This document provides a 3-part summary of a presentation on Kantian philosophy of mathematics and young robots:
[1] It discusses the need for a multi-pronged revolution in intelligence studies, replacing wars between factions with collaboration and focusing more on problems solved by evolution rather than hypothesized solutions.
[2] It outlines some main points of the presentation, including that species have different designs to solve problems, some relying more on genetically determined competences while others can develop new competences through learning.
[3] It notes that the mechanisms for creativity are related to making mathematical discoveries by transforming empirical knowledge into non-empirical knowledge, and gives an example of learning that different counting methods give the same result
The Turing Inspired Meta-Morphogenesis Project -- The self-informing universe...Aaron Sloman
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
Psychology-informed Design of Responsive Open (Personal) Learning EnvironmentsMilos Kravcik
The document summarizes a workshop on psychology-informed design of responsive open learning environments. It discusses how human cognitive biases can be addressed through choice architecture and nudges in system design. The workshop covered the theoretical background of self-regulated learning and personal learning environments. It demonstrated the ROLE widget store and tools for configuring personal learning environments using widgets. Participants worked in groups on conceptual design of PLEs and requirements for the widget store.
Meta-Morphogenesis, Evolution, Cognitive Robotics and Developmental Cognitive...Aaron Sloman
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
Distinguishes Humean (statistics-based) notions of causation and Kantian (deterministic, structure-based) notions of causation, arguing that intelligent robots and animals need both, but each requires a combination of competences, and various kinds of partial competence of both kinds are possible.
Social constructivism as a philosophy of mathematicsPaul Ernest
- Social constructivism views mathematical knowledge as a social and historical construct. It rejects the notion that mathematical knowledge is absolutely valid or certain.
- Key aspects of social constructivism include viewing mathematical concepts and proofs as evolving through a conversational process of proposing ideas and subjecting them to criticism and refinement. Mathematical knowledge is seen as intersubjective rather than purely objective.
- On this view, mathematical texts and concepts can be understood as participating in an ongoing conversation, with proponents putting forth ideas and critics examining them for weaknesses. The acceptance of mathematical ideas and proofs occurs through this social and dialogical process rather than being intrinsically certain.
This document discusses the philosophy of mathematics education. It argues that the traditional view of mathematics as absolute, objective truth is being challenged. Some philosophers now see mathematics as a fallible and changing body of knowledge created by human invention and imagination, rather than certain truths. If mathematics is fallible, it has social responsibilities and its role in education must consider how it transmits values and affects groups in society. How mathematics is viewed significantly impacts education.
The document discusses different philosophical views on the foundations of mathematics. It covers the major schools of thought: logicism, which holds that mathematics can be reduced to logic; formalism, which views mathematics as the study of formal symbols and strings; intuitionism, which sees mathematics as mental constructions; and predicativism, which limits definitions to existing entities. The document also examines views from philosophers like Plato, Aristotle, Leibniz, Kant, Frege, Hilbert, Brouwer, and Gödel on topics like the nature of mathematical objects and truth. More recent perspectives discussed include structuralism, nominalism, fictionalism, and mathematical naturalism.
The Turing Inspired Meta-Morphogenesis Project -- The self-informing universe...Aaron Sloman
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
Psychology-informed Design of Responsive Open (Personal) Learning EnvironmentsMilos Kravcik
The document summarizes a workshop on psychology-informed design of responsive open learning environments. It discusses how human cognitive biases can be addressed through choice architecture and nudges in system design. The workshop covered the theoretical background of self-regulated learning and personal learning environments. It demonstrated the ROLE widget store and tools for configuring personal learning environments using widgets. Participants worked in groups on conceptual design of PLEs and requirements for the widget store.
Meta-Morphogenesis, Evolution, Cognitive Robotics and Developmental Cognitive...Aaron Sloman
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
Distinguishes Humean (statistics-based) notions of causation and Kantian (deterministic, structure-based) notions of causation, arguing that intelligent robots and animals need both, but each requires a combination of competences, and various kinds of partial competence of both kinds are possible.
Social constructivism as a philosophy of mathematicsPaul Ernest
- Social constructivism views mathematical knowledge as a social and historical construct. It rejects the notion that mathematical knowledge is absolutely valid or certain.
- Key aspects of social constructivism include viewing mathematical concepts and proofs as evolving through a conversational process of proposing ideas and subjecting them to criticism and refinement. Mathematical knowledge is seen as intersubjective rather than purely objective.
- On this view, mathematical texts and concepts can be understood as participating in an ongoing conversation, with proponents putting forth ideas and critics examining them for weaknesses. The acceptance of mathematical ideas and proofs occurs through this social and dialogical process rather than being intrinsically certain.
This document discusses the philosophy of mathematics education. It argues that the traditional view of mathematics as absolute, objective truth is being challenged. Some philosophers now see mathematics as a fallible and changing body of knowledge created by human invention and imagination, rather than certain truths. If mathematics is fallible, it has social responsibilities and its role in education must consider how it transmits values and affects groups in society. How mathematics is viewed significantly impacts education.
The document discusses different philosophical views on the foundations of mathematics. It covers the major schools of thought: logicism, which holds that mathematics can be reduced to logic; formalism, which views mathematics as the study of formal symbols and strings; intuitionism, which sees mathematics as mental constructions; and predicativism, which limits definitions to existing entities. The document also examines views from philosophers like Plato, Aristotle, Leibniz, Kant, Frege, Hilbert, Brouwer, and Gödel on topics like the nature of mathematical objects and truth. More recent perspectives discussed include structuralism, nominalism, fictionalism, and mathematical naturalism.
Nota RINGKAS MENGIKUT TOPIK MATEMATIK SEKOLAH RENDAHrichieroyston
The document discusses topics in mathematics including:
1) Place value of whole numbers with units of juta, ribu, puluh, and ribu.
2) Fractions such as proper, mixed, and equivalent fractions.
3) Decimals and adding trailing zeros.
4) Conversions between units of time, length, volume, and mass.
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.)
This document discusses varieties of self-awareness and their uses in natural and artificial systems. It proposes a conceptual framework for metacognition and natural cognition. The document contains slides for presentations on this topic, including:
- Discussing how to analyze requirements by examining natural and artificial systems to understand design discontinuities.
- Explaining how environments can have agent-relative structure that produces varied information processing demands.
- Outlining a conceptual framework that includes reactive and deliberative architectures in natural systems, with different layers providing varieties of self-awareness.
What designers of artificial companions need to understand about biological onesAaron Sloman
This document summarizes Aaron Sloman's presentation at AISB'08 on what designers of artificial companions need to understand about biological ones. Sloman discusses the difficulty of the task and argues that current AI is not capable of replicating the capabilities of young children. He outlines "Type 1" goals for artificial companions focused on engagement, and "Type 2" goals focused on enabling functions like helping users, which are much harder to achieve. Sloman asserts progress requires understanding how human capabilities like understanding environments, minds, and developing new motives emerge from biological and developmental factors. The key is replicating some of the generic learning abilities of young humans to build more advanced functions layer by layer.
Slides 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.
Essay On College Education. 24 Greatest College Essay Examples RedlineSPMelissa Otero
College Essay Examples - 9 in PDF Examples. College and Education - Free Essay Example PapersOwl.com. Essay websites: Essay on the importance of college education. College Education: Should Education be Free Essay. St Joseph Hospital: College Application Essay. Importance of college education essay. Free importance of education .... 004 Essay Example Why Is College Important On Importance Of Education .... College Essay Format: Simple Steps to Be Followed. FREE 11 Sample College Essay Templates in MS Word PDF. Argumentative essay on college education. Sample College Application Essay 5. 021 10067 Thumb College Education Essay Thatsnotus. How to Write In College Essay Format OCC NJ. College Admissions Essay Workshop - 9 Types of Supplemental Essays .... Admission essay: Being a college student essay. This is How You Write a College Essay College application essay .... College Essay: Graduate school essay sample. Why College Should Be Cheaper Essay. Essay On The Importance Of College Education. 24 Greatest College Essay Examples RedlineSP. Why Do You Think College Education Is Important Essay. Impressive Essay On Education Thatsnotus. Essay for education - College Homework Help and Online Tutoring.. College education essay - 24/7 Homework Help.. Education in College - Free Essay Example PapersOwl.com. Everyone Should Enjoy a Free College Education - Free Essay Example .... 26 Outstanding College Essay Examples / - Example of a college essay .... Writing An Essay To Get Into College - Writing a strong college .... College essay: Importance of college education essay. Essay on why college education is important Essay On College Education Essay On College Education. 24 Greatest College Essay Examples RedlineSP
Ontologies for baby animals and robots From "baby stuff" to the world of adul...Aaron Sloman
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.
