Mind Metaphors 

The most recent technical advancement has been likened to the brain (and, by extension, the Mind) in every passing generation. Currently, the computer metaphor is all the rage. Recently, metaphors involving software and (neuronal) networks have supplanted those involving computer hardware. In every area of human knowledge, people often try to understand things by comparing them to other fields. The structural notion of "tensegrity" has recently been proposed by mathematicians and architects as an explanation for the phenomena of life. There is ample evidence that the human propensity to perceive patterns and organisation in seemingly random places has a survival function.  

Dismissing these metaphors as false, pointless, or intentionally misleading is another current trend. Although they are meant to depict something, these metaphors are actually created by the same Mind. Similar to how "brain-children" are the products of "brain-storming" and "minds" are the things or processes that the brain is likened to. If not a physical embodiment of mental processes, then what are computers, software programs, and communications networks?  

Basically, anything that is made by humans has to have some sort of link to human minds. A "mind-correlate" is required even in a petrol pump. Our thoughts may also contain a-priori (not based on experience) or a-posteriori (depending on experience) representations of the "non-human" aspects of the cosmos. The "excretions," "output," "spin-offs," and "products" of the human mind are closely related to the human mind, and this "correlation," "emulation," "simulation," and "representation" between them is crucial to the comprehension of the mind.  

This claim is part of a larger class of claims that state that we can identify an artist by their work, a creator by their creation, and, more generally, that we can identify an origin by any of its offspring, descendants, successors, products, or similes.  

When the product's nature is identical to that of its origin, this general contention becomes more stronger. If the father is human and the child is human as well, then there is a wealth of information that can be securely gleaned from the product that can be used to identify the father. The closer the point of origin is to the product, the more information about the origin can be uncovered. A "thinking machine"—whatever that may mean—that is mechanical, recursive, limiting, and simulated. Mind you, the brain is also a "thinking machine"—albeit one that is considerably more nimble, adaptable, non-linear, and perhaps even qualitatively distinct. However different they may be (and there will definitely be a big difference), they are clearly related. The fact that they are both "thinking machines" and, more importantly, that the latter is an offspring of the former, establishes their intimate relationship. To that end, the computer metaphor is very potent. If an organic computer ever materialises, the analogy will take on more significance. If a quantum computer ever comes to fruition, it will surely improve upon certain parts of the metaphor.  

By the way, it's not always the case that knowing where something comes from allows us to predict what it will be used for. The number of free variables is excessive. To paraphrase Bohr, when a product is available, our set of probability "collapses" and our knowledge grows.  

The starting point is a "wave function"—a set of hypothetical outcomes to which probabilities are associated; the outcomes are the products that are theoretically and logically feasible.  

However, a superficial comparison to the end result reveals very nothing about its source. The majority of these characteristics are structure- and function-related. These can be seen clearly. Does it cover everything? What does the "true nature" of the beginning tell us if anything? No, it is not correct. That humans are ultimately incapable of knowing anything regarding the "true nature" of anything is a pessimistic view. Here we are in the domain of philosophy, not science. Without stating anything substantial about either micro-processes or the Universe, Quantum Mechanics gives an incredibly accurate explanation of both. The goal of modern physics is accurate prediction, not the elaboration of certain worldviews. It only summarises; it offers no explanations. Whenever an interpretation is put out, such as the Copenhagen interpretation of QM, it encounters philosophical roadblocks and impassable problems. Therefore, many metaphors are used in modern science, such as particles and waves, among many others. It has been demonstrated that metaphors are valuable scientific instruments for the "thinking scientist's" toolbox.  

Also, a metaphor can evolve across time, mirroring the stages of its inception. Consider the software metaphor in the context of computers:  

In the early days of computers, programs were written in machine language and had a clear separation between data structures (sometimes called "structures") and instruction code (also called "functions" or "procedures"). It was more accurately described as a "biological" stage, similar to when the embryonic brain develops. The hardware's physical wiring was quite compatible with the machine language. In biology, the instructions are contained within DNA, which is also separated from the data, which consists of amino acids and other things essential to life. Due to their serial nature and "listing" (or "flat file") organisation, databases had any inherent connection to one another. This is in contrast to the intrinsic relationships between rows and columns in a tree structure, which are determined by the "imposer" rather than the data itself. They had reached the substrate state and were prepared to be worked with. Organisational structures could only be "mixed" in the computer during application execution for functions to work on them.  

