�ݺ�ߣshows by User: asloman

�ݺ�ߣshows by User: asloman / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: asloman / Fri, 27 Jul 2018 12:07:38 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: asloman Construction kits for evolving life -- Including evolving minds and mathematical abilities /slideshow/construction-kits-for-evolving-life-including-evolving-minds-and-mathematical-abilities/107709207 construction-kits-180727120738
Darwin's theory of evolution by natural selection does not adequately explain the generative power of biological evolution. For that we need to understand the mechanisms involved in producing new options for natural selection, without which there would always be the same set of possibilities available. This applies also to the construction kits: evolution can produce new construction kits, "Derived" construction kits, based on the Fundamental construction kit provided by the physical universe and its originally lifeless physical and chemical mechanisms. It turns out that life needs both concrete and abstract construction kits, of ever increasing complexity. This paper introduces some basic ideas, though far more empirical and theoretical research is required, combining multiple disciplines. �ݺ�ߣshare no longer allows presentations to be updated, so I no longer use it. For a later version search for: Sloman "Construction kits for evolving life" cogaff. Most of my slideshare presentations have newer versions in the CogAff web site at the University of Birmingham, UK. (Not Alabama)]]>
Darwin's theory of evolution by natural selection does not adequately explain the generative power of biological evolution. For that we need to understand the mechanisms involved in producing new options for natural selection, without which there would always be the same set of possibilities available. This applies also to the construction kits: evolution can produce new construction kits, "Derived" construction kits, based on the Fundamental construction kit provided by the physical universe and its originally lifeless physical and chemical mechanisms. It turns out that life needs both concrete and abstract construction kits, of ever increasing complexity. This paper introduces some basic ideas, though far more empirical and theoretical research is required, combining multiple disciplines. �ݺ�ߣshare no longer allows presentations to be updated, so I no longer use it. For a later version search for: Sloman "Construction kits for evolving life" cogaff. Most of my slideshare presentations have newer versions in the CogAff web site at the University of Birmingham, UK. (Not Alabama)]]> Fri, 27 Jul 2018 12:07:38 GMT /slideshow/construction-kits-for-evolving-life-including-evolving-minds-and-mathematical-abilities/107709207 asloman@slideshare.net(asloman) Construction kits for evolving life -- Including evolving minds and mathematical abilities asloman Darwin's theory of evolution by natural selection does not adequately explain the generative power of biological evolution. For that we need to understand the mechanisms involved in producing new options for natural selection, without which there would always be the same set of possibilities available. This applies also to the construction kits: evolution can produce new construction kits, "Derived" construction kits, based on the Fundamental construction kit provided by the physical universe and its originally lifeless physical and chemical mechanisms. It turns out that life needs both concrete and abstract construction kits, of ever increasing complexity. This paper introduces some basic ideas, though far more empirical and theoretical research is required, combining multiple disciplines. �ݺ�ߣshare no longer allows presentations to be updated, so I no longer use it. For a later version search for: Sloman "Construction kits for evolving life" cogaff. Most of my slideshare presentations have newer versions in the CogAff web site at the University of Birmingham, UK. (Not Alabama) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/construction-kits-180727120738-thumbnail.jpg?width=120&height=120&fit=bounds" /> Darwin's theory of evolution by natural selection does not adequately explain the generative power of biological evolution. For that we need to understand the mechanisms involved in producing new options for natural selection, without which there would always be the same set of possibilities available. This applies also to the construction kits: evolution can produce new construction kits, "Derived" construction kits, based on the Fundamental construction kit provided by the physical universe and its originally lifeless physical and chemical mechanisms. It turns out that life needs both concrete and abstract construction kits, of ever increasing complexity. This paper introduces some basic ideas, though far more empirical and theoretical research is required, combining multiple disciplines. �ݺ�ߣshare no longer allows presentations to be updated, so I no longer use it. For a later version search for: Sloman "Construction kits for evolving life" cogaff. Most of my slideshare presentations have newer versions in the CogAff web site at the University of Birmingham, UK. (Not Alabama)

Construction kits for evolving life -- Including evolving minds and mathematical abilities from Aaron Sloman

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0 The Turing Inspired Meta-Morphogenesis Project -- The self-informing universe(Update 2018) /slideshow/the-turing-inspired-metamorphogenesis-project-the-selfinforming-universeupdate-2018/107709205 meta-morphogenesis-180727120737
This replaces an earlier version. The latest version with clickable links is available at Versions with clickable links available at http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html ]]>
This replaces an earlier version. The latest version with clickable links is available at Versions with clickable links available at http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html ]]> Fri, 27 Jul 2018 12:07:37 GMT /slideshow/the-turing-inspired-metamorphogenesis-project-the-selfinforming-universeupdate-2018/107709205 asloman@slideshare.net(asloman) The Turing Inspired Meta-Morphogenesis Project -- The self-informing universe(Update 2018) asloman This replaces an earlier version. The latest version with clickable links is available at Versions with clickable links available at http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/meta-morphogenesis-180727120737-thumbnail.jpg?width=120&height=120&fit=bounds" /> This replaces an earlier version. The latest version with clickable links is available at Versions with clickable links available at http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html

The Turing Inspired Meta-Morphogenesis Project -- The self-informing universe(Update 2018) from Aaron Sloman

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0 Evolution of language and vision /slideshow/evolution-of-46383806/46383806 ai-icy-vision-language-150327212533-conversion-gate01
Reorganised several times since first uploaded: most recently 25 Jan 2016 ------------------------------------------------------------------------------------------------------- �ݺ�ߣs include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015 ------------------------------------------------------------------------------------------------------------- Two questions are shown to have deep connections: What are the functions of vision in animals? and How did human languages evolve? The answer given here is that the functions of vision need to be supported by richly structured internal languages (forms of representation used for acquiring, storing, manipulating, deriving and using information), from which it follows that internal languages must have evolved before languages for communication. --------------------------------------------------------------------------------------------------------------- The account of the functions of vision mentions early AI vision, the impact of Marr and the even greater impact of Gibson, but argues that they did not recognize all the functions of vision, e.g. the uses of vision in making mathematical discoveries leading to Euclid's elements. --------------------------------------------------------------------------------------------------------------- Many questions are left unanswered by this research, which is part of the Meta-Morphogenesis project, introduced here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html --------------------------------------------------------------------------------------------------------------- A slideshare presentation on "origins of language" by Jasmine Wong, adds some useful additional evidence, but presents a simpler theory: http://www.slideshare.net/JasmineWong6/origins-of-language --------------------------------------------------------------------------------------------------------------- Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015]]>
Reorganised several times since first uploaded: most recently 25 Jan 2016 ------------------------------------------------------------------------------------------------------- �ݺ�ߣs include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015 ------------------------------------------------------------------------------------------------------------- Two questions are shown to have deep connections: What are the functions of vision in animals? and How did human languages evolve? The answer given here is that the functions of vision need to be supported by richly structured internal languages (forms of representation used for acquiring, storing, manipulating, deriving and using information), from which it follows that internal languages must have evolved before languages for communication. --------------------------------------------------------------------------------------------------------------- The account of the functions of vision mentions early AI vision, the impact of Marr and the even greater impact of Gibson, but argues that they did not recognize all the functions of vision, e.g. the uses of vision in making mathematical discoveries leading to Euclid's elements. --------------------------------------------------------------------------------------------------------------- Many questions are left unanswered by this research, which is part of the Meta-Morphogenesis project, introduced here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html --------------------------------------------------------------------------------------------------------------- A slideshare presentation on "origins of language" by Jasmine Wong, adds some useful additional evidence, but presents a simpler theory: http://www.slideshare.net/JasmineWong6/origins-of-language --------------------------------------------------------------------------------------------------------------- Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015]]> Fri, 27 Mar 2015 21:25:33 GMT /slideshow/evolution-of-46383806/46383806 asloman@slideshare.net(asloman) Evolution of language and vision asloman Reorganised several times since first uploaded: most recently 25 Jan 2016 ------------------------------------------------------------------------------------------------------- �ݺ�ߣs include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015 ------------------------------------------------------------------------------------------------------------- Two questions are shown to have deep connections: What are the functions of vision in animals? and How did human languages evolve? The answer given here is that the functions of vision need to be supported by richly structured internal languages (forms of representation used for acquiring, storing, manipulating, deriving and using information), from which it follows that internal languages must have evolved before languages for communication. --------------------------------------------------------------------------------------------------------------- The account of the functions of vision mentions early AI vision, the impact of Marr and the even greater impact of Gibson, but argues that they did not recognize all the functions of vision, e.g. the uses of vision in making mathematical discoveries leading to Euclid's elements. --------------------------------------------------------------------------------------------------------------- Many questions are left unanswered by this research, which is part of the Meta-Morphogenesis project, introduced here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html --------------------------------------------------------------------------------------------------------------- A slideshare presentation on "origins of language" by Jasmine Wong, adds some useful additional evidence, but presents a simpler theory: http://www.slideshare.net/JasmineWong6/origins-of-language --------------------------------------------------------------------------------------------------------------- Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ai-icy-vision-language-150327212533-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reorganised several times since first uploaded: most recently 25 Jan 2016 ------------------------------------------------------------------------------------------------------- �ݺ�ߣs include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015 ------------------------------------------------------------------------------------------------------------- Two questions are shown to have deep connections: What are the functions of vision in animals? and How did human languages evolve? The answer given here is that the functions of vision need to be supported by richly structured internal languages (forms of representation used for acquiring, storing, manipulating, deriving and using information), from which it follows that internal languages must have evolved before languages for communication. --------------------------------------------------------------------------------------------------------------- The account of the functions of vision mentions early AI vision, the impact of Marr and the even greater impact of Gibson, but argues that they did not recognize all the functions of vision, e.g. the uses of vision in making mathematical discoveries leading to Euclid's elements. --------------------------------------------------------------------------------------------------------------- Many questions are left unanswered by this research, which is part of the Meta-Morphogenesis project, introduced here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html --------------------------------------------------------------------------------------------------------------- A slideshare presentation on "origins of language" by Jasmine Wong, adds some useful additional evidence, but presents a simpler theory: http://www.slideshare.net/JasmineWong6/origins-of-language --------------------------------------------------------------------------------------------------------------- Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015

