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Teleoperated Humans
The classic picture of AIs doing things in the world is robots, but I
think a more realistic picture of the near future is computers
telling people what to do. Leaning into the way the world has become
very scifi, we could call this "teleoperating" people. Many things
that are hard for robots are very easy for people, there are strong
economic reasons that push towards teleoperation, and this bypasses
many legal and social limitations on what AI can do. We should expect
this to lead to large and rapid changes in the physical world.
One of the most widespread examples today is driving. I put my
destination into the GPS, and it tells me what to do. I handle the
low-level physical motions and responding to the local circumstances;
the GPS has a broader view of the world and handles the strategy.
When I think about why this happened much earlier than the huge amount
of "teleoperation" I expect to see soon, a few
factors. Driving is a major human activity, so it was worth making
navigation software at a time when AI wasn't very good yet, even
though this meant a ton of human hours going into building the system.
It was also a place where the strategic component was a very strong
fit for automation. You can memorize
the map with enough work, but even then you won't have real-time
street-by-street traffic information. AI solved this problem so well
we don't even
call it "AI" anymore. On the other hand, driving is a realtime
control problem in an unconstrained environment where people die if
you screw up and you can't even always safely stop. This makes it
hard to automate, but also would make it impractical for an AI to
guide non-drivers through the process. The only reason Uber etc have
been able to commodify driving as they have is that so many people
already know how to drive.
Thinking about where else we might see this, most AI use today looks a
lot like management.
You figure out what you want it to do, and describe in detail. It
asks you some questions up front and others while it works. After
some churning you get some a work product to assess. Maybe there's
more back-and-forth, or maybe it's good as is. You set strategy and
give context; the AI handles the implementation. Today's AI is
normally only applied to the implementation to the extent that the
task can happen fully within the computer. In cases when the AI can't
physically, legally, or intellectually do something, the most
efficient path to completing the task will often be the for the AI to
handle strategy while delegating to a human to fill these gaps.
To illustrate what this delegation pattern can look like, let's look at how I
recently got my Whistle Synth
app into the Mac App Store.
At a high level, I set the strategy: "Can you walk me through the
process of getting this into the Mac App Store?" But everything after
that was either handled by the AI or delegated back to me. It handled
included figuring out what tasks needed to be done, modifying the
implementation to be compatible with the App Store restrictions,
building the app, and giving me instructions. And then it delegated
to me to record a demo video involving whistling (physical), register
as a Mac Developer (legal), and clean up its App Store description
(intellectual).
This was mostly pure instruction-following on my part: I was being
teleoperated. Here's one example:
This was relatively mindless work for me. Just like being navigated
through a city I don't expect to return to, I didn't bother trying to
learn how this worked. I was loosely paying attention to make sure I
wasn't doing anything dumb, but for future more capable systems I
expect people to stop attending even that little.
Once it finished walking me through submission I had to wait a few
days for review. It was accepted in the first round with no reviewer
comments. This is a pretty big deal: App Store rules are notoriously
complex, the reviewers very picky, and as a first-time amateur Mac
developer there's n