Following this, as one would assume, came the "relational" method of organising data, the most basic example of which being the spreadsheet. The mathematical formulas established relationships between the data components. As the pregnancy develops, this takes the place of the brain's circuitry.  

The Object-Oriented Programming Systems (OOPS) represent the most recent stage of development. Objects are discrete modules that hold data and instructions. In other words, objects are "black boxes" (an engineering term) whose structure, internal communications, and processes are unknown to the user. However, the user is familiar with the objects' functionalities. How an object's externally helpful functions emerge from its internally concealed ones is something the programmer has no idea about. Things are fleeting, new, and epiphenomenal. Just put, it's a lot more in line with what contemporary physics has come to describe as reality.  

While it is possible to create communication between these black boxes, the system's total efficiency is not determined by the connection itself, nor by how fast or effective it is. The key is in the objects' hierarchical and simultaneously fuzzy organisation. Classes are used to organise objects and specify their properties, both actualised and potential. Being a member of the class defines the object's behaviour, including its actions and the conditions under which it can react. In addition, the "inheritance" principle is at work here: objects can be structured into new (sub)classes, which allow them to take on the definitions and traits of the original class as well as any additional features that set them apart. Essentially, the classes from which these new ones emerged are the source, and the classes from which they emerged are the products. The analogy is strengthened by the fact that this process is so similar to real-life occurrences.  

Therefore, classes can serve as foundational elements. Every problem that may be solved is defined by their permutations. Returning to the Principia Mathematica, it is possible to demonstrate that Turing Machines are an instance of a more general and robust class theory. By modifying the structural and functional aspects of both the hardware (the computer) and software (the computer applications), "framework applications" allow for the integration of the two. There must be a mental analogue somewhere (a priori categories, the collective unconscious, etc.).  

Because one stage gives way to another, we call it evolution. For example, object-oriented databases cannot be merged with relational databases. A "virtual machine" must be built inside the OS in order for Java applets to execute. The maturation of the brain-mind couplet is strikingly similar to these stages.  

At what points does a metaphor work well? When its inclusion sheds light on the genesis that would have been impossible to discover otherwise. That it has to be similar to what we've already found in terms of structure and function. However, this falls short. This is just the metaphor's "quantitative, observational" part. The qualitative one is as follows: it needs to lay the groundwork for a theory and its hypotheses; it needs to be illuminating, beautiful, concise, and educational. A theory that emerges from predetermined aesthetic and logical standards is a metaphor. To be considered trustworthy, it needs to undergo the kind of extensive testing that is required by scientific inquiry.  

The following characteristics of the brain are required if the software metaphor is valid:  

In order to set up a feedback parity loop, the electrochemical signal in a neurone must travel both forward and back (to its origin) at the same time. This allows for parity checks to be performed.  

In contrast to, say, a quantum computer, which will have many states, a neurone cannot have just one. It needs to be exciting on multiple levels (information representation). This "all or nothing" firing threshold theory has to be incorrect.  

Everything about the brain and how it works needs to be redundant. This includes the hardware (so that different parts of the brain can do the same things), the communications (so that different parts of the brain can send and receive information in the same way, so that we can compare them), the retrieval (so that data can be excited in multiple places at once), and the usage of the data (through working memory, or "upper").One must assume that "representation elements" and "models of the world" are fundamental to how the brain functions. The end result is a unified view that facilitates forecasting and efficient, fruitful environmental modification.The brain can only solve recursive problems. We may be able to simplify all of the brain's operations to computationally solvable recursive functions; this would be a huge surprise. The most fantastical visions of AI will materialise if this occurs, elevating the brain to the status of a Turing Machine. But until then, this amazing machine in our brains should be strongly recursive in its operations.There has to be some way for the brain to learn and organise itself.  

The software metaphor can only be considered strong if all six of these conditions are satisfied. Otherwise, we'll have little choice but to ignore it in preference of a more formidable opponent.  

Paranoid machines controlled by Murphy's Laws make up the brain. It is risk-averse, always ready for the worst, and never takes any chances. In control of its own life, precariously balanced and materially fragile, it can and does not take dangers.  

Oh my goodness!

Mind Metaphors (Introduction)

Written by Sam Vaknin, Ph.D.