Evolution of language and vision from Aaron Sloman

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0 Virtuality, causation and the mind-body relationship /slideshow/virtuality-causation/34485482 virtuality-causation-140509101350-phpapp02
Extends my previous introductions to virtual machines and their role both in artefacts and products of biological evolution. This attempts to correct various erroneous assumptions about computation, functionalism, supervenience, life, information, and causation. See also http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html ]]>
Extends my previous introductions to virtual machines and their role both in artefacts and products of biological evolution. This attempts to correct various erroneous assumptions about computation, functionalism, supervenience, life, information, and causation. See also http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html ]]> Fri, 09 May 2014 10:13:50 GMT /slideshow/virtuality-causation/34485482 asloman@slideshare.net(asloman) Virtuality, causation and the mind-body relationship asloman Extends my previous introductions to virtual machines and their role both in artefacts and products of biological evolution. This attempts to correct various erroneous assumptions about computation, functionalism, supervenience, life, information, and causation. See also http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/virtuality-causation-140509101350-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Extends my previous introductions to virtual machines and their role both in artefacts and products of biological evolution. This attempts to correct various erroneous assumptions about computation, functionalism, supervenience, life, information, and causation. See also http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html

Virtuality, causation and the mind-body relationship from Aaron Sloman

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0 How to Build a Research Roadmap (avoiding tempting dead-ends) /slideshow/how-to-build-a-research-roadmap/29712684 building-roadmaps-sloman-140105164253-phpapp01
What's a Research Roadmap For? Why do we need one? How can we avoid the usual trap of making bold promises to do X, Y and Z, then hope that our previous promises will not be remembered the next time we apply for funds to do X, Y and Z? How can we produce a sensible, well informed roadmap? Originally presented at the euCognition Research Roadmap discussion in Munich on 12 Jan 2007 This suggests a way to avoid tempting dead ends (repeating old promises that proved unrealistic) by examining many long term goals, including describing existing human and animal competences not yet achieved by robots, then working backwards systematically by investigating requirements for those competences, and requirements for meeting those requirements, etc. Insread of generating a single linear roadmap this should produce a partially ordered network of intermediate targets, leading back, to short term goals that may be achievable starting from where we are. Such a roadmap will inevitably have mistakes: over-optimistic goals, missing preconditions, unrecognised opportunities. But if the work is done in many teams in a fully open manner with as much collaboration as possible, it should be possible to make faster, deeper, progress than can be achieved by brain-storming discussions of where we can get in a few years.]]>
What's a Research Roadmap For? Why do we need one? How can we avoid the usual trap of making bold promises to do X, Y and Z, then hope that our previous promises will not be remembered the next time we apply for funds to do X, Y and Z? How can we produce a sensible, well informed roadmap? Originally presented at the euCognition Research Roadmap discussion in Munich on 12 Jan 2007 This suggests a way to avoid tempting dead ends (repeating old promises that proved unrealistic) by examining many long term goals, including describing existing human and animal competences not yet achieved by robots, then working backwards systematically by investigating requirements for those competences, and requirements for meeting those requirements, etc. Insread of generating a single linear roadmap this should produce a partially ordered network of intermediate targets, leading back, to short term goals that may be achievable starting from where we are. Such a roadmap will inevitably have mistakes: over-optimistic goals, missing preconditions, unrecognised opportunities. But if the work is done in many teams in a fully open manner with as much collaboration as possible, it should be possible to make faster, deeper, progress than can be achieved by brain-storming discussions of where we can get in a few years.]]> Sun, 05 Jan 2014 16:42:53 GMT /slideshow/how-to-build-a-research-roadmap/29712684 asloman@slideshare.net(asloman) How to Build a Research Roadmap (avoiding tempting dead-ends) asloman What's a Research Roadmap For? Why do we need one? How can we avoid the usual trap of making bold promises to do X, Y and Z, then hope that our previous promises will not be remembered the next time we apply for funds to do X, Y and Z? How can we produce a sensible, well informed roadmap? Originally presented at the euCognition Research Roadmap discussion in Munich on 12 Jan 2007 This suggests a way to avoid tempting dead ends (repeating old promises that proved unrealistic) by examining many long term goals, including describing existing human and animal competences not yet achieved by robots, then working backwards systematically by investigating requirements for those competences, and requirements for meeting those requirements, etc. Insread of generating a single linear roadmap this should produce a partially ordered network of intermediate targets, leading back, to short term goals that may be achievable starting from where we are. Such a roadmap will inevitably have mistakes: over-optimistic goals, missing preconditions, unrecognised opportunities. But if the work is done in many teams in a fully open manner with as much collaboration as possible, it should be possible to make faster, deeper, progress than can be achieved by brain-storming discussions of where we can get in a few years. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/building-roadmaps-sloman-140105164253-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> What's a Research Roadmap For? Why do we need one? How can we avoid the usual trap of making bold promises to do X, Y and Z, then hope that our previous promises will not be remembered the next time we apply for funds to do X, Y and Z? How can we produce a sensible, well informed roadmap? Originally presented at the euCognition Research Roadmap discussion in Munich on 12 Jan 2007 This suggests a way to avoid tempting dead ends (repeating old promises that proved unrealistic) by examining many long term goals, including describing existing human and animal competences not yet achieved by robots, then working backwards systematically by investigating requirements for those competences, and requirements for meeting those requirements, etc. Insread of generating a single linear roadmap this should produce a partially ordered network of intermediate targets, leading back, to short term goals that may be achievable starting from where we are. Such a roadmap will inevitably have mistakes: over-optimistic goals, missing preconditions, unrecognised opportunities. But if the work is done in many teams in a fully open manner with as much collaboration as possible, it should be possible to make faster, deeper, progress than can be achieved by brain-storming discussions of where we can get in a few years.

How to Build a Research Roadmap (avoiding tempting dead-ends) from Aaron Sloman

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0 Evolution euclid-mathematical-cognition /slideshow/evolution-euclidmathematicalcognition/26251563 evolution-euclid-mathematical-cognition-130916200116-phpapp01
If learning maths requires a teacher, where did the first teachers come from? or Why (and how) did biological evolution produce mathematicians? Presentation at Symposium on Mathematical Cognition AISB2010 Part of the Meta-Morphogenesis Project. See also this discussion of toddler theorems: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/toddler-theorems.html Evolution of human mathematics from earlier abilities to perceived, use and reason about affordances, spatial possibilities and constraints. The necessity of mathematical truth does not imply infallibility of mathematical reasoning. (Lakatos). Toddlers discover theorems without knowing it. Later they may learn to reflect on and talk about what they have learnt. Compare Annette Karmiloff-Smith on "Representational re-description". Why is it still so hard to give robots and AI systems the ability to reason spatially as mathematicians do (except for simple special cases, e.g. where space is discretised.)]]>
If learning maths requires a teacher, where did the first teachers come from? or Why (and how) did biological evolution produce mathematicians? Presentation at Symposium on Mathematical Cognition AISB2010 Part of the Meta-Morphogenesis Project. See also this discussion of toddler theorems: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/toddler-theorems.html Evolution of human mathematics from earlier abilities to perceived, use and reason about affordances, spatial possibilities and constraints. The necessity of mathematical truth does not imply infallibility of mathematical reasoning. (Lakatos). Toddlers discover theorems without knowing it. Later they may learn to reflect on and talk about what they have learnt. Compare Annette Karmiloff-Smith on "Representational re-description". Why is it still so hard to give robots and AI systems the ability to reason spatially as mathematicians do (except for simple special cases, e.g. where space is discretised.)]]> Mon, 16 Sep 2013 20:01:16 GMT /slideshow/evolution-euclidmathematicalcognition/26251563 asloman@slideshare.net(asloman) Evolution euclid-mathematical-cognition asloman If learning maths requires a teacher, where did the first teachers come from? or Why (and how) did biological evolution produce mathematicians? Presentation at Symposium on Mathematical Cognition AISB2010 Part of the Meta-Morphogenesis Project. See also this discussion of toddler theorems: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/toddler-theorems.html Evolution of human mathematics from earlier abilities to perceived, use and reason about affordances, spatial possibilities and constraints. The necessity of mathematical truth does not imply infallibility of mathematical reasoning. (Lakatos). Toddlers discover theorems without knowing it. Later they may learn to reflect on and talk about what they have learnt. Compare Annette Karmiloff-Smith on "Representational re-description". Why is it still so hard to give robots and AI systems the ability to reason spatially as mathematicians do (except for simple special cases, e.g. where space is discretised.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/evolution-euclid-mathematical-cognition-130916200116-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> If learning maths requires a teacher, where did the first teachers come from? or Why (and how) did biological evolution produce mathematicians? Presentation at Symposium on Mathematical Cognition AISB2010 Part of the Meta-Morphogenesis Project. See also this discussion of toddler theorems: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/toddler-theorems.html Evolution of human mathematics from earlier abilities to perceived, use and reason about affordances, spatial possibilities and constraints. The necessity of mathematical truth does not imply infallibility of mathematical reasoning. (Lakatos). Toddlers discover theorems without knowing it. Later they may learn to reflect on and talk about what they have learnt. Compare Annette Karmiloff-Smith on "Representational re-description". Why is it still so hard to give robots and AI systems the ability to reason spatially as mathematicians do (except for simple special cases, e.g. where space is discretised.)

