The Story of My Intellectual Life
In the early 1970s I discovered that “Kubla Khan” had a rich, marvelous, and fantastically symmetrical structure. I'd found myself intellectually. I knew what I was doing. I had a specific intellectual mission: to find the mechanisms behind “Kubla Khan.” As defined, that mission failed, and still has not been achieved some 40 odd years later.
It's like this: If you set out to hitch rides from New York City to, say, Los Angeles, and don't make it, well then your hitch-hike adventure is a failure. But if you end up on Mars instead, just what kind of failure is that? Yeah, you’re lost. Really really lost. But you’re lost on Mars! How cool is that!
Of course, it might not actually be Mars. It might just be an abandoned set on a studio back lot.
That's a bit metaphorical. Let's just say I've read and thought about a lot of things having to do with the brain, mind, and culture, and published about them as well. I've written a bunch of academic articles and two general trade books, Visualization: The Second Computer Revolution (Harry Abrams1989), co-authored with Richard Friedhoff, and Beethoven's Anvil: Music in Mind and Culture (Basic Books 2001). Here's what I say about myself at my blog, New Savanna. I've got a conventional CV at Academia.edu. I've also written a lot of stuff that I've not published in a conventional venue. I think of them as working papers. I've got them all at Academia.edu. Some of my best – certainly my most recent – stuff is there.
Posts
Wiki Contributions
Yes, the matching of "mental content" between one mind and another is perhaps the central issue in semantics. You might want to take a look at Warglien and Gärdenfors, Semantics, conceptual spaces, and the meeting of minds:
Abstract: We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions.
You can find those ideas further developed in Gärdenfors' 2014 book, Geometry of Meaning, chapters 4 and 5, "Pointing as Meeting of Minds" and "Meetings of Minds as Fixpoints," respectively. In chapter 5 he develops four levels of communication.
2Around the corner I've got a post that makes use of this post in the final section: Relationships among words, metalingual definition, and interpretability.
YES.
At the moment the A.I. world is dominated by an almost magical believe in large language models. Yes, they are marvelous, a very powerful technology. By all means, let's understand and develop them. But they aren't the way, the truth and the light. They're just a very powerful and important technology. Heavy investment in them has an opportunity cost, less money to invest in other architectures and ideas.
And I'm not just talking about software, chips, and infrastructure. I'm talking about education and training. It's not good to have a whole cohort of researchers and practitioners who know little or nothing beyond the current orthodoxy about machine learning and LLMs. That kind of mistake is very difficult to correct in the future. Why? Because correcting it means education and training. Who's going to do it if no one knows anything else?
Moreover, in order to exploit LLMs effectively we need to understand how they work. Mechanistic interpretability is one approach. But: We're not doing enough of it. And by itself it won't do the job. People need to know more about language, linguistics, and cognition in order to understand what those models are doing.
Whatever one means by "memorize" is by no means self-evident. If you prompt ChatGPT with "To be, or not to be," it will return the whole soliloquy. Sometimes. Other times it will give you an opening chunk and then an explanation that that's the well known soliloquy, etc. By poking around I discovered that I could elicit the soliloquy by giving it prompts that consisting of syntactically coherent phrases, but if I gave it prompts that were not syntactically coherent, it didn't recognize the source, that is, until a bit more prompting. I've never found the idea that LLMs were just memorizing to be very plausible.
In any event, here's a bunch of experiments explicitly aimed at memorizing, including the Hamlet soliloquy stuff: https://www.academia.edu/107318793/Discursive_Competence_in_ChatGPT_Part_2_Memory_for_Texts_Version_3
I was assuming lots of places widely spread. What I was curious about was a specific connection in the available data between the terms I used in my prompts and the levels of language. gwern's comment satisfies that concern.
By labeled data I simply mean that children's stories are likely to be identified as such in the data. Children's books are identified as children's books. Otherwise, how is the model to "know" what language is appropriate for children? Without some link between the language and a certain class of people it's just more text. My prompt specifies 5-year olds. How does the model connect that prompt with a specific kind of language?
Of course, but it does need to know what a definition is. There are certainly lots of dictionaries on the web. I'm willing to assume that some of them made it into the training data. And it needs to know that people of different ages use language at different levels of detail and abstraction. I think that requires labeled data, like children's stories labeled as such.
"Everyone" has known about holography since "forever." That's not the point of the article. Yevick's point is that there are two very different kinds of objects in the world and two very different kinds of computing regimes. One regime is well-suited for one kind of object while the other is well-suited for the other kind of object. Early AI tried to solve all problems with one kind of computing. Current AI is trying to solve all problems with a different kind of computing. If Yevick was right, then both approaches are inadequate. She may have been on to something and she may not have been. But as far as I know, no one has followed up on her insight.
Beating benchmarks, even very difficult ones, is all find and dandy, but we must remember that those tests, no matter how difficult, are at best only a limited measure of human ability. Why? Because they present the test-take with a well-defined situation to which they must respond. Life isn't like that. It's messy and murky. Perhaps the most difficult step is to wade into the mess and the murk and impose a structure on it – perhaps by simply asking a question – so that one can then set about dealing with that situation in terms of the imposed structure. Tests give you a structured situation. That's not what the world does.
Consider this passage from Sam Rodiques, "What does it take to build an AI Scientist"
Right.
How do we put o3, or any other AI, out in the world where it can roam around, poke into things, and come up with its own problems to solve? If you want AGI in any deep and robust sense, that's what you have to do. That calls for real agency. I don't see that OpenAI or any other organization is anywhere close to figuring out how to do this.