Author: Cameron J. Buckner
Publisher: Oxford University Press
ISBN: 0197653308
Category : Machine learning
Languages : en
Pages : 441
Book Description
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"--
From Deep Learning to Rational Machines
Author: Cameron J. Buckner
Publisher: Oxford University Press
ISBN: 0197653308
Category : Machine learning
Languages : en
Pages : 441
Book Description
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"--
Publisher: Oxford University Press
ISBN: 0197653308
Category : Machine learning
Languages : en
Pages : 441
Book Description
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"--
Rational Machines and Artificial Intelligence
Author: Tshilidzi Marwala
Publisher: Elsevier
ISBN: 0128206764
Category : Computers
Languages : en
Pages : 270
Book Description
Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets Discusses the application of Moore's Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality
Publisher: Elsevier
ISBN: 0128206764
Category : Computers
Languages : en
Pages : 270
Book Description
Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets Discusses the application of Moore's Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality
From Deep Learning to Rational Machines
Author: Cameron J. Buckner
Publisher:
ISBN: 9780197653333
Category : Machine learning
Languages : en
Pages : 0
Book Description
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"--
Publisher:
ISBN: 9780197653333
Category : Machine learning
Languages : en
Pages : 0
Book Description
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"--
Rational Machines and Artificial Intelligence
Author: Tshilidzi Marwala
Publisher: Academic Press
ISBN: 0128209445
Category : Science
Languages : en
Pages : 272
Book Description
Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. - Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? - Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions - Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets - Discusses the application of Moore's Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality
Publisher: Academic Press
ISBN: 0128209445
Category : Science
Languages : en
Pages : 272
Book Description
Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. - Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? - Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions - Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets - Discusses the application of Moore's Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality
Minds and Computers
Author: Matt Carter
Publisher: Edinburgh University Press
ISBN: 0748629300
Category : Philosophy
Languages : en
Pages : 232
Book Description
Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by 'mind' anyway?The notion of the 'intelligent' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues 'Artificial Intelligence', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to de
Publisher: Edinburgh University Press
ISBN: 0748629300
Category : Philosophy
Languages : en
Pages : 232
Book Description
Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by 'mind' anyway?The notion of the 'intelligent' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues 'Artificial Intelligence', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to de
World Without Weight
Author: Daniel Povinelli
Publisher: Oxford University Press, USA
ISBN: 0198570961
Category : Philosophy
Languages : en
Pages : 374
Book Description
In every domain of reasoning humans deploy an wide range of intuitive 'theories' about how the world works. So are we alone in trying to make sense of the world by postulating theoretical entities to explain how the world works, or do we share this ability with other species. This is the focus of this new book from Daniel Povinelli
Publisher: Oxford University Press, USA
ISBN: 0198570961
Category : Philosophy
Languages : en
Pages : 374
Book Description
In every domain of reasoning humans deploy an wide range of intuitive 'theories' about how the world works. So are we alone in trying to make sense of the world by postulating theoretical entities to explain how the world works, or do we share this ability with other species. This is the focus of this new book from Daniel Povinelli
Anna's AI Anthology
Author: Daniel Dennett
Publisher: BoD – Books on Demand
ISBN: 3942106981
Category : Philosophy
Languages : en
Pages : 380
Book Description
With the release of ChatGPT, large language models (LLMs) have become a prominent topic of international public and scientific debate. The genie is out of the bottle, but does it have a mind? Can philosophical considerations help us to work out how we can live with such smart machines? In this book, distinguished philosophers explore questions such as whether these new machines are able to act, whether they are social agents, whether they have communicative skills, and if they might even become conscious. The book includes contributions from Syed AbuMusab, Constant Bonard, Stephen Butterfill, Daniel Dennett, Paula Droege, Keith Frankish, Frederic Gilbert, Ying-Tung Lin, Sven Nyholm, Joshua Rust, Eric Schwitzgebel, Henry Shevlin, Anna Strasser, Alessio Tacca, Michael Wilby, and a graphic novel by Anna and Moritz Strasser as a bonus
Publisher: BoD – Books on Demand
ISBN: 3942106981
Category : Philosophy
Languages : en
Pages : 380
Book Description
