The Singularity is Near: When Humans Transcend Biology

by Ray Kurzweil

Still, my view is that, despite our profound limitations of thought, we do have sufficient powers of abstraction to make meaningful statements about the nature of life after the Singularity.

Most important, the intelligence that will emerge will continue to represent the human civilization, which is already a human-machine civilization. In other words, future machines will be human, even if they are not biological.

This will be the next step in evolution, the next high-level paradigm shift, the next level of indirection. Most of the intelligence of our civilization will ultimately be nonbiological. By the end of this century, it will be trillio...

Our civilization will remain human—indeed, in many ways it will be more exemplary of what we regard as human than it is today, although our understanding of the term will move beyond its biological origins.

The potential to augment our own intelligence through intimate connection with other thinking substrates 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.

CHARLES DARWIN: If I may interrupt, it occurred to me that once machine intelligence is greater than human intelligence, it should be in a position to design its own next generation. MOLLY 2004: That doesn’t sound so unusual. Machines are used to design machines today. CHARLES: Yes, but in 2004 they’re still guided by human designers. Once machines are operating at human levels, well, then it kind of closes the loop.

RAY: But we’re just tinkering with a few details. Inherently, DNA-based intelligence is just so very slow and limited. CHARLES: So the machines will design their own next generation rather quickly.

MOLLY 2004: Yes, hiding from the nanobots will be difficult, for sure. RAY: I would expect the intelligence that arises from the Singularity to have great respect for their biological heritage.

RAY: Foglets are nanobots—robots the size of blood cells—that can connect themselves to replicate any physical structure. Moreover, they can direct visual and auditory information in such a way as to bring the morphing qualities of virtual reality into real reality.

The further backward you look, the further forward you can see. —WINSTON CHURCHILL

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. —CARL SAGAN

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”...

The 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.

We see some expected variation, but an unmistakable exponential trend: key events have been occurring at an ever-hastening pace.

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?

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.

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.”

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

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.

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 ...

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).

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.

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 tech...

Sometimes, a deeper order—a better fit to a purpose—is achieved through simplification rather than fu...

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 scie...

Evolution has shown, however, that the general trend toward greater order does typically result in greater complexity.

Thus improving a solution to a problem—which usually increases but sometimes decreases complexity—increases order.

In biological evolution the overall problem has always been to survive. In particular ecological niches this overriding challenge translates into more specific objectives, such as the ability of certain species to survive in extreme environments or to camouflage themselves from predators. As biological evolution moved toward humanoids, the objective itself evolved to the ability to outthink adversaries and to manipulate the environment accordingly.

It may appear that this aspect of the law of accelerating returns contradicts the second law of thermodynamics, which implies that entropy (randomness in a closed system) cannot decrease and, therefore, generally increases.

the law of accelerating returns pertains to evolution, which is not a closed system. It takes place amid great chaos and indeed depends on the disorder in its midst, from which it draws its options for diversity. And from these options, an evolutionary ...

A primary reason that evolution—of life-forms or of technology—speeds up is that it builds on its own increasing order, with ever more sophisticated means of recording and manipulating information.

Innovations created by evolution encourage and enable faster evolution. In the case of the evolution of life-forms, the most notable early example is DNA, which provides a recorded and protected transcription of life’s design from which to launch further experiments.

The first computers were designed on paper and assembled by hand. Today, they are designed on computer workstations, with the computers themselves working out many details of the next generation’s design, and are then produced in fully automated factories with only limited human intervention.

Innovation is multiplicative, not additive. Technology, like any evolutionary process, builds on itself. This aspect will continue to accelerate when the technology itself takes full control of its own progression in Epoch Five.

Evolution applies positive feedback: the more capable methods resulting from one stage of evolutionary progress are used to create the next stage.

Evolution works through indirection: evolution created humans, humans created technology, humans are now working with increasingly advanced technology to create new generations of technology. By the time of the Singularity, there won’t be a distinction between humans and technology.

This is not because humans will have become what we think of as machines today, but rather machines will have progressed to be like humans and beyond. Technology will be the metaphorical opposable thumb that enables our next step in evolution.

Progress (further increases in order) will then be based on thinking processes that occur at the speed of light rather than in very slow electrochemical reactions. Each stage of evolution builds on the fruits of the last stage, so the rate of progress of an evolutionary process increases at least exponentially over time. Over time, the “order” of the information embedded in the evolutionary pro...

An evolutionary process is not a closed system; evolution draws upon the chaos in the larger system in which it takes place for its options for diversity. Because evolution also builds on its own increasing order,...

A correlate of the above observation is that the “returns” of an evolutionary process (such as the speed, efficiency, cost-effectiveness, or overall “power” of a process) also increase at least exponentially over time. We see this in Moore’s Law, in which each new generation of computer chip (which now appears approximately every two years) provides twice as many components per unit cost, each of which operates substantially faster (...

It is also important to note that the term “information technology” is encompassing an increasingly broad class of phenomena and will ultimately include the full range of economic activity and cultural endeavor.

In another positive-feedback loop, the more effective a particular evolutionary process becomes—for example, the higher the capacity and cost-effectiveness that computation attains—the greater the amount of resources that are deployed toward the further progress of that process. This results in a second level of exponential growth; that is, the rate of exponential growth—the exponent—itself grows exponentially.

Biological evolution is one such evolutionary process. Indeed, it is the quintessential evolutionary process. Because it took place in a completely open system (as opposed to the artificial constraints in an evolutionary algorithm), many levels of the system evolved at the same time.

evolution has developed ways to protect genetic information from excessive defects (although a small amount of mutation is allowed, since this is a beneficial mechanism for ongoing evolutionary improvement). One primary means of achieving this is the repetition of genetic information on paired chromosomes. This guarantees that, even if a gene on one chromosome is damaged, its corresponding gene is likely to be correct and effective.

Technological evolution is another such evolutionary process. Indeed, the emergence of the first technology-creating species resulted in the new evolutionary process of technology, which makes technological evolution an outgrowth of—and a continuation of—biological evolution.

Today, the products of major paradigm shifts, such as cell phones and the World Wide Web, are widely adopted in only a few years’ time.

A specific paradigm (a method or approach to solving a problem; for example, shrinking transistors on an integrated circuit as a way to make more powerful computers) generates exponential growth until its potential is exhausted. When this happens, a paradigm shift occurs, which enables exponential growth to continue.