Beginning today, Google Assistant is opening up to new devices around the world. Both Nougat and Marshmallow-powered phones are gaining access to the artificial intelligence platform. Google is making the feature live in select countries to start, so you may not get to use Google Assistant for a couple of days, weeks, and or even months. The first set of people get it are English-speaking users in the United States. After that, English-speaking users in Australia will be next.

A worker picks substrate-grown strawberries at the Driscoll's Inc. facility on the McGrath Ranch in Watsonville, Calif., on Sept. 19, 2016. Buoyed by an inexpensive migrant workforce, California has been the United States' agricultural mainstay for nearly a century, currently producing about 60 percent of the nation's fresh produce. But as the state's minimum wage approaches $15 an hour and competition from a growing Mexican economy mounts, producers face unprecedented operating costs and a workforce that has dropped by 60 percent since the 1990s. Add to this President Trump's moves to restrict immigration, which threatens to significantly curtail the sector's already depressed labor supply. Leading California-based growers like Driscoll's Berries and Taylor Farms are feeling the immediacy of Trump's executive orders, as millions of dollars of specialty crops are growing right now that will require a workforce to pick them at the end of the season. Together they spend over a billion dollars on labor each year.

LG's latest G6 flagship may have become the first non-Pixel phone to have Google's new Assistant feature, but the search giant is focused on bringing its new personal assistant to as many other smartphones as humanly possible. Keeping true to its promise, the Mountain View firm just started rolling out Assistant to smartphones running Android 7.0 Nougat and Android 6.0 Marshmallow versions. With Assistant, you just long press on the Home button or say "Ok Google" to get started. The advanced feature leverages Google's knowledge graph, artificial intelligence and machine learning to better understand context of your questions. Google's conversational artificial intelligence helper debuted at Google I/O in March 2016 on its Pixel smartphones, the Amazon Echo-like Home speaker, in the cross-platform Allo messaging app and as a built-in feature of the latest version of the Android Wear platform.

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.

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.

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.

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.