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The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.
In Feynman’s famous lecture titled “There’s Plenty of Room at the Bottom” [1], he argued that vast technological progress could be achieved by learning to manipulate matter and energy at ever-smaller scales. Such potential could presumably be exploited by natural selection as well. How does biology expand the adaptive function of an existing system? It cannot go down, since there is already something there, exhibiting functional competencies at every level [2]. Instead, it squeezes more action from each level by overloading mechanisms with multiple functions—which we term as polycomputing. We argue that the most effective lens for a wide range of natural and engineered systems must enable a multiple-observers view where the same set of events can be interpreted as different computations (Figure 1 illustrates how artists have recognized this feature). Indeed, depending on their definition of computation, some human observers may conclude that the observed system is not computing at all.
Herein, we review remarkable examples of biological polycomputing, such as spider webs that serve as auditory sensors and prey capture devices [3], and holographic memory storage in the brain [4, 5]. We will also review emerging examples in computer and materials engineering [6]. We provisionally define polycomputing as the ability of a material to provide the results of more than one computation in the same place at the same time. To distinguish this from complex materials that necessarily produce complex results in the same place at the same time, such as the multiple peaks in the frequency spectrum of a vibrating material, polycomputing must be embodied in a material that has been evolved, or can be designed to produce particular results—such as the results of particular mathematical transformations like digital logic—and must be readable by other parts of the material or other devices. That is, the computation, to be considered a computation, must be useful to one or more observers (which, in biology, can exist on multiple scales, with multiple subsystems from the molecular to the whole organism, or swarm levels being able to reap the diverse evolutionary benefits of a single process if they interpret it as processing information that provides an adaptive advantage). These ideas, which describe new ways of understanding and exploiting polycomputing in biology, may suggest ways to improve synthetic polycomputing systems, which, in turn, will shed light on the nature of computation, evolution, and control. Biological systems that polycompute also contribute to an ongoing conceptual debate within interdisciplinary science—the applicability of computer frameworks and metaphors to living systems [7]—in three ways. First: if polycomputing changes our understanding of what computation is, that might change whether we consider a living system to be a computer (Section 1.1). Second: a living system (or inorganic material) may be considered to be polycomputing, depending on one’s point of view, suggesting that observer dependence is unavoidable when considering whether or what a living or engineered system computes (Section 1.2). Third: increasingly intricate admixtures of technological and biological components that compute are forcing a redefinition of life itself (Section 1.3).
Acejmc Self Study By Greenlee School Of Journalism And Communication
The notion of a “computer” needs to be expanded: it no longer only refers to the sequential, deterministic, silicon-embodied, human-programmed, von Neumann/Turing architectures with which biologists are familiar. Those are indeed dissimilar to living systems. There is now a widening array of computational substrates and robots that are often massively parallel (such as GPUs and computational metamaterials [8]), stochastic (hard to predict) [9], able to exploit non-obvious (and potentially not-yet-understood) properties of the exotic substrates they are built from [10], emergent, produced by evolutionary techniques [11], and built by other machines [12] or programmed by other algorithms [13, 14, 15]. The benefit of considering biological systems as members of this broader class is that it avails powerful conceptual frameworks from computer science to be deployed in biology in a deep way, and therefore to understand life far beyond its current limited use in computational biology. Moreover, exploring this powerful invariant between natural and synthetic systems can enrich intervention techniques within biology and improve the capabilities of engineered devices, revealing gaps in our understanding and the capabilities of both computer science and biology. Polycomputing is a powerful but, as of yet, under-appreciated example of the many ways in which the wider class of computer devices can help to revolutionize the life sciences. In the same way that organic and inorganic materials acting as computers increasingly challenges the claim that living materials are not computers, we have argued elsewhere [16] that the widening array of materials that can now be viewed or engineered with as machines is corroding the classic claim that living systems are not machines, and forcing an improved definition of “machine” that escapes the narrow definitions of past decades, which are no longer appropriate [17, 18, 19].
In the statement “living things are (or are not) computers”, “are” implies the existence of an objective, privileged view of both computers and biology that allows an unambiguous, universal decision as to whether they are related. This binary view is untenable and gives rise to numerous pseudo-problems. We argue instead for an observer-dependent view, in which computational formalisms are just metaphors; of course, all scientific concepts are just metaphors, with varying degrees of utility (which is not binary). Once we come to grips with the fact that “all models are wrong but some are useful” [20], it is possible to adopt a pragmatic approach [21] in which anything is a computer in a given context, to the degree to which it enables an observer to predict and control that thing better than any competing metaphors allow us to do. In this view, whether something is computing is not a philosophical question, but one to be settled experimentally by specifying a computational framework and showing empirically what new levels of capability, experiments, and research are enabled by adopting that framework. The only thing left is to enable system subcomponents, not just human scientists, to act as observers
The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.
