Re: Kurzweil's new Singularity/AI page

From: J. R. Molloy (
Date: Sat Feb 24 2001 - 16:57:39 MST

> I have to admit he hooked me with this. Not to give anything away, I
> reveal that I don't have an extra $40 trillion in my pockets right now.
> But it's a pretty good essay anyway.
> Hal

You never know when these things might disappear from the Net, so here's
the whole thing:
      The Singularity is Near
A Book Précis by Raymond Kurzweil

You will get $40 trillion just by reading this précis and understanding
what it says. For complete details, see below. (It's true that authors
will do just about anything to keep your attention, but I'm serious about
this statement. Until I return to a further explanation, however, do read
the first sentence of this paragraph carefully.)

Now back to the future: it's widely misunderstood. Our forebears expected
the future to be pretty much like their present, which had been pretty
much like their past. Although exponential trends did exist a thousand
years ago, they were at that very early stage where an exponential trend
is so flat that it looks like no trend at all. So their lack of
expectations was largely fulfilled. Today, in accordance with the common
wisdom, everyone expects continuous technological progress and the social
repercussions that follow. But the future will be far more surprising than
most observers realize: few have truly internalized the implications of
the fact that the rate of change itself is accelerating.

The Intuitive Linear View versus the Historical Exponential View
Most long range forecasts of technical feasibility in future time periods
dramatically underestimate the power of future technology because they are
based on what I call the "intuitive linear" view of technological progress
rather than the "historical exponential view." To express this another
way, it is not the case that we will experience a hundred years of
progress in the twenty-first century; rather we will witness on the order
of twenty thousand years of progress (at today's rate of progress, that

This disparity in outlook comes up frequently in a variety of contexts,
for example, the discussion of the ethical issues that Bill Joy raised in
his controversial WIRED cover story, Why The Future Doesn't Need Us. Bill
and I have been frequently paired in a variety of venues as pessimist and
optimist respectively. Although I'm expected to criticize Bill's position,
and indeed I do take issue with his prescription of relinquishment, I
nonetheless usually end up defending Joy on the key issue of feasibility.
Recently a Noble Prize winning panelist dismissed Bill's concerns,
exclaiming that, "we're not going to see self-replicating nanoengineered
entities for a hundred years." I pointed out that 100 years was indeed a
reasonable estimate of the amount of technical progress required to
achieve this particular milestone at today's rate of progress. But because
we're doubling the rate of progress every decade, we'll see a century of
progress--at today's rate--in only 25 calendar years.

When people think of a future period, they intuitively assume that the
current rate of progress will continue for future periods. However,
careful consideration of the pace of technology shows that the rate of
progress is not constant, but it is human nature to adapt to the changing
pace, so the intuitive view is that the pace will continue at the current
rate. Even for those of us who have been around long enough to experience
how the pace increases over time, our unexamined intuition nonetheless
provides the impression that progress changes at the rate that we have
experienced recently. From the mathematician's perspective, a primary
reason for this is that an exponential curve approximates a straight line
when viewed for a brief duration. So even though the rate of progress in
the very recent past (e.g., this past year) is far greater than it was ten
years ago (let alone a hundred or a thousand years ago), our memories are
nonetheless dominated by our very recent experience. It is typical,
therefore, that even sophisticated commentators, when considering the
future, extrapolate the current pace of change over the next 10 years or
100 years to determine their expectations. This is why I call this way of
looking at the future the "intuitive linear" view.

But a serious assessment of the history of technology shows that
technological change is exponential. In exponential growth, we find that a
key measurement such as computational power is multiplied by a constant
factor for each unit of time (e.g., doubling every year) rather than just
being added to incrementally. Exponential growth is a feature of any
evolutionary process, of which technology is a primary example. One can
examine the data
in different ways, on different time scales, and for a wide variety of
technologies ranging from electronic to biological, and the acceleration
of progress and growth applies. Indeed, we find not just simple
exponential growth, but "double" exponential growth, meaning that the rate
of exponential growth is itself growing exponentially. These observations
do not rely merely on an assumption of the continuation of Moore's law
(i.e., the exponential shrinking of transistor sizes on an integrated
circuit), but is based on a rich model of diverse technological processes.
What it clearly shows is that technology, particularly the pace of
technological change, advances (at least) exponentially, not linearly, and
has been doing so since the advent of technology, indeed since the advent
of evolution on Earth.

I emphasize this point because it is the most important failure that
would-be prognosticators make in considering future trends. Most
technology forecasts ignore altogether this "historical exponential view"
of technological progress. That is why people tend to overestimate what
can be achieved in the short term (because we tend to leave out necessary
details), but underestimate what can be achieved in the long term (because
the exponential growth is ignored).

The Law of Accelerating Returns
We can organize these observations into what I call the law of
accelerating returns as follows:
Evolution applies positive feedback in that the more capable methods
resulting from one stage of evolutionary progress are used to create the
next stage. As a result, the
rate of progress of an evolutionary process increases exponentially over
time. Over time, the "order" of the information embedded in the
evolutionary process (i.e., the measure of how well the information fits a
purpose, which in evolution is survival) increases.
A correlate of the above observation is that the "returns" of an
evolutionary process (e.g., the speed, cost-effectiveness, or overall
"power" of a process) increase exponentially over time.
In another positive feedback loop, as a particular evolutionary process
(e.g., computation) becomes more effective (e.g., cost effective), greater
resources are deployed toward the further progress of that process. This
results in a second level of exponential growth (i.e., the rate of
exponential growth itself grows exponentially).
Biological evolution is one such evolutionary process.
Technological evolution is another such evolutionary process. Indeed, the
emergence of the first technology creating species resulted in the new
evolutionary process of technology. Therefore, technological evolution is
an outgrowth of--and a continuation of--biological evolution.
A specific paradigm (a method or approach to solving a problem, e.g.,
shrinking transistors on an integrated circuit as an approach to making
more powerful computers) provides exponential growth until the method
exhausts its potential. When this happens, a paradigm shift (i.e., a
fundamental change in the approach) occurs, which enables exponential
growth to continue.
If we apply these principles at the highest level of evolution on Earth,
the first step, the creation of cells, introduced the paradigm of biology.
The subsequent emergence of DNA provided a digital method to record the
results of evolutionary experiments. Then, the evolution of a species who
combined rational thought with an opposable appendage (i.e., the thumb)
caused a fundamental paradigm shift from biology to technology. The
upcoming primary paradigm shift will be from biological thinking to a
hybrid combining biological and nonbiological thinking. This hybrid will
include "biologically inspired" processes resulting from the reverse
engineering of biological brains.

If we examine the timing of these steps, we see that the process has
continuously accelerated. The evolution of life forms required billions of
years for the first steps (e.g., primitive cells); later on progress
accelerated. During the Cambrian explosion, major paradigm shifts took
only tens of millions of years. Later on, Humanoids developed over a
period of millions of years, and Homo sapiens over a period of only
hundreds of thousands of years.

With the advent of a technology-creating species, the exponential pace
became too fast for evolution through DNA-guided protein synthesis and
moved on to human-created technology. Technology goes beyond mere tool
making; it is a process of creating ever more powerful technology using
the tools from the previous round of innovation. In this way, human
technology is distinguished from the tool making of other species. There
is a record of each stage of technology, and each new stage of technology
builds on the order of the previous stage.

The first technological steps-sharp edges, fire, the wheel--took tens of
thousands of years. For people living in this era, there was little
noticeable technological change in even a thousand years. By 1000 A.D.,
progress was much faster and a paradigm shift required only a century or
two. In the nineteenth century, we saw more technological change than in
the nine centuries preceding it. Then in the first twenty years of the
twentieth century, we saw more advancement than in all of the nineteenth
century. Now, paradigm shifts occur in only a few years time. The World
Wide Web did not exist in anything like its present form just a few years
ago; it didn't exist at all a decade ago.

The paradigm shift rate (i.e., the overall rate of technical progress) is
currently doubling (approximately) every decade; that is, paradigm shift
times are halving every decade (and the rate of acceleration is itself
growing exponentially). So, the technological progress in the twenty-first
century will be equivalent to what would require (in the linear view) on
the order of 200 centuries. In contrast, the twentieth century saw only
about 25 years of progress (again at today's rate of progress) since we
have been speeding up to current rates. So the twenty-first century will
see almost a thousand times greater technological change than its

The Singularity Is Near
To appreciate the nature and significance of the coming "singularity," it
is important to ponder the nature of exponential growth. Toward this end,
I am fond of telling the tale of the inventor of chess and his patron, the
emperor of China. In response to the emperor's offer of a reward for his
new beloved game, the inventor asked for a single grain of rice on the
first square, two on the second square, four on the third, and so on. The
Emperor quickly granted this seemingly benign and humble request. One
version of the story has the emperor going bankrupt as the 63 doublings
ultimately totaled 18 million trillion grains of rice. At ten grains of
rice per square inch, this requires rice fields covering twice the surface
area of the Earth, oceans included. Another version of the story has the
inventor losing his head.

It should be pointed out that as the emperor and the inventor went through
the first half of the chess board, things were fairly uneventful. The
inventor was given spoonfuls of rice, then bowls of rice, then barrels. By
the end of the first half of the chess board, the inventor had accumulated
one large field's worth (4 billion grains), and the emperor did start to
take notice. It was as they progressed through the second half of the
chessboard that the situation quickly deteriorated. Incidentally, with
regard to the doublings of computation, that's about where we stand
now--there have been slightly more than 32 doublings of performance since
the first programmable computers were invented during World War II.

This is the nature of exponential growth. Although technology grows in the
exponential domain, we humans live in a linear world. So technological
trends are not noticed as small levels of technological power are doubled.
Then seemingly out of nowhere, a technology explodes into view. For
example, when the Internet went from 20,000 to 80,000 nodes over a two
year period during the 1980s, this progress remained hidden from the
general public. A decade later, when it went from 20 million to 80 million
nodes in the same amount of time, the impact was rather conspicuous.

As exponential growth continues to accelerate into the first half of the
twenty-first century, it will appear to explode into infinity, at least
from the limited and linear perspective of contemporary humans. The
progress will ultimately become so fast that it will rupture our ability
to follow it. It will literally get out of our control. The illusion that
we have our hand "on the plug," will be dispelled.

Can the pace of technological progress continue to speed up indefinitely?
Is there not a point where humans are unable to think fast enough to keep
up with it? With regard to unenhanced humans, clearly so. But what would a
thousand scientists, each a thousand times more intelligent than human
scientists today, and each operating a thousand times faster than
contemporary humans (because the information processing in their primarily
nonbiological brains is faster) accomplish? One year would be like a
millennium. What would they come up with?

Well, for one thing, they would come up with technology to become even
more intelligent (because their intelligence is no longer of fixed
capacity). They would change their own thought processes to think even
faster. When the scientists evolve to be a million times more intelligent
and operate a million times faster, then an hour would result in a century
of progress (in today's terms).

This, then, is the Singularity. The Singularity is technological change so
rapid and so profound that it represents a rupture in the fabric of human
history. Some would say that we cannot comprehend the Singularity, at
least with our current level of understanding, and that it is impossible,
therefore, to look past its "event horizon" and make sense of what lies

My view is that despite our profound limitations of thought, constrained
as we are today to a mere hundred trillion interneuronal connections in
our biological brains, we nonetheless have sufficient powers of
abstraction to make meaningful statements about the nature of life after
the Singularity. Most importantly, it is my view that the intelligence
that will emerge will continue to represent the human civilization, which
is already a human-machine civilization. This will be the next step in
evolution, the next high level paradigm shift.

To put the concept of Singularity into perspective, let's explore the
history of the word itself. Singularity is a familiar word meaning a
unique event with profound implications. In mathematics, the term implies
infinity, the explosion of value that occurs when dividing a constant by a
number that gets closer and closer to zero. In physics, similarly, a
singularity denotes an event or location of infinite power. At the center
of a black hole, matter is so dense that its gravity is infinite. As
nearby matter and energy are drawn into the black hole, an event horizon
separates the region from the rest of the Universe. It constitutes a
rupture in the fabric of space and time. The Universe itself is said to
have begun with just such a Singularity.

In the 1950s, John Von Neumann was quoted as saying that "the ever
accelerating progress of the appearance of approaching
some essential singularity in the history of the race beyond which human
affairs, as we know them, could not continue." In the 1960s, I. J. Good
wrote of an "intelligence explosion," resulting from intelligent machines
designing their next generation without human intervention. In 1986,
Vernor Vinge, a mathematician and computer scientist at San Diego State
University, wrote about a rapidly approaching technological "singularity"
in his science fiction novel, Marooned in Realtime. Then in 1993, Vinge
presented a paper to a NASA-organized symposium which described the
Singularity as an impending event resulting primarily from the advent of
"entities with greater than human intelligence," which Vinge saw as the
harbinger of a run-away phenomenon.