What is computational thinking? Who needs it? Why? How can it be learnt? ...Aaron Sloman
What is computational thinking?
Who needs it? Why? How can it be learnt?
Can it be taught? How?
Slides 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.
2005: Natural Computing - Concepts and ApplicationsLeandro de Castro
The document discusses natural computing, which encompasses computing inspired by nature, simulating natural phenomena using computers, and using natural materials for computing. It surveys ideas from neurocomputing, evolutionary computing, swarm intelligence, immunocomputing, and artificial life. These fields take inspiration from neural networks, evolution, collective animal behavior, the immune system, and the synthesis of life-like behaviors to develop new algorithms and applications. The goal is to develop more robust, adaptive, and fault-tolerant computing approaches.
NG2S: A Study of Pro-Environmental Tipping Point via ABMsKan Yuenyong
A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.
Philosophy of Biological Cell Repair informs Geoethical Nanotechnology: Cellular repair is an age-old function in biology. This talk examines the cellular process of repair in philosophical terms. Biologically, wound-healing is the primary form of cellular repair, drawing on numerous cell types and the extracellular matrix to perform a variety of operations during the phases of inflammation, proliferation, and maturation. Philosophically, these functions can be discussed from a systems theory perspective, through the concepts pairs of parts-whole, autonomy-dependency, self-other, sickness-wellness, and scarcity-abundance. Understanding cellular repair at the theory level could facilitate the development of nanotechnology solutions that augment biological processes in ways that are congruently geoethical with nature’s ethos.
Virtual Machines in Philosophy, Engineering & Biologywpe
This document provides information about upcoming presentations and workshops given by Aaron Sloman on the topic of virtual machines in philosophy, engineering, and biology. It lists details of seminars and talks to be given at the University of Birmingham, Newcastle, Oxford, and the Royal Academy of Engineering in London between October and November 2008. The document acknowledges those who have contributed to Sloman's work and provides links to related materials online.
This document discusses next-generation DNA sequencing technologies and the associated data analysis challenges. It outlines two research projects using these techniques: 1) metagenomic sequencing of soil microbes to study microbial ecology and 2) transcriptomic sequencing of a non-model ascidian to study tail development. It also describes the development of a graduate course to teach computational skills needed for data-intensive biology.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...Aaron Sloman
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
Why symbol-grounding is both impossible and unnecessary, and why theory-tethe...Aaron Sloman
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.
The document discusses integrating invention, innovation and entrepreneurship (i2e) across university curriculums. It proposes requiring students to take a series of courses designated as i2e-intensive. These courses would incorporate modules about innovators and inventions. Pilot programs are being developed at Polytechnic Institute of New York University to incorporate i2e components into existing chemistry, biology, and computer science courses. The goal is to expose students to i2e concepts and encourage innovative thinking throughout their academic programs.
Nota RINGKAS MENGIKUT TOPIK MATEMATIK SEKOLAH RENDAHrichieroyston
The document discusses topics in mathematics including:
1) Place value of whole numbers with units of juta, ribu, puluh, and ribu.
2) Fractions such as proper, mixed, and equivalent fractions.
3) Decimals and adding trailing zeros.
4) Conversions between units of time, length, volume, and mass.
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.)
This document discusses varieties of self-awareness and their uses in natural and artificial systems. It proposes a conceptual framework for metacognition and natural cognition. The document contains slides for presentations on this topic, including:
- Discussing how to analyze requirements by examining natural and artificial systems to understand design discontinuities.
- Explaining how environments can have agent-relative structure that produces varied information processing demands.
- Outlining a conceptual framework that includes reactive and deliberative architectures in natural systems, with different layers providing varieties of self-awareness.
What designers of artificial companions need to understand about biological onesAaron Sloman
This document summarizes Aaron Sloman's presentation at AISB'08 on what designers of artificial companions need to understand about biological ones. Sloman discusses the difficulty of the task and argues that current AI is not capable of replicating the capabilities of young children. He outlines "Type 1" goals for artificial companions focused on engagement, and "Type 2" goals focused on enabling functions like helping users, which are much harder to achieve. Sloman asserts progress requires understanding how human capabilities like understanding environments, minds, and developing new motives emerge from biological and developmental factors. The key is replicating some of the generic learning abilities of young humans to build more advanced functions layer by layer.
Slides 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.
Essay On College Education. 24 Greatest College Essay Examples RedlineSPMelissa Otero
College Essay Examples - 9 in PDF Examples. College and Education - Free Essay Example PapersOwl.com. Essay websites: Essay on the importance of college education. College Education: Should Education be Free Essay. St Joseph Hospital: College Application Essay. Importance of college education essay. Free importance of education .... 004 Essay Example Why Is College Important On Importance Of Education .... College Essay Format: Simple Steps to Be Followed. FREE 11 Sample College Essay Templates in MS Word PDF. Argumentative essay on college education. Sample College Application Essay 5. 021 10067 Thumb College Education Essay Thatsnotus. How to Write In College Essay Format OCC NJ. College Admissions Essay Workshop - 9 Types of Supplemental Essays .... Admission essay: Being a college student essay. This is How You Write a College Essay College application essay .... College Essay: Graduate school essay sample. Why College Should Be Cheaper Essay. Essay On The Importance Of College Education. 24 Greatest College Essay Examples RedlineSP. Why Do You Think College Education Is Important Essay. Impressive Essay On Education Thatsnotus. Essay for education - College Homework Help and Online Tutoring.. College education essay - 24/7 Homework Help.. Education in College - Free Essay Example PapersOwl.com. Everyone Should Enjoy a Free College Education - Free Essay Example .... 26 Outstanding College Essay Examples / - Example of a college essay .... Writing An Essay To Get Into College - Writing a strong college .... College essay: Importance of college education essay. Essay on why college education is important Essay On College Education Essay On College Education. 24 Greatest College Essay Examples RedlineSP
Ontologies for baby animals and robots From "baby stuff" to the world of adul...Aaron Sloman
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.
What is computational thinking? Who needs it? Why? How can it be learnt? ...Aaron Sloman
What is computational thinking?
Who needs it? Why? How can it be learnt?
Can it be taught? How?
Slides 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.
2005: Natural Computing - Concepts and ApplicationsLeandro de Castro
The document discusses natural computing, which encompasses computing inspired by nature, simulating natural phenomena using computers, and using natural materials for computing. It surveys ideas from neurocomputing, evolutionary computing, swarm intelligence, immunocomputing, and artificial life. These fields take inspiration from neural networks, evolution, collective animal behavior, the immune system, and the synthesis of life-like behaviors to develop new algorithms and applications. The goal is to develop more robust, adaptive, and fault-tolerant computing approaches.
NG2S: A Study of Pro-Environmental Tipping Point via ABMsKan Yuenyong
A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.
Philosophy of Biological Cell Repair informs Geoethical Nanotechnology: Cellular repair is an age-old function in biology. This talk examines the cellular process of repair in philosophical terms. Biologically, wound-healing is the primary form of cellular repair, drawing on numerous cell types and the extracellular matrix to perform a variety of operations during the phases of inflammation, proliferation, and maturation. Philosophically, these functions can be discussed from a systems theory perspective, through the concepts pairs of parts-whole, autonomy-dependency, self-other, sickness-wellness, and scarcity-abundance. Understanding cellular repair at the theory level could facilitate the development of nanotechnology solutions that augment biological processes in ways that are congruently geoethical with nature’s ethos.
Virtual Machines in Philosophy, Engineering & Biologywpe
This document provides information about upcoming presentations and workshops given by Aaron Sloman on the topic of virtual machines in philosophy, engineering, and biology. It lists details of seminars and talks to be given at the University of Birmingham, Newcastle, Oxford, and the Royal Academy of Engineering in London between October and November 2008. The document acknowledges those who have contributed to Sloman's work and provides links to related materials online.
