14-16 NOVEMBER 2002:
TOO
MUCH, TOO FAST, WAAAAAAY TOO SOON.
http://www.edge.org/3rd_culture/kurzweil02/kurzweil02_index.html
THE INTELLIGENT UNIVERSE
RAY KURZWEIL: The universe
has been set up in an exquisitely specific way so
that evolution could produce
the people that are sitting here today and we could
use our intelligence to
talk about the universe. We see a formidable power in
the ability to use our minds
and the tools we’ve created to gather evidence, to
use our inferential abilities
to develop theories, to test the theories, and to
understand the universe
at increasingly precise levels. That’s one role of
intelligence. The theories
that we heard on cosmology look at the evidence that
exists in the world today
to make inferences about what existed in the past so
that we can develop models
of how we got here.
Then,
of course, we can run those models and project what might happen in the
future. Even if it’s a little
more difficult to test the future theories, we can
at least deduce, or induce,
that certain phenomena that we see today are
evidence of times past,
such as radiation from billions of years ago. We can’t
really test what will happen
billions or trillions of years from now quite as
directly, but this line
of inquiry is legitimate, in terms of understanding the
past and the derivation
of the universe. As we heard today, the question of the
origin of the universe is
certainly not resolved. There are competing theories,
and at several times we’ve
had theories that have broken down, once we acquired
more precise evidence.
At the
same time, however, we don’t hear discussion about the role of
intelligence in the future.
According to common wisdom, intelligence is
irrelevant to cosmological
thinking. It is just a bit of froth dancing in and
out of the crevices of the
universe, and has no effect on our ultimate
cosmological destiny. That’s
not my view. The universe has been set up
exquisitely enough to have
intelligence. There are intelligent entities like
ourselves that can contemplate
the universe and develop models about it, which
is interesting. Intelligence
is, in fact, a powerful force and we can see that
its power is going to grow
not linearly but exponentially, and will ultimately
be powerful enough to change
the destiny of the universe.
I want
to propose a case that intelligence specifically human intelligence,
but not necessarily biological
human intelligence will trump cosmology, or at
least trump the dumb forces
of cosmology. The forces that we heard discussed
earlier don’t have the qualities
that we posit in intelligent decision-making.
In the grand celestial machinery,
forces deplete themselves at a certain point
and other forces take over.
Essentially you have a universe that’s dominated by
what I call dumb matter,
because it’s controlled by fairly simple mechanical
processes.
Human
civilization possesses a different type of force with a certain scope and
a certain power. It’s changing
the shape and destiny of our planet. Consider,
for example, asteroids and
meteors. Small ones hit us on a fairly regular basis,
but the big ones hit us
every some tens of millions of years and have apparently
had a big impact on the
course of biological evolution. That’s not going to
happen again. If it happened
next year we’re not quite ready to deal with it,
but it doesn’t look like
it’s going to happen next year. When it does happen
again our technology will
be quite sufficient. We’ll see it coming, and we will
deal with it. We’ll use
our engineering to send up a probe and blast it out of
the sky. You can score one
for intelligence in terms of trumping the natural
unintelligent forces of
the universe.
Commanding
our local area of the sky is, of course, very small on a cosmological
scale, but intelligence
can overrule these physical forces, not by literally
repealing the natural laws,
but by manipulating them in such a supremely sublime
and subtle way that it effectively
overrules these laws. This is particularly
the case when you get machinery
that can operate at nano and ultimately femto
and pico scales. Whereas
the laws of physics still apply, they’re being
manipulated now to create
any outcome the intelligence of this civilization
decides on.
Let me
back up and talk about how intelligence came about. Wolfram’s book has
prompted a lot of talk recently
on the computational substrate of the universe
and on the universe as a
computational entity. Earlier today, Seth Lloyd talked
about the universe as a
computer and its capacity for computation and memory.
What Wolfram leaves out
in talking about cellular automata is how you get
intelligent entities. As
you run these cellular automata, they create
interesting pictures, but
the interesting thing about cellular automata, which
was shown long before Wolfram
pointed it out, is that you can get apparently
random behavior from deterministic
processes.
