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One aspect that enters in here is that energy is continuous, and logic never really is. The neural network that constitutes the mind cannot be identical to any logical language with a finite number of symbols, and all languages ultimately have a finite number of symbols.

Languages may point toward a countable set of values, but they cannot really constitute one, so we cannot even have a language that is 'dense' in the range of potential neural states (the way the rational numbers are dense in the reals). We cannot reasonably claim human language has a continuum of reference points, but the projected balance of ionic charges over time -- the stuff of which our memory is constituted, via Hebbian learning -- naturally does.

So there are basic aspects that biology or physics always involve but that cannot, ultimately be captured in language directly. We can only generalize about them indirectly. So whatever language we use to share or communicate the contents of the brain, it will have been simplified by the necessities of storytelling. This requires that it be rendered discrete.

Turing machines never compute anything continuous, unless we impose some definite limit on precision. So they are not a real model for biological activity that has not been reduced to language. The interal state of an analog machine can represent the full complexity of a fractional-dimensional solution to a differential equation, even if we can only read the output to a given precision.

This doesn't really help say anything useful about either of these two languages, but it does prove they have an essential difference. There is a maximum precision of language that can only approximate the precision of reality asymptotically.

One aspect that enters in here is that energy is continuous, and logic never really is. The neural network that constitutes the mind cannot be identical to any logical language with a finite number of symbols, and all languages ultimately have a finite number of symbols.

Languages may point toward a countable set of values, but they cannot really constitute one, so we cannot even have a language that is 'dense' in the range of potential neural states (the way the rational numbers are dense in the reals). We cannot reasonably claim human language has a continuum of reference points, but the projected balance of ionic charges over time -- the stuff of which our memory is constituted, via Hebbian learning -- naturally does.

So there are basic aspects that biology or physics always involve but that cannot, ultimately be captured in language directly. We can only generalize about them indirectly. So whatever language we use to share or communicate the contents of the brain, it will have been simplified by the necessities of storytelling. This requires that it be rendered discrete.

Turing machines never compute anything continuous, unless we impose some definite limit on precision. So they are not a real model for biological activity that has not been reduced to language. The interal state of an analog machine can represent the full complexity of a fractional-dimensional solution to a differential equation, even if we can only read the output to a given precision.

This doesn't really help say anything useful about either of these two languages, but it does prove they have an essential difference. There is a precision of language that can only approximate the precision of reality.

One aspect that enters in here is that energy is continuous, and logic never really is. The neural network that constitutes the mind cannot be identical to any logical language with a finite number of symbols, and all languages ultimately have a finite number of symbols.

Languages may point toward a countable set of values, but they cannot really constitute one, so we cannot even have a language that is 'dense' in the range of potential neural states (the way the rational numbers are dense in the reals). We cannot reasonably claim human language has a continuum of reference points, but the projected balance of ionic charges over time -- the stuff of which our memory is constituted, via Hebbian learning -- naturally does.

So there are basic aspects that biology or physics always involve but that cannot, ultimately be captured in language directly. We can only generalize about them indirectly. So whatever language we use to share or communicate the contents of the brain, it will have been simplified by the necessities of storytelling. This requires that it be rendered discrete.

Turing machines never compute anything continuous, unless we impose some definite limit on precision. So they are not a real model for biological activity that has not been reduced to language. The interal state of an analog machine can represent the full complexity of a fractional-dimensional solution to a differential equation, even if we can only read the output to a given precision.

This doesn't really help say anything useful about either of these two languages, but it does prove they have an essential difference. There is a maximum precision of language that can only approximate the precision of reality asymptotically.

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source | link

One aspect that enters in here is that energy is continuous, and logic never really is. The neural network that constitutes the mind cannot be identical to any logical language with a finite number of symbols, and all languages ultimately have a finite number of symbols.

Languages may point toward a countable set of values, but they cannot really constitute one, so we cannot even have a language that is 'dense' in the range of potential neural states (the way the rational numbers are dense in the reals). We cannot reasonably claim human language has a continuum of reference points, but the projected balance of ionic charges over time -- the stuff of which our memory is constituted, via Hebbian learning -- naturally does.

So there are basic aspects that biology or physics always involve but that cannot, ultimately be captured in language directly. We can only generalize about them indirectly. So whatever language we use to share or communicate the contents of the brain, it will have been simplified by the necessities of storytelling. This requires that it be rendered discrete.

Turing machines never compute anything continuous, unless we impose some definite limit on precision. So they are not a real model for biological activity that has not been reduced to language. The interal state of an analog machine can represent the full complexity of a fractional-dimensional solution to a differential equation, even if we can only read the output to a given precision.

This doesn't really help say anything useful about either of these two languages, but it does prove they have an essential difference. There is a precision of language that can only approximate the precision of reality.