Technology and Human Responsibility

Issue #160                                                January 25, 2005
                 A Publication of The Nature Institute
          Editor:  Stephen L. Talbott (

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Editor's Note

Logic, DNA, and Poetry (Stephen L. Talbott)
   What if geneticists took their words seriously?


About this newsletter


                              EDITOR'S NOTE

My deepest thanks go out to that select group of you who responded to
the December fund appeal.  Your generosity of spirit always humbles me.
NetFuture continues only on the strength of your support.

Also, a new article on The Nature Institute's website may hold interest
for many of you.  It's a broad survey of the ecological, agricultural, and
social issues surrounding the question, "Will Biotechnology Feed the
World?" by my colleague, Craig Holdrege.  He places the claims for
biotechnology within their larger context -- a service rarely or never
offered by those who make the claims, for reasons of self-interest that
will become clear when you read the piece.  You'll find the essay at:


Goto table of contents


                          LOGIC, DNA, AND POETRY

                            Stephen L. Talbott

In January, 1956, Herbert Simon, who would later win the Nobel prize in
economics, walked into his classroom at Carnegie Institute of Technology
and announced, "Over Christmas Allen Newell and I invented a thinking
machine".  His invention was the "Logic Theorist", a computer program
designed to work through and prove logical theorems.  Simon's casual
announcement -- which, had it been true, would surely have rivaled in
importance the Promethean discovery of fire -- galvanized researchers in
the discipline that would soon become known as artificial intelligence
(AI).  The following year Simon spoke of the discipline's promise this

   It is not my aim to surprise or shock you .... But the simplest way I
   can summarize is to say that there are now in the world machines that
   think, that learn and that create.  Moreover, their ability to do these
   things is going to increase rapidly until -- in a visible future -- the
   range of problems they can handle will be coextensive with the range to
   which the human mind has been applied.  (Simon and Newell 1958)

There was good reason for the mention of surprise.  Simon and his
colleagues were, in dramatic fashion, surfing the shock waves produced by
the realization that computers can be made to do much more than merely
crunch numbers; they can also manipulate symbols -- for example, words --
according to rules of logic.  The swiftness with which such programmed
logical activity was equated, in the minds of researchers, to a human-like
capacity for speech and thought was stunning.  And, during an extended
period of apparently rapid progress, their faith in this equation seemed
justified.  In 1965 Simon predicted that "machines will be capable, within
twenty years, of doing any work that a man can do" (Simon 1965, p. 96).
MIT computer scientist Marvin Minsky assured a Life magazine reporter in
1970 that "in from three to eight years we'll have a machine with the
general intelligence of an average human being ... a machine that will be
able to read Shakespeare and grease a car".

The story is well-told by now how the cocksure dreams of AI researchers
crashed during the subsequent years -- crashed above all against the solid
rock of common sense.  Computers could outstrip any philosopher or
mathematician in marching mechanically through a programmed set of logical
maneuvers, but this was only because philosophers and mathematicians --
and the smallest child -- were too smart for their intelligence to be
invested in such maneuvers.  The same goes for a dog.  "It is much
easier", observed AI pioneer, Terry Winograd, "to write a program to carry
out abstruse formal operations than to capture the common sense of a dog"
(Winograd and Flores 1986, p. 98).

A dog knows, through whatever passes for its own sort of common sense,
that it cannot leap over a house in order to reach its master.  It
presumably knows this as the directly given meaning of houses and leaps --
a meaning it experiences all the way down into its muscles and bones.  As
for you and me, we know, perhaps without ever having thought about it,
that a person cannot be in two places at once.  We know (to extract a few
examples from the literature of cognitive science) that there is no
football stadium on the train to Seattle, that giraffes do not wear hats
and underwear, and that a book can aid us in propping up a slide projector
when the image is too low, whereas a sirloin steak probably isn't

We could, of course, record any of these facts in a computer.  The
impossibility arises when we consider how to record and make accessible
the entire, unsurveyable, and ill-defined body of common sense.  We know
all these things, not because our "random access memory" contains
separate, atomic propositions bearing witness to every commonsensical fact
(their number would be infinite), and not because we have ever stopped to
deduce the truth from a few, more general propositions (an adequate
collection of such propositions isn't possible even in principle).  Our
knowledge does not present itself in discrete, logically well-behaved
chunks, nor is it contained within a neat deductive system.