It’s
more than apparent that you literally can’t predict an outcome unless you
can simulate the process.
If the process under consideration is the whole
universe, then presumably
you can’t simulate it unless you step outside the
universe. But when Wolfram
says that this explains the complexity we see in
nature, it’s leaving out
one important step. As you run the cellular automata,
you don’t see the growth
in complexity at least, certainly he’s never run them
long enough to see any growth
in what I would call complexity. You need
evolution.
Marvin
talked about some of the early stages of evolution. It starts out very
slow, but then something
with some power to sustain itself and to overcome other
forces is created and has
the power to self-replicate and preserve that
structure. Evolution works
by indirection. It creates a capability and then uses
that capability to create
the next. It took billions of years until this chaotic
swirl of mass and energy
created the information-processing, structural backbone
of DNA, and then used that
DNA to create the next stage. With DNA, evolution had
an information-processing
machine to record its experiments and conduct
experiments in a more orderly
way. So the next stage, such as the Cambrian
explosion, went a lot faster,
taking only a few tens of millions of years. The
Cambrian explosion then
established body plans that became a mature technology,
meaning that we didn’t need
to evolve body plans any more.
These
designs worked well enough, so evolution could then concentrate on higher
cortical function, establishing
another level of mechanism in the organisms that
could do information processing.
At this point, animals developed brains and
nervous systems that could
process information, and then that evolved and
continued to accelerate.
Homo sapiens evolved in only hundreds of thousands of
years, and then the cutting
edge of evolution again worked by indirection to use
this product of evolution,
the first technology-creating species to survive, to
create the next stage: technology,
a continuation of biological evolution by
other means.
The first
stages of technologies, like stone tools, fire, and the wheel took
tens of thousands of years,
but then we had more powerful tools to create the
next stage. A thousand years
ago, a paradigm shift like the printing press took
only a century or so to
be adopted, and this evolution has accelerated ever
since. Fifty years ago,
the first computers were designed with pencil on paper,
with screwdrivers and wire.
Today we have computers to design computers.
Computer designers will
design some high-level parameters, and twelve levels of
intermediate design are
computed automatically. The process of designing a
computer now goes much more
quickly.
Evolutionary
processes accelerate, and the returns from an evolutionary process
grow in power. I’ve called
this theory “The Law of Accelerating Returns.” The
returns, including economic
returns, accelerate. Stemming from my interest in
being an inventor, I’ve
been developing mathematical models of this because I
quickly realized that an
invention has to make sense when the technology is
finished, not when it was
started, since the world is generally a different
place three or four years
later.
One exponential
pattern that people are familiar with is Moore’s Law, which is
really just one specific
paradigm of shrinking transistors on integrated
circuits. It’s remarkable
how long it’s lasted, but it wasn’t the first, but the
fifth paradigm to provide
exponential growth to computing. Earlier, we had
electro-mechanical calculators,
using relays and vacuum tubes. Engineers were
shrinking the vacuum tubes,
making them smaller and smaller, until finally that
paradigm ran out of steam
because they couldn’t keep the vacuum any more.
Transistors were already
in use in radios and other small, niche applications,
but when the mainstream
technology of computing finally ran out of steam, it
switched to this other technology
that was already waiting in the wings to
provide ongoing exponential
growth. It was a paradigm shift. Later, there was a
shift to integrated circuits,
and at some point, integrated circuits will run
out of steam.
Ten or
15 years from now we’ll go to the third dimension. Of course, research
on
three dimensional computing
is well under way, because as the end of one
paradigm becomes clear,
this perception increases the pressure for the research
to create the next. We’ve
seen tremendous acceleration of molecular computing in
the last several years.
When my book, The Age of Spiritual Machines, came out
about four years ago, the
idea that three-dimensional molecular computing could
be feasible was quite controversial,
and a lot of computer scientists didn’t
believe it was. Today, there
is a universal belief that it’s feasible, and that
it will arrive in plenty
of time before Moore’s Law runs out. We live in a
three-dimensional world,
so we might as well use the third dimension. That will
be the sixth paradigm.
