I thought that we would get the
super-capable kind of blue-collar robots
way before this reading and writing thing
became at least somewhat possible.
The inversion that we don’t know how
to pick parts out of a box,
but we know how to rewrite
the Pledge of Allegiance
the way Donald Trump would write it.
Those two tasks,
the robot task I thought of as much easier,
and so it would come first.
That’s a really sharp observation, Bill,
and there’s actually a thing about it,
which is Moravec’s paradox,
which is that the perceptual tasks
that look seemingly easier for humans
are actually much harder for AI,
compared to say, a chess game,
which is harder for us,
which is actually easier for AI.
In fact, that inversion happens
in other ways as well.
I’m currently proposing this thought,
a generative AI paradox,
where it might be that somehow
generative capabilities are stronger
than the understanding capabilities,
which again, may be
a little bit inversed version
of how humans tend to be able to
understand amazing novels,
but we find it harder to write.
And again, paintings we can appreciate
without being able to
generate those great paintings.
Whereas right now, it looks as if
these capabilities are a little bit reversed.
Because when you look
at DALL-E 2, DALL-E 3,
it’s able to generate amazing images,
but then there’s no amazing current AI
that truly understands the image content
in a way that surprises us.
They are lagging behind, weirdly enough,
so it might be that between generation
and understanding capabilities,
there’s something interestingly
reversed about it.
But it’s almost a paradox
that in the near term,
the risk is that we overuse it,
like take advice from it,
and it would be wrong.
In the long run,
maybe the fear is that it’s too good.
In your talk, you expressed that:
because it’s such a different kind of intelligence,
it’s both the “smartest” by some definitions,
and the “dumbest,”
like in medical applications.
My foundation would love to have
the equivalent of a doctor
for poor people who can
never get access to that expertise.
But how do we test that?
How cautious do we need to be
when we have a hard time characterizing
what we’ve got here?
Part of me wonders
whether that hypothetical, AGI-like capability,
if it did exist, and if it’s so good,
can it actually answer
some of the hardest questions
that humanity faces like climate change?
Again, some people disagree,
what is it doing?
And can AI really help answer
those kinds of questions
in such a satisfactory,
such a high quality, reliable manner?
If AGI really truly comes, I don’t know.
Is it actually going to be good enough
for that kind of purpose?
That relates to your wish about doctors.
We somehow need to create
these AI technologies that can
benefit humanity better,
but are they actually
going to be super-reliable?
How much of a gap will there be?
I think that’s very uncertain right now.
We want to believe that
it’s around the corner, in some sense,
especially those technologies
that can be really beneficial for humanity.
In my twenties, I definitely thought,
like for language translation,
that there would just be
a set of processing steps.
This is a noun, this is a verb,
and that it would be
an explicit piece of logic.
When Google found that
their logic approach,
which was a pretty large team, hundreds,
was just beaten
by their neural net approach –
that was the beginning
of this mind-blowing thing.
So yes, we are often naive,
particularly about what it takes
to match human capability.
I don’t know for sure
whether we’re really around the corner,
or we are just opening the can
of a lot of curious, fundamental questions
about intelligence,
and it might turn out to be that
it’s a lot messier than we expected.
It’s a lot harder than we expected.
Then building really reliable,
trustworthy AI turns out
to be harder than we thought.
I’m not necessarily saying
that that is truth, either.
We just don’t know how far
or close we are.
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