With the release of ChatGPT, large language models (LLMs) have become a prominent topic of international public and scientific debate. The genie is out of the bottle, but does it have a mind? Can philosophical considerations help us to work out how we can live with such smart machines? In this book, distinguished philosophers explore questions such as whether these new machines are able to act, whether they are social agents, whether they have communicative skills, and if they might even become conscious. The book includes contributions from Syed AbuMusab, Constant Bonard, Stephen Butterfill, Daniel Dennett, Paula Droege, Keith Frankish, Frederic Gilbert, Ying-Tung Lin, Sven Nyholm, Joshua Rust, Eric Schwitzgebel, Henry Shevlin, Anna Strasser, Alessio Tacca, Michael Wilby, and a graphic novel by Anna and Moritz Strasser as a bonus
The Brain Abstracted
Author: M. Chirimuuta
Publisher: MIT Press
ISBN: 0262378639
Category : Science
Languages : en
Pages : 377
Book Description
An exciting, new framework for interpreting the philosophical significance of neuroscience. All science needs to simplify, but when the object of research is something as complicated as the brain, this challenge can stretch the limits of scientific possibility. In fact, in The Brain Abstracted, an avowedly “opinionated” history of neuroscience, M. Chirimuuta argues that, due to the brain’s complexity, neuroscientific theories have only captured partial truths—and “neurophilosophy” is unlikely to be achieved. Looking at the theory and practice of neuroscience, both past and present, Chirimuuta shows how the science has been shaped by the problem of brain complexity and the need, in science, to make things as simple as possible. From this history, Chirimuuta draws lessons for debates in philosophy of science over the limits and definition of science and in philosophy of mind over explanations of consciousness and the mind-body problem. The Brain Abstracted is the product of a historical rupture that has become visible in the twenty-first century, between the “classical” scientific approach, which seeks simple, intelligible principles underlying the manifest complexity of nature, and a data-driven engineering approach, which dispenses with the search for elegant, explanatory laws and models. In the space created by this rupture, Chirimuuta finds grounds for theoretical and practical humility. Her aim in The Brain Abstracted is not to reform neuroscience, or offer advice to neuroscientists, but rather to interpret their work—and to suggest a new framework for interpreting the philosophical significance of neuroscience.
Publisher: MIT Press
ISBN: 0262378639
Category : Science
Languages : en
Pages : 377
Book Description
An exciting, new framework for interpreting the philosophical significance of neuroscience. All science needs to simplify, but when the object of research is something as complicated as the brain, this challenge can stretch the limits of scientific possibility. In fact, in The Brain Abstracted, an avowedly “opinionated” history of neuroscience, M. Chirimuuta argues that, due to the brain’s complexity, neuroscientific theories have only captured partial truths—and “neurophilosophy” is unlikely to be achieved. Looking at the theory and practice of neuroscience, both past and present, Chirimuuta shows how the science has been shaped by the problem of brain complexity and the need, in science, to make things as simple as possible. From this history, Chirimuuta draws lessons for debates in philosophy of science over the limits and definition of science and in philosophy of mind over explanations of consciousness and the mind-body problem. The Brain Abstracted is the product of a historical rupture that has become visible in the twenty-first century, between the “classical” scientific approach, which seeks simple, intelligible principles underlying the manifest complexity of nature, and a data-driven engineering approach, which dispenses with the search for elegant, explanatory laws and models. In the space created by this rupture, Chirimuuta finds grounds for theoretical and practical humility. Her aim in The Brain Abstracted is not to reform neuroscience, or offer advice to neuroscientists, but rather to interpret their work—and to suggest a new framework for interpreting the philosophical significance of neuroscience.
Artificial Intuition
Author: Carlos Perez
Publisher: Createspace Independent Publishing Platform
ISBN: 9781983895647
Category :
Languages : en
Pages : 394
Book Description
I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook." The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and those looking to optimize. There are two ways to learn, learning by exploration and learning by exploitation. This book is about exploration into the emerging field of Deep Learning. It's more like a popular science book and less of a business book. It's not going to provide any practical advice of how to use or deploy Deep Learning. However, it's a book that will explore this new field in many more perspectives. So at the very least, you'll walk away with the ability to hold a very informative and impressive conversation about this unique subject. It's my hope that having less constraints on what I can express can lead to a more insightful and novel book. There are plenty of ideas that are either too general or too speculative to fit within a business oriented book. With a business book, you always want to manage expectations. Artificial Intelligence is one of those topics that you want to keep speaking in a conservative manner. That's one reason I felt the need for this book. Perhaps the freedom to be more liberal can give readers more ideas as where this field is heading. Also, it's not just business that needs to understand Deep Learning. We are all going to be profoundly impacted by this new kind of Artificial Intelligence and it is critical we all develop at least a good intuition of how it will change the world.The images in the front cover are all generated using Deep Learning technology.