>From my perspective, the Singularity has many faces. It represents the
nearly vertical phase of exponential growth where the rate of growth is so
extreme that technology appears to be growing at infinite speed. Of
course, from a mathematical perspective, there is no discontinuity, no
rupture, and the growth rates remain finite, albeit extraordinarily large.
But from our currently limited perspective, this imminent event appears to
be an acute and abrupt break in the continuity of progress. However, I
emphasize the word "currently," because one of the salient implications of
the Singularity will be a change in the nature of our ability to
understand. In other words, we will become vastly smarter as we merge with
our technology.

When I wrote my first book, The Age of Intelligent Machines, in the 1980s,
I ended the book with the specter of the emergence of machine intelligence
greater than human intelligence, but found it difficult to look beyond
this event horizon. Now having thought about its implications for the past
20 years, I feel that we are indeed capable of understanding the many
facets of this threshold, one that will transform all spheres of human

Consider a few examples of the implications. The bulk of our experiences
will shift from real reality to virtual reality. Most of the intelligence
of our civilization will ultimately be nonbiological, which by the end of
this century will be trillions of trillions of times more powerful than
human intelligence. However, to address often expressed concerns, this
does not imply the end of biological intelligence, even if thrown from its
perch of evolutionary superiority. Moreover, it is important to note that
the nonbiological forms will be derivative of biological design. In other
words, our civilization will remain human, indeed in many ways more
exemplary of what we regard as human than it is today, although our
understanding of the term will move beyond its strictly biological

Many observers have nonetheless expressed alarm at the emergence of forms
of nonbiological intelligence superior to human intelligence. The
potential to augment our own intelligence through intimate connection with
other thinking mediums does not necessarily alleviate the concern, as some
people have expressed the wish to remain "unenhanced" while at the same
time keeping their place at the top of the intellectual food chain. My
view is that the likely outcome is that on the one hand, from the
perspective of biological humanity, these superhuman intelligences will
appear to be their transcendent servants, satisfying their needs and
desires. On the other hand, fulfilling the wishes of a revered biological
legacy will occupy only a trivial portion of the intellectual power that
the Singularity will bring.

Needless to say, the Singularity will transform all aspects of our lives,
social, sexual, and economic, which I explore herewith.

Wherefrom Moore's Law
Before considering further the implications of the Singularity, let's
examine the wide range of technologies that are subject to the law of
accelerating returns. The exponential trend that has gained the greatest
public recognition has become known as "Moore's Law." Gordon Moore, one of
the inventors of integrated circuits, and then Chairman of Intel, noted in
the mid 1970s that we could squeeze twice as many transistors on an
integrated circuit every 24 months. Given that the electrons have less
distance to travel, the circuits also run twice as fast, providing an
overall quadrupling of computational power.

After sixty years of devoted service, Moore's Law will die a dignified
death no later than the year 2019. By that time, transistor features will
be just a few atoms in width, and the strategy of ever finer
photolithography will have run its course. So, will that be the end of the
exponential growth of computing?

Don't bet on it.

If we plot the speed (in instructions per second) per $1000 (in constant
dollars) of 49 famous calculators and computers spanning the entire
twentieth century, we note some interesting observations.

Moore's Law Was Not the First, but the Fifth Paradigm To Provide
Exponential Growth of Computing
Each time one paradigm runs out of steam, another picks up the pace

It is important to note that Moore's Law of Integrated Circuits was not
the first, but the fifth paradigm to provide accelerating
price-performance. Computing devices have been consistently multiplying in
power (per unit of time) from the mechanical calculating devices used in
the 1890 U.S. Census, to Turing's relay-based "Robinson" machine that
cracked the Nazi enigma code, to the CBS vacuum tube computer that
predicted the election of Eisenhower, to the transistor-based machines
used in the first space launches, to the integrated-circuit-based personal
computer which I used to dictate (and automatically transcribe) this book

But I noticed something else surprising. When I plotted the 49 machines on
an exponential graph (where a straight line means exponential growth), I
didn't get a straight line. What I got was another exponential curve. In
other words, there's exponential growth in the rate of exponential growth.
Computer speed (per unit cost) doubled every three years between 1910 and
1950, doubled every two years between 1950 and 1966, and is now doubling
every year.

But where does Moore's Law come from? What is behind this remarkably
predictable phenomenon? I have seen relatively little written about the
ultimate source of this trend. Is it just "a set of industry expectations
and goals," as Randy Isaac, head of basic science at IBM contends? Or is
there something more profound going on?

In my view, it is one manifestation (among many) of the exponential growth
of the evolutionary process that is technology. The exponential growth of
computing is a marvelous quantitative example of the exponentially growing
returns from an evolutionary process. We can also express the exponential
growth of computing in terms of an accelerating pace: it took ninety years
to achieve the first MIPS (million instructions per second) per thousand
dollars, now we add one MIPS per thousand dollars every day.

Moore's Law narrowly refers to the number of transistors on an integrated
circuit of fixed size, and sometimes has been expressed even more narrowly
in terms of transistor feature size. But rather than feature size (which
is only one contributing factor), or even number of transistors, I think
the most appropriate measure to track is computational speed per unit
cost. This takes into account many levels of "cleverness" (i.e.,
innovation, which is to say, technological evolution). In addition to all
of the innovation in integrated circuits, there are multiple layers of
innovation in computer design, e.g., pipelining, parallel processing,
instruction look-ahead, instruction and memory caching, and many others.

>From the above chart, we see that the exponential growth of computing
didn't start with integrated circuits (around 1958), or even transistors
(around 1947), but goes back to the electromechanical calculators used in
the 1890 and 1900 U.S. Census. This chart spans at least five distinct
paradigms of computing, of which Moore's Law pertains to only the latest

It's obvious what the sixth paradigm will be after Moore's Law runs out of
steam during the second decade of this century. Chips today are flat
(although it does require up to 20 layers of material to produce one layer
of circuitry). Our brain, in contrast, is organized in three dimensions.
We live in a three dimensional world, why not use the third dimension? The
human brain actually uses a very inefficient electrochemical digital
controlled analog computational process. The bulk of the calculations are
done in the interneuronal connections at a speed of only about 200
calculations per second (in each connection), which is about ten million
times slower than contemporary electronic circuits. But the brain gains
its prodigious powers from its extremely parallel organization in three
dimensions. There are many technologies in the wings that build circuitry
in three dimensions. Nanotubes, for example, which are already working in
laboratories, build circuits from pentagonal arrays of carbon atoms. One
cubic inch of nanotube circuitry would be a million times more powerful
than the human brain. There are more than enough new computing
technologies now being researched, including three-dimensional silicon
chips, optical computing, crystalline computing, DNA computing, and
quantum computing, to keep the law of accelerating returns as applied to
computation going for a long time.

Thus the (double) exponential growth of computing is broader than Moore's
Law, which refers to only one of its paradigms. And this accelerating
growth of computing is, in turn, part of the yet broader phenomenon of the
accelerating pace of any evolutionary process. Observers are quick to
criticize extrapolations of an exponential trend on the basis that the
trend is bound to run out of "resources." The classical example is when a
species happens upon a new habitat (e.g., rabbits in Australia), the
species' numbers will grow exponentially for a time, but then hit a limit
when resources such as food and space run out.

But the resources underlying the exponential growth of an evolutionary
process are relatively unbounded:

(i) The (ever growing) order of the evolutionary process itself. Each
stage of evolution provides more powerful tools for the next. In
biological evolution, the advent of DNA allowed more powerful and faster
evolutionary "experiments." Later, setting the "designs" of animal body
plans during the Cambrian explosion allowed rapid evolutionary development
of other body organs such as the brain. Or to take a more recent example,
the advent of computer assisted design tools allows rapid development of
the next generation of computers.
(ii) The "chaos" of the environment in which the evolutionary process
takes place and which provides the options for further diversity. In
biological evolution, diversity enters the process in the form of
mutations and ever changing environmental conditions. In technological
evolution, human ingenuity combined with ever changing market conditions
keep the process of innovation going.
The maximum potential of matter and energy to contain intelligent
processes is a valid issue. But according to my models, we won't approach
those limits during this century (but this will become an issue within a
couple of centuries).

We also need to distinguish between the "S" curve (an "S" stretched to the
right, comprising very slow, virtually unnoticeable growth--followed by
very rapid growth--followed by a flattening out as the process approaches
an asymptote) that is characteristic of any specific technological
paradigm and the continuing exponential growth that is characteristic of
the ongoing evolutionary process of technology. Specific paradigms, such
as Moore's Law, do ultimately reach levels at which exponential growth is
no longer feasible. Thus Moore's Law is an S curve. But the growth of
computation is an ongoing exponential (at least until we "saturate" the
Universe with the intelligence of our human-machine civilization, but that
will not be a limit in this coming century). In accordance with the law of
accelerating returns, paradigm shift, also called innovation, turns the S
curve of any specific paradigm into a continuing exponential. A new
paradigm (e.g., three-dimensional circuits) takes over when the old
paradigm approaches its natural limit. This has already happened at least
four times in the history of computation. This difference also
distinguishes the tool making of non-human species, in which the mastery
of a tool-making (or using) skill by each animal is characterized by an
abruptly ending S shaped learning curve, versus human-created technology,
which has followed an exponential pattern of growth and acceleration since
its inception.

DNA Sequencing, Memory, Communications, the Internet, and Miniaturization
This "law of accelerating returns" applies to all of technology, indeed to
any true evolutionary process, and can be measured with remarkable
precision in information based technologies. There are a great many
examples of the exponential growth implied by the law of accelerating
returns in technologies as varied as DNA sequencing, communication speeds,
electronics of all kinds, and even in the rapidly shrinking size of
technology. The Singularity results not from the exponential explosion of
computation alone, but rather from the interplay and myriad synergies that
will result from manifold intertwined technological revolutions. Also,
keep in mind that every point on the exponential growth curves underlying
these panoply of technologies (see the graphs below) represents an intense
human drama of innovation and competition. It is remarkable therefore that
these chaotic processes result in such smooth and predictable exponential

For example, when the human genome scan started fourteen years ago,
critics pointed out that given the speed with which the genome could then
be scanned, it would take thousands of years to finish the project. Yet
the fifteen year project was nonetheless completed slightly ahead of

Of course, we expect to see exponential growth in electronic memories such
as RAM.

Notice How Exponential Growth Continued through Paradigm Shifts from
Vacuum Tubes to Discrete Transistors to Integrated Circuits
However, growth in magnetic memory is not primarily a matter of Moore's
law, but includes advances in mechanical and electromagnetic systems.

Exponential growth in communications technology has been even more
explosive than in computation and is no less significant in its
implications. Again, this progression involves far more than just
shrinking transistors on an integrated circuit, but includes accelerating
advances in fiber optics, optical switching, electromagnetic technologies,
and others.

Notice how the explosion of the Internet appears to be a surprise from the
Linear Chart, but was perfectly predictable from the Exponential Chart

Ultimately we will get away from the tangle of wires in our cities and in
our lives through wireless communication, the power of which is doubling
every 10 to 11 months.

Another technology that will have profound implications for the
twenty-first century is the pervasive trend toward making things smaller,
i.e., miniaturization. The salient implementation sizes of a broad range
of technologies, both electronic and mechanical, are shrinking, also at a
double exponential rate. At present, we are shrinking technology by a
factor of approximately 5.6 per linear dimension per decade.

The Exponential Growth of Computation Revisited
If we view the exponential growth of computation in its proper perspective
as one example of the pervasiveness of the exponential growth of
information based technology, that is, as one example of many of the law
of accelerating returns, then we can confidently predict its continuation.

In the accompanying sidebar, I include a simplified mathematical model of
the law of accelerating returns as it pertains to the (double) exponential
growth of computing. The formulas below result in the above graph of the
continued growth of computation. This graph matches the available data for
the twentieth century through all five paradigms and provides projections
for the twenty-first century. Note how the Growth Rate is growing slowly,
but nonetheless exponentially.

The Law of Accelerating Returns Applied to the Growth of Computation
The following provides a brief overview of the law of accelerating returns
as it applies to the double exponential growth of computation. This model
considers the impact of the growing power of the technology to foster its
own next generation. For example, with more powerful computers and related
technology, we have the tools and the knowledge to design yet more
powerful computers, and to do so more quickly.

Note that the data for the year 2000 and beyond assume neural net
connection calculations as it is expected that this type of calculation
will ultimately dominate, particularly in emulating human brain functions.
This type of calculation is less expensive than conventional (e.g.,
Pentium III / IV) calculations by a factor of at least 100 (particularly
if implemented using digital controlled analog electronics, which would
correspond well to the brain's digital controlled analog electrochemical
processes). A factor of 100 translates into approximately 6 years (today)
and less than 6 years later in the twenty-first century.