Evolution euclid-mathematical-cognition from Aaron Sloman

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0 A multi-picture challenge for theories of vision /slideshow/a-multipicture/22309007 multipic-challenge-130601190017-phpapp01
(Modified 7th June 2013 to include some droodles.) Some informal experiments are presented whose results help to challenge most theories of vision and proposed mechanisms of vision. A possible explanatory information-processing architecture is proposed, based on multiple dynamical systems, grown during an individual's life time, most of which are dormant most of the time, but which can be very rapidly activated and instantiated so as to build a multi-ontology interpretation of the currently, and recently, available visual information -- e.g. turning a corner into a busy street in an unfamiliar city. As far as I know, there is no working implementation of such a system, though a very early prototype called Popeye (implemented in Pop2) around 1976 is summarised. Many hard unsolved problems remain, though most of them are ignored by research on vision that makes narrow assumptions about the functions of biological vision.]]>
(Modified 7th June 2013 to include some droodles.) Some informal experiments are presented whose results help to challenge most theories of vision and proposed mechanisms of vision. A possible explanatory information-processing architecture is proposed, based on multiple dynamical systems, grown during an individual's life time, most of which are dormant most of the time, but which can be very rapidly activated and instantiated so as to build a multi-ontology interpretation of the currently, and recently, available visual information -- e.g. turning a corner into a busy street in an unfamiliar city. As far as I know, there is no working implementation of such a system, though a very early prototype called Popeye (implemented in Pop2) around 1976 is summarised. Many hard unsolved problems remain, though most of them are ignored by research on vision that makes narrow assumptions about the functions of biological vision.]]> Sat, 01 Jun 2013 19:00:17 GMT /slideshow/a-multipicture/22309007 asloman@slideshare.net(asloman) A multi-picture challenge for theories of vision asloman (Modified 7th June 2013 to include some droodles.) Some informal experiments are presented whose results help to challenge most theories of vision and proposed mechanisms of vision. A possible explanatory information-processing architecture is proposed, based on multiple dynamical systems, grown during an individual's life time, most of which are dormant most of the time, but which can be very rapidly activated and instantiated so as to build a multi-ontology interpretation of the currently, and recently, available visual information -- e.g. turning a corner into a busy street in an unfamiliar city. As far as I know, there is no working implementation of such a system, though a very early prototype called Popeye (implemented in Pop2) around 1976 is summarised. Many hard unsolved problems remain, though most of them are ignored by research on vision that makes narrow assumptions about the functions of biological vision. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/multipic-challenge-130601190017-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> (Modified 7th June 2013 to include some droodles.) Some informal experiments are presented whose results help to challenge most theories of vision and proposed mechanisms of vision. A possible explanatory information-processing architecture is proposed, based on multiple dynamical systems, grown during an individual's life time, most of which are dormant most of the time, but which can be very rapidly activated and instantiated so as to build a multi-ontology interpretation of the currently, and recently, available visual information -- e.g. turning a corner into a busy street in an unfamiliar city. As far as I know, there is no working implementation of such a system, though a very early prototype called Popeye (implemented in Pop2) around 1976 is summarised. Many hard unsolved problems remain, though most of them are ignored by research on vision that makes narrow assumptions about the functions of biological vision.

A multi-picture challenge for theories of vision from Aaron Sloman

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0 Meta-Morphogenesis, Evolution, Cognitive Robotics and Developmental Cognitive science /slideshow/dag13-sloman/19918055 dag13-sloman-130424180656-phpapp01
How could a planet, condensed from a cloud of dust, produce minds -- and products of minds, along with microbes, mice, monkeys, mathematics, music, marmite, murder, megalomania, and all other forms and products of life on earth (and possibly elsewhere)? This presentation introduces the ambitious, multi-disciplinary Meta-Morphogenesis project, partly inspired by Turing's 1952 paper on morphogenesis. It may lead to an answer, by identifying the many transitions between different types and mechanisms of biological information processing, including transitions that changed the mechanisms of change, altering forms of evolution, development, learning, culture and ecosystem dynamics. One of the questions raised is whether chemical information-processing is capable of supporting processes that would be infeasible or impossible on a Turing machine or conventional computer. A 2hour 30 min recording of this tutorial was made by Adam Ford, available here: http://www.youtube.com/watch?v=BNul52kFI74 (new version installed on 14 Jun 2013 with titles and audio problem fixed). Also available here http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#m-m-tut "Information" here is used in Jane Austen's sense, not Claude Shannon's sense. See http://www.cs.bham.ac.uk/research/projects/cogaff/misc/austen-info.html More information about the project is available here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html Adam Ford interviewed the author about some of these topics at the AGI conference in December 2012 in this video: http://www.youtube.com/watch?v=iuH8dC7Snno Related PDF presentations can be found here http://www.cs.bham.ac.uk/research/projects/cogaff/talks]]>
How could a planet, condensed from a cloud of dust, produce minds -- and products of minds, along with microbes, mice, monkeys, mathematics, music, marmite, murder, megalomania, and all other forms and products of life on earth (and possibly elsewhere)? This presentation introduces the ambitious, multi-disciplinary Meta-Morphogenesis project, partly inspired by Turing's 1952 paper on morphogenesis. It may lead to an answer, by identifying the many transitions between different types and mechanisms of biological information processing, including transitions that changed the mechanisms of change, altering forms of evolution, development, learning, culture and ecosystem dynamics. One of the questions raised is whether chemical information-processing is capable of supporting processes that would be infeasible or impossible on a Turing machine or conventional computer. A 2hour 30 min recording of this tutorial was made by Adam Ford, available here: http://www.youtube.com/watch?v=BNul52kFI74 (new version installed on 14 Jun 2013 with titles and audio problem fixed). Also available here http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#m-m-tut "Information" here is used in Jane Austen's sense, not Claude Shannon's sense. See http://www.cs.bham.ac.uk/research/projects/cogaff/misc/austen-info.html More information about the project is available here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html Adam Ford interviewed the author about some of these topics at the AGI conference in December 2012 in this video: http://www.youtube.com/watch?v=iuH8dC7Snno Related PDF presentations can be found here http://www.cs.bham.ac.uk/research/projects/cogaff/talks]]> Wed, 24 Apr 2013 18:06:56 GMT /slideshow/dag13-sloman/19918055 asloman@slideshare.net(asloman) Meta-Morphogenesis, Evolution, Cognitive Robotics and Developmental Cognitive science asloman How could a planet, condensed from a cloud of dust, produce minds -- and products of minds, along with microbes, mice, monkeys, mathematics, music, marmite, murder, megalomania, and all other forms and products of life on earth (and possibly elsewhere)? This presentation introduces the ambitious, multi-disciplinary Meta-Morphogenesis project, partly inspired by Turing's 1952 paper on morphogenesis. It may lead to an answer, by identifying the many transitions between different types and mechanisms of biological information processing, including transitions that changed the mechanisms of change, altering forms of evolution, development, learning, culture and ecosystem dynamics. One of the questions raised is whether chemical information-processing is capable of supporting processes that would be infeasible or impossible on a Turing machine or conventional computer. A 2hour 30 min recording of this tutorial was made by Adam Ford, available here: http://www.youtube.com/watch?v=BNul52kFI74 (new version installed on 14 Jun 2013 with titles and audio problem fixed). Also available here http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#m-m-tut "Information" here is used in Jane Austen's sense, not Claude Shannon's sense. See http://www.cs.bham.ac.uk/research/projects/cogaff/misc/austen-info.html More information about the project is available here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html Adam Ford interviewed the author about some of these topics at the AGI conference in December 2012 in this video: http://www.youtube.com/watch?v=iuH8dC7Snno Related PDF presentations can be found here http://www.cs.bham.ac.uk/research/projects/cogaff/talks <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dag13-sloman-130424180656-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> How could a planet, condensed from a cloud of dust, produce minds -- and products of minds, along with microbes, mice, monkeys, mathematics, music, marmite, murder, megalomania, and all other forms and products of life on earth (and possibly elsewhere)? This presentation introduces the ambitious, multi-disciplinary Meta-Morphogenesis project, partly inspired by Turing's 1952 paper on morphogenesis. It may lead to an answer, by identifying the many transitions between different types and mechanisms of biological information processing, including transitions that changed the mechanisms of change, altering forms of evolution, development, learning, culture and ecosystem dynamics. One of the questions raised is whether chemical information-processing is capable of supporting processes that would be infeasible or impossible on a Turing machine or conventional computer. A 2hour 30 min recording of this tutorial was made by Adam Ford, available here: http://www.youtube.com/watch?v=BNul52kFI74 (new version installed on 14 Jun 2013 with titles and audio problem fixed). Also available here http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#m-m-tut "Information" here is used in Jane Austen's sense, not Claude Shannon's sense. See http://www.cs.bham.ac.uk/research/projects/cogaff/misc/austen-info.html More information about the project is available here: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html Adam Ford interviewed the author about some of these topics at the AGI conference in December 2012 in this video: http://www.youtube.com/watch?v=iuH8dC7Snno Related PDF presentations can be found here http://www.cs.bham.ac.uk/research/projects/cogaff/talks

Meta-Morphogenesis, Evolution, Cognitive Robotics and Developmental Cognitive science from Aaron Sloman

