1. “Computational Wisdom and Self-Computing” research group objectives
Vagan Terziyan
Faculty of Information Technology, University of Jyvaskyla, Finland
Jyvaskyla, 6 October 2016

2. Something from our history as “Industrial Ontologies Group”
“SmartResource” Project ( )
“UBIWARE” Project ( )
“TRUST” Project ( )
“KEYSTONE” Project ( )

3. Smart-Resource: Agent-driven Predictive and Preventive Maintenance
operator
field crew
expert
consumers
owner
manager
administration
USERS
UBIWARE
Production
External
Applications
Services
Maintenance
Intelligence
Enterprise portal
Automation
Data Warehouse

4. AI tools (Knowledge Discovery) Sensors and alarm detectors
Smart-Resource: Agent-driven Predictive and Preventive Maintenance (“personal semantic health record” for things)
AI tools (Knowledge Discovery)
Sensors and alarm detectors
Software and services
Maintenance workers
Operators
Experts
UBIWARE
Resource info
Other users
UBIWARE
Industrial
Resource
Data Warehouse

5. From Smart-Resource and UBIWARE to Smart Health: Agent-driven Predictive and Preventive Healthcare system 2
Human as UBIWARE service provider
Sensing
Online Monitoring
Testing
Diagnostics
Treatment
UBIWARE
Human
as UBIWARE Resource
(i.e. service consumer)
Human as UBIWARE user
(utilizing integrated
data and knowledge)
Human as
UBIWARE
administrator 4
Data Warehouse
UBIWARE 3 1

6. The “Battle” of the New Millennium

7. 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 self-managed entity

8. Collective Intelligence
“Army”
Machine-Learning-
driven agents
Humans
Mind “clones” of humans
Who they are?

9. Collective Intelligence “Army” with cognitive computing “weapon”
Machine-Learning-
driven agents
Humans
Mind “clones” of humans
Who they are?
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.
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.
Cognitive
Computing-
Driven

10. “All you need is love WISDOM” !
Not enough
for Big Data
Instrument
WISDOM
Input
Output
LONG list of alternatives for the decision and relevant input BIG data C B A
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-compressing-connecting) for giving-up something yet making reasonable decision (“wise decision”).

11. 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:
- Focusing
- Filtering
- Forgetting
- Contextualizing
- Compressing
- Structuring
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).

12. WISDOM-II tool: Self-Computing/Self-Management
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 ).

13. 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

14. WISDOM-IV tool: Pi-Mind (“Patented Intelligence”)
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 See also:

15. WISDOM-V: Learning Wisdom
WISDOM-V tool: “Agile” Deep Learning and Wisdom Discovery

16. Traditional Machine Learning Process (“Learning Intelligence”)
Data Mining & Knowledge Discovery
Decision Model
Machine Learning
Decision
Environment

17. Traditional Machine Learning Process (“Learning Intelligence”)
Environment
Decision
Data Mining & Knowledge Discovery
Machine Learning
Decision Model

18. “Agile” Machine Learning Process (“Learning Wisdom”)II
III N …
Decision

19. What kind of skills we need
and we teach to reach
our objectives?

20. … converting problems into products
Good Engineers ...
… converting problems into products
Problems
Products

21. … “talking” about products to make value out of it
Good Engineers vs. Managers
… “talking” about products to make value out of it
Products

22. Good Engineers vs. VIP Engineers
… inventing new problems
Problems
Products

23. VIP Engineers vs. WISE Engineers
… inventing new and SMART problems
AND
… designing SMART problem solvers!
Problems
Products

24. Train your skills with us

25. 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 ("SELF-MINING 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 "SELF-PROTECTION");
FROM ("WEB-OF-INTELLIGENCE")  TO  ("WEB-OF-WISDOM");
FROM ("BIG DATA")  TO  ("WISE DATA").

26. Short Summary
We consider “Computational Wisdom and Self-Computing“ 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 self-discovery (i.e., “self-cognition”) .

27. CoWiSe: “Computational Wisdom and Self-Computing” Research Group contact: