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."

Amazon has a problem, and that problem is humans. Amazon needs humans, lots of them. But humans, as we all know, are the most unreasonable part of any business, constantly demanding things like lights and air. So Amazon has turned to robots (over 100,000 of them) for doing tasks like moving things around in a warehouse. But it's proving to be much more difficult to get the robots to do some other tasks.

Nintendo says the retro version of one of its iconic video game platforms is making a comeback in 2019. The 8-bit NES Classic went on sale last year selling 1.5 million units, but the Japanese gaming firm shocked fans by mysteriously pulling the plug on production in April. The company now says its NES Classic Edition will now return next summer due to popular demand. Nintendo says the retro version of one of its iconic video game platforms will return to stores next year. The Japanese announced in April that it had ended production of the nostalgic NES classic, re-released in November 2016 and selling around 1.5 million units The Nintendo Entertainment System, or NES, was an 8-bit home video game console initially released in Japan as the Family Computer, or Famicom, on July 15, 1983.

Vadim Berman is director of engineering at Aspect Software. He came to Aspect with the acquisition of LinguaSys in 2015. Vadim co-founded LinguaSys in 2010 and was the chief technology officer. He recently moved to Massachusetts from Melbourne, Australia. Give a journalist a buzzword and you've fed him for a day.