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Organic Computing CS 597 March 8, 2004 Christoph von der Malsburg Computer Science Department University of Southern California and Institute for Neural.

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Presentation on theme: "Organic Computing CS 597 March 8, 2004 Christoph von der Malsburg Computer Science Department University of Southern California and Institute for Neural."— Presentation transcript:

1 Organic Computing CS 597 March 8, 2004 Christoph von der Malsburg Computer Science Department University of Southern California and Institute for Neural Computing Ruhr-University Bochum, Germany

2 Moore‘s Law Chip complexity doubles every 18 months

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

4 We expect our systems to become intelligent but Information Technology is running into a Complexity Barrier!

5 SW: Complexity

6 SW: Time

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

8 Life: Computing without Software 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, By Moore‘s Law, the PC will equal the brain in 30 years But: Life is not digital, not deterministic, not algorithmic

9 Evolving Computing Needs: From Algorithms … Arithmetic, Accounting, Differential Equations … To Systems Coordination of Sub-Processes Communication Perception Autonomous Action

10 A New Computing Paradigm: Organisms are Computers! Computers should be Organisms!!

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

12 Human: Detailed Communication Machine : Creative Infrastructure: Goals, Methods, Interpretation, World Knowledge, Diagnostics Algorithms: deterministic, fast, clue-less Algorithmic Division of Labor Algorithmic DOL

13 Human: Loose Communication Machine : Goals Creative Infrastructure: Goals, Methods, Interpretation, World Knowldege, Debugging Data, „Algorithms“ Organic Computers

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

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

16 SW Trends Recognized Trends in Software Development Need to be Taken to their Extreme: Thorough task definition before the first line of code Principled design instead of ad-hoc decisions Thorough testing

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

18 van Essen Anatomy

19 van Essen Wiring

20 Some Relevant Work From my Lab

21 Jochen Triesch Triesch-cue

22 Triesch-confidences

23 Triesch-results

24 One-Click Learning Hartmut Loos

25 Bottles found

26 One person found Hartmut Loos

27 More persons Hartmut Loos

28 Face Finding

29 Title


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