]]> 3444 7 https://cdn.slidesharecdn.com/ss_thumbnails/dag13-sloman-130424180656-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 What is computational thinking? Who needs it? Why? How can it be learnt? (Can it be taught?) /slideshow/what-is-computational-thinking-who-needs-it-why-how-can-it-be-learnt-can-it-be-taught/14365516 alt2012-sloman-120920192728-phpapp02
What is computational thinking? Who needs it? Why? How can it be learnt? Can it be taught? How? �ݺ�ߣs for invited presentation at Conference of ALT (Association for Learning Technology) 11th Sept 2012, University of Manchester. PDF available (easier for printing, selecting text, etc.): http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#talk105 A video of the actual presentation (using no slides because of a projector problem) is now available here http://www.youtube.com/watch?v=QXAFz3L2Qpo It also has been made available as "slide 47" after the PDF presentation on this page. I attempt to generalise Jeannette Wing's notion of "Computational thinking" (ACM 2006) to include attempting to understand much biological information processing, and try to show the necessity for educators to do deep computational thinking if they wish to facilitate processes of learning. ]]>
What is computational thinking? Who needs it? Why? How can it be learnt? Can it be taught? How? �ݺ�ߣs for invited presentation at Conference of ALT (Association for Learning Technology) 11th Sept 2012, University of Manchester. PDF available (easier for printing, selecting text, etc.): http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#talk105 A video of the actual presentation (using no slides because of a projector problem) is now available here http://www.youtube.com/watch?v=QXAFz3L2Qpo It also has been made available as "slide 47" after the PDF presentation on this page. I attempt to generalise Jeannette Wing's notion of "Computational thinking" (ACM 2006) to include attempting to understand much biological information processing, and try to show the necessity for educators to do deep computational thinking if they wish to facilitate processes of learning. ]]> Thu, 20 Sep 2012 19:27:26 GMT /slideshow/what-is-computational-thinking-who-needs-it-why-how-can-it-be-learnt-can-it-be-taught/14365516 asloman@slideshare.net(asloman) What is computational thinking? Who needs it? Why? How can it be learnt? (Can it be taught?) asloman What is computational thinking? Who needs it? Why? How can it be learnt? Can it be taught? How? �ݺ�ߣs for invited presentation at Conference of ALT (Association for Learning Technology) 11th Sept 2012, University of Manchester. PDF available (easier for printing, selecting text, etc.): http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#talk105 A video of the actual presentation (using no slides because of a projector problem) is now available here http://www.youtube.com/watch?v=QXAFz3L2Qpo It also has been made available as "slide 47" after the PDF presentation on this page. I attempt to generalise Jeannette Wing's notion of "Computational thinking" (ACM 2006) to include attempting to understand much biological information processing, and try to show the necessity for educators to do deep computational thinking if they wish to facilitate processes of learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/alt2012-sloman-120920192728-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> What is computational thinking? Who needs it? Why? How can it be learnt? Can it be taught? How? �ݺ�ߣs for invited presentation at Conference of ALT (Association for Learning Technology) 11th Sept 2012, University of Manchester. PDF available (easier for printing, selecting text, etc.): http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#talk105 A video of the actual presentation (using no slides because of a projector problem) is now available here http://www.youtube.com/watch?v=QXAFz3L2Qpo It also has been made available as "slide 47" after the PDF presentation on this page. I attempt to generalise Jeannette Wing's notion of "Computational thinking" (ACM 2006) to include attempting to understand much biological information processing, and try to show the necessity for educators to do deep computational thinking if they wish to facilitate processes of learning.

What is computational thinking? Who needs it? Why? How can it be learnt? (Can it be taught?) from Aaron Sloman

]]> 4809 19 https://cdn.slidesharecdn.com/ss_thumbnails/alt2012-sloman-120920192728-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 What's vision for, and how does it work? From Marr (and earlier)to Gibson and Beyond /slideshow/whats-vision-for-and-how-does-it-work-from-marr-and-earlierto-gibson-and-beyond-with-some-potted-rearranged-history/9275423 sloman-beyond-gibson-110915184121-phpapp02
ABSTRACT Very many researchers assume that it is obvious what vision (e.g. in humans) is for, i.e. what functions it has, leaving only the problem of explaining how those functions are fulfilled. So they postulate mechanisms and try to show how those mechanisms can produce the required effects, and also, in some cases, try to show that those postulated mechanisms exist in humans and other animals and perform the postulated functions. The main point of this presentation is that it is far from obvious what vision is for - and J.J. Gibson's main achievement is drawing attention to some of the functions that other researchers had ignored. I'll present some of the other work, show how Gibson extends and improves it, and then point out much more there is to the functions of vision and other forms of perception than even Gibson had noticed. In particular, much vision research, unlike Gibson, ignores vision's function in on-line control and perception of continuous processes; and nearly all, including Gibson's work, ignores meta-cognitive perception, and perception of possibilities and constraints on possibilities and the associated role of vision in reasoning. If we don't understand that we cannot understand how biological mechanisms arising from requirements for being embodied in a rich, complex and changing 3-D environment underpin human mathematical capabilities, including the ability to reason about topology and Euclidean geometry. Last updated: 1st March 2014, 10 June 2015 (additional links)]]>
ABSTRACT Very many researchers assume that it is obvious what vision (e.g. in humans) is for, i.e. what functions it has, leaving only the problem of explaining how those functions are fulfilled. So they postulate mechanisms and try to show how those mechanisms can produce the required effects, and also, in some cases, try to show that those postulated mechanisms exist in humans and other animals and perform the postulated functions. The main point of this presentation is that it is far from obvious what vision is for - and J.J. Gibson's main achievement is drawing attention to some of the functions that other researchers had ignored. I'll present some of the other work, show how Gibson extends and improves it, and then point out much more there is to the functions of vision and other forms of perception than even Gibson had noticed. In particular, much vision research, unlike Gibson, ignores vision's function in on-line control and perception of continuous processes; and nearly all, including Gibson's work, ignores meta-cognitive perception, and perception of possibilities and constraints on possibilities and the associated role of vision in reasoning. If we don't understand that we cannot understand how biological mechanisms arising from requirements for being embodied in a rich, complex and changing 3-D environment underpin human mathematical capabilities, including the ability to reason about topology and Euclidean geometry. Last updated: 1st March 2014, 10 June 2015 (additional links)]]> Thu, 15 Sep 2011 18:41:18 GMT /slideshow/whats-vision-for-and-how-does-it-work-from-marr-and-earlierto-gibson-and-beyond-with-some-potted-rearranged-history/9275423 asloman@slideshare.net(asloman) What's vision for, and how does it work? From Marr (and earlier)to Gibson and Beyond asloman ABSTRACT Very many researchers assume that it is obvious what vision (e.g. in humans) is for, i.e. what functions it has, leaving only the problem of explaining how those functions are fulfilled. So they postulate mechanisms and try to show how those mechanisms can produce the required effects, and also, in some cases, try to show that those postulated mechanisms exist in humans and other animals and perform the postulated functions. The main point of this presentation is that it is far from obvious what vision is for - and J.J. Gibson's main achievement is drawing attention to some of the functions that other researchers had ignored. I'll present some of the other work, show how Gibson extends and improves it, and then point out much more there is to the functions of vision and other forms of perception than even Gibson had noticed. In particular, much vision research, unlike Gibson, ignores vision's function in on-line control and perception of continuous processes; and nearly all, including Gibson's work, ignores meta-cognitive perception, and perception of possibilities and constraints on possibilities and the associated role of vision in reasoning. If we don't understand that we cannot understand how biological mechanisms arising from requirements for being embodied in a rich, complex and changing 3-D environment underpin human mathematical capabilities, including the ability to reason about topology and Euclidean geometry. Last updated: 1st March 2014, 10 June 2015 (additional links) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sloman-beyond-gibson-110915184121-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> ABSTRACT Very many researchers assume that it is obvious what vision (e.g. in humans) is for, i.e. what functions it has, leaving only the problem of explaining how those functions are fulfilled. So they postulate mechanisms and try to show how those mechanisms can produce the required effects, and also, in some cases, try to show that those postulated mechanisms exist in humans and other animals and perform the postulated functions. The main point of this presentation is that it is far from obvious what vision is for - and J.J. Gibson's main achievement is drawing attention to some of the functions that other researchers had ignored. I'll present some of the other work, show how Gibson extends and improves it, and then point out much more there is to the functions of vision and other forms of perception than even Gibson had noticed. In particular, much vision research, unlike Gibson, ignores vision's function in on-line control and perception of continuous processes; and nearly all, including Gibson's work, ignores meta-cognitive perception, and perception of possibilities and constraints on possibilities and the associated role of vision in reasoning. If we don't understand that we cannot understand how biological mechanisms arising from requirements for being embodied in a rich, complex and changing 3-D environment underpin human mathematical capabilities, including the ability to reason about topology and Euclidean geometry. Last updated: 1st March 2014, 10 June 2015 (additional links)

What's vision for, and how does it work? From Marr (and earlier)to Gibson and Beyond from Aaron Sloman

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0 Sloman cas-teachshare /slideshow/sloman-casteachshare/8264835 sloman-cas-teachshare-110609174844-phpapp02
�ݺ�ߣs prepared for a broadcast presentation to members of Computing at School http://www.computingatschool.org.uk/, about why computing education should be about more than the science and technology required for useful or entertaining applications. Instead, learning about forms of information processing systems can give us new, deeper ways of thinking about many old phenomena, e.g. the nature of mind and the evolution of minds of various kinds. This supports the claim that the study of computation is as much a science as physics or psychology, rather than just a branch of engineering -- as famously suggested by Fred Brooks.]]>
�ݺ�ߣs prepared for a broadcast presentation to members of Computing at School http://www.computingatschool.org.uk/, about why computing education should be about more than the science and technology required for useful or entertaining applications. Instead, learning about forms of information processing systems can give us new, deeper ways of thinking about many old phenomena, e.g. the nature of mind and the evolution of minds of various kinds. This supports the claim that the study of computation is as much a science as physics or psychology, rather than just a branch of engineering -- as famously suggested by Fred Brooks.]]> Thu, 09 Jun 2011 17:48:40 GMT /slideshow/sloman-casteachshare/8264835 asloman@slideshare.net(asloman) Sloman cas-teachshare asloman �ݺ�ߣs prepared for a broadcast presentation to members of Computing at School http://www.computingatschool.org.uk/, about why computing education should be about more than the science and technology required for useful or entertaining applications. Instead, learning about forms of information processing systems can give us new, deeper ways of thinking about many old phenomena, e.g. the nature of mind and the evolution of minds of various kinds. This supports the claim that the study of computation is as much a science as physics or psychology, rather than just a branch of engineering -- as famously suggested by Fred Brooks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sloman-cas-teachshare-110609174844-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs prepared for a broadcast presentation to members of Computing at School http://www.computingatschool.org.uk/, about why computing education should be about more than the science and technology required for useful or entertaining applications. Instead, learning about forms of information processing systems can give us new, deeper ways of thinking about many old phenomena, e.g. the nature of mind and the evolution of minds of various kinds. This supports the claim that the study of computation is as much a science as physics or psychology, rather than just a branch of engineering -- as famously suggested by Fred Brooks.

