The accelerating pace of technological progress means
that our intelligent creations will soon eclipse us--and
that their creations will eventually eclipse them.
By Ray Kurzweil
If we download someone's
"mind file" into a suitable medium,
will the entity that emerges be conscious?
Sometime early in the next century, the intelligence of
machines will exceed that of humans. Within several
decades, machines will exhibit the full range of human
intellect, emotions and skills, ranging from musical and
other creative aptitudes to physical movement. They will
claim to have feelings and, unlike today's virtual
personalities, will be very convincing when they tell us so.
By 2019 a $1,000 computer will at least match the
processing power of the human brain. By 2029 the
software for intelligence will have been largely mastered,
and the average personal computer will be equivalent to
Once computers achieve a level of intelligence comparable to that of
humans, they will necessarily soar past it. For example, if I learn French, I can't readily
download that learning to you. The reason is that for us, learning involves successions of
stunningly complex patterns of interconnections among brain cells (neurons) and
among the concentrations of biochemicals, known as neurotransmitters,
that enable impulses to travel from neuron to neuron. We
have no way of quickly downloading these patterns. But
quick downloading will allow our nonbiological creations
to share immediately what they learn with billions of other
machines. Ultimately, nonbiological entities will master not
only the sum total of their own knowledge but all of ours as
As this happens, there will no longer be a clear distinction
between human and machine. We are already putting
computers--neural implants--directly into people's brains to
counteract Parkinson's disease and tremors from multiple
sclerosis. We have cochlear implants that restore hearing. A
retinal implant is being developed in the U.S. that is
intended to provide at least some visual perception for some
blind individuals, basically by replacing certain
visual-processing circuits of the brain. Recently scientists
from Emory University implanted a chip in the brain of a
paralyzed stroke victim that allows him to use his
brainpower to move a cursor across a computer screen.
In the 2020s neural implants will improve our sensory
experiences, memory and thinking. By 2030, instead of just
phoning a friend, you will be able to meet in, say, a virtual
Mozambican game preserve that will seem compellingly
real. You will be able to have any type of
experience--business, social, sexual--with anyone, real or
simulated, regardless of physical proximity.
How Life and Technology Evolve
To gain insight into the kinds of forecasts I have just made,
it is important to recognize that technology is advancing
exponentially. An exponential process starts slowly, but
eventually its pace increases extremely rapidly. (A fuller
documentation of my argument is contained in my new
book, The Age of Spiritual Machines.)
The evolution of biological life and the evolution of
technology have both followed the same pattern: they take a
long time to get going, but advances build on one another
and progress erupts at an increasingly furious pace. We are
entering that explosive part of the technological evolution
curve right now.
Consider: It took billions of years for Earth to form. It took
two billion more for life to begin and almost as long for
molecules to organize into the first multicellular plants and
animals about 700 million years ago. The pace of evolution
quickened as mammals inherited Earth some 65 million
years ago. With the emergence of primates, evolutionary
progress was measured in mere millions of years, leading to
Homo sapiens perhaps 500,000 years ago.
The evolution of technology has been a continuation of the
evolutionary process that gave rise to us--the
technology-creating species--in the first place. It took tens
of thousands of years for our ancestors to figure out that
sharpening both sides of a stone created useful tools. Then,
earlier in this millennium, the time required for a major
paradigm shift in technology had shrunk to hundreds of
The pace continued to accelerate during the 19th century,
during which technological progress was equal to that of
the 10 centuries that came before it. Advancement in the
first two decades of the 20th century matched that of the
entire 19th century. Today significant technological
transformations take just a few years; for example, the
World Wide Web, already a ubiquitous form of
communication and commerce, did not exist just nine years
Computing technology is experiencing the same
exponential growth. Over the past several decades, a key
factor in this expansion has been described by Moore's
Law. Gordon Moore, a co-founder of Intel, noted in the
mid-1960s that technologists had been doubling the density
of transistors on integrated circuits every 12 months. This
meant computers were periodically doubling both in
capacity and in speed per unit cost. In the mid-1970s Moore
revised his observation of the doubling time to a more
accurate estimate of about 24 months, and that trend has
persisted through the 1990s.
After decades of devoted service, Moore's Law will have
run its course around 2019. By that time, transistor features
will be just a few atoms in width. But new computer
architectures will continue the exponential growth of
computing. For example, computing cubes are already
being designed that will provide thousands of layers of
circuits, not just one as in today's computer chips. Other
technologies that promise orders-of-magnitude increases in
computing density include nanotube circuits built from
carbon atoms, optical computing, crystalline computing and
We can readily see the march of computing by plotting the
speed (in instructions per second) per $1,000 (in constant
dollars) of 49 famous calculating machines spanning the
20th century [see graph below]. The graph is a study in
exponential growth: computer speed per unit cost doubled
every three years between 1910 and 1950 and every two
years between 1950 and 1966 and is now doubling every
year. It took 90 years to achieve the first $1,000 computer
capable of executing one million instructions per second
(MIPS). Now we add an additional MIPS to a $1,000
computer every day.