Moore’s
Law is one paradigm among many that have provided exponential growth in
computation, but computation
is not the only technology that has grown
exponentially. We see something
similar in any technology, particularly in ones
that have any relationship
to information. The genome project, for example, was
not a mainstream project
when it was announced. People thought it was ludicrous
that you could scan the
genome in 15 years, because at the rate at which you
could scan it when the project
began, it could take thousands of years. But the
scanning has doubled in
speed every year, and actually most of the work was done
in the last year of the
project.
Magnetic
data storage is not covered under Moore’s Law, since it involves
packing information on a
magnetic substrate, which is a completely different set
of applied physics, but
magnetic data storage has very regularly doubled every
year. In fact there’s a
second level of acceleration. It took us three years to
double the price-performance
of computing at the beginning of the century, and
two years in the middle
of the century, but we’re now doubling it in less than
one year. This is another
feedback loop that has to do with past technologies,
because as we improve the
price performance, we put more resources into that
technology. If you plot
computers, as I’ve done, on a logarithmic scale, where a
straight line would mean
exponential growth, you see another exponential.
There’s actually a double
rate of exponential growth.
Another
very important phenomenon is the rate of paradigm shift. This is harder
to measure, but even though
people can argue about some of the details and
assumptions in these charts
you still get these same very powerful trends. The
paradigm shift rate itself
is accelerating, and roughly doubling every decade.
When people claim that we
won’t see a particular development for a hundred
years, or that something
is going to take centuries to do accomplish, they’re
ignoring the inherent acceleration
of technical progress.
Bill
Joy and I were at Harvard some months ago and one Nobel Prize-winning
biologist said that we won’t
see self-replicating nanotechnology entities for a
hundred years. That’s actually
a good intuition, because that’s my estimation
at today’s rate of progress
of how long it will take to achieve that technical
milestone. However, since
we’re doubling the rate of progress every decade,
it’ll only take 25 calendar
years to get there this, by the way, is a
mainstream opinion in the
nanotechnology field. The last century is not a good
guide to the next, in the
sense that it made only about 20 years of progress at
today’s rate of progress,
because we were speeding up to this point. At today’s
rate of progress, we’ll
make the same amount of progress as what occurred in the
20th century in 14 years,
and then again in 7 years. The 21st century will
see,
because
of the explosive power of exponential growth, something like 20,000
years
of progress at today’s rate of progress a thousand times greater than
the
20th century, which was no slouch for radical change.
I’ve
been developing these models for a few decades, and made a lot of
predictions about intelligent
machines in the 1980s which people can check out.
They weren’t perfect, but
were a pretty good road map. I’ve been refining these
models. I don’t pretend
that anybody can see the future perfectly, but the power
of the exponential aspect
of the evolution of these technologies, or of
evolution itself, is undeniable.
And that creates a very different perspective
about the future.
Let’s
take computation. Communication is important and shrinkage is important.
Right now, we’re shrinking
technology, apparently both mechanical and
electronic, at a rate of
5.6 per linear dimension per decade. That number is
also moving slowly, in a
double exponential sense, but we’ll get to
nanotechnology at that rate
in the 2020s. There are some early-adopter examples
of nanotechnology today,
but the real mainstream, where the cutting edge of the
operating principles are
in the multi-nanometer range, will be in the 2020s. If
you put these together you
get some interesting observations.
Right
now we have 1026 calculations per second in human civilization in our
biological
brains. We could argue about this figure, but it’s basically, for all
practical
purposes, fixed. I don’t know how much intelligence it adds if you
include
animals, but maybe you then get a little bit higher than 1026.
Non-biological
computation is growing at a double exponential rate, and right
now
is millions of times less than the biological computation in human beings.
Biological intelligence
is fixed, because it’s an old, mature paradigm, but the
new paradigm of non-biological
computation and intelligence is growing
exponentially. The crossover
will be in the 2020s and after that, at least from
a hardware perspective,
non-biological computation will dominate at least
quantitatively.
