The Singularity Is Near: When Humans Transcend Biology (9 page)

R
AY
:
Actually, the entire biosphere is less than one millionth of the matter and energy in the solar system
.

C
HARLES
:
It includes a lot of the carbon
.

R
AY
:
It’s still worth keeping all of it to make sure we haven’t lost anything
.

G
EORGE
2048:
That has been the consensus for at least several years now
.

M
OLLY
2004:
So, basically, I’ll have everything I need at my fingertips?

G
EORGE
2048:
Indeed
.

M
OLLY
2004:
Sounds like King Midas. You know, everything he touched turned to gold
.

N
ED
:
Yes, and as you will recall he died of starvation as a result
.

M
OLLY
2004:
Well, if I do end up going over to the other side, with all of that vast expanse of subjective time, I think I’ll die of boredom
.

G
EORGE
2048:
Oh, that could never happen. I will make sure of it
.

CHAPTER TWO

A Theory of
Technology Evolution

The Law of Accelerating Returns

The further backward you look, the further forward you can see.

                   —W
INSTON
C
HURCHILL

Two billion years ago, our ancestors were microbes; a half-billion years ago, fish; a hundred million years ago, something like mice; ten million years ago, arboreal apes; and a million years ago, proto-humans puzzling out the taming of fire. Our evolutionary lineage is marked by mastery of change. In our time, the pace is quickening.

                   —C
ARL
S
AGAN

Our sole responsibility is to produce something smarter than we are; any problems beyond that are not
ours
to solve. . . . [T]here are no hard problems, only problems that are hard to a certain level of intelligence. Move the smallest bit upwards [in level of intelligence], and some problems will suddenly move from “impossible” to “obvious.” Move a substantial degree upwards, and all of them will become obvious.

                   —E
LIEZER
S. Y
UDKOWSKY
,
S
TARING INTO THE
S
INGULARITY
, 1996

“The future can’t be predicted,” is a common refrain. . . .But . . .when [this perspective] is wrong, it is profoundly wrong.

                   —J
OHN
S
MART
¹

T
he ongoing acceleration of technology is the implication and inevitable result of what I call the law of accelerating returns, which describes the acceleration of the pace of and the exponential growth of the products of an evolutionary process. These products include, in particular, information-bearing technologies such as computation, and their acceleration extends substantially beyond the predictions made by what has become known as Moore’s
Law. The Singularity is the inexorable result of the law of accelerating returns, so it is important that we examine the nature of this evolutionary process.

The Nature of Order
. The previous chapter featured several graphs demonstrating the acceleration of paradigm shift. (Paradigm shifts are major changes in methods and intellectual processes to accomplish tasks; examples include written language and the computer.) The graphs plotted what fifteen thinkers and reference works regarded as the key events in biological and technological evolution from the Big Bang to the Internet. We see some expected variation, but an unmistakable exponential trend: key events have been occurring at an ever-hastening pace.

The criteria for what constituted “key events” varied from one thinker’s list to another. But it’s worth considering the principles they used in making their selections. Some observers have judged that the truly epochal advances in the history of biology and technology have involved increases in complexity.
²
Although increased complexity does appear to follow advances in both biological and technological evolution, I believe that this observation is not precisely correct. But let’s first examine what complexity means.

Not surprisingly, the concept of complexity is complex. One concept of complexity is the minimum amount of information required to represent a process. Let’s say you have a design for a system (for example, a computer program or a computer-assisted design file for a computer), which can be described by a data file containing one million bits. We could say your design has a complexity of one million bits. But suppose we notice that the one million bits actually consist of a pattern of one thousand bits that is repeated one thousand times. We could note the repetitions, remove the repeated patterns, and express the entire design in just over one thousand bits, thereby reducing the size of the file by a factor of about one thousand.

The most popular data-compression techniques use similar methods of finding redundancy within information.
³
But after you’ve compressed a data file in this way, can you be absolutely certain that there are no other rules or methods that might be discovered that would enable you to express the file in even more compact terms? For example, suppose my file was simply “pi” (3.1415 . . .) expressed to one million bits of precision. Most data-compression programs would fail to recognize this sequence and would not compress the million bits at all, since the bits in a binary expression of pi are effectively random and thus have no repeated pattern according to all tests of randomness.

But if we can determine that the file (or a portion of the file) in fact represents pi, we can easily express it (or that portion of it) very compactly as “pi to
one million bits of accuracy.” Since we can never be sure that we have not overlooked some even more compact representation of an information sequence, any amount of compression sets only an upper bound for the complexity of the information. Murray Gell-Mann provides one definition of complexity along these lines. He defines the “algorithmic information content” (AIC) of a set of information as “the length of the shortest program that will cause a standard universal computer to print out the string of bits and then halt.”
⁴

However, Gell-Mann’s concept is not fully adequate. If we have a file with random information, it cannot be compressed. That observation is, in fact, a key criterion for determining if a sequence of numbers is truly random. However, if
any
random sequence will do for a particular design, then this information can be characterized by a simple instruction, such as “put random sequence of numbers here.” So the random sequence, whether it’s ten bits or one billion bits, does not represent a significant amount of complexity, because it is characterized by a simple instruction. This is the difference between a random sequence and an unpredictable sequence of information that has purpose.