My estimate of brain capacity is 100 billion neurons times an average
1,000 connections per neuron (with the calculations taking place primarily
in the connections) times 200 calculations per second. Although these
estimates are conservatively high, one can find higher and lower
estimates. However, even much higher (or lower) estimates by orders of
magnitude only shift the prediction by a relatively small number of years.

Some prominent dates from this analysis include the following:

We achieve one Human Brain capability (2 * 10^16 cps) for $1,000 around
the year 2023.
We achieve one Human Brain capability (2 * 10^16 cps) for one cent around
the year 2037.
We achieve one Human Race capability (2 * 10^26 cps) for $1,000 around the
year 2049.
We achieve one Human Race capability (2 * 10^26 cps) for one cent around
the year 2059.
The Model considers the following variables:

V: Velocity (i.e., power) of computing (measured in CPS/unit cost)
W: World Knowledge as it pertains to designing and building computational
t: Time
The assumptions of the model are:

(1) V = C1 * W
In other words, computer power is a linear function of the knowledge of
how to build computers. This is actually a conservative assumption. In
general, innovations improve V (computer power) by a multiple, not in an
additive way. Independent innovations multiply each other's effect. For
example, a circuit advance such as CMOS, a more efficient IC wiring
methodology, and a processor innovation such as pipelining all increase V
by independent multiples.

(2) W = C2 * Integral (0 to t) V
In other words, W (knowledge) is cumulative, and the instantaneous
increment to knowledge is proportional to V.

This gives us:

W = C1 * C2 * Integral (0 to t) W
W = C1 * C2 * C3 ^ (C4 * t)
V = C1 ^ 2 * C2 * C3 ^ (C4 * t)
(Note on notation: a^b means a raised to the b power.)
Simplifying the constants, we get:

V = Ca * Cb ^ (Cc * t)
So this is a formula for "accelerating" (i.e., exponentially growing)
returns, a "regular Moore's Law."

As I mentioned above, the data shows exponential growth in the rate of
exponential growth. (We doubled computer power every three years early in
the twentieth century, every two years in the middle of the century, and
close to every one year during the 1990s.)

Let's factor in another exponential phenomenon, which is the growing
resources for computation. Not only is each (constant cost) device getting
more powerful as a function of W, but the resources deployed for
computation are also growing exponentially.

We now have:

N: Expenditures for computation
V = C1 * W (as before)
N = C4 ^ (C5 * t) (Expenditure for computation is growing at its own
exponential rate)
W = C2 * Integral(0 to t) (N * V)
As before, world knowledge is accumulating, and the instantaneous
increment is proportional to the amount of computation, which equals the
resources deployed for computation (N) * the power of each (constant cost)

This gives us:

W = C1 * C2 * Integral(0 to t) (C4 ^ (C5 * t) * W)
W = C1 * C2 * (C3 ^ (C6 * t)) ^ (C7 * t)
V = C1 ^ 2 * C2 * (C3 ^ (C6 * t)) ^ (C7 * t)
Simplifying the constants, we get:

V = Ca * (Cb ^ (Cc * t)) ^ (Cd * t)
This is a double exponential--an exponential curve in which the rate of
exponential growth is growing at a different exponential rate.

Now let's consider real-world data. Considering the data for actual
calculating devices and computers during the twentieth century:

CPS/$1K: Calculations Per Second for $1,000
Twentieth century computing data matches:

CPS/$1K = 10^(6.00*((20.40/6.00)^((A13-1900)/100))-11.00)
We can determine the growth rate over a period of time:

Growth Rate =10^((LOG(CPS/$1K for Current Year) - LOG(CPS/$1K for Previous
Year))/(Current Year - Previous Year))
Human Brain = 100 Billion (10^11) neurons * 1000 (10^3) Connections/Neuron
* 200 (2 * 10^2) Calculations Per Second Per Connection = 2 * 10^16
Calculations Per Second
Human Race = 10 Billion (10^10) Human Brains = 2 * 10^26 Calculations Per
These formulas produce the graph above.

Already, IBM's "Blue Gene" supercomputer, now being built and scheduled to
be completed by 2005, is projected to provide 1 million billion
calculations per second (i.e., one billion megaflops). This is already one
twentieth of the capacity of the human brain, which I estimate at a
conservatively high 20 million billion calculations per second (100
billion neurons times 1,000 connections per neuron times 200 calculations
per second per connection). In line with my earlier predictions,
supercomputers will achieve one human brain capacity by 2010, and personal
computers will do so by around 2020. By 2030, it will take a village of
human brains (around a thousand) to match $1000 of computing. By 2050,
$1000 of computing will equal the processing power of all human brains on
Earth. Of course, this only includes those brains still using carbon-based
neurons. While human neurons are wondrous creations in a way, we wouldn't
(and don't) design computing circuits the same way. Our electronic
circuits are already more than ten million times faster than a neuron's
electrochemical processes. Most of the complexity of a human neuron is
devoted to maintaining its life support functions, not its information
processing capabilities. Ultimately, we will need to port our mental
processes to a more suitable computational substrate. Then our minds won't
have to stay so small, being constrained as they are today to a mere
hundred trillion neural connections each operating at a ponderous 200
digitally controlled analog calculations per second.

The Software of Intelligence
So far, I've been talking about the hardware of computing. The software is
even more salient. One of the principal assumptions underlying the
expectation of the Singularity is the ability of nonbiological mediums to
emulate the richness, subtlety, and depth of human thinking. Achieving the
computational capacity of the human brain, or even villages and nations of
human brains will not automatically produce human levels of capability. By
human levels I include all the diverse and subtle ways in which humans are
intelligent, including musical and artistic aptitude, creativity,
physically moving through the world, and understanding and responding
appropriately to emotion. The requisite hardware capacity is a necessary
but not sufficient condition. The organization and content of these
resources--the software of intelligence--is also critical.

Before addressing this issue, it is important to note that once a computer
achieves a human level of intelligence, it will necessarily soar past it.
A key advantage of nonbiological intelligence is that machines can easily
share their knowledge. If I learn French, or read War and Peace, I can't
readily download that learning to you. You have to acquire that
scholarship the same painstaking way that I did. My knowledge, embedded in
a vast pattern of neurotransmitter concentrations and interneuronal
connections, cannot be quickly accessed or transmitted. But we won't leave
out quick downloading ports in our nonbiological equivalents of human
neuron clusters. When one computer learns a skill or gains an insight, it
can immediately share that wisdom with billions of other machines.

As a contemporary example, we spent years teaching one research computer
how to recognize continuous human speech. We exposed it to thousands of
hours of recorded speech, corrected its errors, and patiently improved its
performance. Finally, it became quite adept at recognizing speech (I
dictated most of my recent book to it). Now if you want your own personal
computer to recognize speech, it doesn't have to go through the same
process; you can just download the fully trained patterns in seconds.
Ultimately, billions of nonbiological entities can be the master of all
human and machine acquired knowledge.

In addition, computers are potentially millions of times faster than human
neural circuits. A computer can also remember billions or even trillions
of facts perfectly, while we are hard pressed to remember a handful of
phone numbers. The combination of human level intelligence in a machine
with a computer's inherent superiority in the speed, accuracy, and sharing
ability of its memory will be formidable.

There are a number of compelling scenarios to achieve higher levels of
intelligence in our computers, and ultimately human levels and beyond. We
will be able to evolve and train a system combining massively parallel
neural nets with other paradigms to understand language and model
knowledge, including the ability to read and model the knowledge contained
in written documents. Unlike many contemporary "neural net" machines,
which use mathematically simplified models of human neurons, some
contemporary neural nets are already using highly detailed models of human
neurons, including detailed nonlinear analog activation functions and
other relevant details. Although the ability of today's computers to
extract and learn knowledge from natural language documents is limited,
their capabilities in this domain are improving rapidly. Computers will be
able to read on their own, understanding and modeling what they have read,
by the second decade of the twenty-first century. We can then have our
computers read all of the world's literature--books, magazines, scientific
journals, and other available material. Ultimately, the machines will
gather knowledge on their own by venturing out on the web, or even into
the physical world, drawing from the full spectrum of media and
information services, and sharing knowledge with each other (which
machines can do far more easily than their human creators).

Reverse Engineering the Human Brain
The most compelling scenario for mastering the software of intelligence is
to tap into the blueprint of the best example we can get our hands on of
an intelligent process. There is no reason why we cannot reverse engineer
the human brain, and essentially copy its design. Although it took its
original designer several billion years to develop, it's readily available
to us, and not (yet) copyrighted. Although there's a skull around the
brain, it is not hidden from our view.

The most immediately accessible way to accomplish this is through
destructive scanning: we take a frozen brain, preferably one frozen just
slightly before rather than slightly after it was going to die anyway, and
examine one brain layer--one very thin slice--at a time. We can readily
see every neuron and every connection and every neurotransmitter
concentration represented in each synapse-thin layer.

Human brain scanning has already started. A condemned killer allowed his
brain and body to be scanned and you can access all 10 billion bytes of
him on the Internet

He has a 25 billion byte female companion on the site as well in case he
gets lonely. This scan is not high enough in resolution for our purposes,
but then, we probably don't want to base our templates of machine
intelligence on the brain of a convicted killer, anyway.

But scanning a frozen brain is feasible today, albeit not yet at a
sufficient speed or bandwidth, but again, the law of accelerating returns
will provide the requisite speed of scanning, just as it did for the human
genome scan. Carnegie Mellon University's Andreas Nowatzyk plans to scan
the nervous system of the brain and body of a mouse with a resolution of
less than 200 nanometers, which is getting very close to the resolution
needed for reverse engineering.

We also have noninvasive scanning techniques today, including
high-resolution magnetic resonance imaging (MRI) scans, optical imaging,
near-infrared scanning, and other technologies which are capable in
certain instances of resolving individual somas, or neuron cell bodies.
Brain scanning technologies are also increasing their resolution with each
new generation, just what we would expect from the law of accelerating
returns. Future generations will enable us to resolve the connections
between neurons and to peer inside the synapses and record the
neurotransmitter concentrations.

We can peer inside someone's brain today with noninvasive scanners, which
are increasing their resolution with each new generation of this
technology. There are a number of technical challenges in accomplishing
this, including achieving suitable resolution, bandwidth, lack of
vibration, and safety. For a variety of reasons it is easier to scan the
brain of someone recently deceased than of someone still living. It is
easier to get someone deceased to sit still, for one thing. But
noninvasively scanning a living brain will ultimately become feasible as
MRI, optical, and other scanning technologies continue to improve in
resolution and speed.

Scanning from Inside
Although noninvasive means of scanning the brain from outside the skull
are rapidly improving, the most practical approach to capturing every
salient neural detail will be to scan it from inside. By 2030, "nanobot"
(i.e., nano robot) technology will be viable, and brain scanning will be a
prominent application. Nanobots are robots that are the size of human
blood cells, or even smaller. Billions of them could travel through every
brain capillary and scan every relevant feature from up close. Using high
speed wireless communication, the nanobots would communicate with each
other, and with other computers that are compiling the brain scan data
base (in other words, the nanobots will all be on a wireless local area

This scenario involves only capabilities that we can touch and feel today.
We already have technology capable of producing very high resolution
scans, provided that the scanner is physically proximate to the neural
features. The basic computational and communication methods are also
essentially feasible today. The primary features that are not yet
practical are nanobot size and cost. As I discussed above, we can project
the exponentially declining cost of computation, and the rapidly declining
size of both electronic and mechanical technologies. We can conservatively
expect, therefore, the requisite nanobot technology by around 2030.
Because of its ability to place each scanner in very close physical
proximity to every neural feature, nanobot-based scanning will be more
practical than scanning the brain from outside.

How to Use Your Brain Scan
How will we apply the thousands of trillions of bytes of information
derived from each brain scan? One approach is to use the results to design
more intelligent parallel algorithms for our machines, particularly those
based on one of the neural net paradigms. With this approach, we don't
have to copy every single connection. There is a great deal of repetition
and redundancy within any particular brain region. Although the
information contained in a human brain would require thousands of
trillions of bytes of information (on the order of 100 billion neurons
times an average of 1,000 connections per neuron, each with multiple
neurotransmitter concentrations and connection data), the design of the
brain is characterized by a human genome of only about a billion bytes.

Furthermore, most of the genome is redundant, so the initial design of the
brain is characterized by approximately one hundred million bytes, about
the size of Microsoft Word. Of course, the complexity of our brains
greatly increases as we interact with the world (by a factor of more than
ten million). Because of the highly repetitive patterns found in each
specific brain region, it is not necessary to capture each detail in order
to reverse engineer the significant digital-analog algorithms. With this
information, we can design simulated nets that operate similarly. There
are already multiple efforts under way to scan the human brain and apply
the insights derived to the design of intelligent machines.

