Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.

Artificial Intelligence Set to Transform Insurance Industry but Integration Challenges Remain, According to Accenture Report Insurers are investing in AI technology to enhance the customer experience, empower their own people NEW YORK; Apr. 19, 2017 – Insurance executives believe that artificial intelligence (AI) will significantly transform their industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, according to Accenture's Technology Vision for Insurance 2017. At the same time, however, the report found that insurers face challenges integrating AI into their existing technology, citing issues such as data quality, privacy and infrastructure compatibility. Titled "Technology for People," the report is based on the insights of a technology advisory board, interviews with industry technologists and a survey of more than 550 insurance executives across 31 countries. According to the report, three-quarters (75 percent) of insurance executives believe that AI will either significantly alter or completely transform the overall insurance industry in the next three years. One-third (32 percent) believe that their own company will be "completely transformed" by AI within that timeframe, and an additional 39 percent believe that AI will "significantly change" their company.

A fair amount of our data science projects involve machine learning, and many parts of this workflow are repetitive. Model Diagnostics: Learning curves, partial dependence plots, feature importances, ROC and other diagnostics are extremely useful to generate automatically. AML is a powerful set of techniques for faster data exploration as well as improving model accuracy through model tuning and better diagnostics. The above case study highlights AML's capability to improve model accuracy, however we have realized AMLs other benefits as well.

While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Again, imagine you can repeat the entire model building process multiple times. Fortmann-Roe ends the section on over- and under-fitting by pointing to another of his great essays (Accurately Measuring Model Prediction Error), and then moving on to the highly-agreeable recommendation that "resampling based measures such as cross-validation should be preferred over theoretical measures such as Aikake's Information Criteria." I recommend reading Scott Fortmann-Roe's entire bias-variance tradeoff essay, as well as his piece on measuring model prediction error.

This paper investigates the phase retrieval problem, which aims to recover a signal from the magnitudes of its linear measurements. We develop statistically and computationally efficient algorithms for the situation when the measurements are corrupted by sparse outliers that can take arbitrary values. We propose a novel approach to robustify the gradient descent algorithm by using the sample median as a guide for pruning spurious samples in initialization and local search. Adopting the Poisson loss and the reshaped quadratic loss respectively, we obtain two algorithms termed median-TWF and median-RWF, both of which provably recover the signal from a near-optimal number of measurements when the measurement vectors are composed of i.i.d. Gaussian entries, up to a logarithmic factor, even when a constant fraction of the measurements are adversarially corrupted. We further show that both algorithms are stable in the presence of additional dense bounded noise. Our analysis is accomplished by developing non-trivial concentration results of median-related quantities, which may be of independent interest. We provide numerical experiments to demonstrate the effectiveness of our approach.

This thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modeling and foreground segmentation tasks. Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. Subsequently, we propose a double-constrained version of robust principal component analysis to improve the foreground detection in maritime environments for automated video-surveillance applications. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes.

Back issues are available on-line at www.aimagazine.org The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. Regular features in AI Magazine include feature articles, workshop, symposium, and conference summaries, book reviews, editorials, news about the Association for the Advancement of Artificial Intelligence, letters to the editor, forum discussions, calendar of events, recruitment and product advertising, and columns on various topics including AI in the news. AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and reviews of books.

In this survey, we conduct an investigation on the robustness of several features and classifiers in automatic authorship attribution. Our corpus consists in 25 different documents written by 5 different American philosophers in English. The different documents pass throw a digital conversion into grey-scaled images and several levels of noise are added to corrupt those image documents. The noise consists in a “Salt & Pepper” type, which is randomly added on the surface of the images with the following noise levels: 0%, 1%, 2%, 3%, 4%, 5%, 6% and 7%. Thus, each image goes throw an OCR program (Optical Character Recognition) to extract the text from the image. Then, the obtained text document is kept to be used during the experiments of authorship attribution. Several features and classifiers are employed and evaluated with regards to the classification performances. Results are quite interesting and show that the most robust feature in au-thorship attribution is the character-tetragram, which provides a score of 100% even at a noise level of 7%.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Artificial intelligence (AI) is no longer science fiction. And like any game-changing technology, AI is not exactly what we expected – at least not yet.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human-robot interaction (HRI) community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling (AM) researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses 1) from what underlying dimensions (e.g., homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, 2) what types of emotions have been derived from these dimensions, and 3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.