It is no surprise, then, that the contextual coherence of things -- how
things hold together in fluid, immediately accessible, interpenetrating
patterns of significance rather than in precisely framed logical
relationships -- remains to this day the defining problem for AI.  It is
the problem of meaning.

DNA's Ever-Receding Secrets

On February 28, 1953, Francis Crick and James Watson burst into the Eagle
pub in Cambridge, England, where (as Watson later recalled) Crick spilled
the news that "we had found the secret of life".  The secret, as the world
now knows, lay in the double helical structure of DNA.  Looking back on
Crick and Watson's revelation fifty years later, the editors of Time would
refer to "the Promethean power unleashed that day".

It was, however, slightly strange for Crick and Watson to announce the
revelation of a secret that came in the form of a code they did not
understand and a text they did not possess.  Yet the double helix, by all
accounts, came in just that way.  This is why we have been treated, during
the intervening fifty years, to the celebration of one code-breaking and
text-reading victory after another, culminating most recently in the Human
Genome Project.  Only now, we're told, has the full text of the deciphered
Book of Life been laid out before the glittering eyes of genetic

The celebration -- and also the expense -- of this latest victory has been
unparalleled in the history of science.  So, too, has the orgy of
self-congratulation and utopian prediction.  The completion of the genome
project, many scientists declared, would quickly enable us to slay the
demons of genetically linked disease, after which we would employ designer
genes to create an enhanced race of superhumans.  The giddiness reached
its silly zenith when Nobel laureate and molecular biologist, Walter
Gilbert, observed that you and I will pocket a CD carrying the code for
our personal genomes and say, "Here is a human being; it's me!"

But wait!  Hold off on the celebration.  Now, it appears, there's one,
small, remaining obstacle on the path to unprecedented self-knowledge.
Yes, we have discovered the alphabetic text of the Book of Life, but it
turns out we still can't actually read it.  For this, according to the
current story, we need a new project -- one that will dwarf even the human
genome effort.  We must unravel the functioning of the body's 100,000 or
more proteins -- molecules so deeply implicated in every aspect of the
organism (including its genetic aspects) that the attempt to understand
them looks suspiciously like the entire task we began with: to understand

The secret of life, it appears, is wrapped within layer after layer of
mystery, each one requiring its own decoding, and each one extending
further through the biochemistry of the whole organism.  Where, then, is
the single, controlling secret?  If by their own admission they still
cannot read the DNA text of the Book of Life, how can scientists pronounce
so confidently on the nature and absolute importance of its meaning?  And
if they can achieve the reading only through recourse to everything else
going on in the organism -- that is, if they must in effect read the whole
organism -- then how can they know that the entire secret resides in one,
small, still mostly undeciphered portion of the overall text?

Does Logic Make a Text?

Clearly there is a profound faith at work here.  It is, in fact, the same
faith that motivated Herbert Simon and his fellow AI researchers:  once
they laid hold of an apparently mechanizable logic, they just couldn't
help themselves.  The mechanism and logic must explain everything else!
That is how they expected a mechanically conceived world to work, whether
they were dealing with human speech and thought or the genetic text of the
Book of Life.

What they thirsted after was a world of life and thought driven by a
neatly controlling syntax that played itself out with something like
cause-and-effect necessity.  They imagined this causal necessity much as
they imagined the external impact of particle upon particle, molecule upon
molecule, where one thing "makes" another happen.

