Online volunteers are helping to track slavery from space. A new crowdsourcing project aims to identify South Asian brick kilns – frequently the site of forced labour – in satellite images. This data will then be used to train machine learning algorithms to automatically recognise brick kilns in satellite imagery. If computers can pinpoint the location of possible slavery sites, then the coordinates could be passed to local non-governmental organisations to investigate, says Kevin Bales, who is leading the project at the University of Nottingham in the UK. South Asian brick kilns are notorious sites of modern-day slavery.

When applied to previously-collected atmospheric samples and data, their findings support evidence that on average these bioaerosols globally make up less than 1 percent of the particles in the upper troposphere -- where they could influence cloud formation and by extension, the climate -- and not around 25 to 50 percent as some previous research suggests. While atmospheric and climate modeling suggests that bioaerosols, globally averaged, are not abundant and efficient enough at freezing to significantly influence cloud formation, research findings have varied significantly. The group leveraged the presence of phosphorus in the mass spectra to train the classification machine learning algorithm on known samples and then, primed, applied it to field data acquired from Desert Research Institute's Storm Peak Laboratory in Steamboat Springs, Colorado, and from the Carbonaceous Aerosol and Radiative Effects Study based in the town of Cool, California. Knowing that the principal atmospheric emissions of phosphorus are from mineral dust, combustion products, and biological particles, they exploited the presence of phosphate and organic nitrogen ions and their characteristic ratios in known samples to classify the particles.

The suckers of an octopus have inspired the creation of a new adhesive patch that can stick to wet and dry surfaces. After studying the anatomy of an octopus' tentacle, the scientists made a patch created from flexible sheets of rubber covered in artificial suction cups. The sticky patches could one day be used to create wound dressings that can easily be taken on and off and may even inspire a generation of Spiderman-style robots that can scale walls, according to researchers. After studying the anatomy of an octopus' tentacle, the scientists made a patch created from flexible sheets of rubber covered in tiny holes. To create the adhesive, the scientists concentrated on recreating a dome-shaped bulge found at the bottom of the octopus' suction patch.

Machines won't become intelligent unless they incorporate certain features of the human brain. Europe's massive €1 billion project has shifted focus from simulation to informatics By Megan Scudellari Large-scale brainlike systems are possible with existing technology--if we're willing to spend the money By Jennifer Hasler Researchers in this specialized field have hitched their wagon to deep learning's star By Lee Gomes Running algorithms that mimic a rat's navigation neurons, heavy machines will soon plumb Australia's underground mines By Jean Kumagai Artificial intelligence might endow some computers with self-awareness.

For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (#AI) technologies, which enable'smart machines' to simulate intelligent behavior and make well-informed decisions with little or no human intervention. Let's start by defining both terms first: IoT is defined as a system of interrelated Physical Objects, Sensors, Actuators, Virtual Objects, People, Services, Platforms, and Networks that have separate identifiers and an ability to transfer data independently. By applying the analytic capabilities of AI to data collected by IoT, companies can identify and understand patterns and make more informed decisions. Scientists are trying to find ways to make more intelligent data analysis software and devices in order to make safe and effective IoT a reality.

We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. When the objective is convex, the proposed approach enjoys the same properties as the Catalyst approach of Lin et al. [22]. When the objective is nonconvex, it achieves the best known convergence rate to stationary points for first-order methods. Specifically, the proposed algorithm does not require knowledge about the convexity of the objective; yet, it obtains an overall worst-case efficiency of $\tilde{O}(\varepsilon^{-2})$ and, if the function is convex, the complexity reduces to the near-optimal rate $\tilde{O}(\varepsilon^{-2/3})$. We conclude the paper by showing promising experimental results obtained by applying the proposed approach to SVRG and SAGA for sparse matrix factorization and for learning neural networks.

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis.

Recently we attended the Unearthed Data Science event in Melbourne. A gold mining company -- Newcrest Mining -- provided operating data for a number of its plants, with the aim that some of the teams attending could provide useful solutions grounded in Data Science. One particular system caught our eye -- the autoclaves. This ore is rich is sulphide minerals (sulfide if you're American) such as iron pyrite (FeS2) (aka "Fool's Gold"). Sulphides inhibit the processing techniques used to extract gold from ores, so it's ideal if you can get rid of them.

If you take a common brown rat and drop it into a lab maze or a subway tunnel, it will immediately begin to explore its surroundings, sniffing around the edges, brushing its whiskers against surfaces, peering around corners and obstacles. After a while, it will return to where it started, and from then on, it will treat the explored terrain as familiar. Roboticists have long dreamed of giving their creations similar navigation skills. To be useful in our environments, robots must be able to find their way around on their own. Some are already learning to do that in homes, offices, warehouses, hospitals, hotels, and, in the case of self-driving cars, entire cities. Despite the progress, though, these robotic platforms still struggle to operate reliably under even mildly challenging conditions. Self-driving vehicles, for example, may come equipped with sophisticated sensors and detailed maps of the road ahead, and yet human drivers still have to take control in heavy rain or snow, or at night.

Building things with robots is a nice idea, especially if robots are doing what they're best at: predictable, repetitive tasks like you get with bricklaying. When humans build structures, however, we can be a bit more creative, adapting on the fly to the sizes and shapes of materials available. This is one of those robotic paradoxes--building something that's easy for robots, like an exactly spaced curvy brick wall, is tricky for humans, while building something that's easy for humans, like a wall made out of pile of random rocks that doesn't spontaneously fall over, is tricky for robots. At ICRA this week, researchers from ETH Zurich are presenting a robot that's able to handle some of that variability that humans are so good at effortlessly coping with. With careful planning and a delicate touch, this robot arm is learning to autonomously build towers out of balanced pieces of limestone.