Organic ComputingCS PhD Seminar Mar 3, 2003

Christoph von der Malsburg

Computer Science and Biology DepartmentsUniversity of Southern California

and

Institut für NeuroinformatikRuhr-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, creativeIt 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

Davidson 1

Davidson 2

Neuron

A new computing paradigm –

• From Algorithms …Arithmetic, Accounting, Differential Equations

• … To SystemsCoordination of Sub-ProcessesCommunicationPerceptionAutonomous 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

http://www.ibm.com/research/autonomic

Human:

Detailed Communication

Machine:

Creative Infrastructure: Goals, Methodology, Interpretation, Diagnostics

Algorithms: deterministic, fast, clue-less

Algorithmic Division of LaborAlgorithmic

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 ComputersOrganic

Computers

Algorithmic Machines...are programmed contain no infrastructuremay be simplehave to be simple

Electronic Organisms...growcontain infrastructurehave 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