Here is the link to the article: http://www.articlecity.com/articles/health/article 29.shtml.

current time: 2007-07-25 12:30:10

subject: medical

article:

The most recent technical advancement has been likened to the brain (and, by extension, the Mind) in every passing generation. Currently, the computer metaphor is all the rage. Recently, metaphors involving software and (neuronal) networks have supplanted those involving computer hardware. In every area of human knowledge, people often try to understand things by comparing them to other fields. The structural notion of "tensegrity" has recently been proposed by mathematicians and architects as an explanation for the phenomena of life. There is ample evidence that the human propensity to perceive patterns and organisation in seemingly random places has a survival function.  

Dismissing these metaphors as false, pointless, or intentionally misleading is another current trend. Although they are meant to depict something, these metaphors are actually created by the same Mind. Similar to how "brain-children" are the products of "brain-storming" and "minds" are the things or processes that the brain is likened to. If not a physical embodiment of mental processes, then what are computers, software programs, and communications networks?  

Basically, anything that is made by humans has to have some sort of link to human minds. A "mind-correlate" is required even in a petrol pump. Our thoughts may also contain a-priori (not based on experience) or a-posteriori (depending on experience) representations of the "non-human" aspects of the cosmos. The "excretions," "output," "spin-offs," and "products" of the human mind are closely related to the human mind, and this "correlation," "emulation," "simulation," and "representation" between them is crucial to the comprehension of the mind.  

This claim is part of a larger class of claims that state that we can identify an artist by their work, a creator by their creation, and, more generally, that we can identify an origin by any of its offspring, descendants, successors, products, or similes.  

When the product's nature is identical to that of its origin, this general contention becomes more stronger. If the father is human and the child is human as well, then there is a wealth of information that can be securely gleaned from the product that can be used to identify the father. The closer the point of origin is to the product, the more information about the origin can be uncovered. A "thinking machine"—whatever that may mean—that is mechanical, recursive, limiting, and simulated. Mind you, the brain is also a "thinking machine"—albeit one that is considerably more nimble, adaptable, non-linear, and perhaps even qualitatively distinct. However different they may be (and there will definitely be a big difference), they are clearly related. The fact that they are both "thinking machines" and, more importantly, that the latter is an offspring of the former, establishes their intimate relationship. To that end, the computer metaphor is very potent. If an organic computer ever materialises, the analogy will take on more significance. If a quantum computer ever comes to fruition, it will surely improve upon certain parts of the metaphor.  

By the way, it's not always the case that knowing where something comes from allows us to predict what it will be used for. The number of free variables is excessive. To paraphrase Bohr, when a product is available, our set of probability "collapses" and our knowledge grows.  

The starting point is a "wave function"—a set of hypothetical outcomes to which probabilities are associated; the outcomes are the products that are theoretically and logically feasible.  

However, a superficial comparison to the end result reveals very nothing about its source. The majority of these characteristics are structure- and function-related. These can be seen clearly. Does it cover everything? What does the "true nature" of the beginning tell us if anything? No, it is not correct. That humans are ultimately incapable of knowing anything regarding the "true nature" of anything is a pessimistic view. Here we are in the domain of philosophy, not science. Without stating anything substantial about either micro-processes or the Universe, Quantum Mechanics gives an incredibly accurate explanation of both. The goal of modern physics is accurate prediction, not the elaboration of certain worldviews. It only summarises; it offers no explanations. Whenever an interpretation is put out, such as the Copenhagen interpretation of QM, it encounters philosophical roadblocks and impassable problems. Therefore, many metaphors are used in modern science, such as particles and waves, among many others. It has been demonstrated that metaphors are valuable scientific instruments for the "thinking scientist's" toolbox.  

Also, a metaphor can evolve across time, mirroring the stages of its inception. Consider the software metaphor in the context of computers:  

In the early days of computers, programs were written in machine language and had a clear separation between data structures (sometimes called "structures") and instruction code (also called "functions" or "procedures"). It was more accurately described as a "biological" stage, similar to when the embryonic brain develops. The hardware's physical wiring was quite compatible with the machine language. In biology, the instructions are contained within DNA, which is also separated from the data, which consists of amino acids and other things essential to life. Due to their serial nature and "listing" (or "flat file") organisation, databases had any inherent connection to one another. This is in contrast to the intrinsic relationships between rows and columns in a tree structure, which are determined by the "imposer" rather than the data itself. They had reached the substrate state and were prepared to be worked with. Organisational structures could only be "mixed" in the computer during application execution for functions to work on them.  