Why Returns Accelerate
Why do we see exponential progress occurring in
biological life, technology and computing? It is the result of
a fundamental attribute of any evolutionary process, a
phenomenon I call the Law of Accelerating Returns. As
order exponentially increases (which reflects the essence of
evolution), the time between salient events grows shorter.
Advancement speeds up. The returns--the valuable products
of the process--accelerate at a nonlinear rate. The escalating
growth in the price performance of computing is one
important example of such accelerating returns.
A frequent criticism of predictions is that they rely on an
unjustified extrapolation of current trends, without
considering the forces that may alter those trends. But an
evolutionary process accelerates because it builds on past
achievements, including improvements in its own means for
further evolution. The resources it needs to continue
exponential growth are its own increasing order and the
chaos in the environment in which the evolutionary process
takes place, which provides the options for further diversity.
These two resources are essentially without limit.
The Law of Accelerating Returns shows that by 2019 a
$1,000 personal computer will have the processing power
of the human brain--20 million billion calculations per
second. Neuroscientists came up with this figure by taking
an estimation of the number of neurons in the brain, 100
billion, and multiplying it by 1,000 connections per neuron
and 200 calculations per second per connection. By 2055,
$1,000 worth of computing will equal the processing
power of all human brains on Earth (of course, I may be off
by a year or two).
That's the prediction for processing power, which is a necessary but
not sufficient condition for achieving human-level intelligence in
machines. Of greater importance is the software of intelligence.
One approach to creating this software is to painstakingly
program the rules of complex processes. We are getting
good at this task in certain cases; the CYC (as in
"encyclopedia") system designed by Douglas B. Lenat of
Cycorp has more than one million rules that describe the
intricacies of human common sense, and it is being applied
to Internet search engines so that they return smarter
answers to our queries.
Another approach is "complexity theory" (also known as
chaos theory) computing, in which self-organizing
algorithms gradually learn patterns of information in a
manner analogous to human learning. One such method,
neural nets, is based on simplified mathematical models of
mammalian neurons. Another method, called genetic (or
evolutionary) algorithms, is based on allowing intelligent
solutions to develop gradually in a simulated process of
Ultimately, however, we will learn to program intelligence
by copying the best intelligent entity we can get our hands
on: the human brain itself. We will reverse-engineer the
human brain, and fortunately for us it's not even
The most immediate way to reach this goal is by destructive
scanning: take a brain frozen just before it was about to
expire and examine one very thin slice at a time to reveal
every neuron, interneuronal connection and concentration
of neurotransmitters across each gap between neurons (these
gaps are called synapses). One condemned killer has already
allowed his brain and body to be scanned, and all 15 billion
bytes of him can be accessed on the National Library of
Medicine's Web site. The resolution of these scans is not
nearly high enough for our purposes, but the data at least
enable us to start thinking about these issues.
We also have noninvasive scanning techniques, including
high-resolution magnetic resonance imaging (MRI) and
others. Their increasing resolution and speed will eventually
enable us to resolve the connections between neurons. The
rapid improvement is again a result of the Law of
Accelerating Returns, because massive computation is the
main element in higher-resolution imaging.
Another approach would be to send microscopic robots (or
"nanobots") into the bloodstream and program them to
explore every capillary, monitoring the brain's connections
and neurotransmitter concentrations.
Although sophisticated robots that small are still several
decades away at least, their utility for probing the innermost
recesses of our bodies would be far-reaching. They would
communicate wirelessly with one another and report their
findings to other computers. The result would be a
noninvasive scan of the brain taken from within.
Most of the technologies required for this scenario already
exist, though not in the microscopic size required.
Miniaturizing them to the tiny sizes needed, however,
would reflect the essence of the Law of Accelerating
Returns. For example, the translators on an integrated
circuit have been shrinking by a factor of approximately 5.6
in each linear dimension every 10 years.
The capabilities of these embedded nanobots would not be
limited to passive roles such as monitoring. Eventually they
could be built to communicate directly with the neuronal
circuits in our brains, enhancing or extending our mental
capabilities. We already have electronic devices that can
communicate with neurons by detecting their activity and
either triggering nearby neurons to fire or suppressing them
from firing. The embedded nanobots will be capable of
reprogramming neural connections to provide virtual-reality
experiences and to enhance our pattern recognition and
other cognitive faculties.