The pace of brain reverse engineering is only slightly behind the
availability of the brain scanning and neuron structure information. A
contemporary example is a comprehensive model of a significant portion of
the human auditory processing system that Lloyd Watts (
has developed from both neurobiology studies of specific neuron types and
brain interneuronal connection information. Watts' model includes five
parallel paths and includes the actual intermediate representations of
auditory information at each stage of neural processing. Watts has
implemented his model as real-time software which can locate and identify
sounds with many of the same properties as human hearing. Although a work
in progress, the model illustrates the feasibility of converting
neurobiological models and brain connection data into working simulations.
Also, as Hans Moravec and others have speculated, these efficient
simulations require about 1,000 times less computation than the
theoretical potential of the biological neurons being simulated.

Reverse Engineering the Human Brain: Five Parallel Auditory Pathways

Cochlea: Sense organ of hearing. 30,000 fibers converts motion of the
stapes into spectro-temporal representation of sound.

MC: Multipolar Cells. Measure spectral energy.

GBC: Globular Bushy Cells. Relays spikes from the auditory nerve to the
Lateral Superior.

Olivary Complex (includes LSO and MSO). Encoding of timing and amplitude
of signals for binaural comparison of level.

SBC: Spherical Bushy Cells. Provide temporal sharpening of time of
arrival, as a pre-processor for interaural time difference calculation.

OC: Octopus Cells. Detection of transients.

DCN: Dorsal Cochlear Nucleus. Detection of spectral edges and calibrating
for noise levels.

VNTB: Ventral Nucleus of the Trapezoid Body. Feedback signals to modulate
outer hair cell function in the cochlea.

VNLL, PON: Ventral Nucleus of the Lateral Lemniscus, Peri-Olivary Nuclei.
Processing transients from the Octopus Cells.

MSO: Medial Superior Olive. Computing inter-aural time difference
(difference in time of arrival between the two ears, used to tell where a
sound is coming from).

LSO: Lateral Superior Olive. Also involved in computing inter-aural level

ICC: Central Nucleus of the Inferior Colliculus. The site of major
integration of multiple representations of sound.

ICx: Exterior Nucleus of the Inferior Colliculus. Further refinement of
sound localization.

SC: Superior Colliculus. Location of auditory/visual merging.

MGB: Medial Geniculate Body. The auditory portion of the thalamus.

LS: Limbic System. Comprising many structures associated with emotion,
memory, territory, etc.

AC: Auditory Cortex.

The brain is not one huge "tabula rasa" (i.e., undifferentiated blank
slate), but rather an intricate and intertwined collection of hundreds of
specialized regions. The process of "peeling the onion" to understand
these interleaved regions is well underway. As the requisite neuron models
and brain interconnection data becomes available, detailed and
implementable models such as the auditory example above will be developed
for all brain regions.

After the algorithms of a region are understood, they can be refined and
extended before being implemented in synthetic neural equivalents. For one
thing, they can be run on a computational substrate that is already more
than ten million times faster than neural circuitry. And we can also throw
in the methods for building intelligent machines that we already

Downloading the Human Brain
A more controversial application than this
scanning-the-brain-to-understand-it scenario is
scanning-the-brain-to-download-it. Here we scan someone's brain to map the
locations, interconnections, and contents of all the somas, axons,
dendrites, presynaptic vesicles, neurotransmitter concentrations, and
other neural components and levels. Its entire organization can then be
re-created on a neural computer of sufficient capacity, including the
contents of its memory.

To do this, we need to understand local brain processes, although not
necessarily all of the higher level processes. Scanning a brain with
sufficient detail to download it may sound daunting, but so did the human
genome scan. All of the basic technologies exist today, just not with the
requisite speed, cost, and size, but these are the attributes that are
improving at a double exponential pace.

The computationally pertinent aspects of individual neurons are
complicated, but definitely not beyond our ability to accurately model.
For example, Ted Berger and his colleagues at Hedco Neurosciences have
built integrated circuits that precisely match the digital and analog
information processing characteristics of neurons, including clusters with
hundreds of neurons. Carver Mead and his colleagues at CalTech have built
a variety of integrated circuits that emulate the digital-analog
characteristics of mammalian neural circuits.

A recent experiment at San Diego's Institute for Nonlinear Science
demonstrates the potential for electronic neurons to precisely emulate
biological ones. Neurons (biological or otherwise) are a prime example of
what is often called "chaotic computing." Each neuron acts in an
essentially unpredictable fashion. When an entire network of neurons
receives input (from the outside world or from other networks of neurons),
the signaling amongst them appears at first to be frenzied and random.
Over time, typically a fraction of a second or so, the chaotic interplay
of the neurons dies down, and a stable pattern emerges. This pattern
represents the "decision" of the neural network. If the neural network is
performing a pattern recognition task (which, incidentally, comprises the
bulk of the activity in the human brain), then the emergent pattern
represents the appropriate recognition.

So the question addressed by the San Diego researchers was whether
electronic neurons could engage in this chaotic dance alongside biological
ones. They hooked up their artificial neurons with those from spiney
lobsters in a single network, and their hybrid biological-nonbiological
network performed in the same way (i.e., chaotic interplay followed by a
stable emergent pattern) and with the same type of results as an all
biological net of neurons. Essentially, the biological neurons accepted
their electronic peers. It indicates that their mathematical model of
these neurons was reasonably accurate.

There are many projects around the world which are creating nonbiological
devices to recreate in great detail the functionality of human neuron
clusters. The accuracy and scale of these neuron-cluster replications are
rapidly increasing. We started with functionally equivalent recreations of
single neurons, then clusters of tens, then hundreds, and now thousands.
Scaling up technical processes at an exponential pace is what technology
is good at.

As the computational power to emulate the human brain becomes
available--we're not there yet, but we will be there within a couple of
decades--projects already under way to scan the human brain will be
accelerated, with a view both to understand the human brain in general, as
well as providing a detailed description of the contents and design of
specific brains. By the third decade of the twenty-first century, we will
be in a position to create highly detailed and complete maps of all
relevant features of all neurons, neural connections and synapses in the
human brain, all of the neural details that play a role in the behavior
and functionality of the brain, and to recreate these designs in suitably
advanced neural computers.

Is the Human Brain Different from a Computer?
Is the human brain different from a computer?

The answer depends on what we mean by the word "computer." Certainly the
brain uses very different methods from conventional contemporary
computers. Most computers today are all digital and perform one (or
perhaps a few) computations at a time at extremely high speed. In
contrast, the human brain combines digital and analog methods with most
computations performed in the analog domain. The brain is massively
parallel, performing on the order of a hundred trillion computations at
the same time, but at extremely slow speeds.

With regard to digital versus analog computing, we know that digital
computing can be functionally equivalent to analog computing (although the
reverse is not true), so we can perform all of the capabilities of a
hybrid digital--analog network with an all digital computer. On the other
hand, there is an engineering advantage to analog circuits in that analog
computing is potentially thousands of times more efficient. An analog
computation can be performed by a few transistors, or, in the case of
mammalian neurons, specific electrochemical processes. A digital
computation, in contrast, requires thousands or tens of thousands of
transistors. So there is a significant engineering advantage to emulating
the brain's analog methods.

The massive parallelism of the human brain is the key to its pattern
recognition abilities, which reflects the strength of human thinking. As I
discussed above, mammalian neurons engage in a chaotic dance, and if the
neural network has learned its lessons well, then a stable pattern will
emerge reflecting the network's decision. There is no reason why our
nonbiological functionally equivalent recreations of biological neural
networks cannot be built using these same principles, and indeed there are
dozens of projects around the world that have succeeded in doing this. My
own technical field is pattern recognition, and the projects that I have
been involved in for over thirty years use this form of chaotic computing.
Particularly successful examples are Carver Mead's neural chips, which are
highly parallel, use digital controlled analog computing, and are intended
as functionally similar recreations of biological networks.

Objective and Subjective
The Singularity envisions the emergence of human-like intelligent entities
of astonishing diversity and scope. Although these entities will be
capable of passing the "Turing test" (i.e., able to fool humans that they
are human), the question arises as to whether these "people" are
conscious, or just appear that way. To gain some insight as to why this is
an extremely subtle question (albeit an ultimately important one) it is
useful to consider some of the paradoxes that emerge from the concept of
downloading specific human brains.

Although I anticipate that the most common application of the knowledge
gained from reverse engineering the human brain will be creating more
intelligent machines that are not necessarily modeled on specific
biological human individuals, the scenario of scanning and reinstantiating
all of the neural details of a specific person raises the most immediate
questions of identity. Let's consider the question of what we will find
when we do this.

We have to consider this question on both the objective and subjective
levels. "Objective" means everyone except me, so let's start with that.
Objectively, when we scan someone's brain and reinstantiate their personal
mind file into a suitable computing medium, the newly emergent "person"
will appear to other observers to have very much the same personality,
history, and memory as the person originally scanned. That is, once the
technology has been refined and perfected. Like any new technology, it
won't be perfect at first. But ultimately, the scans and recreations will
be very accurate and realistic.

Interacting with the newly instantiated person will feel like interacting
with the original person. The new person will claim to be that same old
person and will have a memory of having been that person. The new person
will have all of the patterns of knowledge, skill, and personality of the
original. We are already creating functionally equivalent recreations of
neurons and neuron clusters with sufficient accuracy that biological
neurons accept their nonbiological equivalents and work with them as if
they were biological. There are no natural limits that prevent us from
doing the same with the hundred billion neuron cluster of clusters we call
the human brain.

Subjectively, the issue is more subtle and profound, but first we need to
reflect on one additional objective issue: our physical self.

The Importance of Having a Body
Consider how many of our thoughts and thinking are directed toward our
body and its survival, security, nutrition, and image, not to mention
affection, sexuality, and reproduction. Many, if not most, of the goals we
attempt to advance using our brains have to do with our bodies: protecting
them, providing them with fuel, making them attractive, making them feel
good, providing for their myriad needs and desires. Some philosophers
maintain that achieving human level intelligence is impossible without a
body. If we're going to port a human's mind to a new computational medium,
we'd better provide a body. A disembodied mind will quickly get depressed.

There are a variety of bodies that we will provide for our machines, and
that they will provide for themselves: bodies built through nanotechnology
(i.e., building highly complex physical systems atom by atom), virtual
bodies (that exist only in virtual reality), bodies comprised of swarms of
nanobots, and other technologies.

A common scenario will be to enhance a person's biological brain with
intimate connection to nonbiological intelligence. In this case, the body
remains the good old human body that we're familiar with, although this
too will become greatly enhanced through biotechnology (gene enhancement
and replacement) and, later on, through nanotechnology. A detailed
examination of twenty-first century bodies is beyond the scope of this
précis, but recreating and enhancing our bodies will be (and has been) an
easier task than recreating our minds.

So Just Who Are These People?
To return to the issue of subjectivity, consider: is the reinstantiated
mind the same consciousness as the person we just scanned? Are these
"people" conscious at all? Is this a mind or just a brain?

Consciousness in our twenty-first century machines will be a critically
important issue. But it is not easily resolved, or even readily
understood. People tend to have strong views on the subject, and often
just can't understand how anyone else could possibly see the issue from a
different perspective. Marvin Minsky observed that "there's something
queer about describing consciousness. Whatever people mean to say, they
just can't seem to make it clear."

We don't worry, at least not yet, about causing pain and suffering to our
computer programs. But at what point do we consider an entity, a process,
to be conscious, to feel pain and discomfort, to have its own
intentionality, its own free will? How do we determine if an entity is
conscious; if it has subjective experience? How do we distinguish a
process that is conscious from one that just acts as if it is conscious?

We can't simply ask it. If it says "Hey I'm conscious," does that settle
the issue? No, we have computer games today that effectively do that, and
they're not terribly convincing.

How about if the entity is very convincing and compelling when it says
"I'm lonely, please keep me company." Does that settle the issue?

If we look inside its circuits, and see essentially the identical kinds of
feedback loops and other mechanisms in its brain that we see in a human
brain (albeit implemented using nonbiological equivalents), does that
settle the issue?

And just who are these people in the machine, anyway? The answer will
depend on who you ask. If you ask the people in the machine, they will
strenuously claim to be the original persons. For example, if we
scan--let's say myself--and record the exact state, level, and position of
every neurotransmitter, synapse, neural connection, and every other
relevant detail, and then reinstantiate this massive data base of
information (which I estimate at thousands of trillions of bytes) into a
neural computer of sufficient capacity, the person who then emerges in the
machine will think that "he" is (and had been) me, or at least he will act
that way. He will say "I grew up in Queens, New York, went to college at
MIT, stayed in the Boston area, started and sold a few artificial
intelligence companies, 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 biological Ray (that's me) still
exists. I'll still be here in my carbon-cell-based brain. Alas, I will
have to sit back and watch the new Ray succeed in endeavors that I could
only dream of.

A Thought Experiment
Let's consider the issue of just who I am, and who the new Ray is a little
more carefully. First of all, am I the stuff in my brain and body?

