The global security robots market has potential for growth as companies try to establish themselves. Transparency Market Research (TMR) suggests some companies are trying to strengthen their market presence through mergers and acquisitions. Some of the leading companies include Lockheed Martin Corp., BAE Systems PLC, Knight Scope, Boston Dynamics, Northrop Grumman Corp., Liquid Robotics,SMP and Robotic Systems Corp. According to TMR, the global market is likely to reach $3.88 billion by the end of 2025, up from 2016's $1.86 billion. North America made up 30 percent of the global market in 2016, and Europe is likely to experience strong growth as well.

Artificial intelligence (AI) has outperformed doctors at detecting breast cancer in a new study that will further jangle the nerves of medicos, already skittish in the face of a technology whose march into medicine seems unstoppable. The study, led by Babak Ehteshami Bejnordi at Radboud University Medical Centre in the Netherlands, reported the results of the Cancer Metastases in Lymph Nodes Challenge (also known as CAMELYON16), a competition that ran for the 12 months to November 2016. CAMELYON16 threw down the gauntlet to researchers, who had to come up with an automated way of detecting cancer cells in lymph node biopsies from women with breast cancer. During surgery doctors inject a radioactive tracer and blue dye into breast tissue near the tumour, which get funnelled by the lymphatic system to lymph nodes in the armpit. Doctors can then scan the lymph nodes with a Geiger counter, and the naked eye, to find the "hot" blue-coloured node, also called the sentinel node, which is the one the cancer will spread to first.

IMAGE: New machine learning method developed by researchers at the University of Helsinki, Aalto University and Waseda University of Tokyo can use for example data on cell phones while guaranteeing data... view more Modern AI is based on machine learning which creates models by learning from data. Data used in many applications such as health and human behaviour is private and needs protection. New privacy-aware machine learning methods have been developed recently based on the concept of differential privacy. They guarantee that the published model or result can reveal only limited information on each data subject. "Previously you needed one party with unrestricted access to all the data. Our new method enables learning accurate models for example using data on user devices without the need to reveal private information to any outsider", Assistant Professor Antti Honkela of the University of Helsinki says.

Big data, artificial intelligence (AI), Industry 4.0--these terms are thrown around by technologists today, conjuring dystopian images of dark silos of personal data on file, endless automation, and robots running our factories. But what is Industry 4.0? Will Industry 4.0 cut jobs or create them? BusinessBecause caught up with Professor Tobias Meisen, head of the Institute of Information Management in Mechanical Engineering (IMA) at Germany's RWTH Aachen University, who leads a host of new professional courses on Industry 4.0, to find out more. Here's five things you should know about Industry 4.0: A 2011 German government initiative, Industry 4.0 marks a new wave of automation developments in manufacturing, brought about by cutting-edge technologies like big data, artificial intelligence (AI) and the Internet of Things (IoT).

We study the computational complexity of candidate control in elections with few voters, that is, we consider the parameterized complexity of candidate control in elections with respect to the number of voters as a parameter. We consider both the standard scenario of adding and deleting candidates, where one asks whether a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding or deleting few candidates, as well as a combinatorial scenario where adding/deleting a candidate automatically means adding or deleting a whole group of candidates. Considering several fundamental voting rules, our results show that the parameterized complexity of candidate control, with the number of voters as the parameter, is much more varied than in the setting with many voters.

Debate over the existence versus nonexistence of trends in the stellar activity-rotation diagrams continues. Application of modern time series analysis tools to study the mean cycle periods in chromospheric activity index is lacking. We develop such models, based on Gaussian processes, for one-dimensional time series and apply it to the extended Mount Wilson Ca H&K sample. Our main aim is to study how the previously commonly used assumption of strict harmonicity of the stellar cycles affects the results. We introduce three methods of different complexity, starting with the simple harmonic model and followed by Gaussian Process models with periodic and quasi-periodic covariance functions. We confirm the existence of two populations in the activity-period diagram. We find only one significant trend in the inactive population, namely that the cycle periods get shorter with increasing rotation. This is in contrast with earlier studies, that postulate the existence of trends in both of the populations. In terms of rotation to cycle period ratio, our data is consistent with only two activity branches such that the active branch merges together with the transitional one. The retrieved stellar cycles are uniformly distributed over the R'HK activity index, indicating that the operation of stellar large-scale dynamos carries smoothly over the Vaughan-Preston gap. At around the solar activity index, however, indications of a disruption in the cyclic dynamo action are seen. Our study shows that stellar cycle estimates depend significantly on the model applied. Such model-dependent aspects include the improper treatment of linear trends and too simple assumptions of the noise variance model. Assumption of strict harmonicity can result in the appearance of double cyclicities that seem more likely to be explained by the quasi-periodicity of the cycles.

Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability. They use to work as intuition machines with high statistical confidence but unable to give interpretable explanations about the reported results. The vast amount of parameters of these models make difficult to infer a rationale interpretation from them. In this paper we present a diabetic retinopathy interpretable classifier able to classify retine images into the different levels of disease severity and of explaining its results by assigning a score for every point in the hidden and input space, evaluating its contribution to the final classification in a linear way. The generated visual maps can be interpreted by an expert in order to compare its own knowledge with the interpretation given by the model. Keywords: deep learning, classification, explanations, diabetic retinopathy, model interpretation 2010 MSC: 68T10 1. Introduction Deep Learning methods have been used extensively in the last years for many automatic classification tasks. For the case of image analysis, the usual procedure consists on extracting the important features with a set of convolutional layers and, after that, make a final classification with these features using a set of fully connected layers. Finally, a soft-max output layer gives as a result the predicted output probabilities of the set of classes predefined in the model. Once the classifier has been trained (i.e. the parameters of the different layers of the model have been fixed), the quality of the classification outputs predicted is compared against the correct "true" values stored on a labeled dataset. This data is considered as the gold standard, ideally coming from the consensus of the knowledge of a human experts committee. This mapping allows the classification of multidimensional objects into a small number of categories. The model is composed by many neurons that are organized in layers and blocks of layers, piled together in a hierarchical way.

Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accurately predicting the probability of a customer defecting. The predictive churn models are then commonly selected based on accuracy related performance measures such as the area under the ROC curve (AUC). However, these models are often not well aligned with the core business requirement of profit maximization, in the sense that, the models fail to take into account not only misclassification costs, but also the benefits originating from a correct classification. Therefore, the aim is to construct churn prediction models that are profitable and preferably interpretable too. The recently developed expected maximum profit measure for customer churn (EMPC) has been proposed in order to select the most profitable churn model. We present a new classifier that integrates the EMPC metric directly into the model construction. Our technique, called ProfTree, uses an evolutionary algorithm for learning profit driven decision trees. In a benchmark study with real-life data sets from various telecommunication service providers, we show that ProfTree achieves significant profit improvements compared to classic accuracy driven tree-based methods.

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate "worldviews" on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of "we're all equal" (WAE) and "what you see is what you get" (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (college admissions; credit application; insurance contracts) and map out the policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence.

Biomonitoring of waterbodies is vital as the number of anthropogenic stressors on aquatic ecosystems keeps growing. However, the continuous decrease in funding makes it impossible to meet monitoring goals or sustain traditional manual sample processing. In this paper, we review what kind of statistical tools can be used to enhance the cost efficiency of biomonitoring: We explore automated identification of freshwater macroinvertebrates which are used as one indicator group in biomonitoring of aquatic ecosystems. We present the first classification results of a new imaging system producing multiple images per specimen. Moreover, these results are compared with the results of human experts. On a data set of 29 taxonomical groups, automated classification produces a higher average accuracy than human experts.