Medicine has provided the field of artificial intelligence with a plethora of challenging and appealing problems to be solved, particularly in clinical diagnosis ("given a set of signs collected from a patient, select the best diagnosis") and in therapy advice ("given an established diagnosis, select the best course of actions for treatment"). Artificial intelligence, in turn, has offered promising technologies for problem solving in the medical domain [7]. The field of oncology has proven to be particularly fit for modelling and analysis based on artificial intelligence, at least prospectively [5, 3], due to two major reasons: 1. Symptoms in oncology are frequently difficult to identify before later stages of the disease, and cancer can be treated most effectively if identified at early stages of development. Signs of the disease can be diffuse and require high expertise to be selected, collected and analysed. Hence, technologies that can highlight evidence of cancer at early stages are most welcome and challenging at the same time.

Nowadays all industrial sectors are increasingly faced with the explosion in the amount of data. Therefore, it raises the question of the efficient use of this large amount of data. In this research work, we are concerned with process and product traceability data. In some sectors (e.g. pharmaceutical and agro-food), the collection and storage of these data are required. Beyond this constraint (regulatory and / or contractual), we are interested in the use of these data for continuous improvements of industrial performances. Two research axes were identified: product recall and responsiveness towards production hazards. For the first axis, a procedure for product recall exploiting traceability data will be propose. The development of detection and prognosis functions combining process and product data is envisaged for the second axis.

Decision trees, as the name implies, are trees of decisions. You have a question, usually a yes or no (binary; 2 options) question with two branches (yes and no) leading out of the tree. You can get more options than 2, but for this article, we're only using 2 options. Trees are weird in computer science. Instead of growing from a root upwards, they grow downwards.

The Hong Kong Polytechnic University (PolyU) recently developed an intelligent fabric defect detection system, called "WiseEye," which leverages advanced technologies including artificial Intelligence (AI) and Deep Learning in the process of quality control (QC) in textile industry. It helps to save manpower and to enhance the automation management in the textile manufacturing. Supported by AI-based machine-vision technology, "WiseEye" can be installed in a weaving machine to help fabric manufacturers to detect defects instantly in the production process. Through the automatic inspection system, the production line manager can detect the defects, thus helping them to identify the cause of the problems and fix them immediately. "WiseEye" is developed by the Textile and Apparel Artificial Intelligence (TAAI) Research Team, which is spearheaded by Professor Calvin Wong, Cheng Yik Hung Professor in Fashion of Institute of Textiles and Clothing, PolyU.

The information we post online could reveal insights into our mental health. In fact, according to US experts, it may spot key symptoms of depression and low-mood - months before a doctor's formal diagnosis. Researchers believe an algorithm could potentially scan a person's social media posts and alert them to linguistic red flags which are symptomatic of the condition. Indicators of the condition included mentions of hostility and loneliness, words like'tears' and'feelings', plus use of more first-person pronouns like'I' and'me'. Insight: Indicators of the condition included mentions of hostility and loneliness, words like'tears' and'feelings', plus use of more first-person pronouns like'I' and'me' Researchers from the University of Pennsylvania and Stony Brook University published their work in the Proceedings of the National Academy of Sciences.

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.