To decode and understand the brain's
information-processing methods (which, incidentally,
combine both digital and analog methods), it is not
necessary to see every connection, because there is a great
deal of redundancy within each region. We are already
applying insights from early stages of this
reverse-engineering process. For example, in speech
recognition, we have already decoded and copied the brain's
early stages of sound processing.
Perhaps more interesting than this scanning-the-brain-to-
understand-it approach would be scanning the brain for the
purpose of downloading it. We would map the locations,
interconnections, and contents of all the neurons, synapses
and neurotransmitter concentrations. The entire
organization, including the brain's memory, would then be
re-created on a digital-analog computer.
To do this, we would need to understand local brain
processes, and progress is already under way. Theodore W.
Berger and his co-workers at the University of Southern
California have built integrated circuits that precisely match
the processing characteristics of substantial clusters of
neurons. Carver A. Mead and his colleagues at the
California Institute of Technology have built a variety of
integrated circuits that emulate the digital-analog
characteristics of mammalian neural circuits.
Developing complete maps of the human brain is not as
daunting as it may sound. The Human Genome Project
seemed impractical when it was first proposed. At the rate at
which it was possible to scan genetic codes 12 years ago, it
would have taken thousands of years to complete the
genome. But in accordance with the Law of Accelerating
Returns, the ability to sequence DNA has been accelerating.
The latest estimates are that the entire human genome will be
completed in just a few years.
By the third decade of the 21st century, we will be in a
position to create complete, detailed maps of the
computationally relevant features of the human brain and to
re-create these designs in advanced neural computers. We
will provide a variety of bodies for our machines, too, from
virtual bodies in virtual reality to bodies comprising swarms
of nanobots. In fact, humanoid robots that ambulate and
have lifelike facial expressions are already being developed
at several laboratories in Tokyo.
Will It Be Concious?
Such possibilities prompt a host of intriguing issues and
questions. Suppose we scan someone's brain and reinstate
the resulting "mind file" into a suitable computing medium.
Will the entity that emerges from such an operation be
conscious? This being would appear to others to have very
much the same personality, history and memory. For some,
that is enough to define consciousness. For others, such as
physicist and author James Trefil, no logical reconstruction
can attain human consciousness, although Trefil concedes
that computers may become conscious in some new way.
At what point do we consider an entity to be conscious, to
be self-aware, to have free will? How do we distinguish a
process that is conscious from one that just acts as if it is
conscious? If the entity is very convincing when it says,
"I'm lonely, please keep me company," does that settle the
If you ask the "person" in the machine, it will strenuously
claim to be the original person. If we scan, let's say, me and
reinstate that information into a neural computer, the person
who emerges will think he is (and has been) me (or at least
he will act that way). He will say, "I grew up in Queens,
New York, went to college at M.I.T., stayed in the Boston
area, walked into a scanner there and woke up in the
machine here. Hey, this technology really works." But wait,
is this really me? For one thing, old Ray (that's me) still
exists in my carbon-cell-based brain.
Will the new entity be capable of spiritual experiences?
Because its brain processes are effectively identical, its
behavior will be comparable to that of the person it is based
on. So it will certainly claim to have the full range of
emotional and spiritual experiences that a person claims to
No objective test can absolutely determine consciousness.
We cannot objectively measure subjective experience (this
has to do with the very nature of the concepts "objective"
and "subjective"). We can measure only correlates of it, such
as behavior. The new entities will appear to be conscious,
and whether or not they actually are will not affect their
behavior. Just as we debate today the consciousness of
nonhuman entities such as animals, we will surely debate
the potential consciousness of nonbiological intelligent
entities. From a practical perspective, we will accept their
claims. They'll get mad if we don't.
Before the next century is over, the Law of Accelerating
Returns tells us, Earth's technology-creating
species--us--will merge with our own technology. And
when that happens, we might ask: What is the difference
between a human brain enhanced a millionfold by neural
implants and a nonbiological intelligence based on the
reverse-engineering of the human brain that is subsequently
enhanced and expanded?
The engine of evolution used its innovation from one period
(humans) to create the next (intelligent machines). The
subsequent milestone will be for the machines to create their
own next generation without human intervention.
An evolutionary process accelerates because it builds on its
own means for further evolution. Humans have beaten
evolution. We are creating intelligent entities in considerably
less time than it took the evolutionary process that created
us. Human intelligence--a product of evolution--has
transcended it. So, too, the intelligence that we are now
creating in computers will soon exceed the intelligence of its
RAY KURZWEIL is CEO of Kurzweil Technologies, Inc.
He led teams that built a pioneering print-to-speech reading
machine, the first omni-font ("any" font)
optical-character-recognition system, the first text-to-speech
synthesizer, the first music synthesizer capable of re-creating
the grand piano and the first commercially marketed
large-vocabulary speech-recognition system.