According
to the computationalism theory of the human mind, the mind is like a
computer, and one day we will be able to develop computers that
produce outputs just like human consciousness. Such a theory is
assumed by most proponents of the Singularity, the idea that there
will before long be an “intelligence explosion” which results in
machines with intelligence far beyond our own. Such proponents write
books with titles such as The Age of Spiritual Machines. Such
theorists usually don't tell us that they are advancing the
computationalism theory of the human mind. They usually just
pronounce the dubious ideas of such a theory as if such ideas were
self-evident.
But
the computationalism theory of the human mind is not valid. To
explain why, let us look into what happens when computers compute.
The following equation covers most of the types of computation that
occur.
digital
inputs + processing = digital outputs or modification of digital data
There
are various types of variations of this equation. One is simply:
no
inputs + processing = digital outputs or modification of digital data
Another
variation is the following:
digital
inputs + processing + retrieval of other digital inputs = digital
outputs or modification of digital data
By
digital inputs or digital outputs I mean anything at all that can be
represented digitally, by a sequence of binary numbers. Here are some
of the things that we know can be represented digitally, and which
modern computers do use as digital inputs or digital outputs:
Any
number
Any
set of characters or words
Images
Videos
Databases
To the computer, that
pretty gal is just a series of 1's and 0's
Any
text can be digitally represented by means of things such as the
ASCII system that allows you to represent particular characters as
particular numbers. While we don't normally think of an image as
digital, it can be represented digitally as a series of pixels or
picture elements. For example, a photograph might consist of 1
million pixels, which each can be represented by a number
representing a particular shade of color. So the image can be
digitally represented by a million such numbers. A video can also be
representing digitally, since the video can be represented as a
series of images, each of which can be digitally represented.
But
there are two things that we can never hope to produce as digital
outputs. The first is real conceptual understanding, and the second
is experience. By understanding I don't mean “how-to” type
understanding, but the high-level conscious understanding of some
abstract truth or concept. By experience I mean an actual human
experience, such as the life-flow you experience during an hour of
your life.
We
can imagine no possible way to produce a digital output that would
equal a real conceptual understanding of something. Nor can we
imagine any possible way to produce a digital output that would equal
something like a human experience.
Imagine
a conversation like this 200 years from now between a programming
supervisor and a programmer who has been doing his job for over a
century (thanks to the marvel of life-extension pills).
Boss:
Well, I've got an interesting new assignment for you. I want you to
compute an interesting new output.
Programmer:
This should be a breeze. I've
already done functions that compute 12,000 different text outputs,
15,000 different numerical outputs, 25,000 different image outputs,
and 4000 different video outputs.
Boss:
This time I want the computer to
produce waterfalls and Swiss cheese. Not just pictures, but the real
things.
Programmer:
Are you crazy?
There might be a
similar conversation if the boss asked the programmer to produce
understanding or experience as the outputs. Just as waterfalls and
Swiss cheese are not digital outputs, real conceptual understanding
and experience (a slice of life-flow) are not digital outputs. You
can make outputs that might mimic some understanding someone might
have, but you cannot produce real conceptual understanding as a
digital output. You can make outputs that might mimic some sight
someone might see while having an experience, but you cannot produce
actual experience (a slice of life-flow) as a digital output.
But, you may ask,
doesn't that smart computer Watson already understand something –
the game of chess? No, it doesn't. Watson merely can produce a
digital output corresponding to a good move to make as the next move
in a chess game. Watson has zero conceptual understanding of the game
of chess itself, and has zero understanding of the abstract concept
of a game. The only way you can understand the abstract concept of a
game (or the abstract concept of leisure) is if you have been a human
being (or something like a human), and played a game yourself.
A
digital output must always boil down to a series of 1's and 0's. Can
we imagine a series of 1's and 0's that would equal a real
understanding of an abstract concept such as health, matter, life or
world peace? No, we cannot.
Because
conceptual understanding is not a digital output, we should not think
that predictions such as the following recent prediction
are correct:
The
third thing you can expect before the year 2100 is the development of
generalized artificial intelligence (GAI). In other words, machines
that don't just play games like chess or Jeopardy, but can do the
thinking required for any white-collar job, including all the ones at
the top.
Such a prediction is
based on the idea that future computers will be able to produce
conceptual understanding as an output. They won't, because real
understanding of abstract concepts is not a possible digital output,
and digital computers will only be able to produce digital outputs.
Postscript: Digital computers translate inputs into digital content, and translate outputs into content that may not seem digital. For example, a compiler translates English-like computer code into digital inputs a computer can understand, while a purely digital output may be translated into something that doesn't look digital. But at the lowest level inside the computer, it's all digital.
Postscript: Digital computers translate inputs into digital content, and translate outputs into content that may not seem digital. For example, a compiler translates English-like computer code into digital inputs a computer can understand, while a purely digital output may be translated into something that doesn't look digital. But at the lowest level inside the computer, it's all digital.
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