## 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.

I’ve been thinking about ordinary arithmetic computation in this context. We know that models have trouble with it. The issue interests me because arithmetic calculation has well-understood procedures. We know how people do it. And by that I mean that there’s nothing important about the process that’s hidden, unlike our use of ordinary language. The mechanisms of both sentence-level grammar and discourse structure are unconscious.

It’s pretty clear to me that arithmetic requires episodic structure, to introduce a term from old symbolic-systems AI and computational linguistics. That’s obvious from the fact that we don’t teach it to children until grammar school, which is roughly episodic level cognition kicks in (see the paper Hays and I did, Principles and Development of Natural Intelligence).

Arithmetic is not like ordinary language is for humans, which comes to us naturally without much specific training. Fluency in arithmetic requires years of drill. First the child must learn to count; that gives numbers meaning. Once that is well in hand, children are drilled in arithmetic tables for the elementary operations, and so forth. Once this is going smoothly one learns the procedures multiple-digit addition and subtraction, multiple-operand addition and then multiplication and division. Multiple digit division is the most difficult because it requires guessing, which is then checked by actual calculation (multiplication followed by subtraction).

Why do such intellectually simple procedures require so much drill? Because each individual step must be correct. You can’t just go straight ahead. One mistake anywhere, and the whole calculation is thrown off.

Whatever a model is doing in inference mode, I doubt it’s doing anything like what humans do. Where would it pick that up on the web?

I don’t know what’s going on inside model in inference mode, but I’d guess it’s something like this: The inference engine ‘consumes’ a prompt, which moves it to some position in its state space.

It has a number of possibilities for moving to a new position.

It picks one and emits a word.

Is it finished? If so, stop. If not, return to 1.

And so it moves through its state space in a single unbroken traversal. You can’t do arithmetic that way. You have to keep track of partial results and stop to retrieve them so you can integrate them into the ongoing flow of the calculation.

So now the question is: What other kinds of tasks require the computational style that arithmetic does? Perhaps generating a long strong of coherent prose does.

Let me think about that for awhile.