Following this, as one would assume, came the "relational" method of organising data, the most basic example of which being the spreadsheet. The mathematical formulas established relationships between the data components. As the pregnancy develops, this takes the place of the brain's circuitry.  

The Object-Oriented Programming Systems (OOPS) represent the most recent stage of development. Objects are discrete modules that hold data and instructions. In other words, objects are "black boxes" (an engineering term) whose structure, internal communications, and processes are unknown to the user. However, the user is familiar with the objects' functionalities. How an object's externally helpful functions emerge from its internally concealed ones is something the programmer has no idea about. Things are fleeting, new, and epiphenomenal. Just put, it's a lot more in line with what contemporary physics has come to describe as reality.  

While it is possible to create communication between these black boxes, the system's total efficiency is not determined by the connection itself, nor by how fast or effective it is. The key is in the objects' hierarchical and simultaneously fuzzy organisation. Classes are used to organise objects and specify their properties, both actualised and potential. Being a member of the class defines the object's behaviour, including its actions and the conditions under which it can react. In addition, the "inheritance" principle is at work here: objects can be structured into new (sub)classes, which allow them to take on the definitions and traits of the original class as well as any additional features that set them apart. Essentially, the classes from which these new ones emerged are the source, and the classes from which they emerged are the products. The analogy is strengthened by the fact that this process is so similar to real-life occurrences.  

Therefore, classes can serve as foundational elements. Every problem that may be solved is defined by their permutations. Returning to the Principia Mathematica, it is possible to demonstrate that Turing Machines are an instance of a more general and robust class theory. By modifying the structural and functional aspects of both the hardware (the computer) and software (the computer applications), "framework applications" allow for the integration of the two. There must be a mental analogue somewhere (a priori categories, the collective unconscious, etc.).  

Because one stage gives way to another, we call it evolution. For example, object-oriented databases cannot be merged with relational databases. A "virtual machine" must be built inside the OS in order for Java applets to execute. The maturation of the brain-mind couplet is strikingly similar to these stages.  

At what points does a metaphor work well? When its inclusion sheds light on the genesis that would have been impossible to discover otherwise. That it has to be similar to what we've already found in terms of structure and function. However, this falls short. This is just the metaphor's "quantitative, observational" part. The qualitative one is as follows: it needs to lay the groundwork for a theory and its hypotheses; it needs to be illuminating, beautiful, concise, and educational. A theory that emerges from predetermined aesthetic and logical standards is a metaphor. To be considered trustworthy, it needs to undergo the kind of extensive testing that is required by scientific inquiry.  

The following characteristics of the brain are required if the software metaphor is valid:  

In order to set up a feedback parity loop, the electrochemical signal in a neurone must travel both forward and back (to its origin) at the same time. This allows for parity checks to be performed.  

In contrast to, say, a quantum computer, which will have many states, a neurone cannot have just one. It needs to be exciting on multiple levels (information representation). This "all or nothing" firing threshold theory has to be incorrect.  

Everything about the brain and how it works needs to be redundant. This includes the hardware (so that different parts of the brain can do the same things), the communications (so that different parts of the brain can send and receive information in the same way, so that we can compare them), the retrieval (so that data can be excited in multiple places at once), and the usage of the data (through working memory, or "upper").One must assume that "representation elements" and "models of the world" are fundamental to how the brain functions. The end result is a unified view that facilitates forecasting and efficient, fruitful environmental modification.The brain can only solve recursive problems. We may be able to simplify all of the brain's operations to computationally solvable recursive functions; this would be a huge surprise. The most fantastical visions of AI will materialise if this occurs, elevating the brain to the status of a Turing Machine. But until then, this amazing machine in our brains should be strongly recursive in its operations.There has to be some way for the brain to learn and organise itself.  

The software metaphor can only be considered strong if all six of these conditions are satisfied. Otherwise, we'll have little choice but to ignore it in preference of a more formidable opponent.  

Paranoid machines controlled by Murphy's Laws make up the brain. It is risk-averse, always ready for the worst, and never takes any chances. In control of its own life, precariously balanced and materially fragile, it can and does not take dangers.  

Oh my goodness!

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