12^th Mexican International Conference on Artificial Intelligence

Time Series Shape Association Measures

A need in time series shape association measures appears in time series clustering, search in time series data bases, time series data mining, time series pattern recognition, in statistics and in data driven analysis of relationships between elements of dynamic systems. The talk will consider different methods of definition of time series shape similarity and shape association measures and their properties.

Tutorial: Perception Based Approach to Time Series Analysis

Ildar Baryrshin holds a Dr. Sci. in Mathematics (Theoretical Fundamentals of Informatics) from the Institute of Program Systems, Russian Academy of Sciences, and Ph.D. in Computer Science from the Moscow Power Engineering Institute (Technical University), Moscow, USSR. He was Head of Department and Professor at Kazan State Technological University, Russia, and is currently a researcher at the National Oil Institute, Mexico. His research interests include Artificial Intelligence and Expert Systems, Fuzzy Logic and Soft Computing, Decision-Making Systems, Cluster Analysis, and Applied Semiotics. He is author of numerous research publications in the theory of fuzzy systems.

SenticNet: Helping Machines to Learn, Leverage, Love

SenticNet is a publicly available semantic and affective resource for concept-level opinion and sentiment analysis. SenticNet is built by means of sentic computing, a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and common sense computing, which exploits both computer and social sciences to better recognize, interpret, and process opinions and sentiments over the Web. In sentic computing, whose term derives from the Latin sentire (root of words such as sentiment and sentience) and sensus (intended both as capability of feeling and as common sense), the analysis of natural language is based on affective ontologies and common sense reasoning tools, which enable the analysis of text not only at document-, page- or paragraph-level, but also at sentence-, clause-, and concept-level. In particular, sentic computing involves the use of AI and Semantic Web techniques, for knowledge representation and inference; mathematics, for carrying out tasks such as graph mining and multi-dimensionality reduction; linguistics, for discourse analysis and pragmatics; psychology, for cognitive and affective modeling; sociology, for understanding social network dynamics and social influence; finally ethics, for understanding related issues about the nature of mind and the creation of emotional machines.

Erik Cambria received his BEng and MEng with honours in Electronic Engineering from the University of Genova, in 2005 and 2008 respectively. In 2011, he was awarded his PhD in Computing Science and Mathematics, following the completion of an industrial Cooperative Awards in Science and Engineering (CASE) research project, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), which was born from the collaboration between the University of Stirling, the MIT Media Laboratory, and Sitekit Solutions Ltd. Today, Erik is a research scientist at the National University of Singapore (Cognitive Science Programme, Temasek Laboratories) and an associate researcher at the Massachusetts Institute of Technology (Synthetic Intelligence Project, Media Laboratory). His interests include AI, Semantic Web, KR, NLP, big social data analysis, affective and cognitive modelling, intention awareness, HCI, and e-health. Erik is invited speaker/tutor in many international venues, e.g., IEEE SSCI and WWW, associate editor of Springer Cognitive Computation, and guest editor of leading AI journals, e.g., IEEE Intelligent Systems and Elsevier Neural Networks. He is also chair of several international conferences, e.g., Brain Inspired Cognitive Systems (BICS), symposia, e.g., Extreme Learning Machines (ELM), and workshop series, e.g., ICDM SENTIRE, KDD WISDOM, and WWW MABSDA. Additionally, he is a fellow of the Brain Sciences Foundation, the National Laboratory of Pattern Recognition (NLPR - Institute of Automation, Chinese Academy of Sciences), the National Taiwan University, Microsoft Research Asia, and HP Labs India.

Rethinking Artificial Intelligence

The modern school of Artificial Intelligence was originally expected to provide a full working model of intelligence as a set of procedures. Scholars implemented these procedures over time to conceptualize the notion of an intelligent machine. Computer scientists rushed to implement working models that would allegedly reach beyond many limits. Perhaps the most debilitating act was equating what is efficient in procedures to what is artificialized in intelligence. Equally debilitating was interpreting the speed of arithmetic calculations as a quantifier: it led to teams being interpreting speed and accuracy as reflections of intelligence. In order to reach an artificial form of intelligence that is faithful to the amalgam of biological, physical and chemical that it seeks to imitate; scholars of AI must reach a deeper synthesis of its integrative nature, leading to the creation of many artificial synthetic forms of Intelligence, instead of a single vision of intelligence that simply focuses on matching the performance of the human brain. Having said that, we can clearly concur that most of the AI Modern School’s limitations have been discovered and are well-documented and known to the AI community.  Our aim is to discuss a number of these issues, particularly the limits previously described. We avow that these limits emerged from epistemological misunderstandings on the perceived meanings of intelligence itself, leading to the limits imposed in the current interpretations of AI. Future work in AI, or alternatively coined Synthetic Intelligence, must revisit fundamental assumptions about the nature of the brain, cognition, computing, and intelligence. Synthetic Intelligence focuses on the phenomena such as intelligence and consciousness, and mapping them to the physics of the brain and models of brain processes at each of its multiple levels. It is the ‘stack’ of brain subsystems at multiple levels, from cortical down to molecular, joined by a common thread, that make up a mind. What we need are mathematically described mechanisms and information structures to integrate computational discourse analysis, value systems, mapping of cognitive structures to neuron interactions and to the molecular mechanisms of such interactions. The key to this discovery will be the study of emergence of intelligence and consciousness in engineered systems –implemented in silico or in vitro.

