Hello, thanks for spending some
time with me today, I want to
start this talk out by saying
that I am truly honored to
receive the award that's
associated with this talk. It's
been a long career. And I've
worked very hard across that
period of time. But the truth of
the matter is that I have always
taken an approach of ruthlessly
working on whatever it is I
thought was interesting or cool
or especially fun at any given
time. And so when that's, that's
where the fun and the title here
is coming from. And so it's
particularly gratifying to find
out that, in fact, other people
appreciate the results that I've
been able to get. When I was
feeling like I was doing this to
have fun. All right. Now winning
an award like this on inevitably
tends to make you think a little
bit about the past. And I
certainly did that. But I'm not
very interested in the past, I
find the future much more
interesting. And so I'm not
going to do a retrospective of
my work here, if that's what you
came for sorry. Instead, I
really want to talk about the
future of interactive
technology. I know there's not
the future of all of HCI. I'm
interested in the technology
part of that it's not the only
part of it HCI work that I do.
But that's always been sort of
my interest in my concentration.
And so that's what this talk is
going to be about. And my first
thought about the future of
interactive technology is really
that well, the future of
interactive technology has
changed some over the years.
Right? Some of our past futures
have in fact arrived. So video
teleconferencing boy has that
arrived. We are all doing that
today, right now, my smartwatch
that has arrived that you see
here. And in fact, I was able to
take this watch back to 1981, where
this cover was done as a joke,
this would be the fastest
computer on the planet. And we
see this cute little robot
scampering around the floor here
that looks a lot like a Roomba
on that happened. Okay, so some
of our past futures have
arrived. But a lot of our past
futures Well, not so much. We
are not flying around in flying
cars, we do not have the
jetpacks that we all would
really like to be flying around
in. We don't live under
transparent domes. We don't ride
around on monorails. I don't
know what this kid was supposed
to be doing. But we whatever it
is, we're not doing that. Okay.
And so really, a lot of our past
futures didn't show up. And this
means not too surprisingly, that
we're not really particularly
great at predicting the future.
But I think it's still important
to think about the future, even
if we shouldn't have an
expectation that we can predict
it very well. Right. And so
today, what I'm going to do is
to take a little slice out of
this very big and hard topic of
the future and the future of
interactive technology. And in
the end, all I really want to do
is suggest that you should be
thinking a little differently
about the future now that we
have thought about the future in
the past, and that there are
some systematic reasons for why
we should do that. Okay, I will
try to be comprehensive, I'm not
going to try to predict much
very specifically, I'm really
just going to focus on a few
exemplars that I think
illustrate the kinds of points
that I think we should think
about differently, thinking into
the future now than we had
thinking into the future in the
past. So I'm going to talk about
some new approaches to old
problems. As one way to do this,
I want to talk about some old
approaches to new problems.
Somewhere along in here, I may
not do anything new at all, but
maybe I will. But before we do
that, I think we need to talk
about what is the elephant in
the room for almost any
discussion of future technology.
If we look at it, probably the
biggest agent of change has
really changed remarkably little
over time. And that giant agent
of change has been the
exponential growth of computing
that has been predicted and
predicted very well by Moore's
law. Okay, and so here we have
sort of a schematic chart that
represents this huge growth in
computing power that is
predicted by Moore's law. Now I
can hear part of the audience
here saying, Well, wait, wait,
wait, hold on. clockspeeds, stop
doubling years back. Isn't
Moore's Law dead?
Okay. Well, the short answer is,
well, actually, no, Moore's law
is not dead. So this chart here
shows Moore's Law over a
remarkably long period of time.
And if you look at the right
hand side, here, it ends in
2020. So this is relatively
recent data. And remember, this
is a log scale on the y axis
here. So this is exponential
growth. And no, it hasn't
stopped. Right. Now that said,
we do need to recognize that it
is going to be it is and is
going to be harder to make use
of computing power in the future
than it It has been in the past
in the past, we had clockspeed
doublings. And that's not happening
anymore. That meant that we can
take exactly the same,
essentially exactly the same
code, and it ran twice as fast
two years from now than it does
now. Right. And that isn't
happening anymore, we're getting
doubling of transistors, but
it's not going into doubling of
clock speeds, it's going into
number of processors that we can
use simultaneously. And in fact,
we can see that in this chart,
if you are able to get in really
close to this, you would be able
to see that the top seven points
on this chart are actually GPUs,
okay, and those are architected
quite differently, and requires
that you program them quite
differently in order to get the
computational power out of them.
