The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named entities and the effectiveness of pharmaceutical products. The corpus itself consists of two parts, the raw one and the labelled one. The raw part includes 1.4 million health-related user-generated texts collected from various Internet sources, including social media. The labelled part contains 500 consumer reviews about drug therapy with drug- and disease-related information. Labels for sentences include health-related issues or their absence. The sentences with one are additionally labelled at the expression level for identification of fine-grained subtypes such as drug classes and drug forms, drug indications, and drug reactions. Further, we present a baseline model for named entity recognition (NER) and multi-label sentence classification tasks on this corpus. The macro F1 score of 74.85% in the NER task was achieved by our RuDR-BERT model. For the sentence classification task, our model achieves the macro F1 score of 68.82% gaining 7.47% over the score of BERT model trained on Russian data. We make the RuDReC corpus and pretrained weights of domain-specific BERT models freely available at https://github.com/cimm-kzn/RuDReC

The YouTube originals series "The Age of A.I." was released in December 2019. If you haven't already seen it now could be a good time to catch up – with much of the world in enforced or voluntary isolation many of us will be stuck at home with hours to fill. Sit back and marvel at the many incredible, and often heart-warming, applications of AI. Gil makes music using AI and has teamed up other researchers at Georgia Tech to create smart prosthetics for amputees, combining ultrasound signals and machine learning. Episode 3: Using A.I. to build a better human Episode 6: Will a robot take my job?

Insurance is an industry that thrives on predictability. The more certain the outcome, the more insurance firms can be sure to offer fair rates and generate value for customers and shareholders alike. As such, it's an industry that has been slow to adopt new technologies and adapt to global change. Today, however, change is here, and more is on the way. Global megatrends, from the imminent arrival of the self-driving car to accelerating climate change, threaten to disrupt the insurance sector in a way that's never been seen before.

As artificial intelligence (AI) and machine learning (ML) initiatives reshape critical sectors, CXOs need to understand the ethical issues of using AI and ML at their operations. ZDNet and TechRepublic published a PDF ebook: Managing AI and ML in the enterprise 2020, which examines how companies manage, benefit from, and make ethical decisions regarding their AI and ML usage. Artificial intelligence projects are a top priority for many companies, but there are plenty of potential pitfalls for the unwary. Learn more in ZDNet's Daphne Leprince-Ringuet's feature "AI for business: What's going wrong, and how to get it right." How to approach cost justification, identify ROI, and avoid implementation missteps for AI/ML initiatives is the topic of TechRepublic contributor Mary Shacklett's article, "The true costs and ROI of implementing AI in the enterprise."

In a new study in China, a deep learning model detected COVID19 caused pneumonia from CT scans with comparable performance to expert radiologists. CT is the preferred imaging method for evaluating lung infection, assessing progression, and determining treatment options for patients with pneumonia caused by COVID19. In this study, radiologists used AI to help them evaluate the progression of disease, and with the assistance of this model, radiologists' read time decreased by 65%. The model achieved a per-patient sensitivity of 100% and accuracy of 95.24%. This AI could help improve the efficiency of evaluation and diagnosis especially if the number of people with the virus increases. This article is a preprint and has not been peer-reviewed. This paper reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice. Harvard Medical School students have created a COVID19 curriculum. It includes information about epidemiology, clinical management, testing, treatment, vaccine development, and communication. Each section was reviewed by at least two Harvard Medical School faculty experts. Many modules reference supplemental resources that may be worth accessing in the future and to find the most current statistics of the pandemic. Also included are one-page summaries of each module's key takeaways. COVID19 testing in South Korea is free and convenient and over 250,000 people have already been tested. The South Korean data is valuable because they are testing people who have symptoms and people who have no symptoms. This is unusual because most countries are only testing people who are sick to confirm that they have the virus. South Korea is testing everyone, including asymptomatic people, as a public health measure so that anyone who has the virus can isolate even if they don't feel sick. In most countries asymptomatic people are not tested for COVID19. For example Italy is only testing symptomatic people, whereas South Korea tests everyone and picks up more mild cases.

A future driven by artificial intelligence (AI) is often depicted as one paved with improvements across every aspect of life--from health, to jobs, to how we connect. But cracks in this utopia are starting to appear, particularly as we glimpse how AI can also be used to surveil, discriminate, and cause other harms. What existing legal frameworks can protect us from the dark side of this brave new world of technology? Facial recognition is a good example of an AI-driven technology that is starting to have a dramatic human impact. When facial recognition is used to unlock a smartphone, the risk of harm is low, but the stakes are much higher when it is used for policing.

With the rapid emergence of deep learning (DL) technology, it has been successfully used in various fields including aquaculture. This change can create new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on the applications of DL in aquaculture, including live fish identification, species classification, behavioral analysis, feeding decision-making, size or biomass estimation, water quality prediction. In addition, the technical details of DL methods applied to smart fish farming are also analyzed, including data, algorithms, computing power, and performance. The results of this review show that the most significant contribution of DL is the ability to automatically extract features. However, challenges still exist; DL is still in an era of weak artificial intelligence. A large number of labeled data are needed for training, which has become a bottleneck restricting further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs in the handling of complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for the implementation of smart fish farming.

The clustering problem has many applications in Machine Learning, Operations Research, and Statistics. We propose three algorithms to create starting solutions for improvement algorithms for this problem. We test the algorithms on 72 instances that were investigated in the literature. Forty eight of them are relatively easy to solve and we found the best known solution many times for all of them. Twenty four medium and large size instances are more challenging. We found five new best known solutions and matched the best known solution for 18 of the remaining 19 instances.

The information ecosystem today is overwhelmed by an unprecedented quantity of data on versatile topics are with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information spanned over a broad range. To address this gap, in this paper, we applied a data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. We have prepared a labeled dataset with 53012 features and applied different feature selection methods to identify the best feature subset with which our trained classifier achieved an accuracy of 84%-90% varied over 10 criteria. Our semantic analysis of features shows the underpinning associations between the selected features & assessment criteria and further rationalize our assessment approach. Our findings will help in identifying high-quality health articles and thus aiding users in shaping their opinion to make the right choice while picking health-related help from online.

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labeled instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodates to more general conditions. As most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate sample correlations in both the feature space and label space. Benefit from the sample correlation, the proposed method can boost the performance of LE. Extensive experiments on 14 benchmark datasets demonstrate that LESC and gLESC can achieve state-of-the-art results as compared to previous label enhancement baselines.