State of the art and Grand Challenges for Nature-inspired Data Technologies

1. State of the art and Grand Challenges for Nature-inspired Data Technologies D.Anguita Palma de Mallorca – 8 June 2006

2. Problems related to Data Technology • Missing / partial data • Noisy / uncertain data • Heterogeneous data • High dimensionality • Spatial and temporal issues (e.g. delays) • Hierarchy / structure • Representation / Coding • … and Data flood !

3. Data flood … paper Source: How much information 2003 Source: UC Berkeley School of Information

4. Data flood… film Source: How much information 2003 Source: UC Berkeley School of Information

5. Data flood… magnetic media (& flash) Source: How much information 2003 Source: UC Berkeley School of Information

6. Data flood… optical media Source: How much information 2003 Source: UC Berkeley School of Information

7. Data flood… broadcasting and telephony Source: How much information 2003 Source: UC Berkeley School of Information

8. Data flood… Internet Source: How much information 2003 Source: UC Berkeley School of Information

9. Data Technology – Information flow Interaction Data transfer Human interface Data mining Data visualization Data (pre)processing Data storage/Memory Data acquisition Environment

10. Data Acquisition Silicon retina – Inst. of Neuroinformatics, Zurich Amorphous Computing:Paintable sensors, MIT Data acquisition • Auditory / Vision models • Evolutionary and self-organizing sensors • Neuromorphic architectures • Attention/perception modelling Environment

11. Data Technologies • Modularization / Decomposition / Hierarchies • Spatial temporal structures • Morphological structures • Autonomic systems • Learning and adaptive systems • Heterogeneous databases Data mining Data (pre)processing Data storage

12. “Traditional” Data Technologies • Artificial life • Artificial Neural networks • Artificial Autonomous Agents and Adaptive Behavior • Evolutionary Computation • Fuzzy Systems Data mining Data (pre)processing Data storage D.Cliff, Bio-inspired computing approaches, HP Labs

13. Data mining (web mining) Artificial Immune System for Interesting Information Discovery A.Secker, University of Kent

14. Data processing through Natural Computing Adleman’s DNA Computing (but also Paun’s Membrane Computing, etc.)

15. Data storage IBM’s Millipede – 1Tbit/in^2

16. Data distribution / Cooperation Query processing for sensor networks, Cornell University

17. Data Visualization Programmable material, MIT Human interface Nature-inspired visualization,Univ. of Maryland Univ. of Minnesota Data visualization • Visualization concepts • Evolvable interfaces

18. Industries… IBM: Autonomic computing Microsoft: Non classical computation (including bio-inspired)

19. Grand Challenges (Smart Information Systems) How do we bring together all these technologies under a common framework ? Brain metaphor Focus on functionality • Artificial brain • Distributed brain • Digital brain

20. Grand Challenges I Information System = Artificial Brain The artificial brain • Keywords: • “Intelligent” behaviour • Self assembly • Self repairing • Nature-inspired data coding • Nature-inspired data processing • Nature-inspired data mining • Adaptive • “Internal representation” of environment (artificial consciousness ?)

21. Grand Challenges I Information System = Artificial Brain David Rumelhart, Hugo de Garis, John Taylor, …

22. Grand Challenges II Information System = Distributed Brain The artificial brain • Keywords: • Sensor networks • Distributed Micro/Nano Intelligence • Nature-inspired Collaborative Architectures for Complex Adaptive Systems. • “Internal representation” of data (artificial consciousness ?) • Nature-inspired Distributed Data Processing and Mining

23. Grand Challenges II Information System = Distributed Brain Cooperation of (tiny?) brains (Swarm intelligence, Amorphous computing, …) System IS the brain (Autonomic Computing. GRID,…)

24. Grand Challenges III Information System = Digital Brain • Keywords: • Same as “Artificial Brain” but emphasis on artificial computation • Nanotechnology • Combinatorial intelligence • Discrete computation • Rationale: • artificial computation is digital natural computation is analog

25. Grand Challenges III Likharev et al. Stony Brooks University Information System = Digital Brain

26. “Mallorca” proposals (NiDT session) • Artificial Brain • Applications in Speciality Chemistry Industry – Waiting for Ni Solutions • Ni Methods for Knowledge Generation from Data in Real-Time • Ni Methods for Automatic Detection and Classification in Cytogenetic Systems • Ni Methods for Local Pattern Detection • Digital Brain • Discrete and Continuous Aspects of Ni Methods • Physically-inspired Artificial Learning Methods • Distributed Brain • Ni Robustness of Networks

27. Acknowledgments and more refs.: NiSIS Survey on Nature-inspired Data Technologies(January 2006) Cecilio Angulo, Giovanni Bozza, Francisco Bonachela, Emilio Corchado, Michael Haddrell, Bogdan Gabrys, Przemyslaw Kazienko, Dariusz Król, Kauko Leviskä, Katarzyna Musial, Diego Pardo, David Pelta, Janusz Sobecki, Jose Luis Verdegay, Filip Zelezny NiDT Roadmap - Existing and Future Challenges (February 2006) Plamen Angelov, Davide Anguita, Cecilio Angulo, Emilio Corchado, Przemysław Kazienko, Daniel Keim, Katarzyna Musial, David Pelta, José Luis Verdegay, Alejandro Sancho Royo, Juan R.González

28. Conclusion • Grand Challenges: Artificial Brain, Distributed Brain, Digital Brain • Research on Nature-inspired data-related technologies is widespread both in Europe and worldwide • Vast amount of both established and new/emerging technologies • There is little coordination among research groups/areas: NiSIS is a big step in this direction • We need some sort of “Yellow Pages”, listing: Technologies • Research groups • Research Institutions • Courses • Conferences • Publications • … ???