This
brings up the question of software. Lots of people say that even though
things are growing exponentially
in terms of hardware, we’ve made no progress in
software. But we are making
progress in software, even if the doubling factor is
much slower. The real scenario
that I want to address is the reverse engineering
of the human brain. Our
knowledge of the human brain and the tools we have to
observe and understand it
are themselves growing exponentially. Brain scanning
and mathematical models
of neurons and neural structures are growing
exponentially, and there’s
very interesting work going on.
There
is Lloyd Watts, for example, who with his colleagues has collected models
of specific types of neurons
and wiring information about how the internal
connections are wired in
different regions of the brain. He has put together a
detailed model of about
15 regions that deal with auditory processing, and has
applied psychoacoustic tests
of the model, comparing it to human auditory
perception. The model is
at least reasonably accurate, and this technology is
now being used as a front
end for speech recognition software. Still, we’re at
the very early stages of
understanding the human cognitive system. It’s
comparable to the genome
project in its early stages in that we also knew very
little about the genome
in its early stages. We now have most of the data, but
we still don’t have the
reverse engineering to understand how it works.
It would
be a mistake to say that the brain only has a few simple ideas and that
once we can understand them
we can build a very simple machine. But although
there is a lot of complexity
to the brain, it’s also not vast complexity. It is
described by a genome that
doesn’t have that much information in it. There are
about 800 million bytes
in the uncompressed genome. We need to consider
redundancies in the DNA,
as some sequences are repeated hundreds of thousands of
times. By applying routine
data compression, you can compress this information
at a ratio of about 30 to
1, giving you about 23 million bytes which is
smaller than Microsoft Word
to describe the initial conditions of the brain.
But the
brain has a lot more information than that. You can argue about the
exact number, but I come
up with thousands of trillions of bytes of information
to characterize what’s in
a brain, which is millions of times greater than what
is in the genome. How can
that be? Marvin talked about how the methods from
computer science are important
for understanding how the brain works. We know
from computer science that
we can very easily create programs of considerable
complexity from a small
starting condition. You can, with a very small program,
create a genetic algorithm
that simulates some simple evolutionary process and
create something of far
greater complexity than itself. You can use a random
function within the program,
which ultimately creates not just randomness, but
is creating some meaningful
information after the initial random conditions are
evolved using a self-organizing
method, resulting in information that’s far
greater than the initial
conditions.
That
is in large measure how the genome creates the brain. We know that it
specifies certain constraints
for how a particular region is wired, but within
those constraints and methods,
there’s a great deal of stochastic or random
wiring, followed by some
kind of process where the brain learns and
self-organizes to make sense
of its environment. At this point, what began as
random becomes meaningful,
and the program has multiplied the size of its
information.
The point
of all of this is that, since it’s a level of complexity we can
manage, we will be able
to reverse engineer the human brain. We’ve shown that we
can model neurons, clusters
of neurons, and even whole brain regions. We are
well down that path. It’s
rather conservative to say that within 25 years we’ll
have all of the necessary
scanning information and neuron models and will be
able to put together a model
of the principles of operation of how the human
brain works. Then, of course,
we’ll have an entity that has some human-like
qualities. We’ll have to
educate and train it, but of course we can speed up
that process, since we’ll
have access to everything that’s out in the Web, which
will contain all accessible
human knowledge.
One of
the nice things about computer technology is that once you master a
process it can operate much
faster. So we will learn the secrets of human
intelligence, partly from
reverse engineering of the human brain. This will be
one source of knowledge
for creating the software of intelligence.
We can
then combine some advantages of human intelligence with advantages that
we see clearly in non-biological
intelligence. We spent years training our
speech recognition system,
which gives us a combination of rules. It mixes
expert-system approaches
with some self-organizing techniques like neural nets,
Markov models and other
self-organizing algorithms. We automate the training
process by recording thousands
of hours of speech and annotating it, and it
automatically readjusts
all its Markov-model levels and other parameters when it
makes mistakes. Finally,
after years of this process, it does a pretty good job
of recognizing speech. Now,
if you want your computer to do the same thing, you
don’t have to go through
those years of training like we do with every child,
you can actually load the
evolved pattern of this one research computer, which
is called loading the software.
