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
“Computational Wisdom and Self-Computing” research group objectives Vagan Terziyan Faculty of Information Technology, University of Jyvaskyla, Finland Jyvaskyla, 6 October 2016 Smart-Resource: Agent-driven Predictive and Preventive Maintenance operator field crew expert consumers owner manager administration 3 Smart-Resource: Agent-driven Predictive and Preventive Maintenance (“personal semantic health record” for things) Sensors and alarm detectors Resource info Operators Experts Software and services AI tools (Knowledge Discovery) Maintenance workers Other users 4 From Smart-Resource and UBIWARE to Smart Health: Agent-driven Predictive and Preventive Healthcare system 2 Online Monitoring Sensing Testing Diagnostics Treatment 4 3 1 5 The “Battle” of the New Millennium If we want to survive with the Big Data, then we must allow it to be autonomous and self-managed The toolset of “collective intelligence” must be “in the hands” of Big Data itself as autonomous and selfmanaged entity Humans Mind “clones” of humans Machine-Learningdriven agents The technology behind Cognitive Computing relies on advances in the study of Collective Intelligence, in regards to not only physical groups of humans, but more to the conceptual and mechanical systems we build. Humans Mind “clones” of humans Machine-Learningdriven agents Cognitive Computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The goal of cognitive computing is to automate decision-making and problem-solving. “All you need is WISDOM” ! WISDOM LONG list of alternatives for the decision and relevant input BIG data Decision made / chosen alternative “Wise” decision-making includes realizing lack of resources for the optimal decision due to big data to be processed, finding compromise between efficiency and effectiveness of the potential decision and smart utilization of the instrument (focusing-filtering-forgetting-contextualizing-compressingconnecting) for giving-up something yet making reasonable decision (“wise decision”). WISDOM-I: Capability to compromise between the efficiency and the effectiveness when addressing the Big Data challenge Efficiency is achieved if the ratio of the effort (resource) spent is reasonable comparably to the utility of the result. E.g., if a result is not timely the utility of the resulting knowledge will drop. Effectiveness is achieved if: (a) not a single important data/knowledge token is left unattended (completeness); and (b) these tokens are processed adequately for further consumption (expressiveness/granularity). WISDOM-I tools: Ermolayev V., Akerkar R., Terziyan V., Cochez M., Towards Evolving Knowledge Ecosystems for Big Data Understanding, In: R. Akerkar (ed.), Big Data Computing, Chapman and Hall, 2014 (Ch. 1, pp. 3-55). WISDOM-II: Capability to balance between evolving configuration and challenges of the external environment and own (internal) configuration and objectives WISDOM-II tool: Self-Computing/Self-Management Terziyan V., Challenges of the “Global Understanding Environment” based on Agent Mobility, In: V. Sugumaran (ed.), Application of Agents and Intelligent Information Technologies, IGI, 2007, (Ch. 7, pp.121-152). WISDOM-III: Capability to decide “ethically” and “emotionally” correct WISDOM-III tool: Computational Culture and Ethical Computing Terziyan V., Kaikova O., The "Magic Square": A Roadmap towards Emotional Business Intelligence, Journal of Decision Systems, Vol. 24, No.3, 2015, Taylor & Francis, pp. 255-272. WISDOM-IV: Capability to utilize “human-clones” (“mind-robots” of human decision-makers) WISDOM-IV tool: Pi-Mind (“Patented Intelligence”) π (PI) – “Patented Intelligence”, with the meaning of formalizing, licensing, sharing, reuse and integration of the personal wisdom&value-driven decision culture for the quality, transparency and automation of the decision-driven processes in autonomous cyber-physical systems dealing with big data. π -mind (patented mind): a digital patented copy of a human`s decision system providing formalization of his or her wisdom, values system and a decision scheme used for a specific task. π-mind helps keeping and sharing an explicit ontological model of a human`s wisdom and value system for its further use by users of the ecosystem. π-mind characterizes deliberate and formalized rules used by a person for wise decision making in situations defined by a state of the environment to achieve specific goals. Usually in decision support systems it`s a knowledge base which stores expert knowledge in some domain but we propose a more subjective entity: wisdom&value base which stores specific opinions of a concrete person about the importance of various things and phenomena which he/she uses during decision making - so called π-mind. Such space is a complex structure - non-linear and multidimensional. Terziyan V., Golovianko M., Shevchenko O., Semantic Portal as a Tool for Structural Reform of the Ukrainian Educational System, In: Information Technology for Development, Vol. 21, No. 3, 2015, Taylor & Francis, pp. 381-402. See also: http://www.mit.jyu.fi/ai/Quality-3_en.pptx WISDOM-V: Learning Wisdom WISDOM-V tool: “Agile” Deep Learning and Wisdom Discovery Traditional Machine Learning Process (“Learning Intelligence”) Traditional Machine Learning Process (“Learning Intelligence”) “Agile” Machine Learning Process (“Learning Wisdom”) I II III N I II III N Good Engineers ... … converting problems into products Good Engineers vs. Managers … “talking” about products to make value out of it Good Engineers vs. VIP Engineers … inventing new problems VIP Engineers vs. WISE Engineers … inventing new and SMART problems … designing SMART problem solvers! Train your skills with us Our other research ambitions: FROM ("SCIENTIFIC COMPUTING" OR "COMPUTATIONAL SCIENCE") TO ("SELF-COMPUTING") ; FROM ("ARTIFICIAL INTELLIGENCE") TO ("ARTIFICIAL WISDOM") ; FROM ("COMPUTATIONAL INTELLIGENCE") TO ("COMPUTATIONAL WISDOM") ; FROM ("COGNITIVE SCIENCE") TO ("SELF-COGNITION") ; FROM ("DATA MINING AND KNOWLEDGE DISCOVERY") TO ("SELFMINING AND WISDOM DISCOVERY"); FROM ("KNOWLEDGE MANAGEMENT") TO ("WISDOM MANAGEMENT"); FROM ("INTERNET-OF-THINGS") TO ("INTERNET-OF-WISE-THINGS"); FROM ("SMART PRODUCTS") TO ("WISE PRODUCTS"); FROM ("SMART ARCHITECTURE") TO ("WISE ARCHITECTURE"); FROM ("CYBER-SECURITY") TO ("WISE SECURITY" OR "SELFPROTECTION"); FROM ("WEB-OF-INTELLIGENCE") TO ("WEB-OF-WISDOM"); FROM ("BIG DATA") TO ("WISE DATA"). Short Summary We consider “Computational Wisdom and SelfComputing“ to be a new paradigm of "wise" behavior of artificial smart systems against Big Data challenge; Artificial Intelligence and Cognitive Computing are currently based on world cognition instruments like machine learning, data mining and knowledge discovery; while “Artificial Wisdom” (Self-Computing) assumes also [deep, emotional, ethical, pragmatic, intuitive and creative] machine-self-learning, self-mining and selfdiscovery (i.e., “self-cognition”) . CoWiSe: “Computational Wisdom and SelfComputing” Research Group contact: [email protected] http://www.mit.jyu.fi/ai/vagan/index.html