This document discusses next-generation DNA sequencing technologies and the associated data analysis challenges. It outlines two research projects using these techniques: 1) metagenomic sequencing of soil microbes to study microbial ecology and 2) transcriptomic sequencing of a non-model ascidian to study tail development. It also describes the development of a graduate course to teach computational skills needed for data-intensive biology.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...Aaron Sloman
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
Why symbol-grounding is both impossible and unnecessary, and why theory-tethe...Aaron Sloman
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.
The document discusses integrating invention, innovation and entrepreneurship (i2e) across university curriculums. It proposes requiring students to take a series of courses designated as i2e-intensive. These courses would incorporate modules about innovators and inventions. Pilot programs are being developed at Polytechnic Institute of New York University to incorporate i2e components into existing chemistry, biology, and computer science courses. The goal is to expose students to i2e concepts and encourage innovative thinking throughout their academic programs.
Dr Lael Parrott at the Landscape Science Cluster Seminar, May 2009pdalby
This document summarizes how concepts from complex systems studies can inform natural resource management. It discusses how ecosystems and landscapes are complex systems with emergent properties arising from local interactions. Agent-based models are useful for modeling ecological complexity across scales. Examples shown include a model of grassland resilience under disturbance and the relationship between grazing and spatial complexity. Understanding community assembly is explored through a spatial model linking local communities. The document concludes that embracing complexity requires new tools like multi-scale models and monitoring to manage social-ecological systems.
Artificial Intelligence is advancing throughout the world. According to a study by Creative Strategies, 95% of mobile users are using AI-enabled voice assistance. It is hard to seek out a society that doesn’t use AI techniques. This technique brings numerous uses in a number of ways. It includes decision-making capabilities, diagnosis generation, identifying the connection between causes and consequences, forecasting events, controlling devices like smart sensors, mechanical arms, etc.
https://takeoffprojects.com/ai-based-projects
Kenneth Lloyd introduces category theory as a potential language for scientific discourse in agent-based modeling and simulation (ABMS). Category theory defines mathematical structures and relationships between them. Lloyd argues that agents can be considered as structures within a category. He provides an example of applying category theory concepts like functors to represent functional objects and agents. Finally, Lloyd discusses how category theory may provide a formalism for describing emergent properties in multi-agent systems and validating hypotheses through simulation.
A new revolution in e-ducation quality at scale Vijay KumarEIFLINQ2014
This document discusses new technologies and initiatives that are revolutionizing education through open online learning platforms. It describes several MIT educational technology projects that aim to improve learning through interactive simulations, online labs, collaborative tools, and modular course designs. These initiatives have the potential to greatly expand access to quality education and advance educational research on a large scale through data analytics. The document argues that emerging technologies are changing the ecology and economics of education by enabling more customizable, lifelong, blended, and boundary-less learning opportunities.
Similar to Kantian Philosophy of Mathematics and Young Robots: Could a baby robot grow up to be a Mathematician and Philosopher? (20)
Construction kits for evolving life -- Including evolving minds and mathemati...Aaron Sloman
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. Slideshare 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)
Reorganised several times since first uploaded: most recently 25 Jan 2016
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Slides include link to video of lecture (158MB) http://www.cs.bham.ac.uk/research/projects/cogaff/movies/#ailect2-2015
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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.
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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.
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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
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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
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Minor corrections+ additions 30-Mar-2015, 1-Apr-2015, 15-Apr-2015 12-Nov-2015
Virtuality, causation and the mind-body relationshipAaron Sloman
This document discusses virtual machinery and causation. It defines three types of machines: physical machines, abstract mathematical objects called mathematical machines, and running virtual machines that are instances of mathematical machines controlling events in physical machines. It explores how running virtual machines can have causal powers despite being based on abstract mathematical objects, and how causation occurs both physically and through information processing in virtual machines. The document aims to clarify the nature and causal abilities of virtual machines.
How to Build a Research Roadmap (avoiding tempting dead-ends)Aaron Sloman
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.
A multi-picture challenge for theories of visionAaron Sloman
(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.
What's vision for, and how does it work? From Marr (and earlier)to Gibson and...Aaron Sloman
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)
Helping Darwin: How to think about evolution of consciousness (Biosciences ta...Aaron Sloman
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.
Possibilities between form and function (Or between shape and affordances)Aaron Sloman
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
Virtual Machines and the Metaphysics of Science Aaron Sloman
The document is an abstract for a presentation given by Aaron Sloman at the Metaphysics of Science conference in Nottingham on September 12, 2009. The presentation discusses virtual machines and their importance for philosophy. It notes that philosophers regularly use complex virtual machines composed of interacting non-physical subsystems, like operating systems and web browsers. However, philosophers often ignore or misdescribe these virtual machines in discussions of topics like functionalism and causation. The presentation aims to explain virtual machines and how they are relevant to several philosophical problems regarding issues like supervenience, causation, and the mind-body problem.
Why the "hard" problem of consciousness is easy and the "easy" problem hard....Aaron Sloman
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.
Some thoughts and demos, on ways of using computing for deep education on man...Aaron Sloman
1. The document discusses using computing to support deep and liberal education on many topics by stretching young minds through challenging learning opportunities rather than solely teaching skills.
2. It suggests computers can provide powerful learning through playing with AI programs, concepts and graphics-based tools, while also needing simpler textual environments to support abstraction.
3. One way to get this in schools is to make remotely accessible systems like Poplog available through shared Linux machines to introduce simple AI programming and collaborative learning.
Do Intelligent Machines, Natural or Artificial, Really Need Emotions?Aaron Sloman
(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'.
What is science? (Can There Be a Science of Mind?) (Updated August 2010)Aaron Sloman
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.
Evolution of minds and languages: What evolved first and develops first in ch...Aaron Sloman
SLIDESHARE NOW STUPIDLY DOES NOT ALLOW SLIDES TO BE UPDATED. To find the latest version of these slides go to http://www.cs.bham.ac.uk/research/projects/cogaff//talks/#talk111
The version posted here was last updated on 16 March 2015. There have been several changes since then on the alternative site. Why did Slideshare take such a stupid decision (after being bought by Linkedin?)
A theory is presented according to which "languages" with structural variability and compositional semantics evolved in several species for *internal* use (e.g. in perception, planning, learning, forming goals, deciding, etc.) before *external* languages evolved for communication. The theory implies that such internal languages develop in young humans before a language for communication.
It is is also noted that the standard notion of 'compositional semantics' has to allow for the propagation of semantic content from parts to wholes to be potentially context sensitive at every stage: i.e. current context, speaker intentions, user knowledge, shared goals, can all affect how semantics of larger parts are derived from semantics of smaller parts+syntactic structure. This applies as much to non-verbal languages as to verbal ones.
This theory of how human languages evolved from earlier 'internal languages' (GLs) is inconsistent with the best known published theories of evolution or development of language.
But that does not make it wrong. Moreover, this theory is supported by empirical evidence including the example of deaf children in Nicaragua: http://en.wikipedia.org/wiki/Nicaraguan_Sign_Language
This document summarizes a lecture on the relationships between artificial intelligence and philosophy. It discusses how AI both relates to and improves upon philosophical inquiry. Specifically, it notes that AI can help clarify philosophical concepts and provide new examples to investigate philosophical questions. At the same time, philosophy helps clarify AI's goals and concepts. The document provides examples of how AI extends the philosophy of mind by allowing the design of varied mind types and clarifying the relationship between mind and body.
Debate on whether robots will have freewillAaron Sloman
The document discusses the concept of free will and argues that neither humans nor robots have true free will. It defines free will as decisions that are not caused by anything external and allows beings to escape responsibility. However, the document asserts that humans and robots are just complex information processing machines, with humans having deliberative and reactive processes, and robots likely mirroring human architectures. As such, both humans and robots are determined by their internal workings rather than having uncaused or undetermined decisions, and therefore neither possesses free will in the theological sense.