So it's harder to make use of
the increase in power than it
was before. And that is
definitely true. But even if you
take GPUs out of the picture,
then and so this is a different
chart that's just looking at
conventional CPUs here, and now
is running in transistors from
1000 to 50 billion on a log
scale, you can run this right up
to current times, definitely
still happening. But we do
again, need to recognize that
this can't go on absolutely
indefinitely, eventually it has
to plateau, although the
predictions that it would
plateau have been around for a
long time, sometime between here
and there, it's going to start
to slow down is what's gonna
happen. So the doubling times
are going to extend and they're
going to be less predictable.
But we are still talking about
having doubling times and an
exponential growth. So it's
still have so my response to all
this and this objection is
Moore's law is dead. But long
lived Moore's law at least for quite a
bit of time in front of us.
Alright, so let's go back to
where I was in the talk. I think
any talk about the future really
needs to consider this
exponential growth predicted by
Moore's law. Now, I know you're
thinking, really, you're going
to tell me about this. This is
the oldest news in the planet. I
have known about this since I
was 12. It is fully incorporated
into my thinking about my future
research. I'm here to today
to convince you that no,
actually you don't understand
Moore's law, that it really does
matter. And then what what you
should be thinking about should
incorporate much more thinking
about Moore's Law than it has in
the past. Okay, and why is that?
Why is it that we don't
understand Moore's Law, despite
the fact that it's been around
for a very long time? Well, the
reason is that it's very hard
for human beings to understand
exponential growth. So I have
this little schematic chart here
that represents exponential
growth. on the y axis, we have
good stuff resulting from
transistors, nice labeling, and
then I have time in this
direction on the x axis, which
I've conveniently left off the
units of. But schematically, the
important thing here is that
this is a really steep curve, it
increases very rapidly, that's
what exponential growth is
about. Okay. But as I say, it's
very hard for human beings to
understand exponential growth.
So I'm going to do two thought
experiments today to try to help
you understand Moore's Law and
the exponential growth with it
more, more fully. So thought
experiment, one starts by saying
and sort of we I think we all
recognize that there have been
absolutely monumentally huge
gains in computational power,
since the old days, I will just
pick 1965. So this is maybe what
computing look like in 1965.
Okay, computers were very big
things sequestered away in rooms
and so forth, we can compare
that with the computing power
that we have today, we can
compare both of just the
magnitude of the power but also
the impact it has. Okay, and
sort of, so think about for a
second, try to hold it in your
head for just a second, the huge
size of that change, it's
really, really big amount of
change, hold it in your head for
a second.
Now, what does it mean to double
every two years and that's what
Moore's Law predicts? Well, that
means that all of that change
that I'm asking you to hold in
your head, that magnitude of
change all of that every single
bit of that happens again. And
it happens again, in the next
two years, not the next 40
years or the next 60 years. But
it happens again, in the next
two years, every single bit of
that change. That's a lot. Okay.
And again, it's not happening
over those 60 years that it did
in the past. It's happening in
two years. It's happening
between when sophomore CS
students right now, between now
and when they graduate. It is
happening before the if you're
an academic like me, it's
happening before the end of your
next NSF grant or if you're
really an academic like me
before your next NSF grant gets
funded. Hey, all of it out
slowly. Every bit of it is
happening again. In two years,
that is big. Now let's take
let's go to thought experiment
number two. Now, this is a
thought experiment that I heard
in a talk by Douglas Engelbart
quite a few years ago. And it
starts like this. Suppose you
and everything you could can see
suddenly gets exactly 10 times
bigger. So you're in some sort
of a closed room, you and
everything you can see gets
bigger. Could you tell the
difference? Do you perceive a
change? Right? Think about that
for a second. I think the kind
of most common answer and maybe
the common sense answer is no, I
don't I don't, I don't perceive
a change. I don't I can't tell
the difference. And that's going
to turn out to be the right
answer, but not for the reason
that you think. Okay, well,
let's go a little bit of
analysis here. Well, first off
mass scales by volume, okay, so
you just got 1000 times heavier.
But the strength of your chair
depends on sort of the post in
the middle of the chair there.
