Sarah Myers West is the managing director of New York University’s AI Now Institute, which studies the social implications of artificial intelligence. As West explains, ChatGPT is built on more than 60 years of chatbot innovation, starting with the ELIZA program, created at the Massachusetts Institute of Technology in the late 1960s, and including the popular SmarterChild bot on AOL Instant Messenger in the 2000s. West expects this latest version to affect certain industries significantly—but, she says, as uncanny as ChatGPT’s emulation of human writing is, it’s still capable of only a very small subset of functions previously requiring human intelligence. This isn’t to say it represents no meaningful problems, but to West, its future—and the future of artificial intelligence altogether—will be shaped less by the technology itself and more by what humanity does to determine its use.

Sarah Myers West: Artificial intelligence has been around as a field for almost 80 years now, but its meaning has changed a lot along the way.

In its early days, AI focused on what we’d call “expert systems”—technologies that would replicate human intelligence in certain ways. Now, what we refer to as AI is very different—largely, an array of data-centric technologies that, in order to work effectively, rely on a couple of things that didn’t really exist before.

The first is massive amounts of data.

This was enabled by the internet boom of the 2010s, when tech companies developed the capabilities to leverage the production of data on a huge scale—that is, to develop systems that could look for patterns in extremely large data sets. In this sense, when we talk about AI today, it’s essentially what people were talking about as big data starting in the 1990s.

The second thing these systems rely on is massive amounts of computational power to process all this data.

Overall, what this means is that AI, as a field, has become increasingly dependent on the resources of a small number of big tech companies that have built or acquired these two things: huge data sets and huge computational power.

What it doesn’t really mean, though is any close replication of human intelligence. So although it’s very effective at doing a small subset of tasks, what we refer to as AI is very different from what humans are able to do.

Vyse: What’s new about ChatGPT in this history, then?

If you go back to 1966, for example, Joseph Weizenbaum developed a natural-language processing system called ELIZA, an early chatbot. ELIZA was designed specifically to mimic what it was like to be in a therapy session. So if you were to say, I had a really hard day at work, ELIZA would know to say, Tell me more about that.

When people interacted with ELIZA in 1966, they had much the same reaction as people are having to ChatGPT today—that it was this remarkable technology, In fact, Weizenbaum’s secretary—because her conversations with ELIZA felt so intimate—would ask him to close the door when she was speaking to it, even though she knew exactly how the system worked.

Another example that’s a little more recent: I remember, back in the 2000s, playing around with a chatbot called SmarterChild on AOL instant messenger that would offer essentially the same kind of interaction—you’d talk to the system about your day, and it would feel like a very intimate experience.

ChatGPT builds on those precursors, but it does so using huge amounts of data, largely culled from the internet.

“I don’t see ChatGPT being used effectively in tasks that rely on deeper levels of intelligence. I do think we’re likely to see it accelerate a general preexisting trend, though, that’s been devaluing certain categories of work—or making their human elements more mundane. For instance, where someone used to write text themselves, work that required real creativity and ingenuity, they might now be asked just to edit AI-generated text and make it incrementally more meaningful.”

“There’ve already been extensive concerns about how ChatGPT can be used for cheating in education systems, manipulation in the media, new ways to con people into giving over their information, or fraud generally—all of which are legitimate, and all of which OpenAI has acknowledged. But there’s also a deeper concern about the technology’s use by a very few companies, which is that they’ll develop their use without any clear avenue for broader public input as to how it will affect people and the public interest.”

“As to how widespread the effects will be, I honestly don’t know—and I don’t think that even the companies behind the technology necessarily know yet. They certainly haven’t discussed publicly how they intend to make it profitable. Which I think is the critical issue at this juncture: There just isn’t very much transparency from those who’re developing this technology, and those who’ll be deploying it, about where it’s heading.”