LJUBLJANA – Electrical equipment-producer Yaskawa, which is building an industrial robot factory in Slovenia, has decided to build another factory in the country to produce electrical components. The new factory will make inverter drives, servo drives and servo motors, Yaskawa said Monday. "Expanding our production capacity will enable us to further improve the supply chain, shorten our lead times and enhance the service for our European customers," Manfred Stern, head of Yaskawa Europe, said in a statement. Yaskawa did not reveal the value of the new investment, but according to local media it will be worth some €25 million ($30 million) and will create up to 250 new jobs. The company already makes industrial robot parts in Slovenia.

The market for cobots is emerging as a fast-growing segment of the rapidly growing industrial robotics market. Demand for cobots is expected to rise at a CAGR of more than 50 percent over the next decade. Although there are some reasons to assume that the acceptance and implementation of cobots will slow growth somewhat, it is clear that the cobot market is rapidly expanding and the number of use cases will continue to rise. The steadily falling prices of components such as sensors make cobots accessible for SMEs. All markets are expected to see growth rates above 50 percent CAGR, but in a regional sense, expectations are especially high for China, because it still lags in the use of robots, when compared to countries like South Korea, Japan, the US, and Germany.

AI in healthcare is developing rapidly, with many applications currently in use or in development in the UK and worldwide. The Nuffield Council on Bioethics examines the current and potential applications of AI in healthcare, and the ethical issues arising from its use, in a new briefing note, Artificial Intelligence (AI) in healthcare and research, published today. There is much hope and excitement surrounding the use of AI in healthcare. It has the potential to make healthcare more efficient and patient-friendly; speed up and reduce errors in diagnosis; help patients manage symptoms or cope with chronic illness; and help avoid human bias and error. But there are some important questions to consider: who is responsible for the decisions made by AI systems?

AI Science assistant helping researchers and companies to accelerate innovation and solve challenges. Creating new flavor combinations is at the core of being a chef. Foodpairing will vastly expand your repertoire by unlocking a whole new world of hidden flavor combinations. Cobrainer's analytics engine makes expertise visible based on internal data to create a unique map of your organization. Robo-journalism and auto-copywriting: produces your texts in over 27 languages at the touch of a button and in perfect tonality.

This time in AI for Business we are looking at the secret to mass personalization & Personalized content with AI. We know that mass personalization and personalized content is a powerful way of influencing consumer behaviour. The increased availability of artificial intelligence for business combined with marketing automation made Mass Personalization, Personalized Content and sophisticated segmentation less costly and faster to implement. So let's get into depth and see how AI can improve your mass personalization strategy… First of all, computers are now able to perform profiling and classification of consumers, based on the data they actively or passively provide, the so-called digital footprints. How do we make these things in a way that we do not interfere with their privacy and deteriorate trust?

Money2020, the largest finance tradeshow in the world, takes place each year in the Venetian Hotel in Las Vegas. At a recent gathering, above the din of slot machines on the casino floor downstairs, cryptocurrency startups pitched their latest coin offerings, while on the main stage, PayPal President and CEO Dan Schulman made an impassioned speech to thousands about the globe's working poor and their need for access to banking and credit. The future, according to PayPal and many other companies, is algorithmic credit scoring, where payments and social media data coupled to machine learning will make lending decisions that another enthusiast argues are "better at picking people than people could ever be." There's now a whiff of a hope that big data might finally shore up the risky business of consumer credit. Credit in China is now in the hands of a company called Alipay, which uses thousands of consumer data points -- including what they purchase, what type of phone they use, what augmented reality games they play, and their friends on social media -- to determine a credit score.

Computer scientists from Italy's Politecnico di Milano created a set of AI-generated game designs that are so good that AI programs analyzing it pegged it as manmade. The team opted to experiment with legendary first-person shooter, DOOM. First released in 1993, DOOM has been a hotbed for experimentation. While the AI-generated DOOM runs on a standard computer, previously, people have figured out how to run the game on toasters, billboard trucks, calculators, and even thermostats. Scientists trained a Generative Adversarial Network (a model based on Artificial Neural Networks) to create new maps for the game.

The accuracy of police facial recognition systems has been criticised by a UK privacy group. Two forces have been testing facial recognition cameras at public events in an effort to catch wanted criminals. Big Brother Watch said its investigation showed the technology was "dangerous and inaccurate" as it had wrongly flagged up a "staggering" number of innocent people as suspects. But police have defended its use and say additional safeguards are in place. Police facial recognition cameras have been trialled at events such as football matches, festivals and parades.

Analysis of reactive-diffusion simulations requires a large number of independent model runs. For each high-fidelity simulation, inputs are varied and the predicted mixing behavior is represented by changes in species concentration. It is then required to discern how the model inputs impact the mixing process. This task is challenging and typically involves interpretation of large model outputs. However, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. In this paper, we present an application of an unsupervised ML method (called NTFk) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal hidden features in product concentration. An attractive aspect of the proposed ML method is that it ensures the extracted features are non-negative, which are important to obtain a meaningful deconstruction of the mixing processes. The ML method is applied to a large set of high-resolution FEM simulations representing reaction-diffusion processes in perturbed vortex-based velocity fields. The applied FEM ensures that species concentration are always non-negative. The simulated reaction is a fast irreversible bimolecular reaction. The reactive-diffusion model input parameters that control mixing include properties of velocity field, anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product. The presented ML analysis allowed us to identify additive features that characterize mixing behavior.

Embedding representation learning via neural networks is at the core foundation of modern similarity based search. While much effort has been put in developing algorithms for learning binary hamming code representations for search efficiency, this still requires a linear scan of the entire dataset per each query and trades off the search accuracy through binarization. To this end, we consider the problem of directly learning a quantizable embedding representation and the sparse binary hash code end-to-end which can be used to construct an efficient hash table not only providing significant search reduction in the number of data but also achieving the state of the art search accuracy outperforming previous state of the art deep metric learning methods. We also show that finding the optimal sparse binary hash code in a mini-batch can be computed exactly in polynomial time by solving a minimum cost flow problem. Our results on Cifar-100 and on ImageNet datasets show the state of the art search accuracy in precision@k and NMI metrics while providing up to 98X and 478X search speedup respectively over exhaustive linear search.