Who will attend to the machines’ writing? -- Richard Hughes Gibson
<https://hedgehogreview.com/issues/markets-and-the-good/articles/language-machinery>
Generative artificial intelligence is a headspace and a technology—as
much an event playing out in our minds as it is a material reality
emerging at our fingertips. Fast and fluent, AI writing and image-making
machines inspire in us visions of doomsday or a radiant posthuman
future. They raise existential questions about themselves and ourselves.
And, not least, they should lead us to reconsider certain neglected
thinkers of recent intellectual history.
Consider a few of the bolder claims made by experts. Two years ago,
Blaise Agüera y Arcas, vice president of Google Research, had already
declared the end of the animal kingdom’s monopoly on language on the
strength of Google’s experiments with large language models. LLMs, he
argued, “illustrate for the first time the way that language
understanding and intelligence can be dissociated from all the embodied
and emotional characteristics we share with each other and with many
other animals.”^1
<https://hedgehogreview.com/issues/markets-and-the-good/articles/language-machinery#>
In a similar vein, the Stanford University computer scientist
Christopher Manning has argued that if “meaning” constitutes
“understanding of the network of connections between linguistic form and
other things,” be they “objects in the world or other linguistic forms,”
then “there can be no doubt” that LLMs can “learn meanings.”^2
<https://hedgehogreview.com/issues/markets-and-the-good/articles/language-machinery#>
Again, the point is that humans have company. The philosopher Tobias
Rees (among many others) has gone further, arguing that LLMs constitute
a “far-reaching, epoch-making philosophical event” on par with the shift
from the premodern conception of language as a divine gift to the modern
notion of language as a distinctly human trait, even our defining one.
On Rees’s telling, engineers at OpenAI, Google, and Facebook have become
the new Descartes and Locke, “[rendering] untenable the idea that only
humans have language” and thereby undermining the modern paradigm those
philosophers inaugurated. LLMs, for Rees at least, signal modernity’s
end.^3
<https://hedgehogreview.com/issues/markets-and-the-good/articles/language-machinery#>
Rees calls the AI developers “philosophical laboratories” because “they
disrupt the old concepts/ontologies we live by.”^4
<https://hedgehogreview.com/issues/markets-and-the-good/articles/language-machinery#>
That characterization is somewhat misleading. Those disruptive engineers
do not constitute a philosophical school in a traditional sense, since
they aren’t advancing a /positive/ philosophical program (such as
explicit new theories of language or consciousness). And by their own
admission, they lack important answers about how and why LLMs work. Yet
unquestionably, the technology is blazing some kind of trail—whither, no
one knows for sure—leaving us to philosophize in its wake, just as
Manning, Agüera y Arcas, and Rees have done.
In this respect, current debates about writing machines are not as fresh
as they seem. As is quietly acknowledged in the footnotes of scientific
papers, much of the intellectual infrastructure of today’s advances was
laid decades ago. In the 1940s, the mathematician Claude Shannon
demonstrated that language use could be both described by statistics and
/imitated/ with statistics, whether those statistics were in human heads
or a machine’s memory. Shannon, in other words, was the first
statistical language modeler, which makes ChatGPT and its ilk his
distant brainchildren. Shannon never tried to build such a machine, but
some astute early readers of his work recognized that computers were
primed to translate his paper-and-ink experiments into a powerful new
medium. In writings now discussed largely in niche scholarly and
computing circles, these readers imagined—and even made preliminary
sketches of—machines that would translate Shannon’s proposals into
reality. These readers likewise raised questions about the meaning of
such machines’ outputs and wondered what the machines revealed about
/our/ capacity to write.
The current barrage of commentary has largely neglected this backstory,
and our discussions suffer for forgetting that issues that appear novel
to us belong to the mid-twentieth century. Shannon and his first readers
were the original residents of the headspace in which so many of us now
find ourselves. Their ambitions and insights have left traces on our
discourse, just as their silences and uncertainties haunt our exchanges.
If writing machines constitute a “philosophical event” or a “prompt for
philosophizing,” then I submit that we are already living in the event’s
aftermath, which is to say, in Shannon’s aftermath. Amid the rampant
speculation about a future dominated by writing machines, I propose that
we turn in the other direction to listen to field reports from some of
the first people to consider what it meant to read and write in
Shannon’s world.
[...]
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