In Feynman’s famous lecture titled “There’s Plenty of Room at the Bottom” [1], he argued that vast technological progress could be achieved by learning to manipulate matter and energy at ever-smaller scales. Such potential could presumably be exploited by natural selection as well. How does biology expand the adaptive function of an existing system? It cannot go down, since there is already something there, exhibiting functional competencies at every level [2]. Instead, it squeezes more action from each level by overloading mechanisms with multiple functions—which we term as polycomputing. We argue that the most effective lens for a wide range of natural and engineered systems must enable a multiple-observers view where the same set of events can be interpreted as different computations (Figure 1 illustrates how artists have recognized this feature). Indeed, depending on their definition of computation, some human observers may conclude that the observed system is not computing at all.
Herein, we review remarkable examples of biological polycomputing, such as spider webs that serve as auditory sensors and prey capture devices [3], and holographic memory storage in the brain [4, 5]. We will also review emerging examples in computer and materials engineering [6]. We provisionally define polycomputing as the ability of a material to provide the results of more than one computation in the same place at the same time. To distinguish this from complex materials that necessarily produce complex results in the same place at the same time, such as the multiple peaks in the frequency spectrum of a vibrating material, polycomputing must be embodied in a material that has been evolved, or can be designed to produce particular results—such as the results of particular mathematical transformations like digital logic—and must be readable by other parts of the material or other devices. That is, the computation, to be considered a computation, must be useful to one or more observers (which, in biology, can exist on multiple scales, with multiple subsystems from the molecular to the whole organism, or swarm levels being able to reap the diverse evolutionary benefits of a single process if they interpret it as processing information that provides an adaptive advantage). These ideas, which describe new ways of understanding and exploiting polycomputing in biology, may suggest ways to improve synthetic polycomputing systems, which, in turn, will shed light on the nature of computation, evolution, and control. Biological systems that polycompute also contribute to an ongoing conceptual debate within interdisciplinary science—the applicability of computer frameworks and metaphors to living systems [7]—in three ways. First: if polycomputing changes our understanding of what computation is, that might change whether we consider a living system to be a computer (Section 1.1). Second: a living system (or inorganic material) may be considered to be polycomputing, depending on one’s point of view, suggesting that observer dependence is unavoidable when considering whether or what a living or engineered system computes (Section 1.2). Third: increasingly intricate admixtures of technological and biological components that compute are forcing a redefinition of life itself (Section 1.3).
Acejmc Self Study By Greenlee School Of Journalism And Communication
The notion of a “computer” needs to be expanded: it no longer only refers to the sequential, deterministic, silicon-embodied, human-programmed, von Neumann/Turing architectures with which biologists are familiar. Those are indeed dissimilar to living systems. There is now a widening array of computational substrates and robots that are often massively parallel (such as GPUs and computational metamaterials [8]), stochastic (hard to predict) [9], able to exploit non-obvious (and potentially not-yet-understood) properties of the exotic substrates they are built from [10], emergent, produced by evolutionary techniques [11], and built by other machines [12] or programmed by other algorithms [13, 14, 15]. The benefit of considering biological systems as members of this broader class is that it avails powerful conceptual frameworks from computer science to be deployed in biology in a deep way, and therefore to understand life far beyond its current limited use in computational biology. Moreover, exploring this powerful invariant between natural and synthetic systems can enrich intervention techniques within biology and improve the capabilities of engineered devices, revealing gaps in our understanding and the capabilities of both computer science and biology. Polycomputing is a powerful but, as of yet, under-appreciated example of the many ways in which the wider class of computer devices can help to revolutionize the life sciences. In the same way that organic and inorganic materials acting as computers increasingly challenges the claim that living materials are not computers, we have argued elsewhere [16] that the widening array of materials that can now be viewed or engineered with as machines is corroding the classic claim that living systems are not machines, and forcing an improved definition of “machine” that escapes the narrow definitions of past decades, which are no longer appropriate [17, 18, 19].
In the statement “living things are (or are not) computers”, “are” implies the existence of an objective, privileged view of both computers and biology that allows an unambiguous, universal decision as to whether they are related. This binary view is untenable and gives rise to numerous pseudo-problems. We argue instead for an observer-dependent view, in which computational formalisms are just metaphors; of course, all scientific concepts are just metaphors, with varying degrees of utility (which is not binary). Once we come to grips with the fact that “all models are wrong but some are useful” [20], it is possible to adopt a pragmatic approach [21] in which anything is a computer in a given context, to the degree to which it enables an observer to predict and control that thing better than any competing metaphors allow us to do. In this view, whether something is computing is not a philosophical question, but one to be settled experimentally by specifying a computational framework and showing empirically what new levels of capability, experiments, and research are enabled by adopting that framework. The only thing left is to enable system subcomponents, not just human scientists, to act as observers
The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.