This is a mixture of philosophy, artificial intelligence, Cognitive science, Software engineering and theoretical biology, presented at the Workshop on Philosophy and Engineering, London, 10-12 November 2008.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
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تتميز هذهِ الملزمة بعِدة مُميزات :
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4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
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Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
Kantian Philosophy of Mathematics and Young Robots: Could a baby robot grow up to be a Mathematician and Philosopher?
1. 7th International Conference on Mathematical Knowledge Management
Birmingham, UK, 28-30 July 2008
http://events.cs.bham.ac.uk/cicm08/mkm08/
Kantian Philosophy
of Mathematics
and Young Robots
Could a baby robot grow up to be
a Mathematician and Philosopher?
Aaron Sloman
School of Computer Science, University of Birmingham
http://www.cs.bham.ac.uk/∼axs/
Last updated: November 28, 2008 (liable to change).
These PDF slides will be in my ‘talks’ directory.
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#mkm08
See also this longer presentation:
http://www.cs.bham.ac.uk/research/projects/cogaff/talks#math-robot
The conference paper is available here:
http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0802
Kantian Philosophy of Mathematics and Young Robots
MKM08, July 2008 Slide 1 Last revised: November 28, 2008
2. Summary part 1
We need a multi-pronged revolution in studies of intelligence,
– replacing factional wars with healthy collaboration
– and with far more attention paid to the problems solved by evolution
– as opposed to focusing prematurely on supposed solutions to the
problem,
e.g. hypothesised biological mechanisms.
Or even assuming that logic will suffice (McCarthy & Hayes 1969, Sloman 1971)
If we don’t know what problems evolution actually solved,
we (including biologists, neuroscientists, and psychologists)
are likely to make serious mistakes about the mechanisms
used to solve them.
This talk describes one facet of that revolution:
For other facets see my online papers and presentations.
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/
http://www.cs.bham.ac.uk/research/projects/cogaff/
http://www.cs.bham.ac.uk/research/projects/cosy/papers/
MKM08, July 2008 Slide 2 Last revised: November 28, 2008
3. Summary part 2
Some of the main points
• There are many designs for biological organisms – solving different problems.
• Most species (precocial species) are designed with genetically determined,
pre-configured, competences tailored to a particular range of environments
(all they can do is calibrate and adjust parameters).
• A small subset (altricial species) are designed to find out what sort of environment
they are in and to go on extending their understanding of its properties and what can
be done in it: they develop meta-configured competences.
• That can include acquiring new competences to acquire more competences, i.e. new
meta-configured meta-competences, including substantively new ontologies.[*]
Humans in particular can go on learning new ways to learn, indefinitely.
• For some species learning correlations and conditional probabilities is not enough:
they have to learn about structures and structured processes in the environment, and
how they can be combined in novel ways. (E.g. building new tools, weapons, shelters, ...)
..............to be continued
[*] In our IJCAI’05 paper Sloman&Chappell suggested that future robots will need different combinations of
precocial competences and altricial competences. In our IJUC 2007 paper we started calling them
preconfigured and meta-configured competences.
http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0502 (2005)
http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0609 (2007)
MKM08, July 2008 Slide 3 Last revised: November 28, 2008
4. Summary part 3
• The mechanisms required for such creativity are closely related to requirements
for making not only empirical, but also mathematical, discoveries about the
environment.
• As a result, some kinds of knowledge that start empirical in a child can be
transformed, so as to be more like mathematical knowledge, i.e. non-empirical.
Example: Learning that two ways of counting a set give the same result.
Note: The process whereby such discoveries are made is not bug-free. (Lakatos)
Examples of such bugs and how they can be fixed are given in
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#math-robot
MKM08, July 2008 Slide 4 Last revised: November 28, 2008
5. Apologies and Acknowledgements
I apologise
For slides that are too cluttered: I write my slides so that they can be read by people who did not attend
the presentation.
So please ignore what’s on the screen unless I draw attention to something.
NOTE:
To remind me to skip some slides during the presentation they are ‘greyed’ with (X) at the top.
Acknowledgements
The approach taken here is much influenced by reading G.Polya’s How to Solve It.
The deepest influences were
(a) experience of doing mathematics as a student and enjoying mathematical thinking.
(b) Kant’s philosophy of mathematics, expounded in The Critique of Pure Reason (1781) – his
response to David Hume.
Although my interest in these problems goes back to my 1962 DPhil work on Kant’s philosophy of
mathematics, some of the new ideas reported here were stimulated by working on requirements for a
robot that can manipulate 3-D objects in its environment, as part of the EU CoSy project:
“Cognitive Systems for Cognitive Assistants” (2004-2008).
http://www.cognitivesystems.org
http://www.cs.bham.ac.uk/research/projects/cosy/
This included many discussions with Jeremy Wyatt, Nick Hawes and other members of the School of
Computer Science in Birmingham, and Jackie Chappell in the School of Biosciences, who works on
animal cognition.
MKM08, July 2008 Slide 5 Last revised: November 28, 2008
6. Routes from DNA to behaviours: reflexes
Cognitive epigenesis: Multiple routes from DNA to behaviour, some via the environment
The simplest route from
genome to behaviour:
Everything is hard-wired in a design
encoded in the genome (subject to
interpretation by epigenetic
mechanisms).
The physical structures determine
• ongoing behaviours (e.g.
breathing, or respiration, or
pumping of a heart)
• specific reflex responses to
particular stimuli
e.g. the knee-jerk reflex.
Note: during development, the behaviours that produce effects on the environment may
feed back into influencing further development, and learning.
This work is based on collaboration with Jackie Chappell
MKM08, July 2008 Slide 6 Last revised: November 28, 2008
7. Routes from DNA to behaviours:
more flexible competences
Cognitive epigenesis: Multiple routes from DNA to behaviour, some via the environment
A more complex route from genome
to behaviour:
Everything is hard-wired in a design
encoded in the genome (subject to
interpretation by epigenetic mechanisms).
But what is hard-wired is capable of
modifying behaviour on the basis of what
is sensed before and during the
behaviour.
• The details of such behaviours are
products of both the genome and the
current state of the environment.
A “competence” is an ability to produce a family of behaviours capable of serving a
particular goal or need in varied ways, e.g. picking something up, avoiding an obstacle,
getting food from a tree by jumping, catching prey, avoiding predators, migration.
Some competences are “pre-configured” in the genome.
There is not necessarily a sharp division between reflexes and competences: the latter are more flexible
and goal directed, but the degree of sophistication can vary a lot.
MKM08, July 2008 Slide 7 Last revised: November 28, 2008
8. Routes from DNA to behaviours:
The role of meta-competences
Cognitive epigenesis: Multiple routes from DNA to behaviour, some via the environment
A more complex route from genome
to competences:
Instead of competences being hard-wired in
a design encoded in the genome (subject to
interpretation by epigenetic mechanisms),
they may be developed to suit features of
the environment, as a result of play, and
exploration, leading to learning.
There may be hard-wired genetically
preconfigured “meta-competences” that use
information gained by experimenting in the
environment to generate new
competences tailored to the features of the environment – e.g. becoming expert at climbing particular kinds
of tree, or catching particular kinds of fish, while conspecifics in another location develop different
competences produced by the same mechanisms.
The details of such behaviours are products of both the genome and the current state of the environment.
A “meta-competence” is an ability to produce a family of competences capable of serving
varied goals in the environment of the animal. (Compare Alan Bundy’s talk: repair plans)
MKM08, July 2008 Slide 8 Last revised: November 28, 2008
9. Some virtual machines can extend themselves indefinitely
Cognitive epigenesis: Multiple routes from DNA to behaviour, some via the environment
Meta-configured meta-competences:
Humans not only learn new kinds of
things, they can learn to learn even
more varied kinds of things. E.g.
after completing a degree in physics
you are enabled to learn things that
a non-physicist could not learn, e.g.
learning how to do more
sophisticated experiments.
Meta-configured meta-competences:
(towards the right of the diagram) are
produced through interaction of
pre-configured or previously produced
meta-configured competences with
the environment, including possibly
the social environment
http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0609
Natural and artificial meta-configured altricial information-processing systems
Chappell&Sloman, 2007, IJUC
Diagrams developed with Jackie Chappell and Chris Miall.
MKM08, July 2008 Slide 9 Last revised: November 28, 2008
10. Self-monitoring and virtual machines
Systems dealing with complex changing circumstances and needs may
need to monitor themselves, and use the results of such monitoring in
taking high level control decisions.
