Winemaking contributes over A$ 40 billion to the Australian economy each year. Among the many challenges being faced by this centuries-old industry are managing pests and diseases, producing a consistent crop and using water efficiently. According to the report made by the University of Melbourne, artificial intelligence and machine learning have the potential to change the face of winemaking. Dr Sigfredo Fuentes is a plant physiologist and agronomist at the University of Melbourne. He explained how they are able to make use of cutting-edge technology to keep wine racks stocked with high-quality drinks.

The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the motion of a group of objects can even be a challenging task for experts, e.g., in forensics science. Most of the work in the literature relies on physics simulation to draw such infer- ences. The simulation requires a precise model of the under- lying domain to work well and is essentially a black-box from which one can hardly obtain any useful explanation. By contrast, qualitative reasoning methods have the advan- tage in making transparent inferences with ambiguous infor- mation, which makes it suitable for this task. However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space. In this paper, we take this challenge and develop a qualitative theory for the motion of rigid objects. Based on this theory, we develop a reasoning method to solve a very interesting problem: Assuming there are several objects that were initially at rest and now have started to move. We want to infer what action causes the movement of these objects.

This technology could be put in smartphones that understand and predict our behaviour, potentially offering personalised support. They could also be used by robot companions for older people, or in self-driving cars and interactive video games. Dr Loetscher says the findings also provide an important bridge between tightly controlled laboratory studies and the study of natural eye movements in real-world environments. 'This research has tracked and measured the visual behaviour of people going about their everyday tasks, providing more natural responses than if they were in a lab. 'And thanks to our machine-learning approach, we not only validate the role of personality in explaining eye movement in everyday life, but also reveal new eye movement characteristics as predictors of personality traits.' 'Personality traits characterise an individual's patterns of behaviour, thinking, and feeling', researchers wrote previously in their paper published in Frontiers in Human Neuroscience. 'Studies reporting relationships between personality traits and eye movements suggest that people with similar traits tend to move their eyes in similar ways.' Researchers found that people who were neurotic usually blinked faster while people who were open to new experiences moved their eyes more from side-to-side. People who had high levels of conscientiousness had greater fluctuations in their pupil size. Optimists spent less time looking at negative emotional stimuli (such as image of skin cancer) than people who were pessimistic.

Researchers have revealed an AI that they hope will one day write a perfect Shakespearean sonnet. Researchers at IBM used created a sonnet writing AI. Researchers at IBM used created a sonnet writing AI. Sonnets come in many different variations but they always have 14 lines and usually include a regular rhyme scheme of some sort. Shakespeare pioneered his own version in which the lines were divided into 3 verses (or stanzas) of four lines (quatrains), finished with a rhyming couplet.

In January, Amazon opened Amazon Go, a high-tech, cashierless convenience store in Seattle. There are no checkout lines and few employees. The only requirement to shop is downloading an app. Customers just walk in, load up their bags, and go. There's no need to even scan purchases; cameras positioned overhead take note of items in customers' carts and add them to a virtual bill. Amazon Go is both an interesting novelty -- and a profound challenge to the livelihoods of the more than 3.5 million Americans who work as cashiers. Rumors of a coming wave of similar stores and robot-run factories have provoked apocalyptic predictions of mass unemployment among pundits and politicians.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Japan's edition of Hebocon (the "best worst robot competition") took place at the end of last month, I think, and while we hope to get a bunch of really good videos at some point, here's a tiny teaser fight that's too good not to share. On the left, we have "Transparent Robot," which consists mostly of a GoPro pointing backwards, and a phone that displays the video feed from the GoPro pointing forwards, making the robot "invisible."

The scorching heatwave gripping northern Europe was made twice as likely to happen by climate change, scientists have revealed. An initial assessment of the prolonged period of record-breaking hot weather suggests rising temperatures caused by human activity increased the odds of it happening. The preliminary research claims to have found'unambiguous' evidence that human interference has triggered the recent heatwave, which computer models predict will continue until the end of August. People enjoy the Bournemouth beach in Dorset, England, as the hot weather continues across Britain. Britain is experiencing a severe heatwave which has prompted its national weather service to issue an alert for people to'stay out of the sun' 'The logic that climate change will do this is inescapable – the world is becoming warmer and so heatwaves like this are becoming more common,' said one of the authors Dr Friederike Otto, deputy director of the Environmental Change Institute at the University of Oxford and and part of the World Weather Attribution (WWA) consortium that did the latest research.