Machines
can share their knowledge. Machines can do things quickly. Machines
have a type of memory that’s
more accurate than our frail human memories. Nobody
at this table can remember
billions of things perfectly accurately and look them
up quickly. The combination
of the software of biological human intelligence
with the benefits of non-biological
intelligence will be very formidable.
Ultimately, this growing
non-biological intelligence will have the benefits of
human levels of intelligence
in terms of its software and our exponentially
growing knowledge base.
In the
future, maybe only one part of intelligence in a trillion will be
biological, but it will
be infused with human levels of intelligence, which will
be able to amplify itself
because of the powers of non-biological intelligence
to share its knowledge.
How does it grow? Does it grow in or does it grow out?
Growing in means using finer
and finer granularities of matter and energy to do
computation, while growing
out means using more of the stuff in the universe.
Presently, we see some of
both. We see mostly the “in,” since Moore’s Law
inherently means that we’re
shrinking the size of transistors and integrated
circuits, making them finer
and finer. To some extent we’re also expanding out
in that even though the
chips are more and more powerful, we make more chips
every year, and deploy more
economic and material resources towards this non
biological intelligence.
Ultimately,
we’ll get to nanotechnology-based computation, which is at the
molecular level, infused
with the software of human intelligence and the
expanding knowledge base
of human civilization. It’ll continue to expand both
inwards and outwards. It
goes in waves as the expansion inwards reaches certain
points of resistance. The
paradigm shifts will be pretty smooth as we go from
the second to the third
dimension via molecular computing. At that point it’ll
be feasible to take the
next step into femto-engineering on the scale of
trillionths of a meter
and pico engineering on the scale of thousands of
trillionths of a meter
going into the finer structures of matter and
manipulating some of the
really fine forces, such as strings and quarks. That’s
going to be a barrier, however,
so the ongoing expansion of our intelligence is
going to be propelled outward.
Nonetheless, it will go both in and out.
Ultimately,
if you do the math, we will completely saturate our corner of the
universe,
the earth and solar system, sometime in the 22nd century. We’ll
then
want ever-greater horizons,
as is the nature of intelligence and evolution, and
will then expand to the
rest of the universe.
How quickly
will it expand? One premise is that it will expand at the speed of
light, because that’s the
fastest speed at which information can travel. There
are also tantalizing experiments
on quantum disentanglement that show some
effect at rates faster than
the speed of light, even much faster, perhaps
theoretically instantaneously.
Interestingly enough, though, this is not the
transmission of information,
but the transmission of profound quantum
randomness, which doesn’t
accomplish our purpose of communicating intelligence.
You need to transmit information,
not randomness. So far nobody has actually
shown true transmission
of information at faster than the speed of light, at
least not in a way that
has convinced mainstream scientific opinion.
If, in
fact, that is a fundamental barrier, and if things that are far away
really are far away, which
is to say there are no shortcuts through wormholes
through the universe, then
the spread of our intelligence will be slow, governed
by the speed of light. This
process will be initiated within 200 years. If you
do the math, we will be
at near saturation of the available matter and energy in
and around our solar system,
based on current understandings of the limitations
of computation, within that
time period. However, it’s my conjecture that by
going through these other
dimensions that Alan and Paul talked about, there may
be shortcuts. It may be
very hard to do, but we’re talking about supremely
intelligent technologies
and beings. If there are ways to get to parts of the
universe through shortcuts
such as wormholes, they’ll find, deploy, and master
them, and get to other parts
of the universe faster. Then perhaps we can reach
the whole universe, say
1080 protons, photons, and other particles that Seth
Lloyd estimates represents
on the order of 1090 bits, without being limited by
the apparent speed of light.
If the
speed of light is not a limit, and I do have to emphasize that this
particular point is a conjecture
at this time, then within 300 years, we would
saturate the whole universe
with our intelligence, and the whole universe would
become supremely intelligent
and be able to manipulate everything according to
its will. We’re currently
multiplying computational capacity by a factor of at
least 103 every decade.