Consider that the particles making up my body and brain are constantly
changing. We are not at all permanent collections of particles. The cells
in our bodies turn over at different rates, but the particles (e.g., atoms
and molecules) that comprise our cells are exchanged at a very rapid rate.
I am just not the same collection of particles that I was even a month
ago. It is the patterns of matter and energy that are semipermanent (that
is, changing only gradually), but our actual material content is changing
constantly, and very quickly. We are rather like the patterns that water
makes in a stream. The rushing water around a formation of rocks makes a
particular, unique pattern. This pattern may remain relatively unchanged
for hours, even years. Of course, the actual material constituting the
pattern--the water--is replaced in milliseconds. The same is true for Ray
Kurzweil. Like the water in a stream, my particles are constantly
changing, but the pattern that people recognize as Ray has a reasonable
level of continuity. This argues that we should not associate our
fundamental identity with a specific set of particles, but rather the
pattern of matter and energy that we represent. Many contemporary
philosophers seem partial to this "identify from pattern" argument.

But (again) wait.

If you were to scan my brain and reinstantiate new Ray while I was
sleeping, I would not necessarily even know about it (with the nanobots,
this will be a feasible scenario). If you then come to me, and say, "good
news, Ray, we've successfully reinstantiated your mind file, so we won't
be needing your old brain anymore," I may suddenly realize the flaw in
this "identity from pattern" argument. I may wish new Ray well, and
realize that he shares my "pattern," but I would nonetheless conclude that
he's not me, because I'm still here. How could he be me? After all, I
would not necessarily know that he even existed.

Let's consider another perplexing scenario. Suppose I replace a small
number of biological neurons with functionally equivalent nonbiological
ones (they may provide certain benefits such as greater reliability and
longevity, but that's not relevant to this thought experiment). After I
have this procedure performed, am I still the same person? My friends
certainly think so. I still have the same self-deprecating humor, the same
silly grin--yes, I'm still the same guy.

It should be clear where I'm going with this. Bit by bit, region by
region, I ultimately replace my entire brain with essentially identical
(perhaps improved) nonbiological equivalents (preserving all of the
neurotransmitter concentrations and other details that represent my
learning, skills, and memories). At each point, I feel the procedures were
successful. At each point, I feel that I am the same guy. After each
procedure, I claim to be the same guy. My friends concur. There is no old
Ray and new Ray, just one Ray, one that never appears to fundamentally

But consider this. This gradual replacement of my brain with a
nonbiological equivalent is essentially identical to the following

(i) scan Ray and reinstantiate Ray's mind file into new (nonbiological)
Ray, and, then
(ii) terminate old Ray. But we concluded above that in such a scenario new
Ray is not the same as old Ray. And if old Ray is terminated, well then
that's the end of Ray. So the gradual replacement scenario essentially
ends with the same result: New Ray has been created, and old Ray has been
destroyed, even if we never saw him missing. So what appears to be the
continuing existence of just one Ray is really the creation of new Ray and
the termination of old Ray.
On yet another hand (we're running out of philosophical hands here), the
gradual replacement scenario is not altogether different from what happens
normally to our biological selves, in that our particles are always
rapidly being replaced. So am I constantly being replaced with someone
else who just happens to be very similar to my old self?

I am trying to illustrate why consciousness is not an easy issue. If we
talk about consciousness as just a certain type of intelligent skill: the
ability to reflect on one's own self and situation, for example, then the
issue is not difficult at all because any skill or capability or form of
intelligence that one cares to define will be replicated in nonbiological
entities (i.e., machines) within a few decades. With this type of
objective view of consciousness, the conundrums do go away. But a fully
objective view does not penetrate to the core of the issue, because the
essence of consciousness is subjective experience, not objective
correlates of that experience.

Will these future machines be capable of having spiritual experiences?

They certainly will claim to. They will claim to be people, and to have
the full range of emotional and spiritual experiences that people claim to
have. And these will not be idle claims; they will evidence the sort of
rich, complex, and subtle behavior one associates with these feelings. How
do the claims and behaviors--compelling as they will be--relate to the
subjective experience of these reinstantiated people? We keep coming back
to the very real but ultimately unmeasurable issue of consciousness.

People often talk about consciousness as if it were a clear property of an
entity that can readily be identified, detected, and gauged. If there is
one crucial insight that we can make regarding why the issue of
consciousness is so contentious, it is the following:

There exists no objective test that can conclusively determine its

Science is about objective measurement and logical implications therefrom,
but the very nature of objectivity is that you cannot measure subjective
experience-you can only measure correlates of it, such as behavior (and by
behavior, I include the actions of components of an entity, such as
neurons). This limitation has to do with the very nature of the concepts
"objective" and "subjective." Fundamentally, we cannot penetrate the
subjective experience of another entity with direct objective measurement.
We can certainly make arguments about it: i.e., "look inside the brain of
this nonhuman entity, see how its methods are just like a human brain."
Or, "see how its behavior is just like human behavior." But in the end,
these remain just arguments. No matter how convincing the behavior of a
reinstantiated person, some observers will refuse to accept the
consciousness of an entity unless it squirts neurotransmitters, or is
based on DNA-guided protein synthesis, or has some other specific
biologically human attribute.

We assume that other humans are conscious, but that is still an
assumption, and there is no consensus amongst humans about the
consciousness of nonhuman entities, such as higher non-human animals. The
issue will be even more contentious with regard to future nonbiological
entities with human-like behavior and intelligence.

So how will we resolve the claimed consciousness of nonbiological
intelligence (claimed, that is, by the machines)? From a practical
perspective, we'll accept their claims. Keep in mind that nonbiological
entities in the twenty-first century will be extremely intelligent, so
they'll be able to convince us that they are conscious. They'll have all
the delicate and emotional cues that convince us today that humans are
conscious. They will be able to make us laugh and cry. And they'll get mad
if we don't accept their claims. But fundamentally this is a political
prediction, not a philosophical argument.

On Tubules and Quantum Computing
Over the past several years, Roger Penrose, a noted physicist and
philosopher, has suggested that fine structures in the neurons called
tubules perform an exotic form of computation called "quantum computing."
Quantum computing is computing using what are called "qu bits" which take
on all possible combinations of solutions simultaneously. It can be
considered to be an extreme form of parallel processing (because every
combination of values of the qu bits are tested simultaneously). Penrose
suggests that the tubules and their quantum computing capabilities
complicate the concept of recreating neurons and reinstantiating mind

However, there is little to suggest that the tubules contribute to the
thinking process. Even generous models of human knowledge and capability
are more than accounted for by current estimates of brain size, based on
contemporary models of neuron functioning that do not include tubules. In
fact, even with these tubule-less models, it appears that the brain is
conservatively designed with many more connections (by several orders of
magnitude) than it needs for its capabilities and capacity. Recent
experiments (e.g., the San Diego Institute for Nonlinear Science
experiments) showing that hybrid biological-nonbiological networks perform
similarly to all biological networks, while not definitive, are strongly
suggestive that our tubule-less models of neuron functioning are adequate.
Lloyd Watts' software simulation of his intricate model of human auditory
processing uses orders of magnitude less computation than the networks of
neurons he is simulating, and there is no suggestion that quantum
computing is needed.

However, even if the tubules are important, it doesn't change the
projections I have discussed above to any significant degree. According to
my model of computational growth, if the tubules multiplied neuron
complexity by a factor of a thousand (and keep in mind that our current
tubule-less neuron models are already complex, including on the order of a
thousand connections per neuron, multiple nonlinearities and other
details), this would delay our reaching brain capacity by only about 9
years. If we're off by a factor of a million, that's still only a delay of
17 years. A factor of a billion is around 24 years (keep in mind
computation is growing by a double exponential).

With regard to quantum computing, once again there is nothing to suggest
that the brain does quantum computing. Just because quantum technology may
be feasible does not suggest that the brain is capable of it. After all,
we don't have lasers or even radios in our brains. Although some
scientists have claimed to detect quantum wave collapse in the brain, no
one has suggested human capabilities that actually require a capacity for
quantum computing.

However, even if the brain does do quantum computing, this does not
significantly change the outlook for human-level computing (and beyond)
nor does it suggest that brain downloading is infeasible. First of all, if
the brain does do quantum computing this would only verify that quantum
computing is feasible. There would be nothing in such a finding to suggest
that quantum computing is restricted to biological mechanisms. Biological
quantum computing mechanisms, if they exist, could be replicated. Indeed,
recent experiments with small scale quantum computers appear to be
successful. Even the conventional transistor relies on the quantum effect
of electron tunneling.

Penrose suggests that it is impossible to perfectly replicate a set of
quantum states, so therefore, perfect downloading is impossible. Well, how
perfect does a download have to be? I am at this moment in a very
different quantum state (and different in non-quantum ways as well) than I
was a minute ago (certainly in a very different state than I was before I
wrote this paragraph). If we develop downloading technology to the point
where the "copies" are as close to the original as the original person
changes anyway in the course of one minute, that would be good enough for
any conceivable purpose, yet does not require copying quantum states. As
the technology improves, the accuracy of the copy could become as close as
the original changes within ever briefer periods of time (e.g., one
second, one millisecond, one microsecond).

When it was pointed out to Penrose that neurons (and even neural
connections) were too big for quantum computing, he came up with the
tubule theory as a possible mechanism for neural quantum computing. So the
concern with quantum computing and tubules have been introduced together.
If one is searching for barriers to replicating brain function, it is an
ingenious theory, but it fails to introduce any genuine barriers. There is
no evidence for it, and even if true, it only delays matters by a decade
or two. There is no reason to believe that biological mechanisms
(including quantum computing) are inherently impossible to replicate using
nonbiological materials and mechanisms. Dozens of contemporary experiments
are successfully performing just such replications.

The Noninvasive Surgery-Free Reversible Programmable Distributed Brain
Implant, Full-Immersion Shared Virtual Reality Environments, Experience
Beamers, and Brain Expansion
How will we apply technology that is more intelligent than its creators?
One might be tempted to respond "Carefully!" But let's take a look at some

Consider several examples of the nanobot technology, which, based on
miniaturization and cost reduction trends, will be feasible within 30
years. In addition to scanning your brain, the nanobots will also be able
to expand our experiences and our capabilities.

Nanobot technology will provide fully immersive, totally convincing
virtual reality in the following way. The nanobots take up positions in
close physical proximity to every interneuronal connection coming from all
of our senses (e.g., eyes, ears, skin). We already have the technology for
electronic devices to communicate with neurons in both directions that
requires no direct physical contact with the neurons. For example,
scientists at the Max Planck Institute have developed "neuron transistors"
that can detect the firing of a nearby neuron, or alternatively, can cause
a nearby neuron to fire, or suppress it from firing. This amounts to
two-way communication between neurons and the electronic-based neuron
transistors. The Institute scientists demonstrated their invention by
controlling the movement of a living leech from their computer. Again, the
primary aspect of nanobot-based virtual reality that is not yet feasible
is size and cost.

When we want to experience real reality, the nanobots just stay in
position (in the capillaries) and do nothing. If we want to enter virtual
reality, they suppress all of the inputs coming from the real senses, and
replace them with the signals that would be appropriate for the virtual
environment. You (i.e., your brain) could decide to cause your muscles and
limbs to move as you normally would, but the nanobots again intercept
these interneuronal signals, suppress your real limbs from moving, and
instead cause your virtual limbs to move and provide the appropriate
movement and reorientation in the virtual environment.

The web will provide a panoply of virtual environments to explore. Some
will be recreations of real places, others will be fanciful environments
that have no "real" counterpart. Some indeed would be impossible in the
physical world (perhaps, because they violate the laws of physics). We
will be able to "go" to these virtual environments by ourselves, or we
will meet other people there, both real people and simulated people. Of
course, ultimately there won't be a clear distinction between the two.

By 2030, going to a web site will mean entering a full immersion virtual
reality environment. In addition to encompassing all of the senses, these
shared environments can include emotional overlays as the nanobots will be
capable of triggering the neurological correlates of emotions, sexual
pleasure, and other derivatives of our sensory experience and mental

In the same way that people today beam their lives from web cams in their
bedrooms, "experience beamers" circa 2030 will beam their entire flow of
sensory experiences, and if so desired, their emotions and other secondary
reactions. We'll be able to plug in (by going to the appropriate web site)
and experience other people's lives as in the plot concept of 'Being John
Malkovich.' Particularly interesting experiences can be archived and
relived at any time.

We won't need to wait until 2030 to experience shared virtual reality
environments, at least for the visual and auditory senses. Full immersion
visual-auditory environments will be available by the end of this decade
with images written directly onto our retinas by our eyeglasses and
contact lenses. All of the electronics for the computation, image
reconstruction, and very high bandwidth wireless connection to the
Internet will be embedded in our glasses and woven into our clothing, so
computers as distinct objects will disappear.

