1. Organic Computing CS PhD Seminar Mar 3, 2003 Christoph von der Malsburg Computer Science and Biology Departments University of Southern California and Institut für Neuroinformatik Ruhr-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: 10 15 OPS PC today 10 9 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 – From Algorithms … Arithmetic, Accounting, Differential Equations … To Systems Coordination of Sub-Processes Communication Perception Autonomous Action Organic Computing:

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

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

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

19. 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 Electronic Organisms

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. Joachim Triesch Triesch-cue

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

33. Title