Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Perceptual control theory wikipedia , lookup
Person of Interest (TV series) wikipedia , lookup
Concept learning wikipedia , lookup
Incomplete Nature wikipedia , lookup
Expert system wikipedia , lookup
Intelligence explosion wikipedia , lookup
Existential risk from artificial general intelligence wikipedia , lookup
Philosophy of artificial intelligence wikipedia , lookup
Machine learning wikipedia , lookup
Machine Intelligence A special session during the 21st AUTOMATION conference 15-17 March 2017, Warsaw, Poland http://www.piap.pl/automation Theme: Artificial Intelligence (AI) enabled computers to perform some complex tasks that would normally require humanlevel intelligence. Three types of AI systems can be distinguished: Expert Systems (ES), Machine Learning (ML) and Machine Intelligence (MI). Expert systems are traditional software systems, typically tailored for a given domain and built on the knowledge of human experts. Machine Learning enabled the systems to learn from, and continuously adapt to, data without being explicitly programmed for that kind of data and to move from one problem domain to another with very few changes to their algorithmic core. Finally, Machine Intelligence systems gather from machine learning, but additionally posses the ability to perceive and influence their environment, constantly learning from the interactions with that environment about the consequences of their actions. Recent progress in the field of MI enabled several spectacular successes. In particular, combinations of diverse techniques enabled to reach super-human level of performance, e.g. IBM’s Watson crushing the world’s best players in an American question-and-answer TV Jeopardy! in 2011, University of Alberta's Cepheus winning in heads-up limit hold'em poker in 2015 or Google DeepMind's AlphaGo beating South Korean professional Go champion in 2016. But aside of those medial ones, AI technologies cut across a vast array of problems, such as computer vision, natural language processing, language translation, or email security, and as a result are reshaping diverse areas of business world, starting from health care (automated diagnostics, early disease detection based on genomics, algorithmic drug discovery); agriculture (sensor- and vision-based intelligent systems, autonomous farming vehicles); transportation and logistics (self-driving cars, drones, fleet management); robotics (robots learning sophisticated manipulations skills); and financial services (advanced credit decisioning). The main goal of this session is to bring together the Machine Intelligence theorists and practitioners to present and discuss the recent advances in this field. Scope: The covered topics include, but are not limited to: application of various combinations of AI techniques in robotics, automation and measurement techniques, including deep reinforcement learning, neural-based predictive control etc. new techniques and approaches to learning, such as end-to-end training, bootstrapping etc. novel biologically-inspired architectures and types of neural networks, e.g. neural networks with external interfaces/memory, wide and deep neural nets etc. We invite presentations of the work of both theoretical and experimental nature. Paper submission: The papers accepted for session will be published along with the rest of conference papers in the Springer's Advances in Intelligent Systems and Computing (AISC) series (http://www.springer.com/series/11156), indexed in Web of Science. Papers should be submitted via EasyChair system. Please follow the instructions on website: http://www.piap.pl/automation/english/registration_of_interest.php. Important dates: October 10th, 2016 – Submission of papers December 8th, 2016 – Notification of acceptance Organizers: Tomasz Kornuta, Ph.D. IBM Research, Almaden Prof. Cezary Zieliński, Ph.D., D.Sc. Warsaw University of Technology, Institute of Control and Computation Engineering Paweł Wawrzyński, Ph.D. Warsaw University of Technology, Institute of Control and Computation Engineering