And that scales, the strength of
that scales by the cross
sectional area of that, and
that's got 100 times stronger,
but relatively speaking, you
just got 10 times heavier, and
your chair is not designed to
handle that. And so your chair
collapses under the weight of
that tenfold change in the
ratio. And you might notice that
you might, okay. But in fact,
that will not seem so important
to you, because most of your
bones will break for exactly the
same reason, at least by the
time you hit the floor. And
you'll definitely notice that,
okay, except maybe you won't
notice that because it turns out
that oxygen consumption of
yourselves scales by volume, but
the cell's ability to absorb
that oxygen scales by area, you
just instantly lost 90% of your
oxygen, you will be unconscious
before you hit the floor and
dead shortly thereafter. So no,
you won't notice the change at
all, but not for the reason that
you thought. Right? Now, the
moral of the story is that large
changes have a huge impact. And
this was one order of magnitude
of change. And this is
particularly true when you have
differential scaling rates.
Okay, and I want to point out
that this is us now, okay, we
are expecting multiple orders of
change in computational power,
happening over a relatively
small number of years, not just
one order of magnitude, but
multiple orders of magnitude.
And we definitely have
differential scaling. Okay, we
have sort of the potential
provided by computing power down
at the bottom, if you were,
well, they were trying at least
an HCI, to use that power to
change. And to make things
better for people, but people's
abilities are by that scale,
really not changing at all. And
so we definitely have
differential scaling. So these
two thought experiments together
should tell us that the amount
of impact that we could expect
to see from this technological
change is incredibly large. And
it's even much much larger than
the impact that we have seen in
the past despite the fact that
that seems very large. Okay,
now, by the way, how much
computing power do we have right
now? Well, this, this might
surprise some of you. So right
now you can buy about a $1500
graphics card, not cheap, but
you can just stick it in your PC
that has Nvidia ampere
architecture chip on it that has
about 45 billion transistors, I
can tell that's a big number,
but it's a little hard to tell
what that really means. So let's
talk about speed, for instance,
or total computing power. So the
peak speed now you're not going
to get this peak speed, because
you can't get every single part
of this device to deliver all of
this computation at once. But if
you could squeeze every bit of
computation out of this device,
it's going to clock out at 330
trillion floating point
operations per second. Right,
and we can recognize that it's
large, but it's hard to
understand trillion. So let me
relate this to something else.
This means that you have 3300
floating point operations every
second for every single neuron
in your brain. Okay, you may
also be thinking, gee, can I
simulate a neuron with 3300
floating point operations per
second? The answer may be Yes,
but that's a different talk.
Another way to look at this is
we have over 10,000 cores. Now
these aren't all independent,
some a lot of them have to be
executing the same instruction
at the same time. But in
comparison, let's compare this
to the first microcomputer.
Okay, the first microcomputer
really changed things, we often
think of it as sort of starting
the revolution or a era one of
the revolutions that lead to the
big change here. But for every
single transistor in that micro
computer, there were 2300 of
them, we have about five full
computing cores in this device.
So in the end, we have a lot of
computing power right now. Now,
it may seem that that puts us up
at the top of this curve. We've
seen a huge amount of change,
but the interest thing here is
that the really interesting
implications of Moore's law, the
big changes are all in front of
us. Because of the exponential
nature of this change, we are
not at the top of this curve, we
are at the near the bottom of
this curve. And statistically
speaking, almost all of the
change is ahead of us, not
behind us. And for me, that
makes me very optimistic about
the future. Lots of fun stuff is
ahead of us. That's why I think
all the fun is ahead of us. Now,
what should we do with this?
Okay, well, unfortunately, I'll
have 15 minutes here, this was
really just a teaser for a
second part of a longer part of
the talk. But I won't leave you
completely hanging as a preview
of that one of my conclusions
there is that some of our
intuitions, and these are
intuitions that have been
developed over quite a few
doublings, if you think about it
are leading us astray. And to
fight those, we need to take on
a mindset of finding ways to
almost just waste cycles in
service of user to use what in
the past would have been
essentially impossible. But we
certainly would have thought
never thought of using that many
cycles to serve the user
interface and to serve the user.
But that's how we need to think
about things if we're going to
make proper use of what's ahead
of us. Alright, there is this
longer talk, I hope to see you
there. You can find this
continuation near where you
found this recording either on
YouTube or the ACM Digital
Library, but there's also a link
that you can find on this page.
So if you type bit.ly/scotts_talk
into your browser
that will take you
to a page that will provide you
with a link to that second
recording and I hope to see you
on the other side.