E.g. which high priority task to select for action.
Using a high level virtual machine as the control interface may make a very complex
system much more controllable: only relatively few high level factors are involved in
running the system, compared with monitoring and driving every little sub-process, even
at the transistor level.
The history of computer science and software engineering since around 1950 shows
how human engineers introduced more and more abstract and powerful virtual
machines to help them design, implement, test debug, and run very complex systems.
When this happens the human designers of high level systems need to know less and
less about the details of what happens when their programs run.
Making sure that high level designs produce appropriate low level processes is a separate task, e.g. for
people writing compilers, device drivers, etc. Perhaps evolution produced a similar “division of labour”?
Similarly, biological virtual machines monitoring themselves would be aware of only a tiny
subset of what is really going on and would have over-simplified information.
T HAT CAN LEAD TO DISASTERS , BUT MOSTLY DOES NOT.
MKM08, July 2008 Slide 10 Last revised: November 28, 2008
11. Kant on Synthetic A priori Knowledge
(e.g. mathematical knowledge).
Immanuel Kant wrote, over 200 years ago
“There can be no doubt that all our knowledge begins with experience.
For how should our faculty of knowledge be awakened into action did not objects
affecting our senses partly of themselves produce representations, partly arouse
the activity of our understanding to compare these representations, and, by
combining or separating them, work up the raw material of the sensible
impressions into that knowledge of objects which is entitled experience?
In the order of time, therefore, we have no knowledge antecedent to experience,
and with experience all our knowledge begins.”
Critique of Pure Reason
MKM08, July 2008 Slide 11 Last revised: November 28, 2008
12. What about the genome?
Kant wrote before Darwin’s theory of evolution, though he was aware of
and wrote about the question whether humans had evolved from
non-human animals.
However he seems not to have allowed for the possibility that many scientists now take
seriously, that evolution produced genetically-determined cognitive competences and
knowledge that are involved in learning and development from the earliest stages.
Think of precocial species like deer, horses, chickens, whose young are pretty competent after hatching –
more competent than any existing robots. Some robots will need to be precocial also.
Compare
A. Sloman and J. Chappell, 2005, The Altricial-Precocial Spectrum for Robots, Proceedings IJCAI’05 pp. 1187–1192,
http://www.cs.bham.ac.uk/research/cogaff/05.html#200502,
MKM08, July 2008 Slide 12 Last revised: November 28, 2008
13. Yet Kant implicitly supports such a theory
in his question
For how should our faculty of knowledge be awakened into action did not objects
affecting our senses ... work up the raw material of the sensible impressions into that
knowledge of objects which is entitled experience?
Whatever this “faculty of knowledge” is, it could not be awakened without
already existing.
What is this faculty?
Perhaps it is a kind of information-processing architecture that drives
exploration of the environment while it grows itself?
MKM08, July 2008 Slide 13 Last revised: November 28, 2008
14. Genome (+) at work on a rug - after about 6 months
Video available online:
http://www.cs.bham.ac.uk/research/projects/cosy/conferences/mofm-paris-07/sloman/vid/
The infant seems to produce a new combination of previous competences: looking round, identifying an
edge, setting up a goal, rolling over, stretching arm, opening fingers, moving down, closing fingers, pulling.
Human learning seems to progress in layers of competence, where new layers include
components in old layers (including previously learnt actions and hypotheses) as objects
to play with.
One feature of that is specially important for our topic:
Transforming empirical discoveries into non-empirical discoveries.
For the infant there may at first be just empirical discoveries about which movements
produce which effects.
But members of the audience have got long past that:
you know a lot about what actions can and cannot succeed — and why.
MKM08, July 2008 Slide 14 Last revised: November 28, 2008
15. Motivation and Learning are not Reward-based but
Architecture-based
ASK ME LATER WHAT THAT MEANS
MKM08, July 2008 Slide 15 Last revised: November 28, 2008
16. Genome plus much acquired competence
at work with a broom (about 15 months)
Some snapshots from the video (available online):
http://www.cs.bham.ac.uk/research/projects/cosy/conferences/mofm-paris-07/sloman/vid/
At least two different kinds of thing going on in parallel:
• Control of several interacting dynamical systems for maintaining stability and producing motion of the
required sort.
• Various kinds of perceptual and cognitive processes involved in controlling the motion by understanding
how the broom (broom handle and brush) interacts with different things in the environment.
The child feels that the broom handle is caught, looks round, then moves it away from between the railings.
When the brush hits the skirting board he retracts and then redirects it.
Later, he sees that there’s an opening off the corridor on the right and (unconsciously?) works out that
steering into it will require the broom handle to be moved to the left shortly before the opening is reached.
There are also two fleeting bits of social interaction, indicating even more concurrent processing.
MKM08, July 2008 Slide 16 Last revised: November 28, 2008
17. Information initially gained empirically can be
transformed, and recognised as non-empirical.
A key feature of the information-processing architecture produced by
evolution, in humans and possibly a few other species is this:
Acquired knowledge that starts off empirical can be transformed, so that it
has a new status.
E.g. is knowledge about a movement that will stop the railings impeding the handle empirical?
This is because the environment has a great deal of discoverable structure.
Spatial structure (geometrical, topological, continuous, discrete, orderings)
Process structure, combined, adjacent, synchronised processes, mappings, ....
Kinds of matter with distinct properties (rigid, flexible, elastic, inelastic, fluid, sticky, plastic, etc.).
The transformation from empirical to non-empirical (mathematical) is not modelled by any
kind of statistical or probabilistic learning mechanism.
This needs an argument. E.g. changing probabilities of correlations does not allow
creative prediction in new configurations,
or new explanations
or new plans
or new designs
MKM08, July 2008 Slide 17 Last revised: November 28, 2008
18. This is not a change in probability
– but something deeper:
It is a qualitative, not a quantitative change.
Adding a new explanatory theory is not the same as modifying a probability
(Compare Alan Bundy’s talk on ontology/theory changes)
Likewise developing a new form of representation is not a change in probabilities.
Explaining how this happens, and modelling the processes (e.g. in human-like robots),
will require the development of new architectures, and perhaps new forms of
representation.
Probably not using predicate calculus – maybe some formalism closely related to visual competences
and human visual reasoning.
Compare the original presentations of Euclidean geometry.
Several examples, of different kinds, will be given below.
MKM08, July 2008 Slide 18 Last revised: November 28, 2008
19. An underrated, but powerful, learning process:
Creatively playing, exploring, trying things out
This is very different from corpus-based, or data-mining-based learning,
where the only criterion of success is an external judgement.
The learner who observes, plays, explores, and tests ideas in a complex but highly
structured, and in many ways deterministic world, is in control.
Such a learner can tell:
• when predictions are violated
• when goals are not achieved
• when hypotheses pass novel tests
• when unexpected things are observed
• when a new possibility turns up
• when an option has not yet been tried.
Not always, and not infallibly, but often enough to drive learning by play and exploration.
MKM08, July 2008 Slide 19 Last revised: November 28, 2008
20. It can be, but need not be, a social process.
NOTE:
You can’t learn something by imitation or instruction
if you don’t have the cognitive resources in the first place.
This is the obverse of McCarthy’s dictum:
What you can discover for yourself you could learn by being told.
There are exceptions to McCarthy’s dictum.
It’s hard to learn by being told what the taste of pineapple is.
MKM08, July 2008 Slide 20 Last revised: November 28, 2008
21. Forms of representation
What form of representation does an infant or toddler use?
Or a chimpanzee or orangutan?
Or a nest building bird (e.g. Betty, the hook-maker)?
Nobody knows.
Could it be predicate calculus?
Could it be a language like human natural languages, e.g. English?
Could be more like a programming language, e.g. Lisp, Prolog, Java?
Are neural nets capable of expressing what’s needed, and if so how?
Nobody knows – none of those is plausible for a toddler.
Some of us have been suggesting that some non-human animals and pre-verbal children
have ways of manipulating structures specially tailored for spatial reasoning.
E.g. Sloman 1971, 1978 (Chapter 7)
See this presentation on the evolution of language
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#glang
But the problems are deep and difficult and there are many problems to be solved.
