Imagery of the world being taken over by murderous machines that watch over, enslave, and eradicate humanity has been a pop-culture mainstay since the 19th century. These days, however, those clichés and metaphors are set aside for very real fears that AI might one day come along and relieve us of our livelihoods. After all, AI can now drive cars, fly aircraft, help to perform surgery, translate language, write articles... It can even assist in the design of new, more powerful AI-equipped machines, turning the problem into a self-propagating virus for those who fear that one day, they will be replaced. These fears may or may not be rational, but one thing cannot be denied: Artificial intelligence, in its wide spectrum of applications, is already changing the world around us.

In 2013, some folks from Rob Wood's lab at Harvard, including then-postdoc Sawyer Buckminster Fuller, published a paper in Science introducing a (mostly) controllable version RoboBee, an insect-size flying robot that could lift itself, hover, and move around a bit using two flapping wings. Since then, there have been several more generations of RoboBee, including this nutty explosive diving one. The problem with robots at this scale, and especially flying robots at this scale, is energy storage. It takes a lot of oomph to lift off of the ground and stay there, which means that high power is necessary, which means a relatively big battery to provide that power for a significant amount of time, which means a heavier robot over, which means more power is required to lift off, and you can see what the problem is. Fuller has since moved on to a professorship at the University of Washington, where he's been working on ways of solving this problem of power autonomy.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

It was a big deal two and a half years ago when researchers shrunk down an image-recognition program to fit onto a $5 computer the size of a candy bar -- and now it's an even bigger deal for Xnor.ai to re-engineer its artificial intelligence software to fit onto a solar-powered computer chip. "To us, this is as big as when somebody invented a light bulb," Xnor.ai's co-founder, Ali Farhadi, said at the company's Seattle headquarters. Like the candy-bar-sized, Raspberry Pi-powered contraption, the camera-equipped chip flashes a signal when it sees a person standing in front of it. The point is that Xnor.ai has figured out how to blend stand-alone, solar-powered hardware and edge-based AI to turn its vision of "artificial intelligence at your fingertips" into a reality. "This is a key technology milestone, not a product," Farhadi explained.

Wind power, as an alternative to burning fossil fuels, is plentiful and renewable. Data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this paper, we propose a novel classification-based anomaly detection system for icing detection of the wind turbine blades. We effectively combine the deep neural networks and wavelet transformation to identify such failures sequentially across the time. In the training phase, we present a wavelet based fully convolutional neural network (FCNN), namely WaveletFCNN, for the time series classification. We improve the original (FCNN) by augmenting features with the wavelet coefficients. WaveletFCNN outperforms the state-of-the-art FCNN for the univariate time series classification on the UCR time series archive benchmarks. In the detecting phase, we combine the sliding window and majority vote algorithms to provide the timely monitoring of the anomalies. The system has been successfully implemented on a real-world dataset from Goldwind Inc, where the classifier is trained on a multivariate time series dataset and the monitoring algorithm is implemented to capture the abnormal condition on signals from a wind farm.

Quantum Clustering is a powerful method to detect clusters in data with mixed density. However, it is very sensitive to a length parameter that is inherent to the Schr\"odinger equation. In addition, linking data points into clusters requires local estimates of covariance that are also controlled by length parameters. This raises the question of how to adjust the control parameters of the Schr\"odinger equation for optimal clustering. We propose a probabilistic framework that provides an objective function for the goodness-of-fit to the data, enabling the control parameters to be optimised within a Bayesian framework. This naturally yields probabilities of cluster membership and data partitions with specific numbers of clusters. The proposed framework is tested on real and synthetic data sets, assessing its validity by measuring concordance with known data structure by means of the Jaccard score (JS). This work also proposes an objective way to measure performance in unsupervised learning that correlates very well with JS.

Last night, NASA reached out one final time to the Opportunity rover on Mars, hoping the golf-cart-sized machine would phone home with good news. Since June, the robot has been unresponsive, likely because a planet-wide sandstorm coated its solar panels in dust. NASA has pinged it over 1,000 times in those gloomy eight months, to no avail. Last night's attempt was no exception: NASA has announced that Opportunity is officially dead. "I was there yesterday and I was there with the team as these commands went out into the deep sky," said NASA associate administrator Thomas Zurbuchen in a briefing this morning, titled A Lifetime of Opportunity.

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

A digital twin is a digital replica of a living or non-living physical entity.[1] By bridging the physical and the virtual world, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical entity. Digital twin refers to a digital replica of physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes.[2] The digital representation provides both the elements and the dynamics of how an Internet of things device operates and lives throughout its life cycle.[3] Definitions of digital twin technology used in prior research emphasize two important characteristics. Firstly, each definition emphasizes the connection between the physical model and the corresponding virtual model or virtual counterpart[4]. Secondly, this connection is established by generating real time data using sensors. Digital twins integrate internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs[5] to create living digital simulation models that update and change as their physical counterparts change.

Variational inference (VI) is a widely used framework in Bayesian estimation. For most of the non-Gaussian statistical models, it is infeasible to find an analytically tractable solution to estimate the posterior distributions of the parameters. Recently, an improved framework, namely the extended variational inference (EVI), has been introduced and applied to derive analytically tractable solution by employing lower-bound approximation to the variational objective function. Two conditions required for EVI implementation, namely the weak condition and the strong condition, are discussed and compared in this paper. In practical implementation, the convergence of the EVI depends on the selection of the lower-bound approximation, no matter with the weak condition or the strong condition. In general, two approximation strategies, the single lower-bound (SLB) approximation and the multiple lower-bounds (MLB) approximation, can be applied to carry out the lower-bound approximation. To clarify the differences between the SLB and the MLB, we will also discuss the convergence properties of the aforementioned two approximations. Extensive comparisons are made based on some existing EVI-based non-Gaussian statistical models. Theoretical analysis are conducted to demonstrate the differences between the weak and the strong conditions. Qualitative and quantitative experimental results are presented to show the advantages of the SLB approximation.