In Feynman’s famous lecture titled “There’s Plenty of Room at the Bottom” [1], he argued that vast technological progress could be achieved by learning to manipulate matter and energy at ever-smaller scales. Such potential could presumably be exploited by natural selection as well. How does biology expand the adaptive function of an existing system? It cannot go down, since there is already something there, exhibiting functional competencies at every level [2]. Instead, it squeezes more action from each level by overloading mechanisms with multiple functions—which we term as polycomputing. We argue that the most effective lens for a wide range of natural and engineered systems must enable a multiple-observers view where the same set of events can be interpreted as different computations (Figure 1 illustrates how artists have recognized this feature). Indeed, depending on their definition of computation, some human observers may conclude that the observed system is not computing at all.
Herein, we review remarkable examples of biological polycomputing, such as spider webs that serve as auditory sensors and prey capture devices [3], and holographic memory storage in the brain [4, 5]. We will also review emerging examples in computer and materials engineering [6]. We provisionally define polycomputing as the ability of a material to provide the results of more than one computation in the same place at the same time. To distinguish this from complex materials that necessarily produce complex results in the same place at the same time, such as the multiple peaks in the frequency spectrum of a vibrating material, polycomputing must be embodied in a material that has been evolved, or can be designed to produce particular results—such as the results of particular mathematical transformations like digital logic—and must be readable by other parts of the material or other devices. That is, the computation, to be considered a computation, must be useful to one or more observers (which, in biology, can exist on multiple scales, with multiple subsystems from the molecular to the whole organism, or swarm levels being able to reap the diverse evolutionary benefits of a single process if they interpret it as processing information that provides an adaptive advantage). These ideas, which describe new ways of understanding and exploiting polycomputing in biology, may suggest ways to improve synthetic polycomputing systems, which, in turn, will shed light on the nature of computation, evolution, and control. Biological systems that polycompute also contribute to an ongoing conceptual debate within interdisciplinary science—the applicability of computer frameworks and metaphors to living systems [7]—in three ways. First: if polycomputing changes our understanding of what computation is, that might change whether we consider a living system to be a computer (Section 1.1). Second: a living system (or inorganic material) may be considered to be polycomputing, depending on one’s point of view, suggesting that observer dependence is unavoidable when considering whether or what a living or engineered system computes (Section 1.2). Third: increasingly intricate admixtures of technological and biological components that compute are forcing a redefinition of life itself (Section 1.3).
Acejmc Self Study By Greenlee School Of Journalism And Communication
The notion of a “computer” needs to be expanded: it no longer only refers to the sequential, deterministic, silicon-embodied, human-programmed, von Neumann/Turing architectures with which biologists are familiar. Those are indeed dissimilar to living systems. There is now a widening array of computational substrates and robots that are often massively parallel (such as GPUs and computational metamaterials [8]), stochastic (hard to predict) [9], able to exploit non-obvious (and potentially not-yet-understood) properties of the exotic substrates they are built from [10], emergent, produced by evolutionary techniques [11], and built by other machines [12] or programmed by other algorithms [13, 14, 15]. The benefit of considering biological systems as members of this broader class is that it avails powerful conceptual frameworks from computer science to be deployed in biology in a deep way, and therefore to understand life far beyond its current limited use in computational biology. Moreover, exploring this powerful invariant between natural and synthetic systems can enrich intervention techniques within biology and improve the capabilities of engineered devices, revealing gaps in our understanding and the capabilities of both computer science and biology. Polycomputing is a powerful but, as of yet, under-appreciated example of the many ways in which the wider class of computer devices can help to revolutionize the life sciences. In the same way that organic and inorganic materials acting as computers increasingly challenges the claim that living materials are not computers, we have argued elsewhere [16] that the widening array of materials that can now be viewed or engineered with as machines is corroding the classic claim that living systems are not machines, and forcing an improved definition of “machine” that escapes the narrow definitions of past decades, which are no longer appropriate [17, 18, 19].
In the statement “living things are (or are not) computers”, “are” implies the existence of an objective, privileged view of both computers and biology that allows an unambiguous, universal decision as to whether they are related. This binary view is untenable and gives rise to numerous pseudo-problems. We argue instead for an observer-dependent view, in which computational formalisms are just metaphors; of course, all scientific concepts are just metaphors, with varying degrees of utility (which is not binary). Once we come to grips with the fact that “all models are wrong but some are useful” [20], it is possible to adopt a pragmatic approach [21] in which anything is a computer in a given context, to the degree to which it enables an observer to predict and control that thing better than any competing metaphors allow us to do. In this view, whether something is computing is not a philosophical question, but one to be settled experimentally by specifying a computational framework and showing empirically what new levels of capability, experiments, and research are enabled by adopting that framework. The only thing left is to enable system subcomponents, not just human scientists, to act as observers