It's often been said that the eyes are the window to the soul, revealing what we think and how we feel. Now, new research reveals that your eyes may also be an indicator of your personality type, simply by the way they move. Developed by the University of South Australia in partnership with the University of Stuttgart, Flinders University and the Max Planck Institute for Informatics in Germany, the research uses state-of-the-art machine-learning algorithms to demonstrate a link between personality and eye movements. Findings show that people's eye movements reveal whether they are sociable, conscientious or curious, with the algorithm software reliably recognising four of the Big Five personality traits: neuroticism, extroversion, agreeableness, and conscientiousness. Researchers tracked the eye movements of 42 participants as they undertook everyday tasks around a university campus, and subsequently assessed their personality traits using well-established questionnaires. UniSA's Dr Tobias Loetscher says the study provides new links between previously under-investigated eye movements and personality traits and delivers important insights for emerging fields of social signal processing and social robotics.

The state of the art practice in robotics planning is to script behaviors manually, where each behavior is typically generated using trajectory optimization. However, in order for robots to be able to act robustly and adapt to novel situations, they need to plan these activity sequences autonomously. Since the conditions and effects of these behaviors are tightly coupled through time, state and control variables, many problems require that the tasks of activity planning and trajectory optimization are considered together. There are two key issues underlying effective hybrid activity and trajectory planning: the sufficiently accurate modeling of robot dynamics and the capability of planning over long horizons. Hybrid activity and trajectory planners that employ mixed integer programming within a discrete time formulation are able to accurately model complex dynamics for robot vehicles, but are often restricted to relatively short horizons. On the other hand, current hybrid activity planners that employ continuous time formulations can handle longer horizons but they only allow actions to have continuous effects with constant rate of change, and restrict the allowed state constraints to linear inequalities. This is insufficient for many robotic applications and it greatly limits the expressivity of the problems that these approaches can solve. In this work we present the ScottyActivity planner, that is able to generate practical hybrid activity and motion plans over long horizons by employing recent methods in convex optimization combined with methods for planning with relaxed plan graphs and heuristic forward search. Unlike other continuous time planners, ScottyActivity can solve a broad class of robotic planning problems by supporting convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. In order to support planning over long horizons, ScottyActivity does not resort to time, state or control variable discretization. While straightforward formulations of consistency checks are not convex and do not scale, we present an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We also introduce several new realistic domains that demonstrate the capabilities and scalability of our approach, and their simplified linear versions, that we use to compare with other state of the art planners. This work demonstrates the power of integrating advanced convex optimization techniques with discrete search methods and paves the way for extensions dealing with non-convex disjoint constraints, such as obstacle avoidance.

Recently, self-normalizing neural networks (SNNs) have been proposed with the intention to avoid batch or weight normalization. The key step in SNNs is to properly scale the exponential linear unit (referred to as SELU) to inherently incorporate normalization based on central limit theory. SELU is a monotonically increasing function, where it has an approximately constant negative output for large negative input. In this work, we propose a new activation function to break the monotonicity property of SELU while still preserving the self-normalizing property. Differently from SELU, the new function introduces a bump-shaped function in the region of negative input by regularizing a linear function with a scaled exponential function, which is referred to as a scaled exponentially-regularized linear unit (SERLU). The bump-shaped function has approximately zero response to large negative input while being able to push the output of SERLU towards zero mean statistically. To effectively combat over-fitting, we develop a so-called shift-dropout for SERLU, which includes standard dropout as a special case. Experimental results on MNIST, CIFAR10 and CIFAR100 show that SERLU-based neural networks provide consistently promising results in comparison to other 5 activation functions including ELU, SELU, Swish, Leakly ReLU and ReLU.