I’m still trying to figure out how to approach Robert Wright’s The God Test.
How LLMs work
In my previous post – How will I handle The God Test? [GT-1] – I expressed misgivings about how Wright explains the technology. Those misgivings haven’t disappeared. However, Bert Idem has published a useful review at Finite Ape in which he addresses some of those issues in detail. Specifically:
Now, about the history of AI, the story he tells is actually great and it is certainly more than what most non-technical people know about LLMs. However, there are three places where I think the framing goes wrong or at least leaves out context that matters:
- LLMs did not discover the meanings of words on their own by accident. They were designed on top of ideas from older models that were specifically trained to learn the meanings of words.
- Similarly, computer vision models didn’t find out how to “view” an image like we do. Instead, the classical CNN models were heavily inspired by biological vision itself.
- LLM weight training is simply gradient-based optimization and the process has nothing to do with evolution. Of course, we can make a parallel between any kind of change and evolution but then, in that sense, everything evolves and it is not useful to talk about evolution.
I agree with Idem on those three issues, not so sure about the history part. While I may return to some of these issues later on, this will serve as a place holder.
A Rorschach test
There’s something else going on, but I’m not quite sure how to conceptualize it. It seems to me that AI is functioning something like a Rorschach test which, as you may know, is a psychological instrument intended to elicit (potentially) revealing responses from a person. It’s a projective test.
A person is shown a series of ink blot images, like this one (generated by ChatGPT):
They are asked what that they see in the image, what it means to them. Since the image is, though not formless, its form is not that of any specific animal, vegetable, mineral, person, or anything else. It’s just a blot. Whatever the person says about the blot, however they interpret it, that must reveal something about them. Why? Because whatever they see in the blot, isn’t really there.
Broadly and crudely speaking, AI has become something of a cultural Rorschach test.
Understanding computers & LLMs
Until ChatGPT was released in late November of 2022, most people knew very little to nothing about AI. Oh, they may have seen “intelligent” computers and robots in science fiction movies, but that’s science fiction and only tangentially related to AI considered as a line of research dating back to the 1950s. Many people would have heard about IBM’s Deep Blue beating Gary Kasparov in chess in 1997 and then, in 2011, when IBM’s Watson beat Ken Jennings and Brad Rutter in Jeopardy. Those were real AI systems, standing on research extending back decades, but as far as most people were concerned, they were one-off PR stunts. Just how they worked, who cares? They’re computers, and computers are magic, no?
As far as most of us are concerned, computers are magic. Somewhere “out there” someone knows how these things work, but we don’t need to know any of that. It’s complicated, but computers do what they’re programmed to do, no? Yes, but not LLMs.
And that’s the tricky part. LLMs, large language models, aren’t like other computer systems. They aren’t programmed in the way that word processors, photo editors, or phones are programmed. LLMs aren’t programmed at all, not in the ordinary sense of programming – something I may or may not get into in a later post. As far as most users are concerned, how ChatGPT, or Claude, or Gemini work, that’s no more interesting than how a word processor works. It just does. It’s more magic.
But if you have a strong philosophical streak, if you are interested in the mind, in technology, in the technology in the future, then you may not be content with writing LLMs off as just another kind of magic. You want to know what’s going on inside, 1) because you want to know (curiosity), and 2) because you want to know how the technology is going to develop in the future (engagement). Now things get interesting? Why? Because even the people who have created the technology don’t know how it works.
Oh, they know how the transformer program works. That’s the program that creates the language model. It creates the model by performing a (certain kind of) statistical analysis of a huge body of texts, effectively the entire internet. When a person prompts the model with some statement, the model responds by a statement of its own. No one know just how the model does that. That’s a mystery, a deep black hole in the technology ecosystem.
AI as a Rorschach test
If you aren’t content to believe in magic, then you have to come up with something to fill that black hole in your, in our, understanding. This is where the Rorschach aspect of AI reveals itself. To a first approximation, what each of us uses to paper over that black hole has as much to do with ourselves as with AI.
Why do I say, “To a first approximation”? It’s a rhetorical device to get things started. It puts us all in the same boat, despite our different backgrounds. However, whatever LLMs are, they are not magic. It is possible, in principle, to construct a technical account of what they’re up to, but no one knows how to do that, yet. Not even the people in the AI labs who create these beasts.
Those of us who are trying to figure out how LLMs work have widely varying backgrounds. In particular, we have widely varied technical backgrounds and we bring those backgrounds to bear when we think about what LLMs are doing. Those backgrounds influence how we interpret the AI-blot. Wright is a journalist with a wide range of interests, including politics, international affairs, evolutionary psychology, cultural evolution, and Buddhism. As far as I can tell there isn’t much there that’s directly relevant to understanding the mechanisms of LLMs, but he’s done a lot of reading and talked with a lot of experts to fill in the gaps.
My background is quite different. While I happen to know quite a bit about cultural evolution, cognitive psychology, neuroscience, and various other things, my background in computational semantics puts me much closer to LLMs than Wright’s knowledge of evolutionary psychology puts him. Still, like him, I’ve done a lot of reading and talked with experts. In particular, I’ve been collaborating with Ramesh Viswanathan for the last three years. He’s an expert in machine vision Goethe University Frankfurt. He’s got a background in mathematics and AI that I don’t have. Still, there are things he doesn’t know, things he’s trying to figure out.
We are all making stuff up.
To some extent, then, AI is a Rorschach test about how beliefs about the human mind, and human nature. When we try to figure out how the LLM is working we’re also, if only implicitly, trying to figure out how we work, internally, as well. The whole discourse about AL alignment is as much a discourse about us as it is about AI.
The Future
And even if we knew much more about how LLMs work internally we still wouldn’t know how the technology will develop in the future. We? You, me, Robert Wright, Ramesh Viswanathan, Gary Marcus, Tyler Cowen, Geoffrey Hinton, Sam Altman, Dario Amodei, Nick Bostrom, Eliezer Yudkowsky, all of us who are trying to figure it out. We don’t know what will happen. That’s where we’re projecting like mad. We’re hallucinating, to borrow a term from AI-speak.
Thus AI is also a Rorschach test for our visions of the future. When we imagine the future of AI, we’re also imagining our future. Like the two sides of a coin, the two cannot be separated.
The tricky part, the important part, is that the future development of AI is not predestined. It depends on the choices we make, now and in the near future. We can easily and often do imagine things that will not be possible because that’s just not how the world works. But the laws of how the world works are open to a wide range of possibilities. The boundary between the possible and the impossible is fuzzy at best.
Where, and how, does Wright draw that boundary? Perhaps that’s what I’ll be trying to figure out.
More later.
No comments:
Post a Comment