In my view, the most significant implication of the Singularity will be
the merger of biological and nonbiological intelligence. First, it is
important to point out that well before the end of the twenty-first
century, thinking on nonbiological substrates will dominate. Biological
thinking is stuck at 1026 calculations per second (for all biological
human brains), and that figure will not appreciably change, even with
bioengineering changes to our genome. Nonbiological intelligence, on the
other hand, is growing at a double exponential rate and will vastly exceed
biological intelligence well before the middle of this century. However,
in my view, this nonbiological intelligence should still be considered
human as it is fully derivative of the human-machine civilization. The
merger of these two worlds of intelligence is not merely a merger of
biological and nonbiological thinking mediums, but more importantly one of
method and organization of thinking.

One of the key ways in which the two worlds can interact will be through
the nanobots. Nanobot technology will be able to expand our minds in
virtually any imaginable way. Our brains today are relatively fixed in
design. Although we do add patterns of interneuronal connections and
neurotransmitter concentrations as a normal part of the learning process,
the current overall capacity of the human brain is highly constrained,
restricted to a mere hundred trillion connections. Brain implants based on
massively distributed intelligent nanobots will ultimately expand our
memories a trillion fold, and otherwise vastly improve all of our sensory,
pattern recognition, and cognitive abilities. Since the nanobots are
communicating with each other over a wireless local area network, they can
create any set of new neural connections, can break existing connections
(by suppressing neural firing), can create new hybrid
biological-nonbiological networks, as well as add vast new nonbiological

Using nanobots as brain extenders is a significant improvement over the
idea of surgically installed neural implants, which are beginning to be
used today (e.g., ventral posterior nucleus, subthalmic nucleus, and
ventral lateral thalamus neural implants to counteract Parkinson's Disease
and tremors from other neurological disorders, cochlear implants, and
others.) Nanobots will be introduced without surgery, essentially just by
injecting or even swallowing them. They can all be directed to leave, so
the process is easily reversible. They are programmable, in that they can
provide virtual reality one minute, and a variety of brain extensions the
next. They can change their configuration, and clearly can alter their
software. Perhaps most importantly, they are massively distributed and
therefore can take up billions or trillions of positions throughout the
brain, whereas a surgically introduced neural implant can only be placed
in one or at most a few locations.

The Double Exponential Growth of the Economy During the 1990s Was Not a
Yet another manifestation of the law of accelerating returns as it rushes
toward the Singularity can be found in the world of economics, a world
vital to both the genesis of the law of accelerating returns, and to its
implications. It is the economic imperative of a competitive marketplace
that is driving technology forward and fueling the law of accelerating
returns. In turn, the law of accelerating returns, particularly as it
approaches the Singularity, is transforming economic relationships.

Virtually all of the economic models taught in economics classes, used by
the Federal Reserve Board to set monetary policy, by Government agencies
to set economic policy, and by economic forecasters of all kinds are
fundamentally flawed because they are based on the intuitive linear view
of history rather than the historically based exponential view. The reason
that these linear models appear to work for a while is for the same reason
that most people adopt the intuitive linear view in the first place:
exponential trends appear to be linear when viewed (and experienced) for a
brief period of time, particularly in the early stages of an exponential
trend when not much is happening. But once the "knee of the curve" is
achieved and the exponential growth explodes, the linear models break
down. The exponential trends underlying productivity growth are just
beginning this explosive phase.

The economy (viewed either in total or per capita) has been growing
exponentially throughout this century:

There is also a second level of exponential growth, but up until recently
the second exponent has been in the early phase so that the growth in the
growth rate has not been noticed. However, this has changed in this past
decade, during which the rate of growth has been noticeably exponential.

Productivity (economic output per worker) has also been growing
exponentially. Even these statistics are greatly understated because they
do not fully reflect significant improvements in the quality and features
of products and services. It is not the case that "a car is a car;" there
have been significant improvements in safety, reliability, and features.
There are a myriad of such examples. Pharmaceutical drugs are increasingly
effective. Groceries ordered in five minutes on the web and delivered to
your door are worth more than groceries on a supermarket shelf that you
have to fetch yourself. Clothes custom manufactured for your unique body
scan are worth more than clothes you happen to find left on a store rack.
These sorts of improvements are true for most product categories, and none
of them are reflected in the productivity statistics.

The statistical methods underlying the productivity measurements tend to
factor out gains by essentially concluding that we still only get one
dollar of products and services for a dollar despite the fact that we get
much more for a dollar (e.g., compare a $1,000 computer today to one ten
years ago). University of Chicago Professor Pete Klenow and University of
Rochester Professor Mark Bils estimate that the value of existing goods
has been increasing at 1.5% per year for the past 20 years because of
qualitative improvements. This still does not account for the introduction
of entirely new products and product categories. The Bureau of Labor
Statistics, which is responsible for the inflation statistics, uses a
model that incorporates an estimate of quality growth at only 0.5% per
year, reflecting a systematic underestimate of quality improvement and a
resulting overestimate of inflation by at least 1 percent per year.

Despite these weaknesses in the productivity statistical methods, the
gains in productivity are now reaching the steep part of the exponential
curve. Labor productivity grew at 1.6% per year until 1994, then rose at
2.4% per year, and is now growing even more rapidly. In the quarter ending
July 30, 2000, labor productivity grew at 5.3%. Manufacturing productivity
grew at 4.4% annually from 1995 to 1999, durables manufacturing at 6.5%
per year.

The 1990s have seen the most powerful deflationary forces in history. This
is why we are not seeing inflation. Yes, it's true that low unemployment,
high asset values, economic growth, and other such factors are
inflationary, but these factors are offset by the double exponential
trends in the price-performance of all information based technologies:
computation, memory, communications, biotechnology, miniaturization, and
even the overall rate of technical progress. These technologies deeply
affect all industries.

We are also undergoing massive disintermediation in the channels of
distribution through the web and other new communication technologies, as
well as escalating efficiencies in operations and administration.

All of the technology trend charts in this précis e represent massive
deflation. There are many examples of the impact of these escalating
efficiencies. BP Amoco's cost for finding oil is now less than $1 per
barrel, down from nearly $10 in 1991. Processing an internet transaction
costs a bank one penny, compared to over $1 using a teller ten years ago.
A Roland Berger / Deutsche Bank study estimates a cost savings of $1200
per North American car over the next five years. A more optimistic Morgan
Stanley study estimates that Internet-based procurement will save Ford,
GM, and DaimlerChrysler about $2700 per vehicle. Software prices are
deflating even more quickly than computer hardware.

Software Price-Performance Has Also Improved at an Exponential Rate
(Example: Automatic Speech Recognition Software
 1985 1995 2000
Price $5,000 $500 $50
Vocabulary Size (# words) 1,000 10,000 100,000
Continuous Speech? No No Yes
User Training Required (Minutes) 180 60 5
Accuracy Poor Fair Good

Current economic policy is based on outdated models which include energy
prices, commodity prices, and capital investment in plant and equipment as
key driving factors, but do not adequately model bandwidth, MIPs,
megabytes, intellectual property, knowledge, and other increasingly vital
(and increasingly increasing) constituents that are driving the economy.

The economy "wants" to grow more than the 3.5% per year, which constitutes
the current "speed limit" that the Federal Reserve bank and other policy
makers have established as "safe," meaning noninflationary. But in keeping
with the law of accelerating returns, the economy is capable of "safely"
establishing this level of growth in less than a year, implying a growth
rate in an entire year of greater than 3.5%. Recently, the growth rate has
exceeded 5%.

None of this means that cycles of recession will disappear immediately.
The economy still has some of the underlying dynamics that historically
have caused cycles of recession, specifically excessive commitments such
as capital intensive projects and the overstocking of inventories.
However, the rapid dissemination of information, sophisticated forms of
online procurement, and increasingly transparent markets in all industries
have diminished the impact of this cycle. So "recessions" are likely to be
shallow and short lived. The underlying long-term growth rate will
continue at a double exponential rate.

The overall growth of the economy reflects completely new forms and layers
of wealth and value that did not previously exist, or least that did not p
reviously constitute a significant portion of the economy (but do now):
intellectual property, communication portals, web sites, bandwidth,
software, data bases, and many other new technology based categories.

There is no need for high interest rates to counter an inflation that
doesn't exist. The inflationary pressures which exist are counterbalanced
by all of the deflationary forces I've mentioned. The current high
interest rates fostered by the Federal Reserve Bank are destructive, are
causing trillions of dollars of lost wealth, are regressive, hurt business
and the middle class, and are completely unnecessary.

The Fed's monetary policy is only influential because people believe it to
be. It has little real power. The economy today is largely backed by
private capital in the form of a growing variety of equity instruments.
The portion of available liquidity in the economy that the Fed actually
controls is relatively insignificant. The reserves that banks and
financial institutions maintain with the Federal Reserve System are less
than $50 billion, which is only 0.6% of the GDP, and 0.25% of the
liquidity available in stocks.

Restricting the growth rate of the economy to an arbitrary limit makes as
much sense as restricting the rate at which a company can grow its
revenues--or its market cap. Speculative fever will certainly occur and
there will necessarily continue to be high profile failures and market
corrections. However the ability of technology companies to rapidly create
new--real--wealth is just one of the factors that will continue to fuel
ongoing double exponential growth in the economy. These policies have led
to an "Alice in Wonderland" situation in which the market goes up on bad
economic news (because it means that more unnecessary punishment will be
avoided) and goes down on good economic news.

Speaking of market speculative fever and market corrections, the stock
market values for so-called "B to B" (Business to Business) and "B to C"
(Business to Consumer) web portals and enabling technologies is likely to
come back strongly as it becomes clear that economic transactions are
indeed escalating toward e-commerce, and that the (surviving) contenders
are capable of demonstrating profitable business models.

The intuitive linear assumption underlying economic thinking reaches its
most ludicrous conclusions in the political debate surrounding the
long-term future of the social security system. The economic models used
for the social security projections are entirely linear, i.e., they
reflect fixed economic growth. This might be viewed as conservative
planning if we were talking about projections of only a few years, but
they become utterly unrealistic for the three to four decades being
discussed. These projections actually assume a fixed rate of growth of
3.5% per year for the next fifty years! There are incredibly naïve
assumptions that bear on both sides of the argument. On the one hand,
there will be radical extensions to human longevity, while on the other
hand, we will benefit from far greater economic expansion. These factors
do not rule each other out, however, as the positive factors are stronger,
and will ultimately dominate. Moreover, we are certain to rethink social
security when we have centenarians who look and act like 30 year-olds (but
who will think much faster than 30 year-olds circa the year 2000).

Another implication of the law of accelerating returns is exponential
growth in education and learning. Over the past 120 years, we have
increased our investment in K-12 education (per student and in constant
dollars) by a factor of ten. We have a one hundred fold increase in the
number of college students. Automation started by amplifying the power of
our muscles, and in recent times has been amplifying the power of our
minds. Thus, for the past two centuries, automation has been eliminating
jobs at the bottom of the skill ladder while creating new (and better
paying) jobs at the top of the skill ladder. So the ladder has been moving
up, and thus we have been exponentially increasing investments in
education at all levels.

Oh, and about that "offer" at the beginning of this précis, consider that
present stock values are based on future expectations. Given that the
(literally) short sighted linear intuitive view represents the ubiquitous
outlook, the common wisdom in economic expectations are dramatically
understated. Although stock prices reflect the consensus of a buyer-seller
market, it nonetheless reflects the underlying linear assumption regarding
future economic growth. But the law of accelerating returns clearly
implies that the growth rate will continue to grow exponentially because
the rate of progress will continue to accelerate. Although (weakening)
recessionary cycles will continue to cause immediate growth rates to
fluctuate, the underlying rate of growth will continue to double
approximately every decade.

But wait a second, you said that I would get $40 trillion if I read and
understood this précis .

That's right. According to my models, if we replace the linear outlook
with the more appropriate exponential outlook, current stock prices should
triple. Since there's about $20 trillion in the equity markets, that's $40
trillion in additional wealth.

But you said I would get that money.

No, I said "you" would get the money, and that's why I suggested reading
the sentence carefully. The English word "you" can be singular or plural.
I meant it in the sense of "all of you."

I see, all of us as in the whole world. But not everyone will read this
précis .

Well, but everyone could. So if all of you read this précis and understand
it, then economic expectations would be based on the historical
exponential model, and thus stock values would increase.

You mean if everyone understands it, and agrees with it.

Okay, I suppose I was assuming that.

Is that what you expect to happen.

Well, actually, no. Putting on my futurist hat again, my prediction is
that indeed these views will prevail, but only over time, as more and more
evidence of the exponential nature of technology and its impact on the
economy becomes apparent. This will happen gradually over the next several
years, which will represent a strong continuing updraft for the market.

