Seeing Like a Language Model

Last week I wrote that we’d be publishing a few excerpts from a book I’m writing about the worldview I’ve developed by writing, coding, and living with AI. Here’s the first piece, about the differences between the old (pre-GPT-3) worldview and the new.—Dan Shipper

When I say the word “intelligent,” you probably think of being rational. But language models show us that this assumption is wrong.

To us in the West, smarts are about being able to explicitly lay out what you know and why you know it. For us, the root of intelligence is logic and reason to ward off superstition and groupthink; it is clear and concise definitions to eradicate vague and wooly-headed thinking; it is formal theories that explain the hidden laws of the world around us—simple, falsifiable, and parsimonious yet general enough to tie together everything in the universe from atoms to asteroids.

Our romantic picture of ourselves as “rational animals,” as Aristotle said, has produced everything in the modern world—rockets, trains, medicines, computers, smartphones.

But this picture is incomplete. It contains a huge blind spot: It neglects the fundamental importance of intuition in all of intelligent behavior—intuition which is by nature ineffable; that is to say, not fully describable by rational thought or formal systems.

How to build a thinking machine

The best way to understand what I’m talking about is to imagine trying to build a thinking machine. How would you do it?

Let’s start with a task, something easy and basic that humans do every day. Maybe something like scheduling an appointment.

Let’s say we’re a busy executive who gets the following appointment request:

New request

From: Mona Leibnis

Hey,

I’m available Monday at 3 p.m., Tuesday at 4 p.m., Friday at 6 p.m.

When can you meet?

We want to build a machine to intelligently schedule an appointment. How would we go about it?

We’d probably start by giving our machine a couple of rules to follow:

• First, check available time slots on my calendar.

• Then, compare my open slots to the open slots on the invitee’s calendar.

• If you find one, add the appointment to the calendar.

That all seems pretty reasonable. You could definitely write a computer program to follow those rules. But there’s a problem: The rules we’ve specified so far can’t handle urgency or importance.

For example, consider a case where you desperately want to meet with someone and you’d be willing to move another appointment in order to make the time work. Now we have to introduce a new rule:

• If it is urgent that I meet with the invitee, you can reschedule a less urgent appointment in order to make the meeting happen sooner.

But this rule is incomplete because it introduces the concept of urgency without defining it. How do we know what’s urgent? Well, there must be some rules for that too. So we need to delineate them.

In order to measure urgency, we have to have some conception of the different people in your life—who your clients and potential clients are, and which clients are important or not.

Now things are starting to get hairy. In order to determine the relative importance of clients, we have to know about your business aims—and about which clients are likely to close, and which clients are likely to pay a lot of money, and which clients are likely to stay on for a long time. And don’t forget—which clients were introduced by an important friend whom you need to impress, so while they may not be directly responsible for a lot of revenue, they’re still a priority.

This is only a taste of the rules we’d have to define in order to build an adequate automatic scheduling system. And that’s just for dealing with calendars!

The problem that we’re finding is that it’s very hard to lay everything out explicitly—because everything is interconnected. To paraphrase the late astronomer Carl Sagan: If you wish to schedule a meeting from scratch, you must first define the universe.

The old, Western worldview

This approach—which seemed most natural to us—is the exact one that the first generation of artificial intelligence researchers took to try to build AI.

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