And if this is how things work, then why should they worry about what the
Book of Life might turn out to say when they could actually read it?
Their confidence that they had wrested the textual secret of life from the
cell's nucleus even before they had a clue to its reading is the proof
that they were not really thinking in textual terms.  It wasn't the
still-unknown meaning of the text that excited them so much as their
conviction that a cut-and-dried, mechanizable logic had been found for
preserving certain "machine states" from one generation to the next.
Surely, they thought, the discovery of such a mechanism -- seductive and
unqualified in its clarity and reassuringly necessary in the attractions
and repulsions of its logical atoms -- would explain everything.

It was in this spirit that Francis Crick articulated the Central Dogma of
Molecular Biology in 1958.  According to this deeply influential doctrine,
genetic information flows in one direction only, from genes to proteins.
As science historian Evelyn Fox Keller paraphrased the Central Dogma:
"DNA makes RNA, RNA makes protein, and proteins make us" (2000, p. 54).
The doctrine, with its genetic determinism and command-and-control view of
DNA, paved the simplest, most direct highway to a mechanistic
understanding of the organism.

Putting Genes in Context

But the highway proved to be little more than a long, rutted detour.  The
straightforward, neatly determining logical structure envisioned by Crick
-- a structure the lust for which became a feverish obsession during the
Human Genome Project -- has progressively transformed itself into a
seething cauldron of endlessly complex dynamic processes extending
throughout the organism.  The crucial problem for genetic determinism and
the once-prevailing Central Dogma is that biochemical cause and effect
within the cell, as in the organism as a whole, never proceeds in one
direction alone.  To put it coarsely:  everything affects everything else.

The string of discoveries supporting this conclusion is not contested.  We
now know that one gene can produce many different proteins, depending on
complex processes that are orchestrated not only by DNA, but also by
proteins themselves.  Moreover, one protein is not necessarily one
protein.  For example, depending on the presence of so-called chaperon
proteins, a given chain of amino acids (the constituent elements of
protein) may fold in different ways.  These various foldings in turn shape
the overall structure and functioning of cell and organism.

The supposedly linear structure of letters, words, and sentences into
which DNA has been decoded simply does not articulate a clean,
unambiguous, command-and-control authority sitting atop a hierarchical
chain of command.  Only a misguided preoccupation with an imagined set of
well-defined syntactical relationships could have led researchers to
dismiss the greater part of DNA -- nearly all of it, actually -- as "junk
DNA".  The junk didn't seem to participate in the neat controlling
sequences researchers were focused on, and so it seemed irrelevant.  But
more recently the erstwhile junk has been recognized as part of a "complex
system of distributed regulation" in which "the spacing, the positioning,
the separations and the proximities of different elements ... appear to be
of the essence" (Moss 2003, p. 191).

But even more devastating for the centralized command-and-control view has
been the discovery of "epigenetic" processes.  These yield hereditary
changes that are not associated with structural changes in DNA at all.
Rather, they arise from alterations in how the rest of the organism marks
and employs its DNA.  And beyond this, researchers have been exploring
effects upon DNA from the larger environment.  In a dramatic reversal of
traditional doctrine, investigations of bacteria show that gene mutations
can arise from -- can even be guided by -- environmental conditions in a
non-random way.  In sum, genes are no more the self-determining cause of
everything else in the organism, than they are themselves the result of
everything else.

Finally, we have seen a startling demotion of the human genome in size
relative to other organisms.  The most recent and near-final estimate by
the Human Genome Project puts humans in possession of 20,000 - 25,000
genes -- this compared to at least 25,000 for a tiny, primitive,
semi-transparent worm, Caenorhabditis elegans.  If genes constitute
the one-way controlling logic or master program determining the potentials
of the organism, then finding such unexpected gene counts is rather like
discovering we could implement all the programs of the Microsoft Office
suite using only the minuscule amount of program logic required for a
simple daily greeting program.

Reviewing the history of misdirection surrounding the gene, cell biologist
Lenny Moss writes,

   Once upon a time it was believed that something called "genes" were
   integral units, that each specified a piece of a phenotype [that is, a
   trait], that the phenotype as a whole was the result of the sum of
   these units, and that evolutionary change was the result of new genes
   created by random mutation and differential survival.  Once upon a time
   it was believed that the chromosomal location of genes was irrelevant,
   that DNA was the citadel of stability, that DNA which didn't code for
   proteins was biological "junk", and that coding DNA included, as it
   were, its own instructions for use.  Once upon a time it would have
   stood to reason that the complexity of an organism would be
   proportional to the number of its unique genetic units.  (Moss 2003, p.