Sloman cas-teachshare from Aaron Sloman

]]> 2141 4 https://cdn.slidesharecdn.com/ss_thumbnails/sloman-cas-teachshare-110609174844-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Helping Darwin: How to think about evolution of consciousness (Biosciences talk 2010) /slideshow/biosciences-2010/4225013 biosciences-2010-100522103320-phpapp01
ABSTRACT Many of Darwin's opponents, and some of those who accepted the theory of evolution as regards physical forms, objected to the claim that human mental functions, and consciousness in particular, could be products of evolution. There were several reasons for this opposition, including unanswered questions as to how physical mechanisms could produce mental states and processes an old, and still surviving, philosophical problem. A new answer is now available. Evolution could have produced the "mysterious" aspects of consciousness if, like engineers developing computing systems in the last six or seven decades, evolution encountered and "solved" increasingly complex problems of representation and control (including self-monitoring and self-control) by using systems with increasingly abstract mechanisms based on virtual machines, including most recently self-monitoring virtual machines. These capabilities are, like many capabilities of computer-based systems, implemented in non-physical virtual machinery which, in turn, are implemented in lower level physical mechanisms. This would require far more complex virtual machines than human engineers have so far created. Noone knows whether the biological virtual machines could have been implemented in the discrete-switch technology used in current computers. These ideas were not available to Darwin and his contemporaries: most of the concepts, and the technology, involved in creation and use of sophisticated virtual machines were developed only in the last half century, as a by-product of a large number of design decisions by hardware and software engineers solving different problems. ]]>
ABSTRACT Many of Darwin's opponents, and some of those who accepted the theory of evolution as regards physical forms, objected to the claim that human mental functions, and consciousness in particular, could be products of evolution. There were several reasons for this opposition, including unanswered questions as to how physical mechanisms could produce mental states and processes an old, and still surviving, philosophical problem. A new answer is now available. Evolution could have produced the "mysterious" aspects of consciousness if, like engineers developing computing systems in the last six or seven decades, evolution encountered and "solved" increasingly complex problems of representation and control (including self-monitoring and self-control) by using systems with increasingly abstract mechanisms based on virtual machines, including most recently self-monitoring virtual machines. These capabilities are, like many capabilities of computer-based systems, implemented in non-physical virtual machinery which, in turn, are implemented in lower level physical mechanisms. This would require far more complex virtual machines than human engineers have so far created. Noone knows whether the biological virtual machines could have been implemented in the discrete-switch technology used in current computers. These ideas were not available to Darwin and his contemporaries: most of the concepts, and the technology, involved in creation and use of sophisticated virtual machines were developed only in the last half century, as a by-product of a large number of design decisions by hardware and software engineers solving different problems. ]]> Sat, 22 May 2010 08:57:44 GMT /slideshow/biosciences-2010/4225013 asloman@slideshare.net(asloman) Helping Darwin: How to think about evolution of consciousness (Biosciences talk 2010) asloman ABSTRACT Many of Darwin's opponents, and some of those who accepted the theory of evolution as regards physical forms, objected to the claim that human mental functions, and consciousness in particular, could be products of evolution. There were several reasons for this opposition, including unanswered questions as to how physical mechanisms could produce mental states and processes an old, and still surviving, philosophical problem. A new answer is now available. Evolution could have produced the "mysterious" aspects of consciousness if, like engineers developing computing systems in the last six or seven decades, evolution encountered and "solved" increasingly complex problems of representation and control (including self-monitoring and self-control) by using systems with increasingly abstract mechanisms based on virtual machines, including most recently self-monitoring virtual machines. These capabilities are, like many capabilities of computer-based systems, implemented in non-physical virtual machinery which, in turn, are implemented in lower level physical mechanisms. This would require far more complex virtual machines than human engineers have so far created. Noone knows whether the biological virtual machines could have been implemented in the discrete-switch technology used in current computers. These ideas were not available to Darwin and his contemporaries: most of the concepts, and the technology, involved in creation and use of sophisticated virtual machines were developed only in the last half century, as a by-product of a large number of design decisions by hardware and software engineers solving different problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/biosciences-2010-100522103320-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> ABSTRACT Many of Darwin's opponents, and some of those who accepted the theory of evolution as regards physical forms, objected to the claim that human mental functions, and consciousness in particular, could be products of evolution. There were several reasons for this opposition, including unanswered questions as to how physical mechanisms could produce mental states and processes an old, and still surviving, philosophical problem. A new answer is now available. Evolution could have produced the "mysterious" aspects of consciousness if, like engineers developing computing systems in the last six or seven decades, evolution encountered and "solved" increasingly complex problems of representation and control (including self-monitoring and self-control) by using systems with increasingly abstract mechanisms based on virtual machines, including most recently self-monitoring virtual machines. These capabilities are, like many capabilities of computer-based systems, implemented in non-physical virtual machinery which, in turn, are implemented in lower level physical mechanisms. This would require far more complex virtual machines than human engineers have so far created. Noone knows whether the biological virtual machines could have been implemented in the discrete-switch technology used in current computers. These ideas were not available to Darwin and his contemporaries: most of the concepts, and the technology, involved in creation and use of sophisticated virtual machines were developed only in the last half century, as a by-product of a large number of design decisions by hardware and software engineers solving different problems.

Helping Darwin: How to think about evolution of consciousness (Biosciences talk 2010) from Aaron Sloman

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0 Ontologies for baby animals and robots From "baby stuff" to the world of adult science: Developmental AI from a Kantian viewpoint. /slideshow/ontologies-for-baby-animals-and-robots-from-baby-stuff-to-the-world-of-adult-science-developmental-ai-from-a-kantian-viewpoint/3091448 sloman-brown-slides-100206171636-phpapp01
In contrast with ontology developers concerned with a symbolic or digital environment (e.g. the internet), I draw attention to some features of our 3-D spatio-temporal environment that challenge young humans and other intelligent animals and will also challenge future robots. Evolution provides most animals with an ontology that suffices for life, whereas some animals, including humans, also have mechanisms for substantive ontology extension based on results of interacting with the environment. Future human-like robots will also need this. Since pre-verbal human children and many intelligent non-human animals, including hunting mammals, nest-building birds and primates can interact, often creatively, with complex structures and processes in a 3-D environment, that suggests (a) that they use ontologies that include kinds of material (stuff), kinds of structure, kinds of relationship, kinds of process (some of which are process-fragments composed of bits of stuff changing their properties, structures or relationships), and kinds of causal interaction and (b) since they don't use a human communicative language they must use information encoded in some form that existed prior to human communicative languages both in our evolutionary history and in individual development. Since evolution could not have anticipated the ontologies required for all human cultures, including advanced scientific cultures, individuals must have ways of achieving substantive ontology extension. The research reported here aims mainly to develop requirements for explanatory designs. The attempt to develop forms of representation, mechanisms and architectures that meet those requirements will be a long term research project. ]]>
In contrast with ontology developers concerned with a symbolic or digital environment (e.g. the internet), I draw attention to some features of our 3-D spatio-temporal environment that challenge young humans and other intelligent animals and will also challenge future robots. Evolution provides most animals with an ontology that suffices for life, whereas some animals, including humans, also have mechanisms for substantive ontology extension based on results of interacting with the environment. Future human-like robots will also need this. Since pre-verbal human children and many intelligent non-human animals, including hunting mammals, nest-building birds and primates can interact, often creatively, with complex structures and processes in a 3-D environment, that suggests (a) that they use ontologies that include kinds of material (stuff), kinds of structure, kinds of relationship, kinds of process (some of which are process-fragments composed of bits of stuff changing their properties, structures or relationships), and kinds of causal interaction and (b) since they don't use a human communicative language they must use information encoded in some form that existed prior to human communicative languages both in our evolutionary history and in individual development. Since evolution could not have anticipated the ontologies required for all human cultures, including advanced scientific cultures, individuals must have ways of achieving substantive ontology extension. The research reported here aims mainly to develop requirements for explanatory designs. The attempt to develop forms of representation, mechanisms and architectures that meet those requirements will be a long term research project. ]]> Sat, 06 Feb 2010 17:16:29 GMT /slideshow/ontologies-for-baby-animals-and-robots-from-baby-stuff-to-the-world-of-adult-science-developmental-ai-from-a-kantian-viewpoint/3091448 asloman@slideshare.net(asloman) Ontologies for baby animals and robots From "baby stuff" to the world of adult science: Developmental AI from a Kantian viewpoint. asloman In contrast with ontology developers concerned with a symbolic or digital environment (e.g. the internet), I draw attention to some features of our 3-D spatio-temporal environment that challenge young humans and other intelligent animals and will also challenge future robots. Evolution provides most animals with an ontology that suffices for life, whereas some animals, including humans, also have mechanisms for substantive ontology extension based on results of interacting with the environment. Future human-like robots will also need this. Since pre-verbal human children and many intelligent non-human animals, including hunting mammals, nest-building birds and primates can interact, often creatively, with complex structures and processes in a 3-D environment, that suggests (a) that they use ontologies that include kinds of material (stuff), kinds of structure, kinds of relationship, kinds of process (some of which are process-fragments composed of bits of stuff changing their properties, structures or relationships), and kinds of causal interaction and (b) since they don't use a human communicative language they must use information encoded in some form that existed prior to human communicative languages both in our evolutionary history and in individual development. Since evolution could not have anticipated the ontologies required for all human cultures, including advanced scientific cultures, individuals must have ways of achieving substantive ontology extension. The research reported here aims mainly to develop requirements for explanatory designs. The attempt to develop forms of representation, mechanisms and architectures that meet those requirements will be a long term research project. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sloman-brown-slides-100206171636-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> In contrast with ontology developers concerned with a symbolic or digital environment (e.g. the internet), I draw attention to some features of our 3-D spatio-temporal environment that challenge young humans and other intelligent animals and will also challenge future robots. Evolution provides most animals with an ontology that suffices for life, whereas some animals, including humans, also have mechanisms for substantive ontology extension based on results of interacting with the environment. Future human-like robots will also need this. Since pre-verbal human children and many intelligent non-human animals, including hunting mammals, nest-building birds and primates can interact, often creatively, with complex structures and processes in a 3-D environment, that suggests (a) that they use ontologies that include kinds of material (stuff), kinds of structure, kinds of relationship, kinds of process (some of which are process-fragments composed of bits of stuff changing their properties, structures or relationships), and kinds of causal interaction and (b) since they don't use a human communicative language they must use information encoded in some form that existed prior to human communicative languages both in our evolutionary history and in individual development. Since evolution could not have anticipated the ontologies required for all human cultures, including advanced scientific cultures, individuals must have ways of achieving substantive ontology extension. The research reported here aims mainly to develop requirements for explanatory designs. The attempt to develop forms of representation, mechanisms and architectures that meet those requirements will be a long term research project.

