In “The Evolution of Cognition” (1990) David Hays and I argued that the long-term evolution of human culture flows from the architectural foundations of thought and communication: first speech, then writing, followed by systematized calculation, and most recently, computation. In discussing the importance of the computer, we remark:
One of the problems we have with the computer is deciding what kind of thing it is, and therefore what sorts of tasks are suitable to it. The computer is ontologically ambiguous. Can it think, or only calculate? Is it a brain or only a machine?
The steam locomotive, the so-called iron horse, posed a similar problem for people at Rank 3. It is obviously a mechanism and it is inherently inanimate. Yet it is capable of autonomous motion, something heretofore only within the capacity of animals and humans. So, is it animate or not? Perhaps the key to acceptance of the iron horse was the adoption of a system of thought that permits separation of autonomous motion from autonomous decision. The iron horse is fearsome only if it may, at any time, choose to leave the tracks and come after you like a charging rhinoceros. Once the system of thought had shaken down in such a way that autonomous motion did not imply the capacity for decision, people made peace with the locomotive.
The computer is similarly ambiguous. It is clearly an inanimate machine. Yet we interact with it through language; a medium heretofore restricted to communication with other people. To be sure, computer languages are very restricted, but they are languages. They have words, punctuation marks, and syntactic rules. To learn to program computers we must extend our mechanisms for natural language.
Back then the question was mostly an academic one. That is to say, it had little purchase on the daily lives of most people. Consequently, however intently a relatively small cadre of academics debated the question, it was of relatively little interest to ordinary people.
That changed quite dramatically when, late in November 2022, OpenAI released ChatGPT on the web where anyone with an internet account and a web browser to access it and play with it. Overnight millions did so. The question of whether or not this thing was dead or alive, that is inanimate or animate, mindless or conscious, impressed itself on millions of users. It was no longer an academic question. It was a live question, and to some it was even existential: How long before this, this, this THING, goes rogue and destroys us?
Fortunately, that has not happened. We are all alive to debate the issue. And we do so, using terms that existed long prior to the release of ChatGPT. That’s a problem.
Back in the days when the questions of computer intelligence, of computational minds, and of artificial consciousness were academic, we had no examples of devices whose behavior was phenomenologically problematic. Computers played an inferior game of chess, though that ended in 1997 when IBM’s Deep Blue defeated Gary Kasparov, and were at best halting, clumsy, and relentlessly stupid with language. You could take whatever position you wished about the possibility of artificial intelligence (AI), artificial general intelligence (AGI), a term coined early in the millennium, or even superintelligence, a term popularized by Nick Bostrom’s 2013 book of that title, when it came to actual devices, it was clear that they were not intelligent or conscious.
ChatGPT could “talk,” just like a human being, or so much so that one had to work hard to find a meaningful difference. Many users proceeded as though there were no meaningful difference. Now the question of AI, AGI, or even superintelligence has taken on a different valence. Any chatbot “knows” a wider range of subjects than even the most brilliant and learned of humans. In that specific and limited sense, these things are superintelligent. While no one, so far as I know, has claimed that these chatbots are superintelligent in the fullest sense (as in Bostrom’s book, Superintelligence) you see the problem. Don’t you?
Just as we don’t know how the human mind, the human brain, works. So we don’t know how these chatbots, these large language models (LLMs) work. Do they work like we do or not? At some level obviously not. Computer hardware is quite different from biological “wetware” (brains). But when we consider function, that we don’t know. As long as we stick to symbolic behavior, the ability to write natural language, and increasingly to speak it, to write computer code, and to worth with mathematics, our ability to distinguish the real, that is, humans, from the artificial, that is, computers, is problematic. Thus the terms, the concepts, we have inherited from the pre-GPT-3 era are no longer adequate to problems we now face.
That is what makes the question of computer intelligence both so urgent and so deeply problematic. For the moment, I’m fond of a formulation Steven Harnad expressed somewhere on the web: The behavior chatbots exhibit is astonishing when you consider the fact that they don’t understand another. Those words are mine, but the thought is Harnad’s. This formulation, however, is no more than a stop gap.
We need new concepts, and new conceptual framework. That’s easier called for than accomplished. The accomplishment will take an intellectual generation.
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I’ve made this general point, but at greater length and in different terms in a paper I finished in January of 2023: ChatGPT intimates a tantalizing future; its core LLM is organized on multiple levels; and it has broken the idea of thinking.
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