Today, as Evelyn Fox Keller tell us, the findings of the past few decades
"have brought the concept of the gene to the verge of collapse".  In fact,
"it seems evident that the primacy of the gene as the core explanatory
concept of biological structure and function is more a feature of the
twentieth century than it will be of the twenty first" (Keller 2000, pp.

Taking Our Words Seriously

To point out the failure of the Central Dogma will strike most geneticists
today as anachronistic.  "We long ago quit believing such a simplistic
doctrine".  And, in fact, you will find them regularly disclaiming the
"gene-for" view -- that is, the belief that for many or most traits of the
organism there is a gene, or a few genes, that account for the trait.  "We
know it's much more complicated than that" -- so the disclaimer runs.  In
the face of such protestations, recital of the history of misdirection
begins to seem unfair.  After all, scientists must be allowed to make
mistakes, as long as they are willing to learn from them.  What's
important is the knowledge they eventually arrive at.

But does the painfully repetitive history of genetics and AI suggest that
they have in fact learned from their mistakes?  The best way I know to
answer this question is to elucidate the central misdirection in the
history under discussion.

The real significance of the overheated rhetoric of the Human Genome
Project lies in the seemingly unstoppable appeal by geneticists to
language and thought -- that is, to book, word, letter, code, translation,
transcription, message, signal, and all the rest.  Or, to employ the most
universal term today:  information.  This resort to a terminology so
brazenly mental in origin appears to be a stunning reversal.  Just a few
decades ago we still lived within the long historical era during which it
was unpardonable for the natural scientist to draw his explanatory terms
from intelligent activity.  What changed?

Crucially, the age of cybernetics and computation arrived.  This brought
with it, for many researchers, the promise of the mechanization of
language and thought.  Suddenly it became respectable to invoke human
mentality in scientific explanation because everyone knew you weren't
really talking about mentality at all -- certainly not about anything
remotely resembling our actual mental experience.  You were invoking
computational mechanisms.  So the change was less a matter of assigning
human intelligence to the mechanically conceived world than of
reconceiving human intelligence itself as mechanical performance.

Of course, we have seen that the equation of mechanical computation with
mentality was based on the extraordinarily naïve assumption that machine
logic is the essence of thinking and language.  But if we can look past
this reductionism, what we find is that geneticists have glimpsed  more
truth than they realize, and the reason for their confusion is that, due
to their mechanistic compulsions, they cannot bring themselves to accept
their own inchoate insight.  If they have been driven to textual metaphors
with such compelling, seemingly inescapable force, it is because these
metaphors capture a truth of the matter.  The creative processes within
the organism are word-like processes.  Something does speak through every
part of the organism -- and certainly through DNA along with all the rest.
Geneticists are at least vaguely aware of this speaking -- and of the
unity of being it implies -- and therefore they naturally resort to
explanations that seem to invoke a being who speaks.

The problem is that their insistence upon textual mechanisms blinds them
even to the most obvious aspects of language -- aspects that prove crucial
for understanding the organism.  If I am speaking to you in a logically or
grammatically proper fashion, then you can safely predict that my next
sentence will respect the rules of logic and grammar.  But this does not
even come close to telling you what I will say.  Really, it's not a hard
truth to see:  neither grammatical nor logical rules determine the speech
in which they are found.  Rather, they only tell us something about how we

If geneticists would reckon fully with this one central truth, it would
transform their discipline.  They would no longer imagine they could read
the significance of the genetic text merely by laying bare the rules of a
molecular syntax.  And they would quickly realize other characteristics of
the textual language they incessantly appeal to -- for example, that
meaning flows from the context into the words, altering the significance
of the words.  This is something you experience every time you find
yourself able, while hearing a sentence, to select between words that
sound alike but have different meanings.  The context tells you which one
makes sense.

