1. “The origins of intelligence” -Luc Steels
Presentation by Vincent Bivona

2. Evolving Complex Adaptive Systems
4 defining characteristics:
Self-maintenance-
actively establishing itself (rebuilding/establishing boundary btw. Itself & rest of world)
Adaptivity-
maintain own equilibrium/reacts to changes in environment to enhance chances of further existence
Information preservation-
role of components preserved; does not depend on continued existence of components to survive
Spontaneous increase of complexity-
Increasingly more parts, more complex relationships btw. Parts, more complex behaviors of parts, etc.

3. Major Instantiations Autocatalytic chemical reactions (uncoded life)
Living systems
Intelligent systems
Cultural systems

4. Interrelationships
Complex interrelations between these different types of systems because one system is built on top of another one
The complex self-reinforcing relations between levels is probably crucial for understanding intelligence

5. Language As the key of a symbolic layer
Classical symbolic AI systems exhibit great complexity for particular functionalities (e.g. expert problem solving, chess playing, etc.)
Functionality is typically completely programmed by hand based on an analysis of human competence
Frozen instances of intelligence as opposed to evolving adaptive intelligence systems
Do not have any direct relationship to reality but require a human to interpret reality and supply the symbolic descriptions that are needed.

6. Language As the key of a symbolic layer cont.
Experiments exploring issues like:
Ambiguity & semantic indeterminacy
Formation of morphological & syntactic structures
Indirect mapping of meanings to words (one word may capture different meanings)
Emergence & handling of ambiguity
Grounding of language in robotic agents
Creation of new meaning
Through such experiments, better understand how complex symbolic representations form themselves