Ontologies for baby animals and robots From "baby stuff" to the world of adult science: Developmental AI from a Kantian viewpoint. from Aaron Sloman

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0 Possibilities between form and function (Or between shape and affordances) /slideshow/possibilities-between-form-and-function-or-between-shape-and-affordances/2855483 dagstuhl-slides-100107173416-phpapp01
I discuss the need for an intelligent system, whether it is a robot, or some sort of digital companion equipped with a vision system, to include in its ontology a range of concepts that appear not to have been noticed by most researchers in robotics, vision, and human psychology. These are concepts that lie between (a) concepts of "form", concerned with spatially located objects, object parts, features, and relationships and (b) concepts of affordances and functions, concerned with how things in the environment make possible or constrain actions that are possible for a perceiver and which can support or hinder the goals of the perceiver. Those intermediate concepts are concerned with processes that *are* occurring and processes that *can* occur, and the causal relationships between physical structures/forms/configurations and the possibilities for and constraints on such processes, independently of whether they are processes involving anyone's actions or goals. These intermediate concepts relate motions and constraints on motion to both geometric and topological structures in the environment and the kinds of 'stuff' of which things are composed, since, for example, rigid, flexible, and fluid stuffs support and constrain different sorts of motions. They underlie affordance concepts. Attempts to study affordances without taking account of the intermediate concepts are bound to prove shallow and inadequate. Notes for invited talk at Dagstuhl Seminar: ``From Form to Function'' Oct 18-23, 2009 http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=09431 ]]>
I discuss the need for an intelligent system, whether it is a robot, or some sort of digital companion equipped with a vision system, to include in its ontology a range of concepts that appear not to have been noticed by most researchers in robotics, vision, and human psychology. These are concepts that lie between (a) concepts of "form", concerned with spatially located objects, object parts, features, and relationships and (b) concepts of affordances and functions, concerned with how things in the environment make possible or constrain actions that are possible for a perceiver and which can support or hinder the goals of the perceiver. Those intermediate concepts are concerned with processes that *are* occurring and processes that *can* occur, and the causal relationships between physical structures/forms/configurations and the possibilities for and constraints on such processes, independently of whether they are processes involving anyone's actions or goals. These intermediate concepts relate motions and constraints on motion to both geometric and topological structures in the environment and the kinds of 'stuff' of which things are composed, since, for example, rigid, flexible, and fluid stuffs support and constrain different sorts of motions. They underlie affordance concepts. Attempts to study affordances without taking account of the intermediate concepts are bound to prove shallow and inadequate. Notes for invited talk at Dagstuhl Seminar: ``From Form to Function'' Oct 18-23, 2009 http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=09431 ]]> Thu, 07 Jan 2010 17:33:50 GMT /slideshow/possibilities-between-form-and-function-or-between-shape-and-affordances/2855483 asloman@slideshare.net(asloman) Possibilities between form and function (Or between shape and affordances) asloman I discuss the need for an intelligent system, whether it is a robot, or some sort of digital companion equipped with a vision system, to include in its ontology a range of concepts that appear not to have been noticed by most researchers in robotics, vision, and human psychology. These are concepts that lie between (a) concepts of "form", concerned with spatially located objects, object parts, features, and relationships and (b) concepts of affordances and functions, concerned with how things in the environment make possible or constrain actions that are possible for a perceiver and which can support or hinder the goals of the perceiver. Those intermediate concepts are concerned with processes that *are* occurring and processes that *can* occur, and the causal relationships between physical structures/forms/configurations and the possibilities for and constraints on such processes, independently of whether they are processes involving anyone's actions or goals. These intermediate concepts relate motions and constraints on motion to both geometric and topological structures in the environment and the kinds of 'stuff' of which things are composed, since, for example, rigid, flexible, and fluid stuffs support and constrain different sorts of motions. They underlie affordance concepts. Attempts to study affordances without taking account of the intermediate concepts are bound to prove shallow and inadequate. Notes for invited talk at Dagstuhl Seminar: ``From Form to Function'' Oct 18-23, 2009 http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=09431 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dagstuhl-slides-100107173416-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> I discuss the need for an intelligent system, whether it is a robot, or some sort of digital companion equipped with a vision system, to include in its ontology a range of concepts that appear not to have been noticed by most researchers in robotics, vision, and human psychology. These are concepts that lie between (a) concepts of "form", concerned with spatially located objects, object parts, features, and relationships and (b) concepts of affordances and functions, concerned with how things in the environment make possible or constrain actions that are possible for a perceiver and which can support or hinder the goals of the perceiver. Those intermediate concepts are concerned with processes that *are* occurring and processes that *can* occur, and the causal relationships between physical structures/forms/configurations and the possibilities for and constraints on such processes, independently of whether they are processes involving anyone's actions or goals. These intermediate concepts relate motions and constraints on motion to both geometric and topological structures in the environment and the kinds of 'stuff' of which things are composed, since, for example, rigid, flexible, and fluid stuffs support and constrain different sorts of motions. They underlie affordance concepts. Attempts to study affordances without taking account of the intermediate concepts are bound to prove shallow and inadequate. Notes for invited talk at Dagstuhl Seminar: ``From Form to Function'' Oct 18-23, 2009 http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=09431

Possibilities between form and function (Or between shape and affordances) from Aaron Sloman

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0 Virtual Machines and the Metaphysics of Science /slideshow/virtual-machines-and-the-metaphysics-of-science/2554767 2554767
Philosophers regularly use complex (running) virtual machines (not virtual realities) composed of enduring interacting non-physical subsystems (e.g. operating systems, word-processors, email systems, web browsers, and many more). These VMs can be subdivided into different kinds with different types of functions, e.g. "specific-function VMs" and "platform VMs" (including language VMs, and operating system VMs) that provide support for a variety of different (possibly concurrent) "higher level" VMs, with different functions. Yet, almost all ignore (or misdescribe) these VMs when discussing functionalism, supervenience, multiple realisation, reductionism, emergence, and causation. Such VMs depend on many hardware and software designs that interact in very complex ways to maintain a network of causal relationships between physical and virtual entities and processes. I'll try to explain this, and show how VMs are important for philosophy, in part because evolution long ago developed far more sophisticated systems of virtual machinery (e.g. running on brains and their surroundings) than human engineers so far. Most are still not understood. This partly accounts for the apparent intractability of several philosophical problems. E.g. running VM subsystems can be disconnected from input-output interactions for extended periods, and some can have more complexity than the available input/output bandwidth can reveal. Moreover, despite the advantages of VMs for self-monitoring and self control, they can also lead to self-deception. For a lot of related material see Steve Burbeck's web site http://evolutionofcomputing.org/Multicellular/Emergence.html (A related presentation debunking the "hard problem" of consciousness is also in this collection.)]]>
Philosophers regularly use complex (running) virtual machines (not virtual realities) composed of enduring interacting non-physical subsystems (e.g. operating systems, word-processors, email systems, web browsers, and many more). These VMs can be subdivided into different kinds with different types of functions, e.g. "specific-function VMs" and "platform VMs" (including language VMs, and operating system VMs) that provide support for a variety of different (possibly concurrent) "higher level" VMs, with different functions. Yet, almost all ignore (or misdescribe) these VMs when discussing functionalism, supervenience, multiple realisation, reductionism, emergence, and causation. Such VMs depend on many hardware and software designs that interact in very complex ways to maintain a network of causal relationships between physical and virtual entities and processes. I'll try to explain this, and show how VMs are important for philosophy, in part because evolution long ago developed far more sophisticated systems of virtual machinery (e.g. running on brains and their surroundings) than human engineers so far. Most are still not understood. This partly accounts for the apparent intractability of several philosophical problems. E.g. running VM subsystems can be disconnected from input-output interactions for extended periods, and some can have more complexity than the available input/output bandwidth can reveal. Moreover, despite the advantages of VMs for self-monitoring and self control, they can also lead to self-deception. For a lot of related material see Steve Burbeck's web site http://evolutionofcomputing.org/Multicellular/Emergence.html (A related presentation debunking the "hard problem" of consciousness is also in this collection.)]]> Sat, 21 Nov 2009 12:37:14 GMT /slideshow/virtual-machines-and-the-metaphysics-of-science/2554767 asloman@slideshare.net(asloman) Virtual Machines and the Metaphysics of Science asloman Philosophers regularly use complex (running) virtual machines (not virtual realities) composed of enduring interacting non-physical subsystems (e.g. operating systems, word-processors, email systems, web browsers, and many more). These VMs can be subdivided into different kinds with different types of functions, e.g. "specific-function VMs" and "platform VMs" (including language VMs, and operating system VMs) that provide support for a variety of different (possibly concurrent) "higher level" VMs, with different functions. Yet, almost all ignore (or misdescribe) these VMs when discussing functionalism, supervenience, multiple realisation, reductionism, emergence, and causation. Such VMs depend on many hardware and software designs that interact in very complex ways to maintain a network of causal relationships between physical and virtual entities and processes. I'll try to explain this, and show how VMs are important for philosophy, in part because evolution long ago developed far more sophisticated systems of virtual machinery (e.g. running on brains and their surroundings) than human engineers so far. Most are still not understood. This partly accounts for the apparent intractability of several philosophical problems. E.g. running VM subsystems can be disconnected from input-output interactions for extended periods, and some can have more complexity than the available input/output bandwidth can reveal. Moreover, despite the advantages of VMs for self-monitoring and self control, they can also lead to self-deception. For a lot of related material see Steve Burbeck's web site http://evolutionofcomputing.org/Multicellular/Emergence.html (A related presentation debunking the "hard problem" of consciousness is also in this collection.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2554767-thumbnail.jpg?width=120&height=120&fit=bounds" /> Philosophers regularly use complex (running) virtual machines (not virtual realities) composed of enduring interacting non-physical subsystems (e.g. operating systems, word-processors, email systems, web browsers, and many more). These VMs can be subdivided into different kinds with different types of functions, e.g. "specific-function VMs" and "platform VMs" (including language VMs, and operating system VMs) that provide support for a variety of different (possibly concurrent) "higher level" VMs, with different functions. Yet, almost all ignore (or misdescribe) these VMs when discussing functionalism, supervenience, multiple realisation, reductionism, emergence, and causation. Such VMs depend on many hardware and software designs that interact in very complex ways to maintain a network of causal relationships between physical and virtual entities and processes. I'll try to explain this, and show how VMs are important for philosophy, in part because evolution long ago developed far more sophisticated systems of virtual machinery (e.g. running on brains and their surroundings) than human engineers so far. Most are still not understood. This partly accounts for the apparent intractability of several philosophical problems. E.g. running VM subsystems can be disconnected from input-output interactions for extended periods, and some can have more complexity than the available input/output bandwidth can reveal. Moreover, despite the advantages of VMs for self-monitoring and self control, they can also lead to self-deception. For a lot of related material see Steve Burbeck's web site http://evolutionofcomputing.org/Multicellular/Emergence.html (A related presentation debunking the "hard problem" of consciousness is also in this collection.)

