A new software system developed by a European Union-funded research project can determine if industrial machinery requires maintenance based on the sounds it makes. A European Union-funded research project has developed software based on the human auditory system that can analyze sound to determine if industrial machinery requires maintenance. The Horizon2020 neuronSW team integrated advanced algorithms, machine learning, and big data analysis to mimic the human auditory cortex and enable early detection and prediction of mechanical breakdowns. Said SME NeuronSW Ltd.'s Jiri Cermak, "The technology leverages machine learning, the cloud, and the Internet of Things to deliver a detection service which emulates human intuition about sound." The neuronSW solution lets manufacturers perform intelligent audio diagnostics and monitor key pieces of machinery by the sounds they generate.

Artificial intelligence (AI) is emerging as a key tool in just about every industry, from marketing to recruitment and beyond. But one particularly powerful application for AI is in health care, where we're already seeing early signs of its potential. Iowa-based Idx is one startup using AI to detect early signs of specific medical conditions. Its first system, IDx-DR, is an AI diagnostic system that analyzes images of the retina for signs of diabetic retinopathy, a complication of diabetes caused by high sugar levels. This means that health care providers, including doctors who are not eye care specialists, can use the IDx-DR system to detect diabetic retinopathy without needing to bring in a specialist clinician to interpret the image scan or results.

I first entered the informatics field in the late 1980s, at the tail end of the first era of artificial intelligence (AI) in medicine. Initial systems focused on making medical diagnoses using symbolic processing, which was appropriate for a time of relatively little digital data, both for individual patients and healthcare as whole, and underpowered hardware. Systems like MYCIN [1], INTERNIST-1/QMR [2], and DXPLAIN [3] provided relatively accurate diagnostic performance, but were slow and difficult to use. They also provided a single likely diagnosis, which was not really what clinicians needed. Because of these shortcomings, they never achieved significant real-world adoption, and their "Greek Oracle" style of approach was abandoned. There was also some early enthusiasm for neural networks around that time [5], although in retrospect those systems were hampered by lack of data and computing power.

In this paper we study the problem of making predictions using multiple structural casual models defined by different agents, under the constraint that the prediction satisfies the criterion of counterfactual fairness. Relying on the frameworks of causality, fairness and opinion pooling, we build upon and extend previous work focusing on the qualitative aggregation of causal Bayesian networks and causal models. In order to complement previous qualitative results, we devise a method based on Monte Carlo simulations. This method enables a decision-maker to aggregate the outputs of the causal models provided by different experts while guaranteeing the counterfactual fairness of the result. We demonstrate our approach on a simple, yet illustrative, toy case study.

The scikit tree does a good job of representing the tree structure, but we have a few quibbles. The colors aren't the best and it's not immediately obvious why some of the nodes are colored and some aren't. If the colors represent predicted class for this classifier, one would think just the leaves would be colored because only leaves have predictions. The count of samples of the various target classes in each node is somewhat useful, but a histogram would be even better. A target class color legend would be nice.

Diagnosis of such problems before the baby is born, allowing for prompt treatment within a week after birth, is known to markedly improve the prognosis, so there have been many attempts to develop technology to enables accurate and rapid diagnosis. However, today, fetal diagnosis depends heavily on observations by experienced examiners using ultrasound imaging, so it is unfortunately not uncommon for children to be born without having been properly diagnosed. In recent years, machine learning techniques such as deep learning have been developing rapidly, and there is great interest in the adoption of machine learning for medical applications. Machine learning can allow diagnostic systems to detect diseases more rapidly and accurately than human beings, but this requires the availability of adequate datasets on normal and abnormal subjects for a certain disease. Unfortunately, however, since congenital heart problems in children are relatively rare, there are no complete datasets, and up until now, prediction based on machine learning was not accurate enough for practical use in the clinic.

The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway may be of interest as well. For example, we may wish to maximize the chemical effect of a drug given data from an observational study where the chemical effect of the drug on the outcome is entangled with the indirect effect mediated by differential adherence. In such cases, we may wish to optimize the direct effect of a drug, while keeping the indirect effect to that of some reference treatment. [16] shows how to combine mediation analysis and dynamic treatment regime ideas to defines policies associated with causal pathways and counterfactual responses to these policies. In this paper, we derive a variety of methods for learning high quality policies of this type from data, in a causal model corresponding to a longitudinal setting of practical importance. We illustrate our methods via a dataset of HIV patients undergoing therapy, gathered in the Nigerian PEPFAR program.

Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods.

Industrial robots play an important role in manufacturing process. Since robots are usually set up in parallel-serial settings, breakdown of a single robot has a negative effect on the entire manufacturing process in that it slows down the process. Therefore, fault diagnostic systems based on the internal signals of robots have gained a lot of attention as essential components of the services provided for industrial robots. The current work in fault diagnostic algorithms extract features from the internal signals of the robot while the robot is healthy in order to build a model representing the normal robot behavior. During the test, the extracted features are compared to the normal behavior for detecting any deviation. The main challenge with the existing fault diagnostic algorithms is that when the task of the robot changes, the extracted features differ from those of the normal behavior. As a result, the algorithm raises false alarm. To eliminate the false alarm, fault diagnostic algorithms require the model to be retrained with normal data of the new task. In this paper, domain adaptation, {\it a.k.a} transfer learning, is used to transfer the knowledge of the trained model from one task to another in order to prevent the need for retraining and to eliminate the false alarm. The results of the proposed algorithm on real dataset show the ability of the domain adaptation in distinguishing the operation change from the mechanical condition change.

In 2018, FireEye – a company based in California – informed Facebook and Google about a large group of fake Iranian social media accounts that was running movements to control the U.S people. As a result, Facebook and Google identified them, along with fake YouTube channels and blogs, using back-end data and then removed them. "Right now, you know something's automated just by the sheer volume of content pushing out," Lee Foster, information operations manager at FireEye, says. "It's not possible for a human to do this, so it's clearly not organically created. Often you'll see automated retweeting of some list of accounts that just to boost out a message. But the situation is about to take a new turn, he claims, as Artificial Intelligence (AI) system that covers its automated heritage is available now. "Imagine having a capability out there that can automate the organic creation of original content effectively enough that it looks real, but you don't even have to have it operate or touch it," Foster says. "In the very near term, the evolution of AI and machine learning, combined with the increasing availability of big data, will begin to transform human communication and interaction in the digital space.