NB: Beware of assuming the representing structures are isomorphic with what they represent.
(See the impossible object pictures below.)
MKM08, July 2008 Slide 21 Last revised: November 28, 2008
22. Empirical vs Non-empirical knowledge
Example of the distinction, from Kant.
• Empirical:
All solid bodies are heavy
Many solid things require effort to raise them off a surface they are resting on. (But exceptions exist.)
• Non-empirical
All solid bodies occupy space
Kant: “All bodies are extended”
Question:
Is all non-empirical knowledge essentially just a matter of definition?
Isn’t it part of the concept of a solid object that it occupies space?
Arguably, it is impossible for a child to discover (by observation or experiment) that solid objects
occupy space, for where else could it look for solid objects except in space?
Does that make all non-empirical knowledge trivial?
(Or “analytic”, in Kant’s terminology)
Or is some of it non-trivial, substantive, an addition to knowledge?
(Or “synthetic” in Kant’s terminology)
According to Kant, some non-trivial, substantive knowledge, may initially be acquired as a result of
experience, but then come to be understood as non-empirical, and as necessarily true.
MKM08, July 2008 Slide 22 Last revised: November 28, 2008
23. Examples for babies and toddlers
The following are empirical discoveries at first:
• A certain sort of visual experience (of the edge of a rug) indicates the possibility of a
certain sort of action (grasping the edge).
The action requires certain intermediate stages,
e.g. reaching, opening fingers, moving hand down, closing fingers, lifting.
• A rigid rod (e.g. broom-handle) poked between a pair of vertical railings cannot be
moved sideways in the plane of the railings, but can be moved in the perpendicular
direction.
A result of the second motion can be that the previously impossible direction of motion
becomes possible.
Are these empirical facts for you?
Beware: That question has different interpretations.
Care is needed in the formulation/interpretation.
Another version:
• Could you imagine developing a mathematical theory of rug grasping?
• Could you imagine developing a mathematical theory of how rigid, impenetrable, rods or poles can move
in the presence of other rigid, impenetrable things?
MKM08, July 2008 Slide 23 Last revised: November 28, 2008
24. Somatic and exosomatic ontologies
There are many “biologically inspired” researchers who think brains learn
correlations between sensory and motor signals.
For such a researcher, committed to “the sensorimotor approach” the statement
“A certain sort of visual experience (of the edge of a rug) indicates the
possibility of a certain sort of action (grasping the edge).”
would be a summary of a collection of correlations between visual input signals (or retinal
patterns) and motor output signals.
It would summarise statistical facts about events on and inside the skin (taking sensors as
part of the skin).
Such generalisations use a purely somatic ontology.
But some biological systems, e.g. humans, are capable of using an exosomatic ontology,
referring to objects, properties, relationships, events, and processes existing outside the
perceiver’s body, and capable of existing independently of any individuals sensory or
motor patterns.
Without using an exosomatic ontology you would not be able to think about
• what’s going on out of sight,
• what happened in the past,
• what might happen in the future,
• what you should do in the future, and
• what’s going inside objects whose innards are not accessible to you.
MKM08, July 2008 Slide 24 Last revised: November 28, 2008
25. An exosomatic infant ontology
Whatever the state at birth, humans are eventually able to use an
exosomatic ontology.
That can give a new meaning to
“A certain sort of visual experience (of the edge of a rug) indicates the
possibility of a certain sort of action (grasping the edge).”
i.e. something of the form
“When you see a certain 3-D structure in the environment related to you in certain ways, you can
infer that certain 3-D processes are possible, involving your hands and the perceived structure.”
The exosomatic ontology provides many advantages of economy, and power, including
being usable for reasoning about processes in the environment, including actions and
affordances of others.
Vicarious affordances can be important: a point ignored by J.J.Gibson, I think.
Warneken’s experiments show that even pre-verbal children can detect them
http://email.eva.mpg.de/∼warneken/video.htm
See also
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/nature-nurture-cube.html
Learning to see a set of moving lines as a rotating cube.)
http://www.cs.bham.ac.uk/research/projects/cosy/papers/#dp0603
Sensorimotor vs objective contingencies
MKM08, July 2008 Slide 25 Last revised: November 28, 2008
26. Ontologies and theories develop together
(Compare Alan Bundy’s talk.)
Whereas discoveries of correlations involving sensory and motor signals
are empirical, within a theory of “external” 3-D structures and processes,
discoveries can have a mathematical status, like theorems of geometry
and topology.
What is possible and what is impossible within a configuration of structures can be
derived from the specifications of the structures and their relationships.
MKM08, July 2008 Slide 26 Last revised: November 28, 2008
27. A theorem for infant science
E.g.
Theorem 1:
If you stand with your soles next to a pair of slippers with their soles on the floor you
cannot get your feet into the slippers just by sliding them horizontally.
(This might first be learnt empirically.)
(This is a special case of a much more general theorem about things with openings.)
Theorem 2:
You can’t shut an open door by grasping its edge and pulling it towards you until it is
shut.
(Don’t try that!)
Of course you can start the process that way if you can generate enough momentum in the door for it to
complete the shutting process on its own.
For more on substantive ontology development and how meanings can be extended see this attack on
“symbol grounding” theory:
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#models
MKM08, July 2008 Slide 27 Last revised: November 28, 2008
28. More theorems for infants and toddlers
In the first few years of life, as infant then toddler, a normal human learns
many hundreds, possibly thousands, of facts about things that are and are
not possible in the environment.
Because they become part of our common sense, and are “second-nature” to us, we do
not notice that we and others learn them, nor do we find them remarkable. Things like
• How to adjust/improve grip on a held object.
• Results of pushing, prodding, squeezing, rotating objects of various shapes and sizes, made of different
kinds of stuff.
• Results of moving around in various ways in relation to obstacles, openings, occluders, etc.
• Results of moving markers (pens, pencils, crayons) on markable surfaces.
E.g. What movements of a pencil point are needed to make it draw a square: A four year old tried,
and failed and was annoyed – he could only draw curves, including more or less closed curves. He
knew he had not drawn a square, but did not know how to do it. Several months later he found it trivial.
• How to get liquid out of a half-empty bottle, first when sucking and later when pouring.
At first it’s an empirical discovery that tilting your head back is needed for sucking. Later ???
• How to correct a mis-aimed teat on bottle (i.e. making a corrective movement depending on which part
of the face it makes contact with).
• What changes in the mouth when one end of a rigid object is in the mouth and the far end is moved in
various ways – e.g. a toothbrush.
• Various actions required at various stages of putting on and taking off various sorts of garments.
You can’t put a sweater on just by holding it against you and pressing hard. (Contrast a hat or cap.)
MKM08, July 2008 Slide 28 Last revised: November 28, 2008
29. Learning how to bring something to you
A child can learn various ways of getting hold of a visible object, depending
on whether it is within reach or not, whether it is resting on something that
is within reach, whether something that is within reach is attached to it, etc.
If the toy is not on the mat pulling the mat will
not move the toy, but pulling the string may
move the toy.
If the string is straight, pulling it will move the toy,
but not if the string is curved.
However, pulling a string that is not straight can transform
it into a straight string, after which the object can be
moved towards the puller.
But not if the string goes round a fixed remote obstacle,
e.g. a remote chair leg.
Generalisations are learnt, and then counter-examples
discovered, causing the generalisations to be abandoned
or de-bugged, and in the process ontologies are extended, e.g. length of a curved string,
knots, loops, forces being transmitted indirectly, elasticity, etc.
But some of what is learnt is about non-contingent truths that, once recognised as such,
require no more empirical evidence.
A fixed-length (inelastic) string gets straighter as its ends are moved apart.
MKM08, July 2008 Slide 29 Last revised: November 28, 2008
30. From action affordances to epistemic affordances
In addition to discoveries about possible actions and processes
• what actions the individual can and cannot perform in what circumstances
• more generally what processes can and cannot occur in various circumstances,
• what the consequences of those actions and processes must be
a child also needs to learn things about available information
• What information about the world is available to the perceiver in various situations
• how the available information can be changed by various actions and other processes.
In other words, a child has to learn about both
• action affordances, and
• epistemic affordances, and
• how they are related.
