go at experiencing the joy of flying," said Mr Xiong, 28, who is also EHang's chief marketing officer.

In this paper, we present an initial attempt to learn evolution PDEs from data. Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models. The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses. Comparing with existing approaches, which either assume the form of the nonlinear response is known or fix certain finite difference approximations of differential operators, our approach has the most flexibility by learning both differential operators and the nonlinear responses. A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network. These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory). We also discuss relations of the PDE-Net with some existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network (ResNet). Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.