A Clear and Future Danger
Technology has always been a double edged sword, bringing us longer and
healthier life spans, freedom from physical and mental drudgery, and many
new creative possibilities on the one hand, while introducing new and
salient dangers on the other. We still live today with sufficient nuclear
weapons (not all of which appear to be well accounted for) to end all
mammalian life on the planet. Bioengineering is in the early stages of
enormous strides in reversing disease and aging processes. However, the
means and knowledge will soon exist in a routine college bioengineering
lab (and already exists in more sophisticated labs) to create unfriendly
pathogens more dangerous than nuclear weapons. As technology accelerates
toward the Singularity, we will see the same intertwined potentials: a
feast of creativity resulting from human intelligence expanded a
trillion-fold combined with many grave new dangers.

Consider unrestrained nanobot replication. Nanobot technology requires
billions or trillions of such intelligent devices to be useful. The most
cost effective way to scale up to such levels is through self-replication,
essentially the same approach used in the biological world. And in the
same way that biological self-replication gone awry (i.e., cancer) results
in biological destruction, a defect in the mechanism curtailing nanobot
self-replication would endanger all physical entities, biological or

Other primary concerns include "who is controlling the nanobots?" and "who
are the nanobots talking to?" Organizations (e.g., governments, extremist
groups) or just a clever individual could put trillions of undetectable
nanobots in the water or food supply of an individual or of an entire
population. These "spy" nanobots could then monitor, influence, and even
control our thoughts and actions. In addition to introducing physical spy
nanobots, existing nanobots could be influenced through software viruses
and other software "hacking" techniques. When there is software running in
our brains, issues of privacy and security will take on a new urgency.

My own expectation is that the creative and constructive applications of
this technology will dominate, as I believe they do today. But there will
be a valuable (and increasingly vocal) role for a concerned and
constructive Luddite movement (i.e., anti-technologists inspired by early
nineteenth century weavers who destroyed labor-saving machinery in

If we imagine describing the dangers that exist today to people who lived
a couple of hundred years ago, they would think it mad to take such risks.
On the other hand, how many people in the year 2000 would really want to
go back to the short, brutish, disease-filled, poverty-stricken,
disaster-prone lives that 99 percent of the human race struggled through a
couple of centuries ago? We may romanticize the past, but up until fairly
recently, most of humanity lived extremely fragile lives where one all too
common misfortune could spell disaster. Substantial portions of our
species still live in this precarious way, which is at least one reason to
continue technological progress and the economic enhancement that
accompanies it.

People often go through three stages in examining the impact of future
technology: awe and wonderment at its potential to overcome age old
problems, then a sense of dread at a new set of grave dangers that
accompany these new technologies, followed, finally and hopefully, by the
realization that the only viable and responsible path is to set a careful
course that can realize the promise while managing the peril.

In his cover story for WIRED Why The Future Doesn't Need Us, Bill Joy
eloquently described the plagues of centuries' past, and how new
self-replicating technologies, such as mutant bioengineered pathogens, and
"nanobots" run amok, may bring back long forgotten pestilence. Indeed
these are real dangers. It is also the case, which Joy acknowledges, that
it has been technological advances, such as antibiotics and improved
sanitation, which has freed us from the prevalence of such plagues.
Suffering in the world continues and demands our steadfast attention.
Should we tell the millions of people afflicted with cancer and other
devastating conditions that we are canceling the development of all
bioengineered treatments because there is a risk that these same
technologies may someday be used for malevolent purposes? Having asked the
rhetorical question, I realize that there is a movement to do exactly
that, but I think most people would agree that such broad based
relinquishment is not the answer.

The continued opportunity to alleviate human distress is one important
motivation for continuing technological advancement. Also compelling are
the already apparent economic gains I discussed above which will continue
to hasten in the decades ahead. The continued acceleration of many
intertwined technologies are roads paved with gold (I use the plural here
because technology is clearly not a single path). In a competitive
environment, it is an economic imperative to go down these roads.
Relinquishing technological advancement would be economic suicide for
individuals, companies, and nations.

Which brings us to the issue of relinquishment, which is Bill Joy's most
controversial recommendation and personal commitment. I do feel that
relinquishment at the right level is part of a responsible and
constructive response to these genuine perils. The issue, however, is
exactly this: at what level are we to relinquish technology?

Ted Kaczynski would have us renounce all of it. This, in my view, is
neither desirable nor feasible, and the futility of such a position is
only underscored by the senselessness of Kaczynski's deplorable tactics.

Another level would be to forego certain fields; nanotechnology, for
example, that might be regarded as too dangerous. But such sweeping
strokes of relinquishment are equally untenable. Nanotechnology is simply
the inevitable end result of the persistent trend toward miniaturization
which pervades all of technology. It is far from a single centralized
effort, but is being pursued by a myriad of projects with many diverse

One observer wrote:

"A further reason why industrial society cannot be reformed. . . is that
modern technology is a unified system in which all parts are dependent on
one another. You can't get rid of the "bad" parts of technology and retain
only the "good" parts. Take modern medicine, for example. Progress in
medical science depends on progress in chemistry, physics, biology,
computer science and other fields. Advanced medical treatments require
expensive, high-tech equipment that can be made available only by a
technologically progressive, economically rich society. Clearly you can't
have much progress in medicine without the whole technological system and
everything that goes with it."
The observer I am quoting is, again, Ted Kaczynski. Although one might
properly resist Kaczynski as an authority, I believe he is correct on the
deeply entangled nature of the benefits and risks. However, Kaczynski and
I clearly part company on our overall assessment on the relative balance
between the two. Bill Joy and I have dialogued on this issue both publicly
and privately, and we both believe that technology will and should
progress, and that we need to be actively concerned with the dark side. If
Bill and I disagree, it's on the granularity of relinquishment that is
both feasible and desirable.

Abandonment of broad areas of technology will only push them underground
where development would continue unimpeded by ethics and regulation. In
such a situation, it would be the less stable, less responsible
practitioners (e.g., the terrorists) who would have all the expertise.

I do think that relinquishment at the right level needs to be part of our
ethical response to the dangers of twenty first century technologies. One
constructive example of this is the proposed ethical guideline by the
Foresight Institute, founded by nanotechnology pioneer Eric Drexler, that
nanotechnologists agree to relinquish the development of physical entities
that can self-replicate in a natural environment. Another is a ban on
self-replicating physical entities that contain their own codes for
self-replication. In what nanotechnologist Ralph Merkle calls the
"Broadcast Architecture," such entities would have to obtain such codes
from a centralized secure server, which would guard against undesirable
replication. The Broadcast Architecture is impossible in the biological
world, which represents at least one way in which nanotechnology can be
made safer than biotechnology. In other ways, nanotech is potentially more
dangerous because nanobots can be physically stronger than protein-based
entities and more intelligent. It will eventually be possible to combine
the two by having nanotechnology provide the codes within biological
entities (replacing DNA), in which case biological entities can use the
much safer Broadcast Architecture.

Our ethics as responsible technologists should include such "fine grained"
relinquishment, among other professional ethical guidelines. Other
protections will need to include oversight by regulatory bodies, the
development of technology-specific "immune" responses, as well as computer
assisted surveillance by law enforcement organizations. Many people are
not aware that our intelligence agencies already use advanced technologies
such as automated word spotting to monitor a substantial flow of telephone
conversations. As we go forward, balancing our cherished rights of privacy
with our need to be protected from the malicious use of powerful twenty
first century technologies will be one of many profound challenges. This
is one reason that such issues as an encryption "trap door" (in which law
enforcement authorities would have access to otherwise secure information)
and the FBI "Carnivore" email-snooping system have been so contentious.

As a test case, we can take a small measure of comfort from how we have
dealt with one recent technological challenge. There exists today a new
form of fully nonbiological self replicating entity that didn't exist just
a few decades ago: the computer virus. When this form of destructive
intruder first appeared, strong concerns were voiced that as they became
more sophisticated, software pathogens had the potential to destroy the
computer network medium they live in. Yet the "immune system" that has
evolved in response to this challenge has been largely effective. Although
destructive self-replicating software entities do cause damage from time
to time, the injury is but a small fraction of the benefit we receive from
the computers and communication links that harbor them. No one would
suggest we do away with computers, local area networks, and the Internet
because of software viruses.

One might counter that computer viruses do not have the lethal potential
of biological viruses or of destructive nanotechnology. Although true,
this strengthens my observation. The fact that computer viruses are not
usually deadly to humans only means that more people are willing to create
and release them. It also means that our response to the danger is that
much less intense. Conversely, when it comes to self replicating entities
that are potentially lethal on a large scale, our response on all levels
will be vastly more serious.

Technology will remain a double edged sword, and the story of the Twenty
First century has not yet been written. It represents vast power to be
used for all humankind's purposes. We have no choice but to work hard to
apply these quickening technologies to advance our human values, despite
what often appears to be a lack of consensus on what those values should

Living Forever
Once brain porting technology has been refined and fully developed, will
this enable us to live forever? The answer depends on what we mean by
living and dying. Consider what we do today with our personal computer
files. When we change from one personal computer to a less obsolete model,
we don't throw all our files away; rather we copy them over to the new
hardware. Although our software files do not necessary continue their
existence forever, the longevity of our personal computer software is
completely separate and disconnected from the hardware that it runs on.
When it comes to our personal mind file, however, when our human hardware
crashes, the software of our lives dies with it. However, this will not
continue to be the case when we have the means to store and restore the
thousands of trillions of bytes of information represented in the pattern
that we call our brains.

The longevity of one's mind file will not be dependent, therefore, on the
continued viability of any particular hardware medium. Ultimately
software-based humans, albeit vastly extended beyond the severe
limitations of humans as we know them today, will live out on the web,
projecting bodies whenever they need or want them, including virtual
bodies in diverse realms of virtual reality, holographically projected
bodies, physical bodies comprised of nanobot swarms, and other forms of

A software-based human will be free, therefore, from the constraints of
any particular thinking medium. Today, we are each confined to a mere
hundred trillion connections, but humans at the end of the twenty-first
century can grow their thinking and thoughts without limit. We may regard
this as a form of immortality, although it is worth pointing out that data
and information do not necessarily last forever. Although not dependent on
the viability of the hardware it runs on, the longevity of information
depends on its relevance, utility, and accessibility. If you've ever tried
to retrieve information from an obsolete form of data storage in an old
obscure format (e.g., a reel of magnetic tape from a 1970 minicomputer),
you will understand the challenges in keeping software viable. However, if
we are diligent in maintaining our mind file, keeping current backups, and
porting to current formats and mediums, then a form of immortality can be
attained, at least for software-based humans. Our mind file--our
personality, skills, memories--all of that is lost today when our
biological hardware crashes. When we can access, store, and restore that
information, then its longevity will no longer be tied to our hardware

Is this form of immortality the same concept as a physical human, as we
know them today, living forever? In one sense it is, because as I pointed
out earlier, our contemporary selves are not a constant collection of
matter either. Only our pattern of matter and energy persists, and even
that gradually changes. Similarly, it will be the pattern of a software
human that persists and develops and changes gradually.

But is that person based on my mind file, who migrates across many
computational substrates, and who outlives any particular thinking medium,
really me? We come back to the same questions of consciousness and
identity, issues that have been debated since the Platonic dialogues. As
we go through the twenty-first century, these will not remain polite
philosophical debates, but will be confronted as vital, practical,
political, and legal issues.

A related question is "is death desirable?" A great deal of our effort
goes into avoiding it. We make extraordinary efforts to delay it, and
indeed often consider its intrusion a tragic event. Yet we might find it
hard to live without it. We consider death as giving meaning to our lives.
It gives importance and value to time. Time could become meaningless if
there were too much of it.

The Next Step in Evolution and the Purpose of Life
But I regard the freeing of the human mind from its severe physical
limitations of scope and duration as the necessary next step in evolution.
Evolution, in my view, represents the purpose of life. That is, the
purpose of life--and of our lives--is to evolve. The Singularity then is
not a grave danger to be avoided. In my view, this next paradigm shift
represents the goal of our civilization.

What does it mean to evolve? Evolution moves toward greater complexity,
greater elegance, greater knowledge, greater intelligence, greater beauty,
greater creativity, and more of other abstract and subtle attributes such
as love. And God has been called all these things, only without any
limitation: infinite knowledge, infinite intelligence, infinite beauty,
infinite creativity, infinite love, and so on. Of course, even the
accelerating growth of evolution never achieves an infinite level, but as
it explodes exponentially, it certainly moves rapidly in that direction.
So evolution moves inexorably toward our conception of God, albeit never
quite reaching this ideal. Thus the freeing of our thinking from the
severe limitations of its biological form may be regarded as an essential
spiritual quest.

In making this statement, it is important to emphasize that terms like
evolution, destiny, and spiritual quest are observations about the end
result, not the basis for these predictions. I am not saying that
technology will evolve to human levels and beyond simply because it is our
destiny and because of the satisfaction of a spiritual quest. Rather my
projections result from a methodology based on the dynamics underlying the
(double) exponential growth of technological processes. The primary force
driving technology is economic imperative. We are moving toward machines
with human level intelligence (and beyond) as the result of millions of
small advances, each with their own particular economic justification.

