Causality discovery has been one of the core tasks in scientific research since the beginning of human scientific history. In the age of data tsunami, the causality discovery task involves identification of causality among millions of variables which cannot be done manually by humans. However, the identification of causality relationships using artificial intelligence and statistical techniques in non-experimental settings faces several challenges. In this work, we address three of the challenges regarding Granger causality, one of the most popular causality inference techniques. First, we analyze the consistency of two most popular Granger causality techniques and show that the significance test is not consistent in high dimensions. Second, we review our nonparametric generalization of the Lasso-Granger technique called Generalized Lasso Granger (GLG) to uncover Granger causality relationships among irregularly sampled time series. Finally, we describe two techniques to uncover the casual dependence in non-linear datasets. Extensive experiments are provided to show the significant advantages of the proposed algorithms over their state-of-the-art counterparts.

Artificial Intelligence researchers have long sought to understand and replicate processes of scientific discovery. This article discusses Discovery Informatics as an emerging area of research that builds on that tradition and applies principles of intelligent computing and information systems to understand, automate, improve, and innovate processes of scientific discovery.

His informatics group built the reusable software platform for Stembook Despite the fact that we now have access to almost all peer reviewed (www.stembook.org), William Cohen exchanged and is orthogonal to any specific biomedical domain The growing size of the scientific literature has led to a number of ontology. We believe this approach will be extremely useful in attempts to automatically extract entities and relationships from drug discovery to break down information silos, increase information scientific papers, and then to populate databases with this extracted awareness and sharing, and integrate terminologies and information. In my group we have been exploring techniques data with documents and text, both public and private. We will for using this sort of extracted information for specific tasks, discuss applications we are currently developing in collaboration including "bootstrapping" to improve the coverage of an extraction with a major pharma.

Addressing the ambitious research agendas put forward by many scientific disciplines requires meeting a multitude of challenges in intelligent systems, information sciences, and human-computer interaction. Many aspects of the scientific discovery process are often largely manual and could be automated, improved, or made more efficient. Better interfaces for collaboration, visualization, and understanding would significantly improve scientific practice. Scientific data, publications, and tools could be published in open formats with appropriate semantic descriptions and metadata annotations to improve sharing and dissemination. Opportunities for broader participation in well-defined scientific tasks enable human contributors to provide large amounts of data, annotations, or complex processing results that could not otherwise be obtained. Improvements and innovations across the spectrum of scientific processes and activities will have a profound impact on the rate of scientific discoveries.

This talk will analyze responses to funniness of five versions of 10 different jokes. The responses of one of them will then be compared to theoretical analysis and representation of the same joke based on Script-based Semantics Theory of Humor, General Theory of Verbal Humor, and Ontological Semantic Theory of Humor.

The notion of constructions, from Construction Grammar, is borrowed for use in joke recognition by a knowledge-based computational text analysis system. The joke recognizer is a proposed addition to an existing text analysis framework, Ontological Semantic Technology. Joke recognition is based upon calculation that the candidate text exhibits qualities similar to jokes already collected and represented in a taxonomy, with other processing input. Joke templates, based on constructions, provide semantic scripts against which texts are judged. With these scripts, meta-jokes, which conform almost but not completely to a known joke script, may also be recognized.

Creating an AI device that is both easy to control and comfortable to interact with will likely require algorithms for accurately interpreting conversational speech. Homonyms and homophones represent a particular challenge in this regard, thus the study of puns and other forms of humorous wordplay can be informative. Moving beyond the simple resolution of word uncertainty to an understanding of humor is, however, problematic. The Mutual Vulnerability Theory of Laughter identifies numerous variables involved in our differentiating humorous and nonhumorous stimuli. These include available information, type and degree of relationship with others, personal history, culture, and even mood. It also suggests there will be potential liabilities for AI users, retailers, and developers resulting from even successful attempts to identify, respond to, and create humor, as all require the highlighting of vulnerabilities.

In this article some formal, content-related and procedural considerations towards the sense of humor are articulated and the analysis of both everyday humor behavior and of comic styles leads to the initial proposal of a four factor-model of humor (4FMH). This model is tested in a new dataset and it is also examined whether two forms of comic styles (benevolent humor and moral mockery) do fit in. The model seems to be robust but further studies on the structure of the sense of humor as a personality trait are required.

This exercise in metatheory presents what any theory consists of and what properties it should have. It, then, adjust the general recipe to a theory of humor and computational humor. In this light, it reviews the state of the art in computational humor and suggests the main lines of development.

Based on recent experiences between a laughing virtual agent and a human user at the intersection AI and humor and laughter, this paper aims to highlight some of the psychological considerations, when conducting AI and humor experiments. The systematic and standardized approach outlined in this paper will demonstrate how to reduce error variance that may be caused by confound variables such as having poor experimental controls. From the necessity of cover stories, protocols and procedures, the differences to the pros and cons of measuring subjectively and objectively and what is required so that both give valid and reliable results are offered as solutions to achieving this goal. Furthermore, the psychological individual differences that need consideration, such as the appreciation of different types of humor, mood, personality variables, for example, trait and state cheerfulness, and gelotophobia- the fear of being laughed at are discussed.