The role of context is pervasive.  As poets know very well, even the word
"prophet" in the two phrases, "old prophets" and "prophets old", carries
different ranges of meaning (Barfield 1973, p. 41).  If DNA is like a
text, then plasticity of the gene must be one of the rock-bottom,
fundamental principles of heredity.

Conversation and Poetry

There is no need for geneticists to endure lectures from philologists,
however.  As we have seen, all this is exactly what their own discoveries
of the past fifty years have been shouting at them.  In the ongoing
conversation between word and text, part and whole -- and contrary to the
Central Dogma -- we find the context of the organism informing the genetic
text at least as much as the genes can be said to inform the organism.
This is the underlying truth that science historian Lily Kay trades
on when she writes: "once the genetic, cellular, organismic, and
environmental complexities of DNA's context-dependence are taken
into account", we might find that genetic messages "read less like an
instruction manual and more like poetry, in all their exquisite polysemy
[multiplicity of meaning], ambiguity, and biological nuances" (2000, pp.

What this means practically is that, in Craig Holdrege's words:

   We gain a knowledge of genes ... only through knowledge of the organism
   as a whole. The more knowledge we have of the organism as a whole, the
   more information we have. This information is not in the genes; it
   is the conceptual thread that weaves together the various details into
   a meaningful whole. (Holdrege 1996, p. 80. Emphasis in original)

The weaving together is a conversation, not a merely mechanical unrolling
of a logically compelling sequence.  When we speak of such things as
messenger RNA, the conversational context should be obvious.  It makes no
sense -- or, at least, no sense that biologists have yet explained -- to
speak of a message without a recipient capable of a certain understanding,
and without a context for determining how the message is to be construed.
If we eliminate these things from the picture, we have a message without
meaning, which is no message at all.  The question, then, is whether
geneticists really believe their own terminology.

They ought to.  Everything we have been learning about the genome points
to the significance of its conversational context.  As Lenny Moss puts it:
"If the sum total of coding sequences in the genome be a script, then it
is a script that has become wizened and perhaps banal.  It wouldn't be the
script that continued to make life interesting but rather the ongoing and
widespread conversations about it" within the biochemistry of the organism
(2003, p. 190; emphasis in original).

Actually, it is not so much the script that is banal as the reduced,
syntactic reading of it.  As Moss himself reminds us, the script is a
dynamic one, subject to continual and rapid changes with profound
significance -- "transpositions, amplifications, recombinations, and the
like, as well as modulation by direct chemical modification" (p. 110).
There is a lively conversation going on here, but it is one in which our
genes are caught up, not one they are single-handedly dictating.

Words of Explanation

Language is the very soul and substance of explanation itself.  The reason
for this can only be that the world we are explaining has something
language-like about it.  When we offer a scientific explanation for some
aspect of the world, we necessarily assume that the meaning of our words
is at the same time the meaning of the chosen aspect of the world.  If
this so-called "intentionality" of language -- its being about something
else and not just about itself -- were not born of the world's word-like
character, then our scientific explanations could tell us nothing about
reality.  The world must in some sense be a text waiting to be deciphered.
This is why the scientist can, in fact, decipher it into the text of a
scientific description.

So in reality all scientific explanation is founded upon an appeal to
the word.  The irony lies in the fact that precisely where the computer
scientist and geneticist resort explicitly to "word" and "text", what
we actually see is a concerted attempt to substitute wordless logic and
computational mechanisms for language2.

Most fundamentally, this stance takes the form of an attempt to explain
words themselves as if they were objects.  No longer standing consciously
within the transparent meaning of the words we speak, allowing the world
to become visible and meaningful through their transparency, we instead
take these words as additional things in the world to be explained.  That
is, we want to understand our explanatory words as if they themselves were
nothing more than causal results of processes going on in the world they
explain.  There is something gravely misconceived in this effort to
explain explanation itself -- and all the more when the effort involves an
appeal to mechanisms stripped as far as possible of their word-like (and
therefore of their explaining) nature.  It is rather like trying to
prove the validity of logic -- or, in other words, trying to prove the
validity of the instruments of proof -- and to do so by invoking physical
laws.  A fool's task.