To use an example from my own experience at one of my companies (Kurzweil
Applied Intelligence), whenever we came up with a slightly more
intelligent version of speech recognition, the new version invariably had
greater value than the earlier generation and, as a result, sales
increased. It is interesting to note that in the example of speech
recognition software, the three primary surviving competitors stayed very
close to each other in the intelligence of their software. A few other
companies that failed to do so (e.g., Speech Systems) went out of
business. At any point in time, we would be able to sell the version prior
to the latest version for perhaps a quarter of the price of the current
version. As for versions of our technology that were two generations old,
we couldn't even give those away. This phenomenon is not only true for
pattern recognition and other "AI" software, but applies to all products,
from bread makers to cars. And if the product itself doesn't exhibit some
level of intelligence, then intelligence in the manufacturing and
marketing methods have a major effect on the success and profitability of
an enterprise.

There is a vital economic imperative to create more intelligent
technology. Intelligent machines have enormous value. That is why they are
being built. There are tens of thousands of projects that are advancing
intelligent machines in diverse incremental ways. The support for "high
tech" in the business community (mostly software) has grown enormously.
When I started my optical character recognition (OCR) and speech synthesis
company (Kurzweil Computer Products, Inc.) in 1974, there were only a
half-dozen high technology IPO's that year. The number of such deals has
increased one hundred fold and the number of dollars invested has
increased by more than one thousand fold in the past 25 years. In the four
years between 1995 and 1999 alone, high tech venture capital deals
increased from just over $1 billion to approximately $15 billion.

We will continue to build more powerful computational mechanisms because
it creates enormous value. We will reverse-engineer the human brain not
simply because it is our destiny, but because there is valuable
information to be found there that will provide insights in building more
intelligent (and more valuable) machines. We would have to repeal
capitalism and every visage of economic competition to stop this

By the second half of this next century, there will be no clear
distinction between human and machine intelligence. On the one hand, we
will have biological brains vastly expanded through distributed
nanobot-based implants. On the other hand, we will have fully
nonbiological brains that are copies of human brains, albeit also vastly
extended. And we will have a myriad of other varieties of intimate
connection between human thinking and the technology it has fostered.

Ultimately, nonbiological intelligence will dominate because it is growing
at a double exponential rate, whereas for all practical purposes
biological intelligence is at a standstill. Human thinking is stuck at
1026 calculations per second (for all biological humans), and that figure
will never appreciably change (except for a small increase resulting from
genetic engineering). Nonbiological thinking is still millions of times
less today, but the cross over will occur before 2030. By the end of the
twenty-first century, nonbiological thinking will be trillions of
trillions of times more powerful than that of its biological progenitors,
although still of human origin. It will continue to be the human-machine
civilization taking the next step in evolution.

Most forecasts of the future seem to ignore the revolutionary impact of
the Singularity in our human destiny: the inevitable emergence of
computers that match and ultimately vastly exceed the capabilities of the
human brain, a development that will be no less important than the
evolution of human intelligence itself some thousands of centuries ago.
And the primary reason for this failure is that they are based on the
intuitive but short sighted linear view of history.

Before the next century is over, the Earth's technology-creating species
will merge with its computational technology. There will not be a clear
distinction between human and machine. After all, what is the difference
between a human brain enhanced a trillion fold by nanobot-based implants,
and a computer whose design is based on high resolution scans of the human
brain, and then extended a trillion-fold?

Why SETI Will Fail (and why we are alone in the Universe)
The law of accelerating returns implies that by 2099, the intelligence
that will have emerged from human-machine civilization will be trillions
of trillions of times more powerful than it is today, dominated of course
by its nonbiological form.

So what does this have to do with SETI (the Search for Extra Terrestrial
Intelligence)? The naïve view, going back to pre-Copernican days, was that
the Earth was at the center of the Universe, and human intelligence its
greatest gift (next to God). The more informed recent view is that even if
the likelihood of a star having a planet with a technology creating
species is very low (e.g., one in a million), there are so many stars
(i.e., billions of trillions of them), that there are bound to be many
with advanced technology.

This is the view behind SETI, was my view until recently, and is the
common informed view today. Although SETI has not yet looked everywhere,
it has already covered a substantial portion of the Universe.

In the above diagram (courtesy of Scientific American), we can see that
SETI has already thoroughly searched all star systems within 107
light-years from Earth for alien civilizations capable (and willing) to
transmit at a power of at least 1025 watts, a so-called Type II
civilization (and all star systems within 106 light-years for transmission
of at least 1018 watts, and so on). No sign of intelligence has been found
as of yet.

In a recent email to my research assistant, Dr. Seth Shostak of the SETI
Institute points out that a new comprehensive targeted search, called
Project Phoenix, which has up to 100 times the sensitivity and covers a
greater range of the radio dial as compared to previous searches, has only
been applied thus far to 500 star systems, which is, of course only a
minute fraction of the half trillion star systems in just our own galaxy.

However, according to my model, once a civilization achieves our own level
("Earth-level") of radio transmission, it takes no more than one century,
two at the most, to achieve what SETI calls a Type II civilization. If the
assumption that there are at least millions of radio capable civilizations
out there, and that these civilizations are spread out over millions
(indeed billions) of years of development, then surely there ought to be
millions that have achieved Type II status.

Incidentally, this is not an argument against the SETI project, which in
my view should have the highest possible priority because the negative
finding is no less significant than a positive result.

It is odd that we find the cosmos so silent. Where is everybody? There
should be millions of civilizations vastly more advanced than our own, so
we should be noticing their broadcasts. A sufficiently advanced
civilization would not be likely to restrict its broadcasts to subtle
signals on obscure frequencies. Why are they so silent, and so shy?

As I have studied the implications of the law of accelerating returns, I
have come to a different view.

Because exponential growth is so explosive, it is the case that once a
species develops computing technology, it is only a matter of a couple of
centuries before the nonbiological form of their intelligence explodes. It
permeates virtually all matter in their vicinity, and then inevitably
expands outward close to the maximum speed that information can travel.
Once the nonbiological intelligence emerging from that species' technology
has saturated its vicinity (and the nature of this saturation is another
complex issue, which I won't deal with in this précis), it has no other
way to continue to evolve but to expand outwardly. The expansion does not
start out at the maximum speed, but quickly achieves a speed within a
vanishingly small delta from the maximum speed.

What is the maximum speed? We currently understand this to be the speed of
light, but there are already tantalizing hints that this may not be an
absolute limit. There were recent experiments that measured the flight
time of photons at nearly twice the speed of light, a result of quantum
uncertainty on their position. However, this result is actually not useful
for this analysis, because it does not actually allow information to be
communicated at faster than the speed of light, and we are fundamentally
interested in communication speed.

Quantum disentanglement has been measured at many times the speed of
light, but this is only communicating randomness (profound quantum
randomness) at speeds far greater than the speed of light; again, this is
not communication of information (but is of great interest for restoring
encryption, after quantum computing destroys it). There is the potential
for worm holes (or folds of the Universe in dimensions beyond the three
visible ones), but this is not really traveling at faster than the speed
of light, it just means that the topology of the Universe is not the
simple three dimensional space that naïve physics implies. But we already
knew that. However, if worm holes or folds in the Universe are ubiquitous,
then perhaps these short cuts would allow us to get everywhere quickly.
Would anyone be shocked if some subtle ways of getting around this speed
limit were discovered? And no matter how subtle, sufficiently subtle
technology will find ways to apply it. The point is that if there are ways
around this limit (or any other currently understood limit), then the
extraordinary levels of intelligence that our human-machine civilization
will achieve will find those ways and exploit them.

So for now, we can say that ultra high levels of intelligence will expand
outward at the speed of light, but recognize that this may not be the
actual limit of the speed of expansion, or even if the limit is the speed
of light that this limit may not restrict reaching other locations

Consider that the time spans for biological evolution are measured in
millions and billions of years, so if there are other civilizations out
there, they would be spread out by huge spans of time. If there are a lot
of them, as contemporary thinking implies, then it would be very unlikely
that at least some of them would not be ahead of us. That at least is the
SETI assumption. And if they are ahead of us, they likely would be ahead
of us by huge spans of time. The likelihood that any civilization that is
ahead of us is ahead of us by only a few decades is extremely small.

If the SETI assumption that there are many (e.g., millions) of
technological (at least radio capable) civilizations is correct, then at
least some of them (i.e., millions of them) would be way ahead of us. But
it takes only a few centuries at most from the advent of computation for
that civilization to expand outward at at least light speed. Given this,
how can it be that we have not noticed them?

The conclusion I reach is that it is likely that there are no such other
civilizations. In other words, we are in the lead. That's right, our
humble civilization with its Dodge pick up trucks, fried chicken fast
food, and ethnic cleansings (and computation!) is in the lead.

Now how can that be? Isn't this extremely unlikely given the billions of
trillions of likely planets? Indeed it is very unlikely. But equally
unlikely is the existence of our Universe with a set of laws of physics so
exquisitely precisely what is needed for the evolution of life to be
possible. But by the Anthropic principle, if the Universe didn't allow the
evolution of life we wouldn't be here to notice it. Yet here we are. So by
the same Anthropic principle, we're here in the lead in the Universe.
Again, if we weren't here, we would not be noticing it.

Let's consider some arguments against this perspective.

Perhaps there are extremely advanced technological civilizations out
there, but we are outside their light sphere of intelligence. That is,
they haven't gotten here yet. Okay, in this case, SETI will still fail
because we won't be able to see (or hear) them, at least not before we
reach Singularity.

Perhaps they are amongst us, but have decided to remain invisible to us.
Incidentally, I have always considered the science fiction notion of large
space ships with large squishy creatures similar to us to be very
unlikely. Any civilization sophisticated enough to make the trip here
would have long since passed the point of merging with their technology
and would not need to send such physically bulky organisms and equipment.
Such a civilization would not have any unmet material needs that require
it to steal physical resources from us. They would be here for observation
only, to gather knowledge, which is the only resource of value to such a
civilization. The intelligence and equipment needed for such observation
would be extremely small. In this case, SETI will still fail because if
this civilization decided that it did not want us to notice it, then it
would succeed in that desire. Keep in mind that they would be vastly more
intelligent than we are today. Perhaps they will reveal themselves to us
when we achieve the next level of our evolution, specifically merging our
biological brains with our technology, which is to say, after the
Singularity. Moreover, given that the SETI assumption implies that there
are millions of such highly developed civilizations, it seems odd that all
of them have made the same decision to stay out of our way.

Why Intelligence is More Powerful than Physics
As intelligence saturates the matter and energy available to it, it turns
dumb matter into smart matter. Although smart matter still nominally
follows the laws of physics, it is so exquisitely intelligent that it can
harness the most subtle aspects of the laws to manipulate matter and
energy to its will. So it would at least appear that intelligence is more
powerful than physics.

Perhaps what I should say is that intelligence is more powerful than
cosmology. That is, once matter evolves into smart matter (matter fully
saturated with intelligence), it can manipulate matter and energy to do
whatever it wants. This perspective has not been considered in discussions
of future cosmology. It is assumed that intelligence is irrelevant to
events and processes on a cosmological scale. Stars are born and die;
galaxies go through their cycles of creation and destruction. The Universe
itself was born in a big bang and will end with a crunch or a whimper,
we're not yet sure which. But intelligence has little to do with it.
Intelligence is just a bit of froth, an ebullition of little creatures
darting in and out of inexorable universal forces. The mindless mechanism
of the Universe is winding up or down to a distant future, and there's
nothing intelligence can do about it.

That's the common wisdom, but I don't agree with it. Intelligence will be
more powerful than these impersonal forces. Once a planet yields a
technology creating species and that species creates computation (as has
happened here on Earth), it is only a matter of a few centuries before its
intelligence saturates the matter and energy in its vicinity, and it
begins to expand outward at the speed of light or greater. It will then
overcome gravity (through exquisite and vast technology) and other
cosmological forces (or, to be fully accurate, will maneuver and control
these forces) and create the Universe it wants. This is the goal of the

What kind of Universe will that be? Well, just wait and see.

Plan to Stick Around
Most of you (again I'm using the plural form of the word) are likely to be
around to see the Singularity. The expanding human life span is another
one of those exponential trends. In the eighteenth century, we added a few
days every year to human longevity; during the nineteenth century we added
a couple of weeks each year; and now we're adding almost a half a year
every year. With the revolutions in genomics, proteomics, rational drug
design, therapeutic cloning of our own organs and tissues, and related
developments in bio-information sciences, we will be adding more than a
year every year within ten years. So take care of yourself the old
fashioned way for just a little while longer, and you may actually get to
experience the next fundamental paradigm shift in our destiny.

Copyright (C) Raymond Kurzweil 2001

Stay hungry,

--J. R.

Useless hypotheses: consciousness, phlogiston, philosophy, vitalism, mind,
free will

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