This is conservative as this rate of exponential growth
is itself growing exponentially.
Thus it is conservative to project that within
30 decades (300 years),
we would multiply current computational capacities by a
factor of 1090, and thus
exceed Seth Lloyd’s estimate of 1090 bits in the
Universe. We can speculate
about identity will this be multiple people or
beings, or one being, or
will we all be merged? but nonetheless, we’ll be very
intelligent and we’ll be
able to decide whether we want to continue expanding.
Information is very sacred,
which is why death is a tragedy. Whenever a person
dies, you lose all that
information in a person. The tragedy of losing
historical artifacts is
that we’re losing information. We could realize that
losing information is bad,
and decide not to do that any more. Intelligence will
have a profound effect on
the cosmological destiny of the universe at that
point.
I’ll
end with a comment about the SETI project. Regardless of this ultimate
resolution of this issue
of the speed of light and it is my speculation (and
that of others as well)
that there are ways to circumvent it if there are
ways, they’ll be found,
because intelligence is intelligent enough to master any
mechanism that is discovered.
Regardless of that, I think the SETI project will
fail it’s actually a very
important failure, because sometimes a negative
finding is just as profound
as a positive finding for the following reason:
we’ve looked at a lot of
the sky with at least some level of power, and we don’t
see anybody out there. The
SETI assumption is that even though it’s very
unlikely that there is another
intelligent civilization like we have here on
Earth, there are billions
of trillions of planets. So even if the probability is
one in a million, or one
in a billion, there are still going to be millions, or
billions, of life-bearing
and ultimately intelligence-bearing planets out there.
If that’s
true, they’re going to be distributed fairly evenly across
cosmological time, so some
will be ahead of us, and some will be behind us.
Those that are ahead of
us are not going to be ahead of us by only a few years.
They’re going to be ahead
of us by billions of years. But because of the
exponential nature of evolution,
once we get a civilization that gets to our
point, or even to the point
of Babbage, who was messing around with mechanical
linkages in a crude 19th
century technology, it’s only a matter of a few
centuries before they get
to a full realization of nanotechnology, if not femto
and pico-engineering, and
totally infuse their area of the cosmos with their
intelligence. It only takes
a few hundred years!
So if
there are millions of civilizations that are millions or billions of years
ahead of us, there would
have to be millions that have passed this threshold and
are doing what I’ve just
said, and have really infused their area of the cosmos.
Yet we don’t see them, nor
do we have the slightest indication of their
existence, a challenge known
as the Fermi paradox. Someone could say that this
“silence of the cosmos”
is because the speed of light is a limit, therefore we
don’t see them, because
even though they’re fantastically intelligent, they’re
outside of our light sphere.
Of course, if that’s true, SETI won’t find them,
because they’re outside
of our light sphere. But let’s say they’re inside our
light sphere, or that light
isn’t a limitation, for the reasons I’ve mentioned,
then perhaps they decided,
in their great wisdom, to remain invisible to us. You
can imagine that there’s
one civilization out there that made that decision, but
are we to believe that this
is the case for every one of the millions, or
billions, of civilizations
that SETI says should be out there?
That’s
unlikely, but even if it’s true, SETI still won’t find them, because if
a
civilization like that has
made that decision, it is so intelligent they’ll be
able to carry that out,
and remain hidden from us. Maybe they’re waiting for us
to evolve to that point
and then they’ll reveal themselves to us. Still, if you
analyze this more carefully,
it’s very unlikely in fact that they’re out there.
You might
ask, isn’t it incredibly unlikely that this planet, which is in a very
random place in the universe
and one of trillions of planets and solar systems,
is ahead of the rest of
the universe in the evolution of intelligence? Of course
the whole existence of our
universe, with the laws of physics so sublimely
precise to allow this type
of evolution to occur is also very unlikely, but by
the anthropic principles,
we’re here, and by an analogous anthropic principle we
are here in the lead. After
all, if this were not the case, we wouldn’t be
having this conversation.
So by a similar anthropic principle we’re able to
appreciate this argument.
I’ll end on that note.
