On Wednesday, amid neon lights and smoke machines (but no mirrors) Mr Meij, a former pizza delivery driver who is estimated to be worth at least $130 million, unveiled Domino's latest high-tech weapons. After unveiling a stream of disruptive digital initiatives over the past six years – including mobile phone apps, GPS driver tracking, SMS ordering, electronic bikes, robotic pizza delivery vehicles and drones – Domino's is now harnessing the power of artificial intelligence to encourage Australian and New Zealand consumers to eat more pizza. Over the next week Domino's will add new features and devices to its DRU (Domino's Robotic Unit) platform, starting with a beta version of DRU Assist, a virtual, voice-activated digital assistant which takes customers' orders on mobile phones, desktops and laptops.

In this paper we introduce a new classification algorithm called Optimization of Distributions Differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another while the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the Quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a non-linear transformation in this paper. We show that the algorithm can outperform 6 other state-of-the-art classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in 12 standard classification datasets. Our results show that the method is less sensitive to the imbalanced number of instances comparing to these methods. We also show that ODD maintains its performance better than other classification methods in these datasets, hence, offers a better generalization ability.

When using reinforcement learning (RL) algorithms to evaluate a policy it is common, given a large state space, to introduce some form of approximation architecture for the value function (VF). The exact form of this architecture can have a significant effect on the accuracy of the VF estimate, however, and determining a suitable approximation architecture can often be a highly complex task. Consequently there is a large amount of interest in the potential for allowing RL algorithms to adaptively generate approximation architectures. We investigate a method of adapting approximation architectures which uses feedback regarding the frequency with which an agent has visited certain states to guide which areas of the state space to approximate with greater detail. This method is "unsupervised" in the sense that it makes no direct reference to reward or the VF estimate. We introduce an algorithm based upon this idea which adapts a state aggregation approximation architecture on-line. A common method of scoring a VF estimate is to weight the squared Bellman error of each state-action by the probability of that state-action occurring. Adopting this scoring method, and assuming $S$ states, we demonstrate theoretically that - provided (1) the number of cells $X$ in the state aggregation architecture is of order $\sqrt{S}\log_2{S}\ln{S}$ or greater, (2) the policy and transition function are close to deterministic, and (3) the prior for the transition function is uniformly distributed - our algorithm, used in conjunction with a suitable RL algorithm, can guarantee a score which is arbitrarily close to zero as $S$ becomes large. It is able to do this despite having only $O(X \log_2S)$ space complexity and negligible time complexity. The results take advantage of certain properties of the stationary distributions of Markov chains.

While many firms are looking to the moon for mining opportunities, one Australian firm believes there could be precious metals a lot nearer to home. Deep-sea robots will be sent to mine mineral deposits in the deep ocean in 2019 in a test for a controversial new scheme. As land-based mineral stores are becoming depleted, the ocean floor is becoming a more attractive mining prospect, containing gold, copper and other precious metal deposits used to make electronics, renewable energy tools and even medical imaging machines. But deep-sea excavation may have a negative impact on deep ocean marine life, as robot mining may destroy their homes and disturb these sensitive species. The Canadian mining company Nautilus Minerals plans to send robots to mine deposits rich in copper and gold in the waters of Papua New Guinea.

Domino's latest technical achievement may well provoke a sense of déjà vu in many customers who used to order pizza by phone. The next time you place an order on the Domino's app or website, you'll be able to talk to a robot that will take the order. DRU Assist is a virtual assistant developed by Domino's in conjunction with voice technology provider Nuance Communications. "DRU Assist is not just a toy. We genuinely see this as a real platform change," Domino's CEO Don Meij said at the company's regular technology showcase in Sydney today.

Domino's has delivered new additions to its Domino's Robotics Unit (DRU) family, announcing the DRU Platform, an artificial intelligence (AI)-based technology that will allow customers to order a pizza using their voice. As of Monday, customers will be able to use DRU Assist, an in-app AI virtual assistant that was built in partnership with natural language company Nuance. DRU Assist takes the pizza giant somewhat back to the start of its digital journey, with customers speaking to order a pizza via their phone. Powered by Nuance's Nina, Domino's DRU Assist engages with customers in human-like conversation via text or speech recognition. "DRU Assist is not just a toy, this is real platform change," Domino's Group CEO and Managing Director Don Meij said.

Conferences, workshops and other meetings provide opportunities to learn where the jobs and technology is headed and a chance to learn and practice the skills necessary to keep up. Why you should attend: The focus of this Summit is "the rise of intelligent machines to make sense of data." "Deep-dive workshops" will give attendees the opportunity to explore specific topics, from natural language processing to pattern recognition. Speakers include development engineers and scientists from top Bay Area companies such as Flickr, Airbnb and Pandora. Why you should attend: The MLconf, which began as a partnership with Carnegie Mellon University's GraphLab, focuses on solutions to organizing and analyzing large, noisy data sets.

Imagine you're part of a great swelling crowd, one of 60,000 people who fill up the cauldron of noise and chaos that is a sold-out football stadium. For you and everyone around you, the game is an open-air gathering place, a chance to steam and scream and worry about nothing except the other team's menacing D. To the security officials responsible for your safety, it is a constant source of worst-case-scenario planning. They install metal detectors; they enlist a kennel's worth of bomb-sniffing dogs; they plant concrete pillars around the perimeter to keep out cars; they train personnel in the dark art of bag searching; they even obtain a temporary flight restriction from the FAA to keep all aircraft above 3,000 feet for a radius of 3 miles. They spend millions of dollars and thousands of hours to keep you safe, yet they know that none of it can stop a 3-pound off-the-shelf drone from flying in and dropping something on the crowd. Whatever it is, you'll never see it coming, and because there is currently no legal way to bring down a drone with any accuracy or reliability, there's nothing anyone can do but wait for it. In the summer of 2015, Ross Lamm and Dave Romero watched just such a scenario unfold from within a skybox at a large university stadium. The head of security for the college, fearful of the damage drones could do, had decided to run a simulation of a drone attack inside his 60,000- capacity football stadium.

SINGAPORE, 22 February 2017 -- In our increasingly digital world, new and emerging innovations are set to disrupt the way people live, work and play. According to youth across the Asia Pacific region, the most exciting technologies expected to have the largest impact on their future lives will be artificial intelligence (AI), virtual/mixed/augmented reality (VR/MR/AR), and Internet of Things (IoT), based on survey findings released today by Microsoft. In the Microsoft Asia Digital Future Survey, 1,400 youth were polled across 14 markets across the Asia Pacific region, comprising Australia, China, Hong Kong, India, Indonesia, Japan, Korea, Malaysia, New Zealand, Philippines, Singapore, Taiwan, Thailand and Vietnam. Artificial intelligence (AI) is ranked as the top technology that youth expect to have the biggest impact on their lives. In recent years, the confluence of power devices, cloud and data has enabled bold visions on how AI can be an integrated part of our digital future.

Microsoft Monday is a weekly column that focuses on updates in regards to the Redmond giant. This week, Microsoft Monday includes details about Windows 10 upgrade revealed, EU still not satisfied with data collection transparency, HoloLens used for designing operating rooms, the launch of Skype Lite, an investment in AirMap, Halo split-screen is returning, new features for the Mail and Calendar apps for Windows 10 and much more! Microsoft has recently announced that a fourth major Windows 10 upgrade is being planned. Microsoft made the announcement at Ignite Australia roughly about two weeks ago. The first three updates are known as November Update, Anniversary Update and Creators Update.