What Kind of Mind Does ChatGPT Have? Large language models seem startlingly intelligent. But what’s really happening under the hood?

“What kinds of new minds are being released into our world? The response to ChatGPT, and to the other chatbots that have followed in its wake, has often suggested that they are powerful, sophisticated, imaginative, and possibly even dangerous. But is that really true? If we treat these new artificial-intelligence tools as mysterious black boxes, it’s impossible to say. Only by taking the time to investigate how this technology actually works—from its high-level concepts down to its basic digital wiring—can we understand what we’re dealing with. We send messages into the electronic void, and receive surprising replies. But what, exactly, is writing back? [..]

The computer scientists behind systems like ChatGPT found a clever solution to this problem [generating enough rules for the software to address all possible user requests]. They equipped their programs with the ability to devise their own rules, by studying many, many examples of real text. We could do the same with our program. We start by giving it a massive rule book filled with random rules that don’t do anything interesting. The program will then grab an example passage from a real text, chop off the last word, and feed this truncated passage through its rule book, eventually spitting out a guess about what word should come next. It can then compare this guess to the real word that it deleted, allowing it to calculate how well its rules are currently operating. For example, if the program feeds itself an excerpt of Act III of “Hamlet” that ends with the words “to be or not to,” then it knows the correct next word is “be.” If this is still early in the program’s training, relying on largely random rules, it’s unlikely to output this correct response; maybe it will output something nonsensical, like “dog.” But this is O.K., because since the program knows the right answer—“be”—it can now nudge its existing rules until they produce a response that is slightly better. Such a nudge, accomplished through a careful mathematical process, is likely to be small, and the difference it makes will be minor. If we imagine that the input passing through our program’s rules is like the disk rattling down the Plinko board on “The Price Is Right,” then a nudge is like removing a single peg—it will change where the disk lands, but only barely.

The key to this strategy is scale. If our program nudges itself enough times, in response to a wide enough array of examples, it will become smarter. If we run it through a preposterously large number of trials, it might even evolve a collection of rules that’s more comprehensive and sophisticated than any we could ever hope to write by hand.

The numbers involved here are huge. Though OpenAI hasn’t released many low-level technical details about ChatGPT, we do know that GPT-3, the language model on which ChatGPT is based, was trained on passages extracted from an immense corpus of sample text that includes much of the public Web. This allowed the model to define and nudge a lot of rules, covering everything from “Seinfeld” scripts to Biblical verses. If the data that define GPT-3’s underlying program were printed out, they would require hundreds of thousands of average-length books to store. [..]

A user types a prompt into a chat interface; this prompt is transformed into a big collection of numbers, which are then multiplied against the billions of numerical values that define the program’s constituent neural networks, creating a cascade of frenetic math directed toward the humble goal of predicting useful words to output next. The result of these efforts might very well be jaw-dropping in its nuance and accuracy, but behind the scenes its generation lacks majesty. The system’s brilliance turns out to be the result less of a ghost in the machine than of the relentless churning of endless multiplications. [..]

The idea that programs like ChatGPT might represent a recognizable form of intelligence is further undermined by the details of their architecture. Consciousness depends on a brain’s ability to maintain a constantly updated conception of itself as a distinct entity interacting with a model of the external world. The layers of neural networks that make up systems like ChatGPT, however, are static: once they’re trained, they never change. ChatGPT maintains no persistent state, no model of its surroundings that it modifies with new information, no memory of past conversations. It just cranks out words one at a time, in response to whatever input it’s provided, applying the exact same rules for each mechanistic act of grammatical production—regardless of whether that word is part of a description of VCR repair or a joke in a sitcom script. It doesn’t even make sense for us to talk about ChatGPT as a singular entity. There are actually many copies of the program running at any one time, and each of these copies is itself divided over multiple distinct processors (as the total program is too large to fit in the memory of a single device), which are likely switching back and forth rapidly between serving many unrelated user interactions. Combined, these observations provide good news for those who fear that ChatGPT is just a small number of technological improvements away from becoming HAL, from “2001: A Space Odyssey.” It’s possible that super-intelligent A.I. is a looming threat, or that we might one day soon accidentally trap a self-aware entity inside a computer—but if such a system does emerge, it won’t be in the form of a large language model. [..]

Based on what we’ve learned so far, ChatGPT’s functionality seems limited to, more or less, writing about combinations of known topics using a combination of known styles, where “known” means that the program encountered a given topic or style enough times during its training. Although this ability can generate attention-catching examples, the technology is unlikely in its current form to significantly disrupt the job market. Much of what occurs in offices, for example, doesn’t involve the production of text, and even when knowledge workers do write, what they write often depends on industry expertise and an understanding of the personalities and processes that are specific to their workplace. Recently, I collaborated with some colleagues at my university on a carefully worded e-mail, clarifying a confusing point about our school’s faculty-hiring process, that had to be sent to exactly the right person in the dean’s office. There’s nothing in ChatGPT’s broad training that could have helped us accomplish this narrow task. Furthermore, these programs suffer from a trustworthiness crisis: they’re designed to produce text that sounds right, but they have limited ability to determine if what they’re saying is true. The popular developer message board Stack Overflow has had to ban answers generated by ChatGPT because, although they looked convincing, they had “a high rate of being incorrect.” Presumably, most employers will hesitate to outsource jobs to an unrepentant fabulist.

This isn’t to say that large language models won’t have any useful professional applications. They almost certainly will. But, given the constraints of these technologies, the applications will likely be more focussed and bespoke than many suspect. ChatGPT won’t replace doctors, but it might make their jobs easier by automatically generating patient notes from electronic medical-record entries. ChatGPT cannot write publishable articles from scratch, but it might provide journalists with summaries of relevant information, collected into a useful format.

[..] once we’ve taken the time to open up the black box and poke around the springs and gears found inside, we discover that programs like ChatGPT don’t represent an alien intelligence with which we must now learn to coexist; instead, they turn out to run on the well-worn digital logic of pattern-matching, pushed to a radically larger scale. It’s hard to predict exactly how these large language models will end up integrated into our lives going forward, but we can be assured that they’re incapable of hatching diabolical plans, and are unlikely to undermine our economy. ChatGPT is amazing, but in the final accounting it’s clear that what’s been unleashed is more automaton than golem.”

Full article, C Newport, The New Yorker, 2023.4.13