Publisher: Createspace Independent Publishing Platform
ISBN: 9781983895647
Category :
Languages : en
Pages : 394
Book Description
I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook." The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and those looking to optimize. There are two ways to learn, learning by exploration and learning by exploitation. This book is about exploration into the emerging field of Deep Learning. It's more like a popular science book and less of a business book. It's not going to provide any practical advice of how to use or deploy Deep Learning. However, it's a book that will explore this new field in many more perspectives. So at the very least, you'll walk away with the ability to hold a very informative and impressive conversation about this unique subject. It's my hope that having less constraints on what I can express can lead to a more insightful and novel book. There are plenty of ideas that are either too general or too speculative to fit within a business oriented book. With a business book, you always want to manage expectations. Artificial Intelligence is one of those topics that you want to keep speaking in a conservative manner. That's one reason I felt the need for this book. Perhaps the freedom to be more liberal can give readers more ideas as where this field is heading. Also, it's not just business that needs to understand Deep Learning. We are all going to be profoundly impacted by this new kind of Artificial Intelligence and it is critical we all develop at least a good intuition of how it will change the world.The images in the front cover are all generated using Deep Learning technology.
Beyond Reduction
Author: Steven Horst
Publisher: Oxford University Press
ISBN: 0190295589
Category : Philosophy
Languages : en
Pages : 387
Book Description
Contemporary philosophers of mind tend to assume that the world of nature can be reduced to basic physics. Yet there are features of the mind consciousness, intentionality, normativity that do not seem to be reducible to physics or neuroscience. This explanatory gap between mind and brain has thus been a major cause of concern in recent philosophy of mind. Reductionists hold that, despite all appearances, the mind can be reduced to the brain. Eliminativists hold that it cannot, and that this implies that there is something illegitimate about the mentalistic vocabulary. Dualists hold that the mental is irreducible, and that this implies either a substance or a property dualism. Mysterian non-reductive physicalists hold that the mind is uniquely irreducible, perhaps due to some limitation of our self-understanding. In this book, Steven Horst argues that this whole conversation is based on assumptions left over from an outdated philosophy of science. While reductionism was part of the philosophical orthodoxy fifty years ago, it has been decisively rejected by philosophers of science over the past thirty years, and for good reason. True reductions are in fact exceedingly rare in the sciences, and the conviction that they were there to be found was an artifact of armchair assumptions of 17th century Rationalists and 20th century Logical Empiricists. The explanatory gaps between mind and brain are far from unique. In fact, in the sciences it is gaps all the way down.And if reductions are rare in even the physical sciences, there is little reason to expect them in the case of psychology. Horst argues that this calls for a complete re-thinking of the contemporary problematic in philosophy of mind. Reductionism, dualism, eliminativism and non-reductive materialism are each severely compromised by post-reductionist philosophy of science, and philosophy of mind is in need of a new paradigm. Horst suggests that such a paradigm might be found in Cognitive Pluralism: the view that human cognitive architecture constrains us to understand the world through a plurality of partial, idealized, and pragmatically-constrained models, each employing a particular representational system optimized for its own problem domain. Such an architecture can explain the disunities of knowledge, and is plausible on evolutionary grounds.
Publisher: Oxford University Press
ISBN: 0190295589
Category : Philosophy
Languages : en
Pages : 387
Book Description
Contemporary philosophers of mind tend to assume that the world of nature can be reduced to basic physics. Yet there are features of the mind consciousness, intentionality, normativity that do not seem to be reducible to physics or neuroscience. This explanatory gap between mind and brain has thus been a major cause of concern in recent philosophy of mind. Reductionists hold that, despite all appearances, the mind can be reduced to the brain. Eliminativists hold that it cannot, and that this implies that there is something illegitimate about the mentalistic vocabulary. Dualists hold that the mental is irreducible, and that this implies either a substance or a property dualism. Mysterian non-reductive physicalists hold that the mind is uniquely irreducible, perhaps due to some limitation of our self-understanding. In this book, Steven Horst argues that this whole conversation is based on assumptions left over from an outdated philosophy of science. While reductionism was part of the philosophical orthodoxy fifty years ago, it has been decisively rejected by philosophers of science over the past thirty years, and for good reason. True reductions are in fact exceedingly rare in the sciences, and the conviction that they were there to be found was an artifact of armchair assumptions of 17th century Rationalists and 20th century Logical Empiricists. The explanatory gaps between mind and brain are far from unique. In fact, in the sciences it is gaps all the way down.And if reductions are rare in even the physical sciences, there is little reason to expect them in the case of psychology. Horst argues that this calls for a complete re-thinking of the contemporary problematic in philosophy of mind. Reductionism, dualism, eliminativism and non-reductive materialism are each severely compromised by post-reductionist philosophy of science, and philosophy of mind is in need of a new paradigm. Horst suggests that such a paradigm might be found in Cognitive Pluralism: the view that human cognitive architecture constrains us to understand the world through a plurality of partial, idealized, and pragmatically-constrained models, each employing a particular representational system optimized for its own problem domain. Such an architecture can explain the disunities of knowledge, and is plausible on evolutionary grounds.