To gain some further insight into the nature of complexity, consider the complexity of a rock. If we were to characterize all of the properties (precise location, angular momentum, spin, velocity, and so on) of every atom in the rock, we would have a vast amount of information. A one-kilogram (2.2-pound) rock has 10
²⁵
atoms which, as I will discuss in the next chapter, can hold up to 10
²⁷
bits of information. That’s one hundred million billion times more information than the genetic code of a human (even without compressing the genetic code).
⁵
But for most common purposes, the bulk of this information is largely random and of little consequence. So we can characterize the rock for most purposes with far less information just by specifying its shape and the type of material of which it is made. Thus, it is reasonable to consider the complexity of an ordinary rock to be far less than that of a human even though the rock theoretically contains vast amounts of information.
⁶

One concept of complexity is the minimum amount of
meaningful, non-random, but unpredictable
information needed to characterize a system or process.

In Gell-Mann’s concept, the AIC of a million-bit random string would be about a million bits long. So I am adding to Gell-Mann’s AIC concept the idea of replacing each random string with a simple instruction to “put random bits” here.

However, even this is not sufficient. Another issue is raised by strings of arbitrary data, such as names and phone numbers in a phone book, or periodic measurements of radiation levels or temperature. Such data is not random, and
data-compression methods will only succeed in reducing it to a small degree. Yet it does not represent complexity as that term is generally understood. It is just data. So we need another simple instruction to “put arbitrary data sequence” here.

To summarize my proposed measure of the complexity of a set of information, we first consider its AIC as Gell-Mann has defined it. We then replace each random string with a simple instruction to insert a random string. We then do the same for arbitrary data strings. Now we have a measure of complexity that reasonably matches our intuition.

It is a fair observation that paradigm shifts in an evolutionary process such as biology—and its continuation through technology—each represent an increase in complexity, as I have defined it above. For example, the evolution of DNA allowed for more complex organisms, whose biological information processes could be controlled by the DNA molecule’s flexible data storage. The Cambrian explosion provided a stable set of animal body plans (in DNA), so that the evolutionary process could concentrate on more complex cerebral development. In technology, the invention of the computer provided a means for human civilization to store and manipulate ever more complex sets of information. The extensive interconnectedness of the Internet provides for even greater complexity.

“Increasing complexity” on its own is not, however, the ultimate goal or end-product of these evolutionary processes. Evolution results in
better
answers, not necessarily more complicated ones. Sometimes a superior solution is a simpler one. So let’s consider another concept: order. Order is not the same as the opposite of disorder. If disorder represents a random sequence of events, the opposite of disorder should be “not randomness.” Information is a sequence of data that is meaningful in a process, such as the DNA code of an organism or the bits in a computer program. “Noise,” on the other hand, is a random sequence. Noise is inherently unpredictable but carries no information. Information, however, is also unpredictable. If we can predict future data from past data, that future data stops being information. Thus, neither information nor noise can be compressed (and restored to exactly the same sequence). We might consider a predictably alternating pattern (such as 0101010 . . .) to be orderly, but it carries no information beyond the first couple of bits.

Thus, orderliness does not constitute order, because order requires information.
Order is information that fits a purpose. The measure of order is the measure of how well the information fits the purpose
. In the evolution of life-forms, the purpose is to survive. In an evolutionary algorithm (a computer program that simulates evolution to solve a problem) applied to, say, designing
a jet engine, the purpose is to optimize engine performance, efficiency, and possibly other criteria.
⁷
Measuring order is more difficult than measuring complexity. There are proposed measures of complexity, as I discussed above. For order, we need a measure of “success” that would be tailored to each situation. When we create evolutionary algorithms, the programmer needs to provide such a success measure (called the “utility function”). In the evolutionary process of technology development, we could assign a measure of economic success.

Simply having more information does not necessarily result in a better fit. Sometimes, a deeper order—a better fit to a purpose—is achieved through simplification rather than further increases in complexity. For example, a new theory that ties together apparently disparate ideas into one broader, more coherent theory reduces complexity but nonetheless may increase the “order for a purpose.” (In this case, the purpose is to accurately model observed phenomena.) Indeed, achieving simpler theories is a driving force in science. (As Einstein said,“Make everything as simple as possible, but no simpler.”)

An important example of this concept is one that represented a key step in the evolution of hominids: the shift in the thumb’s pivot point, which allowed more precise manipulation of the environment.
⁸
Primates such as chimpanzees can grasp but they cannot manipulate objects with either a “power grip,” or sufficient fine-motor coordination to write or to shape objects. A change in the thumb’s pivot point did not significantly increase the complexity of the animal but nonetheless did represent an increase in order, enabling, among other things, the development of technology. Evolution has shown, however, that the general trend toward greater order does typically result in greater complexity.
⁹