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

Moore‘s Law Chip complexity doubles every 18 months

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

We expect our systems to become intelligent!

SW: Complexity

SW: Time

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

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: 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

Davidson 1

Davidson 2

Neuron

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

Organisms are Computers! Computers should be Organisms!!

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

IBM‘s Autonomic Computing Campaign

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

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

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

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

In-out vs. out-in

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

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

van Essen Anatomy

van Essen Wiring

Joachim Triesch Triesch-cue

Triesch-confidences

Triesch-results

One-Click Learning Hartmut Loos

Bottles found

One person found Hartmut Loos

More persons Hartmut Loos

Face Finding

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