Virtual Machines and the Metaphysics of Science from Aaron Sloman

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0 Why the "hard" problem of consciousness is easy and the "easy" problem hard. (And how to make progress) /slideshow/why-the-hard-problem-of-consciousness-is-easy-and-the-easy-problem-hard-and-how-to-make-progress/2549963 2549963
The "hard" problem of concsiousness can be shown to be a non-problem because it is formulated using a seriously defective concept (the concept of "phenomenal consciousness" defined so as to rule out cognitive functionality and causal powers). So the hard problem is an example of a well known type of philosophical problem that needs to be dissolved (fairly easily) rather than solved. For other examples, and a brief introduction to conceptual analysis, see http://www.cs.bham.ac.uk/research/projects/cogaff/misc/varieties-of-atheism.html In contrast, the so-called "easy" problem requires detailed analysis of very complex and subtle features of perceptual processes, introspective processes and other mental processes, sometimes labelled "access consciousness": these have cognitive functions, but their complexity (especially the way details change as the environment changes or the perceiver moves) is considerable and very hard to characterise. "Access consciousness" is complex also because it takes many different forms, since what individuals are conscious of and what uses being conscious of things can be put to, can vary hugely, from simple life forms, through many other animals and human infants, to sophisticated adult humans, Finding ways of modelling these aspects of consciousness, and explaining how they arise out of physical mechanisms, requires major advances in the science of information processing systems -- including computer science and neuroscience. There are empirical facts about introspection that have generated theories of consciousness but some of the empirical facts go unnoticed by philosophers. The notion of a virtual machine is introduced briefly and illustrated using Conway's "Game of life" and other examples of virtual machinery that explain how contents of consciousness can have causal powers and can have intentionality (be able to refer to other things). The beginnings of a research program are presented, showing how more examples can be collected and how notions of virtual machinery may need to be developed to cope with all the phenomena.]]>
The "hard" problem of concsiousness can be shown to be a non-problem because it is formulated using a seriously defective concept (the concept of "phenomenal consciousness" defined so as to rule out cognitive functionality and causal powers). So the hard problem is an example of a well known type of philosophical problem that needs to be dissolved (fairly easily) rather than solved. For other examples, and a brief introduction to conceptual analysis, see http://www.cs.bham.ac.uk/research/projects/cogaff/misc/varieties-of-atheism.html In contrast, the so-called "easy" problem requires detailed analysis of very complex and subtle features of perceptual processes, introspective processes and other mental processes, sometimes labelled "access consciousness": these have cognitive functions, but their complexity (especially the way details change as the environment changes or the perceiver moves) is considerable and very hard to characterise. "Access consciousness" is complex also because it takes many different forms, since what individuals are conscious of and what uses being conscious of things can be put to, can vary hugely, from simple life forms, through many other animals and human infants, to sophisticated adult humans, Finding ways of modelling these aspects of consciousness, and explaining how they arise out of physical mechanisms, requires major advances in the science of information processing systems -- including computer science and neuroscience. There are empirical facts about introspection that have generated theories of consciousness but some of the empirical facts go unnoticed by philosophers. The notion of a virtual machine is introduced briefly and illustrated using Conway's "Game of life" and other examples of virtual machinery that explain how contents of consciousness can have causal powers and can have intentionality (be able to refer to other things). The beginnings of a research program are presented, showing how more examples can be collected and how notions of virtual machinery may need to be developed to cope with all the phenomena.]]> Fri, 20 Nov 2009 19:33:58 GMT /slideshow/why-the-hard-problem-of-consciousness-is-easy-and-the-easy-problem-hard-and-how-to-make-progress/2549963 asloman@slideshare.net(asloman) Why the "hard" problem of consciousness is easy and the "easy" problem hard. (And how to make progress) asloman The "hard" problem of concsiousness can be shown to be a non-problem because it is formulated using a seriously defective concept (the concept of "phenomenal consciousness" defined so as to rule out cognitive functionality and causal powers). So the hard problem is an example of a well known type of philosophical problem that needs to be dissolved (fairly easily) rather than solved. For other examples, and a brief introduction to conceptual analysis, see http://www.cs.bham.ac.uk/research/projects/cogaff/misc/varieties-of-atheism.html In contrast, the so-called "easy" problem requires detailed analysis of very complex and subtle features of perceptual processes, introspective processes and other mental processes, sometimes labelled "access consciousness": these have cognitive functions, but their complexity (especially the way details change as the environment changes or the perceiver moves) is considerable and very hard to characterise. "Access consciousness" is complex also because it takes many different forms, since what individuals are conscious of and what uses being conscious of things can be put to, can vary hugely, from simple life forms, through many other animals and human infants, to sophisticated adult humans, Finding ways of modelling these aspects of consciousness, and explaining how they arise out of physical mechanisms, requires major advances in the science of information processing systems -- including computer science and neuroscience. There are empirical facts about introspection that have generated theories of consciousness but some of the empirical facts go unnoticed by philosophers. The notion of a virtual machine is introduced briefly and illustrated using Conway's "Game of life" and other examples of virtual machinery that explain how contents of consciousness can have causal powers and can have intentionality (be able to refer to other things). The beginnings of a research program are presented, showing how more examples can be collected and how notions of virtual machinery may need to be developed to cope with all the phenomena. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2549963-thumbnail.jpg?width=120&height=120&fit=bounds" /> The "hard" problem of concsiousness can be shown to be a non-problem because it is formulated using a seriously defective concept (the concept of "phenomenal consciousness" defined so as to rule out cognitive functionality and causal powers). So the hard problem is an example of a well known type of philosophical problem that needs to be dissolved (fairly easily) rather than solved. For other examples, and a brief introduction to conceptual analysis, see http://www.cs.bham.ac.uk/research/projects/cogaff/misc/varieties-of-atheism.html In contrast, the so-called "easy" problem requires detailed analysis of very complex and subtle features of perceptual processes, introspective processes and other mental processes, sometimes labelled "access consciousness": these have cognitive functions, but their complexity (especially the way details change as the environment changes or the perceiver moves) is considerable and very hard to characterise. "Access consciousness" is complex also because it takes many different forms, since what individuals are conscious of and what uses being conscious of things can be put to, can vary hugely, from simple life forms, through many other animals and human infants, to sophisticated adult humans, Finding ways of modelling these aspects of consciousness, and explaining how they arise out of physical mechanisms, requires major advances in the science of information processing systems -- including computer science and neuroscience. There are empirical facts about introspection that have generated theories of consciousness but some of the empirical facts go unnoticed by philosophers. The notion of a virtual machine is introduced briefly and illustrated using Conway's "Game of life" and other examples of virtual machinery that explain how contents of consciousness can have causal powers and can have intentionality (be able to refer to other things). The beginnings of a research program are presented, showing how more examples can be collected and how notions of virtual machinery may need to be developed to cope with all the phenomena.

