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Organic Computing CS PhD Seminar Mar 3, 2003

Organic Computing CS PhD Seminar Mar 3, 2003. Christoph von der Malsburg Computer Science and Biology Departments University of Southern California and Institut f ü r Neuroinformatik Ruhr-Universit ä t Bochum. Moore‘s Law. Chip complexity doubles every 18 months. Expectations.

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Organic Computing CS PhD Seminar Mar 3, 2003

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  1. Organic ComputingCS PhD Seminar Mar 3, 2003 Christoph von der Malsburg Computer Science and Biology Departments University of Southern California and Institut für NeuroinformatikRuhr-Universität Bochum

  2. Moore‘s Law Chip complexity doubles every 18 months

  3. Expectations More Complex Functions Flexibility, Robustness Adaptivity, Evolability Autonomy User Friendliness Situation Awareness

  4. We expect our systems to become intelligent!

  5. SW: Complexity

  6. SW: Time

  7. SW: Failure NIST study 02: yearly US losses due to SW failure: $ 60 Billion

  8. Life as Model • Living Cell: as complex as PC, but • flexible, robust, autonomous, adaptive, evolvable, situation aware • Organism: more complex than all existing software • Human Brain: intelligent, conscious, creative • It is the source of all algorithms!! • Estimated computing power: 1015 OPS • PC today 109 OPS, will equal brain in 30 years according to Moore‘s Law • But: Life is not digital, not deterministic, not algorithmic

  9. Davidson 1

  10. Davidson 2

  11. Neuron

  12. A new computing paradigm – Organic Computing: • From Algorithms … • Arithmetic, Accounting, Differential Equations • … To Systems • Coordination of Sub-Processes • Communication • Perception • Autonomous Action

  13. Organisms are Computers! Computers should be Organisms!!

  14. Organic Computing is not Molecular computing, about faster computers but being fault-tolerant and self-organizing, it will lay the foundation for molecular and massively parallel computers

  15. IBM‘s Autonomic Computing Campaign http://www.ibm.com/research/autonomic

  16. Algorithmic DOL Algorithmic Division of Labor Human: Creative Infrastructure: Goals, Methodology, Interpretation, Diagnostics Detailed Communication Machine: Algorithms: deterministic, fast, clue-less

  17. Debugging Comparison of actual result with original goal Autonomous debugging: Goals must be represented in the machine

  18. Organic Computers Organic Computers Human: Goals Loose Communication Machine: Creative Infrastructure: Methodology, Interpretation, Diagnostics,Debugging, Goals, Data, „Algorithms“

  19. Electronic Organisms Algorithmic Machines... are programmed contain no infrastructure may be simple have to be simple Electronic Organisms... grow contain infrastructure have to be complex may be complex

  20. In-out vs. out-in

  21. Relevant Methodologies Neural Networks Fuzzy Logic Genetic Algorithms Artificial Life Autonomous Agents Amorphous Computing Belief Propagation

  22. First Application Domains • Artificial Vision • Autonomous Robots • Autonomous Vehicles • Toy Robots • Service Robots • User Interfaces • Natural Language Understanding • Computer Security

  23. van Essen Anatomy

  24. van Essen Wiring

  25. Triesch-cue Joachim Triesch

  26. Triesch-confidences

  27. Triesch-results

  28. One-Click Learning Hartmut Loos

  29. Bottles found

  30. One person found Hartmut Loos

  31. More persons Hartmut Loos

  32. Face Finding

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