MKM08, July 2008 Slide 30 Last revised: November 28, 2008
31. Getting information about the world from the world
Things you probably know:
• You can get more information about the contents
of a room from outside an open doorway
(a) if you move closer to the doorway,
(b) if you keep your distance but move sideways.
Why do those procedures work? How do they differ?
• Why do perceived aspect-ratios of visible
objects change as you change your viewpoint?
A circle becomes an ellipse, with changing ratio of
lengths of major/minor axes.
Rectangles become parallelograms
Why? How? Is it just a brute empirical fact?
MKM08, July 2008 Slide 31 Last revised: November 28, 2008
32. Towards a mathematics of door action affordances
and other domestic action affordances?
• In order to shut a door, why do you sometimes need to push it, sometimes to pull it?
• Why do you need a handle to pull the door shut, but not to push it shut?
• Why do you see different parts of an object as you move round it?
• When can you can avoid bumping into the left doorpost while going through a doorway
by aiming further to the right – and what problem does that raise?
• How could you use the lid of one coffee tin to open the lid of another which you cannot
prise out using your fingers?
• Why it is easier to carry a tray full of cups and saucers using a hand at each side than
using only one hand on one side?
and so on and so on ....
Try to work out what theorems underlie such common sense knowledge.
How might the theoremhood become apparent to a child?
MKM08, July 2008 Slide 32 Last revised: November 28, 2008
33. Examples with discrete event sequences
Compare these discoveries a child might make by playing with things in
the environment.
1. On one of the objects the child has access to there is a button, and whenever the
button is pressed down the end of the object glows brightly, until the button is released
– which makes the glow stop.
2. Another object is a circular disc, with a picture on each face, only one of which is
visible when the disc is lying on the floor, but every time it is turned over the other
picture becomes visible.
Are both of those contingent truths? Depends on how the processes are specified.
3. Another variant:
If the child has learnt to count, then she can count in synchrony with turning the disc
over several times, noticing both the last number named and the state of the disc.
The following empirical discoveries could be made:
• If the last number is “four”, the picture on top is the same as the initial picture.
• If the last number is “six”, the picture on top is the same as the initial picture.
• If the last number is “three”, the picture on top is not the same as the initial picture.
• If the last number is “seven”, the picture on top is not the same as the initial picture.
What else could be discovered, from such experiments?
MKM08, July 2008 Slide 33 Last revised: November 28, 2008
34. Example: Counting invariances
A child may first learn to count.
Then learn empirically that counting objects from left to right gives the
same result as counting from right to left.
Then learn that the two results must be the same: it is not empirical.
Then he may generalise to the notion of an invariant property of a set.
Then he may learn more about empirically discoverable non-empirical
facts about numbers.
The process of identifying non-empirical truths is not bug free: faulty conjectures and proofs may have to be
fixed.
MKM08, July 2008 Slide 34 Last revised: November 28, 2008
35. Example: Linear order
If you move along a row of objects in a certain direction you will see them
in a particular order (first the square, then the circle, then,. ...).
If nothing changes in the row of objects, and you reverse direction and
move back to the starting point, you will see them in the opposite temporal
order.
Is this an empirical discovery?
Could some experiment (in special conditions, on another planet perhaps) refute it?
What information-processing mechanisms would enable a child to notice that two
sequences occur in the opposite order?
What mechanisms are required for a child to understand the necessary connection
between the two temporal sequences?
Are YOU convinced? If so, why?
MKM08, July 2008 Slide 35 Last revised: November 28, 2008
36. Example: Circular order
If you move round an unchanging
configuration of objects arranged
against the wall of a building, you will
see them in a particular order, and as
you continue moving round the
building you will see the same
sequence over and over again.
If you reverse direction of revolution
you will see the unending sequence in
the opposite order.
The geometric and topological relationships that hold between the objects
in the environment make what is perceived predictable
What information-processing mechanisms allow a child to make this
discovery, or to use it?
This is based on Kant’s example of moving round a house in his discussion of causation
in Critique of Pure Reason 1780
MKM08, July 2008 Slide 36 Last revised: November 28, 2008
37. Example: More on linear order
If you
• move the rightmost object (hexagon) to a location below the leftmost object (square)
• then repeatedly move the rightmost object remaining in the original row to the right of
the object last moved
then when all the objects have been moved they will be in the opposite order.
Become puzzled: What does being “in the opposite order” mean?
“Opposite” is a pattern linking two patterns – spatial or temporal or abstract.
A logician might give an inductive definition: does a child need that?
How many different ways do we have of specifying to ourselves what we mean by something?
Compare
• being in the opposite order
and
• being seen in the opposite order (previous slide).
MKM08, July 2008 Slide 37 Last revised: November 28, 2008
38. Doing a thought experiment
Suppose you don’t move objects physically, as in the last slide, but merely
think about doing it, using an abstract spatial representation, perhaps
containing only a few objects, which you re-order in the manner required.
Can you tell by inspecting this representation of the process that all instances will have
some common features?
Initial state Next state: Next state: Final state:
A B C | A B | A |
| C | C B | C B A
Things that clearly can be varied without affecting the outcome include:
• the number of items in the row,
• their sizes,
• what they are made of, their colours,
• how heavy they are, ...
• which continent or planet they are on, .... and many more.
The only thing that is important is
• the position of each item in the initial row and
• the fact that each item is moved immediately after the item on its right.
Another pattern:
This procedure guarantees that taken two at a time neighbouring items will have their order reversed.
As noted in Sloman 1971 there is no real difference between doing this in your head or on paper. Why?
MKM08, July 2008 Slide 38 Last revised: November 28, 2008
39. Experiments with orderings
It is possible to do many experiments with orderings.
• If you have a row of cards with pictures or labels on them, ordered in a certain way,
from left to right, e.g.
“A”, “B”, “C”, “D”, “E”
You can take the cards one by one from left to right and pile them up.
• If you then unstack them, i.e. take the top one off, then the next one, and put each
card to the right of the previous one, in what order will the results come out?
• Many computer programs make use of stacks in virtual machines, with the important
property that the last thing stacked is the first one found on the top of the stack.
• Other things you can do is find out how many different ways there are of ordering a
given set of objects.
• If you have a few classes of objects, e.g. three red, two black, four blue: in how many
significantly different ways can you order them, i.e. ignoring which individual cards are
where, taking account only of where the colours are in the order.
• Theorem: If there is only one colour, there is only one ordering
• What happens if you have two colours, and there are N cards of each colour?
Anyone playing with this sort of question is taking an early step towards understanding
Bose-Einstein statistics: important for Quantum Mechanics.
MKM08, July 2008 Slide 39 Last revised: November 28, 2008
40. Adding non-geometric features
The ability to understand and predict what is going on can be enhanced if
the exosomatic ontology goes beyond geometric and topological relations
and processes, to include properties of materials, e.g. rigidity,
impenetrability, weight, thermal properties, etc. Kinds of “stuff”.
(a) (b) (c)
Assuming impenetrability and rigidity makes it possible in (a) to predict that whichever way the left wheel
is turned the other will turn the opposite way. This uses geometric reasoning about movement of gears.
In case (b) the only way to predict is to use statistics/probability information from many observations.
Case (c) shows that sometimes (if relative distances are used carefully) hidden mechanisms can be
guessed on the basis of geometrical/causal reasoning, and used to explain what is observed.
Humean (nowadays Bayesian) causation based on collection of evidence of correlations,
or statistics, is the only kind of causation available when the mechanism is not
understood.
Kantian (structure-based, deterministic) causation is sometimes available, and is often
more useful, but it requires a richer ontology and more general reasoning abilities.
MKM08, July 2008 Slide 40 Last revised: November 28, 2008
41. Chains of causation
You can probably imagine various chains of causation by doing “What if
reasoning” about this 3-D structure, with the initial causation being located
in different places, including rotating, sliding, in various directions, etc.
Snapshot of the ‘glxgears’ program running on linux
E.g. if the small blue wheel moves towards you then starts rotating it may leave the other two unaffected,
but not if it rotates where it is.
Our ability to represent different combinations of processes that are possible in any situation is rich and
varied – but limited, and sometimes partly dependent on previous practice.
