Framing my discussion of The God Test, Part 1: Rorschach, reason, and whaling – [GT-3]

I’ve got to bite the bullet: I’m just going to have to go through a bunch of (preliminary) stuff before I can really engage with The God Test. My current target is to be in a position to publish a proper review of the book in 3 Quarks Daily for the week of August 9.

Rorschach Recap

I want start by recapping the Rorschach metaphor I introduced in the previous post, More on how I’m approaching The God Test – Rorschach! [GT-2]. What I like about it is that has a shape, there’s something there, but it’s not clear what. So we have little choice but to project onto it in order to (begin to) make sense of it.

First: It is a new kind of thing, an artifact we can converse with in an open-ended and natural way. The steam engine was the same kind of thing. It was an inanimate object that moved over the surface of the earth under its own power. Previously only animals (& humans as animals) had that power. So it becomes an iron horse. Just what are AIs? What’s their nature? That’s one thing.

Second: How it works is opaque. We know how to create large language models (LLMs), but we don’t know how they work. That’s new. We may not have understood the deep physics of the steam engine, but we certainly knew how they worked.

Third: We don’t know what they portend for the future. To some extent this is a function of the first two: How can we, how should we, interact. But it is also a function of the future, which is undetermined. We just don’t know.

Rhetorical force over reason

This is an argument I made in the first working paper I published after the release of ChatGPT in November of 2022: ChatGPT intimates a tantalizing future; its core LLM is organized on multiple levels; and it has broken the idea of thinking (February 6, 2023).

What do I mean by that, has broken the idea of thinking? Prior to ChatGPT it was obvious that humans could think and computers could not. [Yeah, I know, there’s Deep Blue defeating Kasparov in chess. That just changes the dates, not the argument.] The difference in performance was so obvious that the fact that we don’t really know how humans think wasn’t much of an issue. Now it is. Sure, we can still say that we can think and the AI’s can’t, but that’s just a line and without good explanations on both sides of the line, it seems a bit arbitrary, if not desperate.

I made a particular argument about Searles’ (in)famous Chinese Room thought experiment. I read it when it was first published in Brain and Behavioral Science in 1980. I wasn’t impressed. Why not? He didn’t say anything about any of the techniques used in AI or computational linguistics (CL). How could anyone possibly take that seriously?

He talked about intention, that’s how. Meaning requires intention and only living things can have intention, a remark he made at the end of the article. Without intention the most you get is syntax, but no meaning. Searle could get away with that because, in the first place, the concept of intention has a long history within philosophy – it has a subtle meaning, but that can wait for a later post – and so philosophers, his main audience, were comfortable with it. That’s one thing.

But there’s something more important, something that we can see only in retrospect, and that’s the simple fact computers very obviously could not translate from Chinese into English or into any other language. That difference carried tremendous weight. We don’t have a subtle behavioral difference between computers and humans that requires a subtle and sophisticated argument. To a first approximation, almost any argument would do. As far as I was concerned, “intention” was just a fancy word for something we don’t understand. But that’s not an argument anyone needs to take seriously. The behavioral distance is quite sufficient to carry the argument for those who insist that computers can’t and will never be able to think like humans.

Now the behavioral evidence has changed. Sure, differences remain, but the evidence is shifting. The old arguments remain and those who believed them still do so, but it’s getting harder. The need for explicit arguments grounded in explicit accounts of computers, and also brains, is growing.

Whaling and expertise

What’s an expert in machine learning and LLMs actually expert in? For some time now I’ve been arguing that investing in AI is like investing in a whaling venture where the captain and crew of the ship know all there is to know about the ship and how to handle it but know little or nothing about whales and their behavior and about navigating around the Cape Horn and in the South Pacific, where the whales live. What are the chances of that voyage being successful? Not very good.

The people who have created the current AI technology are like that captain and crew. The know how to sail the ship. But they don’t know much about language or cognition. They don’t actually know much about the human mind. Here my point is not about the fact that the models are opaque, but that human language and cognition are highly structured and they don’t believe that one needs to know (much of) anything about that not only to build AI but to make confident prediction about the future of AI.

Gary Marcus, Subbarao Kambhampati, and others have been consistently arguing that, yes, the current technology is remarkable, but we are going to have to adopt classical symbolic techniques if we are to fully develop the technology so that we have accurate and safe systems. Marcus is arguing from his knowledge of human language and cognition. As far as I can tell, Wright doesn’t take that seriously. I know that he had Marcus on his NonZero podcast, and that he lists Marcus in his acknowledgements, but that he doesn’t discuss Marcus’s ideas. I conclude that he doesn’t take that line of argument seriously.

That’s a mistake, but this is not the place to make my own arguments on this issue. My point is simply that expertise in AI is no generally construed to encompass knowledge of, expertise in, human cognition and language. I can’t see how that is going to work out well in the future.

[Note: If you’re curious about my views, on this subject, read the article linked in the first paragraph of this section. My views all over the place here at New Savanna, particularly around the work of the mathematician Miriam Yevick. Also, check out the experimental work I’ve done with LLMs.]