We can recognize something like the fruitless struggle to explain
explanation in the difficulties that beset twentieth-century physics when
the attempt was made to understand light -- that is, to illuminate
illumination.  But light is that by which the world becomes manifest, so
that the attempt to understand it in terms of manifest entities -- for
example, in terms of materially conceived particles or waves -- led only
to universally acknowledged confusion.

The discipline of artificial intelligence went down an analogous path when
computer scientists came to believe they could explain speech (and
thought) as manifestations of computational devices.  Their aim was to
explain our powers of explanation by appealing to something not having the
essential character of explanation.  The result could only be nonsense,
which is why the researchers quickly began arbitrarily projecting language
back into their wordless explanatory devices.

At its worst, the projecting became extraordinarily crude.  All one needed
to do was to label programs and data structures with terms like UNDERSTAND
and GOAL, and then mindlessly assume that the programs actually had
something to do with understanding or goal-seeking.  Such nonsense
eventually became downright embarrassing.  In 1981 computer scientist Drew
McDermott published an essay entitled "Artificial Intelligence Meets
Natural Stupidity" in which he ridiculed the use of "wishful mnemonics".
He wondered aloud whether, if programmers used labels such as G0034
instead of UNDERSTAND, they would be equally impressed with their clever

Likewise, McDermott commented on Herbert Simon's "GPS" program, written as
a much more ambitious successor to the Logic Theorist:  "By now, 'GPS' is
a colorless term denoting a particularly stupid program to solve puzzles.
But it originally meant 'General Problem Solver', which caused everybody a
lot of needless excitement and distraction." He went on to say,

   As AI progresses (at least in terms of money spent), this malady gets
   worse.  We have lived so long with the conviction that robots are
   possible, even just around the corner, that we can't help hastening
   their arrival with magic incantations.  Winograd ... explored some of
   the complexity of language in sophisticated detail; and now everyone
   takes 'natural-language interfaces' for granted, though none has been
   written.  Charniak ... pointed out some approaches to understanding
   stories, and now the OWL interpreter includes a 'story-understanding
   module'.  (And, God help us, a top-level 'ego loop.')  (McDermott 1981,
   pp. 145-46)

The geneticist's strategy was much like the computer scientist's --
unsurprisingly, given that DNA is often imagined as a genetic program.
All that was needed was to put a label on the gene saying that it was the
gene for such-and-such a trait and -- presto! -- the gene now spoke a
meaningful language.  The only problem is that these neatly labeled
genes are forever disappearing as rapidly as they are discovered -- or,
rather, they lose their neat, causal identity against a background of
extraordinary complexity.  What stands on the biochemical and supposedly
causal side of the relation never clearly relates to the trait, and
certainly fails to explain it in any adequate sense.  This is because the
trait -- whether it is dark skin, green eyes, cancer, or an aggressive
tendency -- is quite properly understood in qualitative and meaningful
(word-like) terms, whereas the "causal" gene remains at the level of
mechanism, not language.  Causes and mechanisms, as we prefer to have
them, do not mean (Talbott 1995).


After centuries of doing its best to ignore the self-contained,
illuminating reality of the language with which it describes phenomena,
science is now broadly engaged in the task of trying to seize the word by
killing it.  This is to destroy the means of all explanation, and the
result can only be a continuing loss of coherence within scientific
discourse.  This helps us to understand how we could possibly encounter
statements like this one from the geneticist, Maxim Frank-Kamenetskii:

   Under [DNA's] inexorable laws, the ill fate that befell the father may
   also threaten the son.  (1997, p. 180)

Here, within a single sentence, "inexorable" mutates unaccountably into
"may threaten".  What does he mean to say?  An inexorable law is one
thing, and a possible threat quite another.  To juxtapose the two as if
one were equivalent to the other is sloppiness one should hardly expect to
find in a prominent scientist.

