Aaron Sloman is a philosopher and researcher on artificial intelligence and cognitive science. He is the author of several papers on philosophy, epistemology and artificial intelligence. Aaron gave this talk during the Winter Intelligence Oxford Conference on Monday 10 December 2012. The conference was organized by the Future of Humanity Institute.
The universe is made up of matter, energy and information, interacting with each other and producing new kinds of matter, energy, information and interaction. How? How did all this come out of a cloud of dust? In order to find explanations we first need much better descriptions of what needs to be explained. This is a multi-disciplinary project attempting to describe and explain the variety of biological information-processing mechanisms involved in the production of new biological information-processing mechanisms, on many time scales, between the earliest days of the planet with no life, only physical and chemical structures, including volcanic eruptions, asteroid impacts, solar and stellar radiation, and many other physical/chemical processes (or perhaps starting even earlier, when there was only a dust cloud in this part of the solar system?).
Evolution can be thought of as a (blind) Theorem Prover (or theorem discoverer). Proving (discovering) theorems about what is possible (possible types of information, possible types of information-processing, possible uses of information-processing) Proving (discovering) many theorems in parallel (including especially theorems about new types of information and new useful types of information-processing) Sharing partial results among proofs of different things (Very different biological phenomena may share origins, mechanisms, information, ...) Combining separately derived old theorems in constructions of new proofs (One way of thinking about symbiogenesis.)
Delegating some theorem-discovery to neonates and toddlers (epigenesis/ontogenesis). (Including individuals too under-developed to know what they are discovering.) Delegating some theorem-discovery to social/cultural developments. (Including memes and other discoveries shared unwittingly within and between communities.) Using older products to speed up discovery of new ones (Using old and new kinds of architectures, sensori-motor morphologies, types of information, types of processing mechanism, types of control & decision making, types of testing.) The "proofs" of discovered possibilities are implicit in evolutionary and/or developmental trajectories. They demonstrate the possibility of development of new forms of development evolution of new types of evolution learning new ways to learn evolution of new types of learning (including mathematical learning: by working things out without requiring empirical evidence) evolution of new forms of development development of new forms of learning (why can't a toddler learn quantum mechanics?) how new forms of learning support new forms of evolution how new forms of development support new forms of evolution (e.g. postponing sexual maturity until mate-selection mating and nurturing can be influenced by much learning) .... .... and ways in which social cultural evolution add to the mix These processes produce new forms of representation, new ontologies and information contents, new information-processing mechanisms, new sensory-motor morphologies, new forms of control, new forms of social interaction, new forms of creativity, ... and more. Some may even accelerate evolution.
A draft growing list of transitions in types of biological information-processing: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/evolution-info-transitions.html
An attempt to identify a major type of mathematical reasoning with precursors in perception and reasoning about affordances, not yet replicated in AI systems: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/triangle-theorem.html
Even in microbes I suspect there's much still to be learnt about the varying challenges and opportunities faced by microbes at various stages in their evolution, including new challenges produced by environmental changes and new opportunities (e.g. for control) produced by previous evolved features and competences -- and the mechanisms that evolved in response to those challenges and opportunities. Example: which organisms were first able to learn about an enduring spatial configuration of resources, obstacles and dangers, only a tiny fragment of which can be sensed at any one time?
What changes occurred to meet that need? Use of "external memories" (e.g. stigmergy) Use of "internal memories" (various kinds of "cognitive maps") More examples to be collected here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/evolution-info-transitions.html
Aaron Sloman is a philosopher and researcher on artificial intelligence and cognitive science. He is the author of several papers on philosophy, epistemology and artificial intelligence. Aaron gave this talk during the Winter Intelligence Oxford Conference on Monday 10 December 2012. The conference was organized by the Future of Humanity Institute.
The universe is made up of matter, energy and information, interacting with each other and producing new kinds of matter, energy, information and interaction. How? How did all this come out of a cloud of dust? In order to find explanations we first need much better descriptions of what needs to be explained. This is a multi-disciplinary project attempting to describe and explain the variety of biological information-processing mechanisms involved in the production of new biological information-processing mechanisms, on many time scales, between the earliest days of the planet with no life, only physical and chemical structures, including volcanic eruptions, asteroid impacts, solar and stellar radiation, and many other physical/chemical processes (or perhaps starting even earlier, when there was only a dust cloud in this part of the solar system?).
Evolution can be thought of as a (blind) Theorem Prover (or theorem discoverer). Proving (discovering) theorems about what is possible (possible types of information, possible types of information-processing, possible uses of information-processing) Proving (discovering) many theorems in parallel (including especially theorems about new types of information and new useful types of information-processing) Sharing partial results among proofs of different things (Very different biological phenomena may share origins, mechanisms, information, ...) Combining separately derived old theorems in constructions of new proofs (One way of thinking about symbiogenesis.)
Delegating some theorem-discovery to neonates and toddlers (epigenesis/ontogenesis). (Including individuals too under-developed to know what they are discovering.) Delegating some theorem-discovery to social/cultural developments. (Including memes and other discoveries shared unwittingly within and between communities.) Using older products to speed up discovery of new ones (Using old and new kinds of architectures, sensori-motor morphologies, types of information, types of processing mechanism, types of control & decision making, types of testing.) The "proofs" of discovered possibilities are implicit in evolutionary and/or developmental trajectories. They demonstrate the possibility of development of new forms of development evolution of new types of evolution learning new ways to learn evolution of new types of learning (including mathematical learning: by working things out without requiring empirical evidence) evolution of new forms of development development of new forms of learning (why can't a toddler learn quantum mechanics?) how new forms of learning support new forms of evolution how new forms of development support new forms of evolution (e.g. postponing sexual maturity until mate-selection mating and nurturing can be influenced by much learning) .... .... and ways in which social cultural evolution add to the mix These processes produce new forms of representation, new ontologies and information contents, new information-processing mechanisms, new sensory-motor morphologies, new forms of control, new forms of social interaction, new forms of creativity, ... and more. Some may even accelerate evolution.
A draft growing list of transitions in types of biological information-processing: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/evolution-info-transitions.html
An attempt to identify a major type of mathematical reasoning with precursors in perception and reasoning about affordances, not yet replicated in AI systems: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/triangle-theorem.html
Even in microbes I suspect there's much still to be learnt about the varying challenges and opportunities faced by microbes at various stages in their evolution, including new challenges produced by environmental changes and new opportunities (e.g. for control) produced by previous evolved features and competences -- and the mechanisms that evolved in response to those challenges and opportunities. Example: which organisms were first able to learn about an enduring spatial configuration of resources, obstacles and dangers, only a tiny fragment of which can be sensed at any one time?
What changes occurred to meet that need? Use of "external memories" (e.g. stigmergy) Use of "internal memories" (various kinds of "cognitive maps") More examples to be collected here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/evolution-info-transitions.html
http://www.youtube.com/watch?v=BNul52kFI74