Tobias Rees, Non-Human Words: On GPT-3 as a Philosophical Laboratory, Dædalus, Spring 2020.
In [Saussure's] words, “language is a system of signs that expresses ideas.”
Put differently, language is a freestanding arbitrary system organized by an inner combinatorial logic. If one wishes to understand this system, one must discover the structure of its logic. De Saussure, effectively, separated language from the human. There is much to be said about the history of structuralism post de Saussure.
However, for my purposes here, it is perhaps sufficient to highlight that every thinker that came after the Swiss linguist, from Jakobson (who developed Saussure’s original ideas into a consistent research program) to Claude Lévi-Strauss (who moved Jakobson’s method outside of linguistics and into cultural anthropology) to Michel Foucault (who developed a quasi-structuralist understanding of history that does not ground in an intentional subject), ultimately has built on the two key insights already provided by de Saussure: 1) the possibility to understand language, culture, or history as a structure organized by a combinatorial logics that 2) can be–must be–understood independent of the human subject.
GPT-3, wittingly or not, is an heir to structuralism. Both in terms of the concept of language that structuralism produced and in terms of the antisubject philosophy that it gave rise to. GPT-3 is a machine learning (ML) system that assigns arbitrary numerical values to words and then, after analyzing large amounts of texts, calculates the likelihood that one particular word will follow another. This analysis is done by a neural network, each layer of which analyzes a different aspect of the samples it was provided with: meanings of words, relations of words, sentence structures, and so on. It can be used for translation from one language to another, for predicting what words are likely to come next in a series, and for writing coherent text all by itself.
GPT-3, then, is arguably a structural analysis of and a structuralist production of language. It stands in direct continuity with the work of de Saussure: language comes into view here as a logical system to which the speaker is merely incidental.
That view has some similarity with the one I advanced on Pages 15-19 of my 2020 working paper about GPT-3.
Moreover,
All prior structuralists were at home in the human sciences and analyzed what they themselves considered human-specific phenomena: language, culture, history, thought. They may have embraced cybernetics, they may have conducted a formal, computer-based analysis of speech or art or kinship systems. And yet their focus was on things human, not on machines. GPT-3, in short, extends structuralism beyond the human.
The second, in some ways even more far-reaching, difference is that the structuralism that informs LLMs like GPT-3 is not a theoretical analysis of something. Quite to the contrary, it is a practical way of building things. If up until the early 2010s the term structuralism referred to a way of analyzing, of decoding, of relating to language, then now it refers to the actual practice of building machines “that have words.”
A new ontology?
Machine learning engineers in companies like OpenAI, Google, Facebook, or Microsoft have experimentally established a concept of language at the center of which does not need to be the human, either as a knowing thing or as an existential subject. According to this new concept, language is a system organized by an internal combinatorial logic that is independent from whomever speaks (human or machine). Indeed, they have shown, in however rudimentary a way, that if a machine discovers this combinatorial logic, it can produce and participate in language (have words). By doing so, they have effectively undermined and rendered untenable the idea that only humans have language–or words.
What is more, they have undermined the key logical assumptions that organized the modern Western experience and understanding of reality: the idea that humans have what animals and machines do not have, language and logos. [...]
In fact, the new concept of language–the structuralist concept of language–that they make practically available makes possible a whole new ontology.
What is this new ontology? Here is a rough, tentative sketch, based on my current understanding.
By undoing the formerly exclusive link between language and humans, GPT-3 created the condition of the possibility of elaborating a much more general concept of language: as long as language needed human subjects, only humans could have language. But once language is understood as a communication system, then there is in principle nothing that separates human language from the language of animals or microbes or machines.
A brave new world?
Language, almost certainly, is just a first field of application, a first radical transformation of the human provoked by experimental structuralism. That is, we are likely to see the transformation of aspects previously thought of as exclusive human qualities–intelligence, thought, language, creativity–into general themes: into series of which humans are but one entry.
What will it mean to be surrounded by a multitude of non-human forms of intelligence? What is the alternative to building large-scale collaborations between philosophers and technologists that ground in engineering as well as an acute awareness of the philosophical stakes of building LLMs and other foundational models?
It is naive to think we can simply navigate–or regulate–the new world that surrounds us with the help of the old concepts. And it is equally naive to assume engineers can do a good job at building the new epoch without making these philosophical questions part of the building itself: for articulating new concepts is not a theoretical but a practical challenge; it is at stake in the experiments happening in (the West at) places like OpenAI, Google, Microsoft, Facebook, and Amazon.
Addendum, 5.17-19.22: We're not quite there yet, but Rees' final point still stands, we do need new concepts. It's not at all clear in what sense GPT-3 is "arguably a structural analysis of" language, or any kind of analysis at all, and it certainly is not a stand-alone language automaton. It does not in fact constitute a/the language system divorced from a human agent. There is only a partial separation, a distancing. We're heading in that direction, but I doubt that we'll get there on extensions of current tech alone. We're going to need something new. Neurosymbolic? Maybe.
For a different kind of analysis of GPT, but also deeper because it gets closer to the mechanism, see my working paper, GPT-3: Waterloo or Rubicon? Here be Dragons, Version 4.1.
Anthropomorphism re-imagined.
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