Newton Howard is the Director of the Synthetic Intelligence Lab and a resident scientist at MIT. He holds PhD in Cognitive Informatics and Mathematics from La Sorbonne, France where he was also awarded the Habilitation a Diriger des Recherches for his leading work on the Physics of Cognition (PoC) and its applications to complex medical, economical and security equilibriums. While a graduate member of the Faculty of Mathematical Sciences at the University of Oxford, England, he proposed the Theory of Intention Awareness (IA), which made a significant impact on the design of command and control systems and information exchange systems at tactical operational and strategic levels. He has served as the Chairman of the Center for Advanced Defense Studies (CADS), the leading Washington, D.C, National Security Group and is currently its board director. He is a national security advisor to several U.S. Government organizations. He recently published The Mood State Indicators (MSI) algorithm, which models and explains the mental processes involved in human speech and writing, to predict emotional states. His natural-language approach to systems understanding and design pointed to the multi-dimensional structures embedded and nested within speech-based cognitive systems, which have led to much advancement in building more accurate cognitive engines for modeling behavioral and cognitive feedback. Dr. Howard works with multi-disciplinary teams of physicists, chemists, biologists, brain scientists, computer scientists, and engineers to reach a deeper understanding of the brain. His research aims to improve the quality of life for so many who suffer from degenerating conditions currently considered incurable. He founded the Mind Machine Project at MIT; an interdisciplinary initiative to reconcile natural intelligence with machine intelligence. He established the Brain Sciences Foundation (BSF), a multidisciplinary research foundation dedicated to developing novel paradigms for the study of both mind and brain and ultimately the treatment of neurological disorders. He currently works on functional brain and neuron interfacing abilities using theoretical mathematical models to represent the exchange of information inside the human brain. This work, called the Fundamental Code Unit (FCU), has proven applicable in the diagnosis and study of brain disorders and has led to important pharmacological and therapeutic tools for physicians. He has developed individualized strategies to incorporate solutions for psychiatric and brain prosthetics and has been working on interventions for early detection and novel treatment strategies for neurodegenerative diseases and affective disorders.

Multi-Agent Ontology Mapping Framework for the Semantic Web

Ontology mapping is prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies present on the web need to be aligned before one can make use of them for example, a question answering. At the same time these ontologies can be used as domain specific background knowledge by the ontology mapping systems in order to increase the mapping precision. These ontologies however, can differ in representation, quality and size that pose different challenges for ontology mapping. In this talk  I will  analyse these challenges and introduce a multi-agent ontology mapping framework that has been designed to operate effectively in the Semantic Web environment.

Maria Vargas-Vera received her PhD from the Artificial Intelligence Department at Edinburgh University in 1995 and she was awarded Fellow of the British Computer Society (FBCS) from November 2009. Dr. Vargas-Vera is a Professor at Adolfo Ibanez University, Chile. Her current research focuses on Ontology Mapping, Question Answering and Applications using Semantic Web Technologies. Dr Vargas-Vera has published many research papers in international conferences and journals and she is also a member of program committees of international Conferences. Dr Vargas-Vera is an Associated Editor, the International Journal of Knowledge and Learning (IJKL) and the Journal of Emerging Technologies in Web Intelligence (JETWI).

First-Order Theorem Proving and Vampire

Automatic theorem proving has a number of important applications, such as Software Verification, Hardware Verification, Hardware Design, Knowledge Representation and Reasoning, Semantic Web, Algebra and Proving Theorems in Mathematics. Over 50 years of research in theorem proving have resulted in one of the most advanced and elegant theories in computer science. This area is an ideal target for scientific engineering: implementation techniques have to be developed to realise an advanced theory in practically valuable tools.

Vampire is a theorem prover, that is, a system able to prove theorems. More precisely, it proves theorems in first-order logic. Vampire is very fast, as can be judged by its awards.

 Note by the organizers:  Andrei is the creator of the EasyChair system used by about 27 thousand conferences and almost a million users! Without his huge work on this system, world's science would not be what it is: 27 thousand conferences, including MICAI, would not be possible. A huge thank you to Andrei for the very existence of MICAI!