Why the "hard" problem of consciousness is easy and the "easy" problem hard. (And how to make progress) from Aaron Sloman

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0 Some thoughts and demos, on ways of using computing for deep education on many topics /slideshow/some-thoughts-and-demos-on-ways-of-using-computing-for-deep-education-on-many-topics/1881097 1881097
Revised expanded version of presentation at Opensource Schools Unconference, July 2009, Nottingham, uk]]>
Revised expanded version of presentation at Opensource Schools Unconference, July 2009, Nottingham, uk]]> Wed, 19 Aug 2009 09:47:54 GMT /slideshow/some-thoughts-and-demos-on-ways-of-using-computing-for-deep-education-on-many-topics/1881097 asloman@slideshare.net(asloman) Some thoughts and demos, on ways of using computing for deep education on many topics asloman Revised expanded version of presentation at Opensource Schools Unconference, July 2009, Nottingham, uk <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1881097-thumbnail.jpg?width=120&height=120&fit=bounds" /> Revised expanded version of presentation at Opensource Schools Unconference, July 2009, Nottingham, uk

Some thoughts and demos, on ways of using computing for deep education on many topics from Aaron Sloman

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0 Why symbol-grounding is both impossible and unnecessary, and why theory-tethering is more powerful anyway. /slideshow/why-symbolgrounding-is-both-impossible-and-unnecessary-and-why-theorytethering-is-more-powerful-anyway/1117994 1117994
Introduction to key ideas of semantic models, implicit definitions and symbol tethering, using ideas from philosophy of science and model theoretic semantics to explain why symbol ground theory is misguided: there is no need for all symbols used by an intelligent agent to be 'grounded' in terms of experience, or sensory-motor patterns. Rather, most of the meaning of a symbol may come from its role in a powerful explanatory theory, though the theory should have some connection with experiments and observations in order to be applicable to the world. That is not the same as requiring every symbol to be linked to experiences, experiments or measurements. Symbol grounding theory is a modern version of the philosophical theory of 'concept empiricism', which was refuted by the philosopher Immanuel Kant in the 18th century.]]>
Introduction to key ideas of semantic models, implicit definitions and symbol tethering, using ideas from philosophy of science and model theoretic semantics to explain why symbol ground theory is misguided: there is no need for all symbols used by an intelligent agent to be 'grounded' in terms of experience, or sensory-motor patterns. Rather, most of the meaning of a symbol may come from its role in a powerful explanatory theory, though the theory should have some connection with experiments and observations in order to be applicable to the world. That is not the same as requiring every symbol to be linked to experiences, experiments or measurements. Symbol grounding theory is a modern version of the philosophical theory of 'concept empiricism', which was refuted by the philosopher Immanuel Kant in the 18th century.]]> Sun, 08 Mar 2009 14:48:09 GMT /slideshow/why-symbolgrounding-is-both-impossible-and-unnecessary-and-why-theorytethering-is-more-powerful-anyway/1117994 asloman@slideshare.net(asloman) Why symbol-grounding is both impossible and unnecessary, and why theory-tethering is more powerful anyway. asloman Introduction to key ideas of semantic models, implicit definitions and symbol tethering, using ideas from philosophy of science and model theoretic semantics to explain why symbol ground theory is misguided: there is no need for all symbols used by an intelligent agent to be 'grounded' in terms of experience, or sensory-motor patterns. Rather, most of the meaning of a symbol may come from its role in a powerful explanatory theory, though the theory should have some connection with experiments and observations in order to be applicable to the world. That is not the same as requiring every symbol to be linked to experiences, experiments or measurements. Symbol grounding theory is a modern version of the philosophical theory of 'concept empiricism', which was refuted by the philosopher Immanuel Kant in the 18th century. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1117994-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to key ideas of semantic models, implicit definitions and symbol tethering, using ideas from philosophy of science and model theoretic semantics to explain why symbol ground theory is misguided: there is no need for all symbols used by an intelligent agent to be 'grounded' in terms of experience, or sensory-motor patterns. Rather, most of the meaning of a symbol may come from its role in a powerful explanatory theory, though the theory should have some connection with experiments and observations in order to be applicable to the world. That is not the same as requiring every symbol to be linked to experiences, experiments or measurements. Symbol grounding theory is a modern version of the philosophical theory of 'concept empiricism', which was refuted by the philosopher Immanuel Kant in the 18th century.

Why symbol-grounding is both impossible and unnecessary, and why theory-tethering is more powerful anyway. from Aaron Sloman

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0 Do Intelligent Machines, Natural or Artificial, Really Need Emotions? /slideshow/do-machines-natural-or-artificial-really-need-emotions-presentation/955115 emotionsmachines-1233004714538727-1
(Updated on 14 Jan 2014 -- with substantial revisions.) Many people believe that emotions are required for intelligence. I argue that this is mostly based on (a) wishful thinking and (b) a failure adequately to analyse the variety of types of affective states and processes that can arise in different sorts of architectures produced by biological evolution or required for artificial systems. This work is a development of ideas presented by Herbert Simon in the 1960s in his 'Motivational and emotional controls of cognition'.]]>
(Updated on 14 Jan 2014 -- with substantial revisions.) Many people believe that emotions are required for intelligence. I argue that this is mostly based on (a) wishful thinking and (b) a failure adequately to analyse the variety of types of affective states and processes that can arise in different sorts of architectures produced by biological evolution or required for artificial systems. This work is a development of ideas presented by Herbert Simon in the 1960s in his 'Motivational and emotional controls of cognition'.]]> Mon, 26 Jan 2009 15:24:57 GMT /slideshow/do-machines-natural-or-artificial-really-need-emotions-presentation/955115 asloman@slideshare.net(asloman) Do Intelligent Machines, Natural or Artificial, Really Need Emotions? asloman (Updated on 14 Jan 2014 -- with substantial revisions.) Many people believe that emotions are required for intelligence. I argue that this is mostly based on (a) wishful thinking and (b) a failure adequately to analyse the variety of types of affective states and processes that can arise in different sorts of architectures produced by biological evolution or required for artificial systems. This work is a development of ideas presented by Herbert Simon in the 1960s in his 'Motivational and emotional controls of cognition'. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/emotionsmachines-1233004714538727-1-thumbnail.jpg?width=120&height=120&fit=bounds" /> (Updated on 14 Jan 2014 -- with substantial revisions.) Many people believe that emotions are required for intelligence. I argue that this is mostly based on (a) wishful thinking and (b) a failure adequately to analyse the variety of types of affective states and processes that can arise in different sorts of architectures produced by biological evolution or required for artificial systems. This work is a development of ideas presented by Herbert Simon in the 1960s in his 'Motivational and emotional controls of cognition'.

Do Intelligent Machines, Natural or Artificial, Really Need Emotions? from Aaron Sloman

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0 What is science? (Can There Be a Science of Mind?) (Updated August 2010) /slideshow/what-is-science-can-there-be-a-science-of-mind/912054 912054-1231926577
This presentation gives an introduction to philosophy of science, though a rather idiosyncratic one, stressing science as the search for powerful new ontologies rather than merely laws. You can't express a law unless you have an ontology including the items referred to in the law (e.g. pressure, volume, temperature). The talk raises a number of questions about the aims and methods of science, about the differences between the physical sciences and the science of information-processing systems (e.g. organisms, minds, computers), whether there is a unique truth or final answers to be found by science, whether scientists ever prove anything (no -- at most they show that some theory is better than any currently available rival theory), and why science does not require faith (though obstinacy can be useful). The slides end with a section on whether a science of mind is possible, answering yes, and explaining how.]]>
This presentation gives an introduction to philosophy of science, though a rather idiosyncratic one, stressing science as the search for powerful new ontologies rather than merely laws. You can't express a law unless you have an ontology including the items referred to in the law (e.g. pressure, volume, temperature). The talk raises a number of questions about the aims and methods of science, about the differences between the physical sciences and the science of information-processing systems (e.g. organisms, minds, computers), whether there is a unique truth or final answers to be found by science, whether scientists ever prove anything (no -- at most they show that some theory is better than any currently available rival theory), and why science does not require faith (though obstinacy can be useful). The slides end with a section on whether a science of mind is possible, answering yes, and explaining how.]]> Mon, 12 Jan 2009 20:20:13 GMT /slideshow/what-is-science-can-there-be-a-science-of-mind/912054 asloman@slideshare.net(asloman) What is science? (Can There Be a Science of Mind?) (Updated August 2010) asloman This presentation gives an introduction to philosophy of science, though a rather idiosyncratic one, stressing science as the search for powerful new ontologies rather than merely laws. You can't express a law unless you have an ontology including the items referred to in the law (e.g. pressure, volume, temperature). The talk raises a number of questions about the aims and methods of science, about the differences between the physical sciences and the science of information-processing systems (e.g. organisms, minds, computers), whether there is a unique truth or final answers to be found by science, whether scientists ever prove anything (no -- at most they show that some theory is better than any currently available rival theory), and why science does not require faith (though obstinacy can be useful). The slides end with a section on whether a science of mind is possible, answering yes, and explaining how. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/912054-1231926577-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation gives an introduction to philosophy of science, though a rather idiosyncratic one, stressing science as the search for powerful new ontologies rather than merely laws. You can't express a law unless you have an ontology including the items referred to in the law (e.g. pressure, volume, temperature). The talk raises a number of questions about the aims and methods of science, about the differences between the physical sciences and the science of information-processing systems (e.g. organisms, minds, computers), whether there is a unique truth or final answers to be found by science, whether scientists ever prove anything (no -- at most they show that some theory is better than any currently available rival theory), and why science does not require faith (though obstinacy can be useful). The slides end with a section on whether a science of mind is possible, answering yes, and explaining how.

What is science? (Can There Be a Science of Mind?) (Updated August 2010) from Aaron Sloman

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0 https://cdn.slidesharecdn.com/profile-photo-asloman-48x48.jpg?cb=1661856034 Stopped adding stuff here several years ago. See my home page. www.cs.bham.ac.uk/~axs Jack of many trades and master of none. Interested in philosophy of mind, philosophy of science, philosophy of mathematics, cognitive science, artificial intelligence, biology, evolution, nature-nurture issues. www.cs.bham.ac.uk/~axs https://cdn.slidesharecdn.com/ss_thumbnails/construction-kits-180727120738-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/construction-kits-for-evolving-life-including-evolving-minds-and-mathematical-abilities/107709207 Construction kits for ... https://cdn.slidesharecdn.com/ss_thumbnails/meta-morphogenesis-180727120737-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-turing-inspired-metamorphogenesis-project-the-selfinforming-universeupdate-2018/107709205 The Turing Inspired Me... https://cdn.slidesharecdn.com/ss_thumbnails/ai-icy-vision-language-150327212533-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/evolution-of-46383806/46383806 Evolution of language ...