It's the same degeneration of the word we've been noting in the language
of geneticists generally, and also in the language of artificial
intelligence researchers.  The unhappy truth is that many scientists,
secure amid their precise, mechanically conceived formulas, can scarcely
bring themselves to worry very much about the meaning of their words -- a
fact that coincides neatly with a second one:  they are doing their best
to demonstrate that words are not vessels of meaning at all, but rather
genetic or silicon mechanisms for delivering inexorable, cause-and-effect

Here, then, is a formula for the destruction of science as a discipline of
understanding rather than merely of effective technique.  There could
hardly be a surer indication of the insecure and disturbed foundations of
science than we find in all the confusion over word and text.  How much
confidence can we place in the understandings conveyed through an
enterprise whose verbal and conceptual instruments of understanding are so
badly damaged?  If there is to be a scientific Prometheus for our day, he
must bring the fire of meaning into our various theoretical languages --
languages that, in their current, desiccated state, are like dry tinder
eager for the blaze.  And it is almost as if geneticists, with their
ceaseless invocation of word and text, have been unconsciously calling
down the tongues of flame.

Such a conflagration will doubtless consume a great deal.  But it may also
purify and transform.  If, as we heard Evelyn Fox Keller suggest, the
concept of the gene has been brought to the verge of collapse, we can hope
that in our revitalized understanding the gene will truly speak with all
the creative and clarifying power of the word -- because the entire
organism speaks through it.  Then its language of wholeness will belong as
much to the poet as to the scientist, and we will hear within its rhythms
and cadences a song of destiny in which we ourselves are singers.


1. Something similar is true of the laws of nature.  See "Do Physical Laws
Make Things Happen?" (Talbott 2004).

2. The same denial of the word is increasingly evident throughout all the sciences wherever we find the word explicitly or implicitly appealed to, as it is when researchers speak of information, signal, message, program, computation, and so on. Bibliography ------------ Barfield, Owen (1973). Poetic Diction: A Study in Meaning. Middletown, CT: Wesleyan University Press. Originally published in 1928. Frank-Kamenetskii, Maxim D. (1997). Unraveling DNA: The Most Important Molecule of Life. Reading MA: Perseus Books. Holdrege, Craig (1996). Genetics and the Manipulation of Life: The Forgotten Factor of Context. Hudson NY: Lindisfarne. Kay, Lily E. (2000). Who Wrote the Book of Life? A History of the Genetic Code. Stanford CA: Stanford University Press. Keller, Evelyn Fox (2000). The Century of the Gene. Cambridge MA: Harvard University Press. McDermott, Drew (1981). "Artificial Intelligence Meets Natural Stupidity", in Mind Design, edited by John Haugeland. Cambridge MA: MIT Press. Moss, Lenny (2003). What Genes Can't Do. Cambridge MA: MIT Press. Simon, Herbert A. and Allen Newell (1958). "Heuristic Problem Solving: The Next Advance in Operations Research", Operations Research vol. 6, pp. 1-10. Simon, Herbert A. (1965). The Shape of Automation for Men and Management. New York: Harper and Row. Talbott, Stephen L. (1995). The Future Does Not Compute: Transcending the Machines in Our Midst. Sebastopol CA: O'Reilly and Associates. Talbott, Stephen L. (2004). "Do Physical Laws Make Things Happen?". Winograd, Terry and Fernando Flores (1986). Understanding Computers and Cognition: A New Foundation for Design. Norwood NJ: Ablex. Goto table of contents ========================================================================== ABOUT THIS NEWSLETTER Copyright 2005 by The Nature Institute. You may redistribute this newsletter for noncommercial purposes. You may also redistribute individual articles in their entirety, provided the NetFuture url and this paragraph are attached. NetFuture is supported by freely given reader contributions, and could not survive without them. For details and special offers, see . Current and past issues of NetFuture are available on the Web: To subscribe or unsubscribe to NetFuture: This issue of NetFuture: Steve Talbott :: NetFuture #160 :: January 25, 2005

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