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.

America has a proud tradition of garage inventors, but this one needs to spend more time in the garage.

Mikko Marsio, Vice President of Digital Business and IoT at Empower group, says that what has unfolded over the past two decades and led companies to where they are today can be understood as both an evolution from a technological perspective, as well as a revolution from an industry and business perspective. From the speculative nature of the IT bubble, to the profoundness of the Internet of Things, Marsio explains how consolidating technology with business is now more imperative than ever before. "I remember a prediction that was made before I attended an MIT Executive Education course on the Internet in 2000. It envisioned the Internet becoming like electricity, meaning something that we don't even acknowledge when using," Marsio reminisces. "If you look at what was laid out in 2000 in conjunction with the IT bubble – for example that the best years for the pulp and paper industry were then and there – no one could actually have predicted how many paper mills would be shut down over the following 15 years. In order for these mills to stay relevant, they must adapt what they are producing. Companies in general need to understand how both digitalization and end-users are causing their businesses to change. Over the past few years, increasingly many have come to recognize this," he continues.

While Elon Musk, CEO of SpaceX may have ambitions to set up a colony on Mars, Jeff Bezos, CEO of rival, Amazon, is setting his sights slightly closer to home. In a talk this week, Bezos has revealed that wants to colonise the moon. And his plans don't just involve sending a handful of people to live on the lunar surface, as he has said that he'want(s) to see millions of people living and working in space.' In March, Bezos announced plans to ship packages to the moon by 2020 to help set up a human settlement. He has already reserved a parking spot near the Shackleton Crater on the south pole.

Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from smart home environments. However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts. We show that abstracting the events to a higher-level interpretation can enable discovery of more precise and more comprehensible models. We present a framework for the extraction of features that can be used for abstraction with supervised learning methods that is based on the XES IEEE standard for event logs. This framework can automatically abstract sensor-level events to their interpretation at the human activity level, after training it on training data for which both the sensor and human activity events are known. We demonstrate our abstraction framework on three real-life smart home event logs and show that the process models that can be discovered after abstraction are more precise indeed.