MKM08, July 2008 Slide 41 Last revised: November 28, 2008
42. Building A Collection Infant/Toddler Theorems
I would like to get help in building a much larger collection of examples of
things that could be learnt empirically by human infants and toddlers, and
perhaps some other animals, but which have an explanation that shows
they are not merely contingent facts but can be understood as necessary
truths within a more general theory of the nature of the environment, e.g.
as containing 3 dimensional structures and processes, with a locally
euclidean geometry, and containing materials with various properties,
including rigidity, impenetrability, various kinds of elasticity, etc.
Please send examples to me at A.Sloman@cs.bham.ac.uk
A lot more needs to be done to specify more precisely what should and should not be
included, how it should be organised, what experimental and other procedures could be
used to justify their inclusion, what new experiments could be generated on the basis of
the examples, e.g.
(a) to find out which children (and other animals?) learn which examples, and under what conditions they
make the transition from purely empirical understanding to grasping what must be the case;
(b) to find out what brain mechanisms might be involved in the transition from empirical to “mathematical”
understanding of particular cases;
and what sorts of robotic experiments could be constructed to demonstrate how such
learning and the transitions between different sorts of understanding could work.
(Compare the work of Piaget, Karmiloff-Smith and others.)
MKM08, July 2008 Slide 42 Last revised: November 28, 2008
43. What can be built from a pile of cubes 1?
If you were given a box full of cubes and rectangular bricks could you
assemble a configuration roughly like this?
(It’s not a trick question. Compare the next slide.)
MKM08, July 2008 Slide 43 Last revised: November 28, 2008
44. What can be built from a pile of cubes 2?
Young children don’t see anything wrong with these pictures?
(Compare Escher’s pictures.)
Why not?
What has not developed yet?
How does it develop later?
Did you see the similarity between the picture on the right and the one on the previous
page? Only one block was moved.
MKM08, July 2008 Slide 44 Last revised: November 28, 2008
45. Should a robot be able to see impossible objects?
Impossible triangle by Reutersvard – Swedish artist 1934
At first this might simply look like a configuration
of cubes in the form of a triangle.
However you should be able to convince
yourself that the 3-D configuration cannot exist.
If you remove any two adjacent cubes at one of
the corners, the remaining seven cubes form a
consistent configuration.
But if you put back all the cubes, the relative
distances from you become impossible, like
A > B > C > D..... > A
Should a robot looking at something like this
notice the impossibility?
How can child, or you, see a lot of 3-D
fragments that do not add up to a possible 3-D
whole, without noticing the impossibility?
What does that tell us about how a robot should see a 3-D environment?
MKM08, July 2008 Slide 45 Last revised: November 28, 2008
46. The need for patterns of motion, or change
Several of the examples have involved things changing in some
experimental situation:
• A person moving nearer to a door and seeing more of a room
• A person moving past objects and seeing them in some order
• A person moving sideways and seeing different parts of a room
• A person moving past objects and seeing them in some order
• Blocks moving from one location to another.
• Counting processes
• Coin-turning processes
• Processes of re-ordering items
Perceiving a process clearly produces processes in the perceiver: things change in the
perceiver.
What changes, and how? Don’t assume it’s an isomorphic model of the environment.
If what is seen is remembered and re-usable, that implies that some information structure
is stored which can be accessed later: a representation of the process.
(Not necessarily representing every detail of the process).
Much is unknown about what forms of representations are good ones, what forms brains use, what forms
should/could be used by robots – though there has been a lot of work on auditory memories suggesting that
what is stored is itself a process, in some of those cases: rehearsal.
If you have learnt a dance, do you have a constantly running simulation of your dancing in your brain?
MKM08, July 2008 Slide 46 Last revised: November 28, 2008
47. Re-usable information about processes
The ability to use a remembered process to produce or recognise a new
process of the same type implies that there is some sort of pattern
structure in the process representation: it can be re-instantiated to new
instances of the pattern – perceived or created.
So our claim that patterns could be discovered in processes is not a very surprising claim
– if the discovery of patterns is already a requirement for repetition or recognition.
However that leaves entirely unspecified what the form of that pattern is.
E.g. it could be an algorithm for generating instances.
If the pattern allows different forms, e.g. counting sequences of different lengths, the
pattern may be stored in the form of a grammar of some kind,
or perhaps a program,
or perhaps both:
a grammar for recognition, and
a program for production?
or something completely different, not yet thought of by any programmer or robot
designer.
MKM08, July 2008 Slide 47 Last revised: November 28, 2008
48. The need for a self-monitoring architecture
The previous slides showed examples of your knowledge about both
1. action affordances: things you can and cannot do
2. epistemic affordances: information you can and cannot acquire
3. meta-affordances: how your actions can change both the action affordances and the
epistemic affordances in various situations.
The ability to discover such things requires information-processing abilities that include
not only
• the ability to perceive, form goals, make plans, and execute plans to achieve goals,
but also
• the ability to observe oneself in doing those things, and to notice recurring patterns and constraints that
can be used in future to work out in advance what can and cannot happen in various situations.
(This is one of the functions of the meta-management architectural layer in H-CogAff:
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#enf07
http://www.cogs.susx.ac.uk/users/ronc/cogs.pdf)
I shall try to show that such discoveries, though they may be hard to come by without experiment and
exploration, are not empirical, like the discovery that most things expand on being heated and the discovery
that ice is an exception: they don’t have to be tested by doing experiments on things in the environment.
The discoveries are both non-empirical, and also synthetic (knowledge-extending) as Kant claimed.
They are typically mathematical discoveries.
MKM08, July 2008 Slide 48 Last revised: November 28, 2008
49. My claims
• In the first five years of life, every child encounters hundreds, or perhaps thousands, of
examples of knowledge each of which starts empirical but then acquires a new status,
though the transitions mostly go unnoticed both by the child and by others (e.g.
developmental psychologists).
• These capabilities depend both on the nature of human perception of spatial
structures and processes, and on the ability to notice features of such perception
when it occurs.
• These capabilities are also at the root of human mathematical competences
• Similar transitions will be needed for human-like robots.
• Very few (if any) developmental psychologists or AI researchers have noticed this
transformation of empirical knowledge to apriori knowledge.
I expect any who have noticed it have wrongly characterised it as a change in subjective or objective
probability produced by accumulating evidence.
(Compare philosophers who think mathematical knowledge is empirical, e.g. J.S. Mill.)
• There are no AI models that I know of that are capable of explaining how this happens.
• It also seems to be beyond what neuroscientists are currently capable of studying.
MKM08, July 2008 Slide 49 Last revised: November 28, 2008
50. Two very different views of mathematics
Bertrand Russell: Mathematics may be defined as the subject in which
we never know what we are talking about, nor whether what we are saying
is true. (In Mysticism and Logic 1918)
Richard Feynman: To those who do not know Mathematics it is difficult to
get across a real feeling as to the beauty, the deepest beauty of nature. ...
If you want to learn about nature, to appreciate nature, it is necessary to
understand the language that she speaks in.
In The Character of Physical Law
Russell’s view makes it at first sight puzzling that mathematics can be relevant to the world we live in, or any
other specific, non-mathematical subject matter.
Previous slides point towards an explanation of how mathematics can be applicable to the world in
something like the way that Feynman suggested.
These quotes about mathematics, many other quotes, and lots of mathematics-related fun and information,
can be found in:
http://www.cut-the-knot.org
MKM08, July 2008 Slide 50 Last revised: November 28, 2008
51. Conclusion
It’s an old idea that a child is a young scientist.
We can now add more content to that idea.
Not just human children – maybe some other altricial species.
But humans have some special features (indefinitely many levels of
meta-competences).
This may be connected with both special architectures and special forms
of representation.
It may not be an all-or-nothing matter: perhaps some other intelligent animals, e.g. corvids, orangutans,
and hunting mammals, have portions of the architecture and special cases of the forms of representation
that give humans their extended capabilities.
There are forms of representation used by pre-verbal children and
non-verbal animals that seem to be very powerful in discovering and
creatively using facts about how the environment works.
This has deep implications for the nature of language learning and
language evolution.
Maybe AI will look somewhat different when we know how to model all this.
In 100 years time – or in the next decade ???
MKM08, July 2008 Slide 51 Last revised: November 28, 2008