One of AWS' most highly decorated partners has arrived in Australia, bagging Origin Energy as its foundation client.

In the 1920s, the Soviet scientist Ilya Ivanovich Ivanov used artificial insemination to breed a'humanzee' – a cross between a human and our closest relative species, the chimpanzee. Given the moral quandaries a humanzee might create, we can be thankful that Ivanov failed: when the winds of Soviet scientific preferences changed, he was arrested and exiled. But Ivanov's endeavour points to the persistent, post-Darwinian fear and fascination with the question of whether humans are a creature apart, above all other life, or whether we're just one more animal in a mad scientist's menagerie. Humans have searched and repeatedly failed to rescue ourselves from this disquieting commonality. Numerous dividers between humans and beasts have been proposed: thought and language, tools and rules, culture, imitation, empathy, morality, hate, even a grasp of'folk' physics. But they've all failed, in one way or another. I'd like to put forward a new contender – strangely, the very same tendency that elicits the most dread and excitement among political and economic commentators today. We lost our exclusive position in the animal kingdom, not because we overestimated ourselves, but because we underestimated our cousins.

Conventional models, while still solid, are no longer up to the heightened challenges of the present. Exponentially improving technologies for the Internet of Things (IoT) and artificial intelligence are enabling urban developments with much higher levels of efficiency and flexibility to conserve resources, promote security, and boost the quality of life. The key development is not the technologies themselves, but their integration around a holistic view of urbanization that enables a series of smart services. Instead of focusing on single services, or specific buildings or highways, leading organizations around the world are using IoT and analytics to optimize infrastructure generally and evolve with changing needs. While getting there will take a great deal of investment and expertise, the result will be places where residents thrive in unexpected ways in their personalized urban developments.

In this episode of Robots in Depth, Per Sjöborg speaks with Peter Corke, distinguished professor of robotic vision from Queensland University of Technology, and Director of the ARC Centre of Excellence for Robotic Vision. Peter is well known for his work in computer vision and has written one of the books that defines the area. He talks about how serendipity made him build a checkers playing robot and then move on to robotics and machine vision. We get to hear about how early experiments with "Blob Vision" got him interested in analyzing images and especially moving images, and his long and interesting journey giving robots eyes to see the world. The interview ends with Peter adding a new item to the CV, fashion model, when he shows us the ICRA 2018 T-shirt!

New Zealand-based artificial intelligence company IMAGR announced today that it's upping the grocery game by launching its innovative solution SMARTCART to a Four Square store in the Auckland suburb of Ellerslie. The move comes as retailers around the globe look for ways to give consumers more streamlined methods and checkout-less options for shopping, including the recent launch of Amazon Go in Seattle. The New Zealand store will be the flagship Foodstuffs retail outlet to trail SMARTCART, a computer vision technology retrofitted to shopping baskets and carts that recognises products as soon as they are placed inside, eliminating the need for barcode scanning, checkouts and queueing. "We're delighted Foodstuffs is the first retailer in the world that we're partnering with to make this happen This is the first significant step in enhancing the way we do our grocery shopping here in New Zealand and abroad," said IMAGR founder William Chomley. "It's great to see Foodstuffs embracing technology like this to empower customer experiences."

In order to design and operate the process it is important to know which variables have the strongest influence on performance. To estimate this we compared each state with the length and diameter of the resulting fibers, performing second order polynomial fit using samples over all 9 experiments, and noting the value of the resulting correlation coefficients R. The correlations between process parameters and the Length and Diameter is contained in Supplementary Table S.1. The angle and position have very little influence on the characteristics of the produced fibers. Polymer flow has a moderate influence (0.37) on length, but only a weak influence on diameter. Thus the most significant influence on overall performance is solvent speed followed by channel width.

Whether financial institutions are looking for improved customer service, risk management, fraud prevention, investment prediction or cybersecurity, the scopes of machine learning and artificial intelligence are limitless. In the modern era of the digital economy, technological advancements are no longer a luxury for the organizations, but a necessity to outsmart their competitors and business growth. With the technological advancements in the recent times, the impact of Machine Learning (ML) and Artificial Intelligence (AI) are very critical than ever before. Previously, we discussed the scopes of big data and data science in banking and financial services. In this article will explain in detail about ML and AI, and their scopes in banking and financial services. Apparently, in order to be successful and making an impact, the banks and financial institutions need to make machine learning and artificial intelligence an expansion of their big data and data analytics approach.

While first-order optimization methods such as stochastic gradient descent (SGD) are popular in machine learning (ML), they come with well-known deficiencies, including relatively-slow convergence, sensitivity to the settings of hyper-parameters such as learning rate, stagnation at high training errors, and difficulty in escaping flat regions and saddle points. These issues are particularly acute in highly non-convex settings such as those arising in neural networks. Motivated by this, there has been recent interest in second-order methods that aim to alleviate these shortcomings by capturing curvature information. In this paper, we report detailed empirical evaluations of a class of Newton-type methods, namely sub-sampled variants of trust region (TR) and adaptive regularization with cubics (ARC) algorithms, for non-convex ML problems. In doing so, we demonstrate that these methods not only can be computationally competitive with hand-tuned SGD with momentum, obtaining comparable or better generalization performance, but also they are highly robust to hyper-parameter settings. Further, in contrast to SGD with momentum, we show that the manner in which these Newton-type methods employ curvature information allows them to seamlessly escape flat regions and saddle points.

The actual impact of artificial intelligence (AI) on the world's economy and jobs will likely be somewhere between the utopian and dystopian futures that it is often discussed in terms of, according to a new report from the Economist Intelligence Unit. The report, commissioned by Google, examined how AI will impact certain industries in the US, the UK, Australia, Japan, and Asia as a whole. The findings are based on econometric modelling, desk research, and interviews with academic and industry experts. Firms developing and using machine learning need to better communicate among themselves as well as with the public and policymakers, the report stated. This means doing more to manage expectations around the impact of machine learning, acknowledging the potential risks and rewards, improving trust and transparency, and educating the public.

It's far less common to see video games tackle the other side of war and competition: the human side. Love, for instance, is rarely a central selling point for a video game. Plenty of classic action and adventure games use love as a motivation for the main character -- usually by kidnapping or killing the male protagonist's wife -- but less often do these experiences dive into the dense complexities of relationships and romance. Wong's latest game, Florence, does just that. It landed on iOS devices this week, just in time for Valentine's Day, and it's the first title out of Wong's Melbourne, Australia, studio, Mountains.