The term "industrial Internet of Things" has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. But the focus of IIoT -- on industry at large -- is broader. Here, we take a comprehensive view, rounding up 20 IIoT leaders and pioneers, drawing on the feedback from industry analysts and consultants. The focus here is not on vendors offering, say, a cloud-based platform for monitoring industrial machines but on the companies that themselves are using IIoT technology to drive their business forward. For the sake of this feature, we focus on organizations that use connected technology in tandem with cloud-based analytics to drive efficiencies and launch new business models.

This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility on her own opinion, and show that this general model is analogous to the Friedkin-Johnsen model, which assumes a constant susceptibility for each individual. We then consider two specific functions in which the individual's susceptibility depends on the \emph{polarity} of her opinion, and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. A court jury is used as a motivating example. Second, we consider stubborn neutrals, who have reduced susceptibility when their opinions are in the middle of the spectrum, and our motivating examples are social networks discussing established social norms or institutionalized behavior. For each specific susceptibility model, we establish the initial and graph topology conditions in which consensus is reached, and develop necessary and sufficient conditions on the initial conditions for the final consensus value to be at either extreme of the opinion interval. Simulations are provided to show the effects of the susceptibility function when compared to the DeGroot model.

If you've got a friend you're dying to set up on a date, then there's good news as a new online dating app lets you play'virtual Cupid.' Hinge has unveiled a new standalone app called Hinge Matchmaker that lets people help their single friends to find love by suggesting matches based on your Facebook friends. The beta test version of the app is free to download, and is available to all Hinge markets in the UK, US, Canada, Australia and India. Hinge Matchmaker gives people in a relationship a chance to set up their single friends, based on your Facebook friends who have a profile on Hinge. As the matchmaker, the power is left in your hands to decide whether your friend should connect on Hinge, with the option to send an icebreaker message to get the conversation started. As well as randomly recommending potential couples, Hinge Matchmaker also has an option for you to take control and select a specific friend you'd like to help by selecting the'lock' option on their profile.

The power of machine learning, data science and big data is no longer considered a hype or fad. Data has emerged as a real competitive edge and disruptive force for enterprises. Companies that make the most of data by using machine learning and data science will win and outlast in this digital world. IBM recently extended its partnership with Hortonworks to better help businesses accelerate data-driven decision making. Hortonworks is the leading industry and only pure open source Hadoop platform.

ETF Securities Australia has launched an exchange-traded product on the ASX that provides access to global robotics, artificial intelligence and automation stocks. The ETF Securities ROBO Global Robotics and Automation ETF (ROBO), which tracks the ROBO Global Robotics and Automation Index, began trading on the ASX yesterday. According to a statement by ETF Securities, the robotics economy is estimated to be worth US$1.2 trillion by 2025, driven by demand for higher productivity and applications in various industries. Commenting on the launch of the ETF, head of ETF Securities Australia Kris Walesby said, "The robotics and automation industries are part of a global megatrend which is expected to outperform the broader market in coming decades. "ROBO Global is the pioneer in this area, having created the first robotics and automation ETF on the NASDAQ in 2013, and continues to work with a strategic advisory team including leading robotics experts.

Abstract-- An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than twofold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of 0.9 achieved for apples and mangoes. I. INTRODUCTION Vision based fruit detection is a critical component for infield automation in agriculture. With accurate knowledge of individual fruit locations in the field, it is possible to perform yield estimation and mapping, which is important for growers as it facilitates efficient utilisation of resources and improves returns per unit area and time. Precise localisation of the fruit is also a necessary component of an automated robotic harvesting system, which can help mitigate one of the most labour intensive tasks in an orchard [1].

Artificial intelligence has jumped from sci-fi movie plots into mainstream news headlines in just a couple of years. And the headlines are often contradictory. AI is either a technological leap into greater prosperity or mass unemployment; it will either be our most valuable servant or terrifying master. But what is AI, how does it work, and what are the benefits and the concerns? AI is a computer system that can do tasks that humans need intelligence to do. "An intelligent computer system could be as simple as a program that plays chess or as complex as a driverless car," Mary-Anne Williams, professor of social robotics at the University of Technology, Sydney, said.

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Snuffling through the underbrush, the shaggy little creature wanders through the sylvan night, sticking its nose in one place, then another, seeking the aroma of its soft-bodied dinner. The forest is dark and the pixie's eyesight poor, but long whiskers and a keen sense of smell allow it to get around. Threatened, it takes off at breakneck speed, barreling through the vegetation, ducking through holes, soon lost from sight. Many animals spend their nights cruising the forest floor, searching for small prey in a similar fashion: Hedgehogs, shrews, weasels, to name a few, and bigger ones, too, like opossums and even pigs. The world is full of them. But this one is different. All the others are hairy. This one's pelage is also soft, made up of millions of thin strands. All the others move about on four legs and bear live young. And as the male calls, he identifies himself: "Kee-wee, kee-wee."

Game studios and enthusiasts may soon have a new tool at their disposal to speed up game development and experiment with different styles of play. Georgia Institute of Technology researchers have developed a new approach using an artificial intelligence to learn a complete game engine, the basic software of a game that governs everything from character movement to rendering graphics. Their AI system watches less than two minutes of gameplay video and then builds its own model of how the game operates by studying the frames and making predictions of future events, such as what path a character will choose or how enemies might react. To get their AI agent to create an accurate predictive model that could account for all the physics of a 2D platform-style game, the team trained the AI on a single "speedrunner" video, where a player heads straight for the goal. This made "the training problem for the AI as difficult as possible."