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Grand Challenges Nature-inspired Data Technologies

Grand Challenges Nature-inspired Data Technologies. NiDT Focus Group Palma de Mallorca – 9 June 2006. Data Technology – Information flow. Interaction. Data transfer. Human interface. Data mining. Data visualization. Data (pre)processing. Data storage/ Memory. Data acquisition.

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Grand Challenges Nature-inspired Data Technologies

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  1. Grand Challenges Nature-inspired Data Technologies NiDT Focus Group Palma de Mallorca – 9 June 2006

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

  3. Nature-inspired ICT (Information and Communication Technology)

  4. Grand Challenge (Computational) Artificial Nervous (Sensing) Systems Goal: Robust data and information acquisition Smart Sensors Autonomous sensors Self Adapting in a changing world Self Repairing Management of uncertainty Expected benefits: Industrial Control/Monitoring, Additional safety, Quality, Increasing ability to acquire information, Sensor failure robustness, better/fitter representation of external world Technologies: Sensor batteries, Machine learning ?, Robustness / stability ?, Evolutionary Algorithms / Survival / Decay, Redundancy, Signal processing, Adaptation

  5. Grand Challenge (Autonomous Intelligent) Brain-like computing Goals: Find solutions for complex problems like nature does Matching problems to solutions Knowledge extraction, maintenance, management Real-time decisions Smart memory (e.g. more efficient, smart compression) Expected benefits: Real time information processing / understanding “Natural” (e.g. medical) data processing / understanding Better understanding of external world Robustness Technologies: Heterogeneous / Hierarchical Data repres. and processing Continuous / Discrete representation and processing Bio-inspired methodologies (ANN, etc.), Physics-inspired methodologies , Self configuration, Data mining, Machine learning

  6. Grand Challenge Distributed (cooperative) intelligence Goal: System Survival / Fitness / Improvement Team Performance Expected benefits: Industry Computer networks, autonomic computing Computational efficiency Robustness (e.g. to failure, to computation errors, of run time) Technologies: Networks, swarms, ants, agents,

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