The Singularity is Near: When Humans Transcend Biology

by Ray Kurzweil

What, then, is the Singularity? It’s a future period during which the pace of technological change will be so rapid, its impact so deep, that human life will be irreversibly transformed.

the 1950s John von Neumann, the legendary information theorist, was quoted as saying that “the ever-accelerating progress of technology … gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”3 Von Neumann makes two important observations here: acceleration and singularity. The first idea is that human progress is exponential (that is, it expands by repeatedly multiplying by a constant) rather than linear (that is, expanding by repeatedly adding a constant).

The second is that exponential growth is seductive, starting out slowly and virtually unnoticeably, but beyond the knee of the curve it turns explosive and profoundly transformative.

Thus the twentieth century was gradually speeding up to today’s rate of progress; its achievements, therefore, were equivalent to about twenty years of progress at the rate in 2000. We’ll make another twenty years of progress in just fourteen years (by 2014), and then do the same again in only seven years. To express this another way, we won’t experience one hundred years of technological advance in the twenty-first century; we will witness on the order of twenty thousand years of progress (again, when measured by today’s rate of progress), or about one thousand times greater than what was achieved in the twentieth century.4

curves are typical of biological growth: replication of a system of relatively fixed complexity (such as an organism of a particular species), operating in a competitive niche and struggling for finite local resources. This often occurs, for example, when a species happens upon a new hospitable environment. Its numbers will grow exponentially for a while before leveling off. The overall exponential growth of an evolutionary process (whether molecular, biological, cultural, or technological) supersedes the limits to growth seen in any particular paradigm (a specific S-curve) as a result of the increasing power and efficiency developed in each successive paradigm. The exponential growth of an evolutionary process, therefore, spans multiple S-curves. The most important contemporary example of this phenomenon is the five paradigms of computation discussed below.

VL Business growth

Early biological life could observe local events several millimeters away, using chemical gradients. When sighted animals evolved, they were able to observe events that were miles away. With the invention of the telescope, humans could see other galaxies millions of light-years away. Conversely, using microscopes, they could also see cellular-size structures. Today humans armed with contemporary technology can see to the edge of the observable universe, a distance of more than thirteen billion light-years, and down to quantum-scale subatomic particles. Consider the duration of observation. Single-cell animals could remember events for seconds, based on chemical reactions. Animals with brains could remember events for days. Primates with culture could pass down information through several generations. Early human civilizations with oral histories were able to preserve stories for hundreds of years. With the advent of written language the permanence extended to thousands of years. As one of many examples of the acceleration of the technology paradigm-shift rate, it took about a half century for the late-nineteenth-century invention of the telephone to reach significant levels of usage (see the figure below).17 In comparison, the late-twentieth-century adoption of the cell phone took only a decade.18

As has been discussed, while technology progresses exponentially, we experience it in the linear domain.

the genetics (or biotechnology) revolution is bringing the information revolution, with its exponentially increasing capacity and price-performance, to the field of biology. Similarly, the nanotechnology revolution will bring the rapidly increasing mastery of information to materials and mechanical systems.

all three of the overlapping transformations—genetics, nanotechnology, and robotics—that will dominate the first half of this century represent different facets of the information revolution.

The Singularity as Economic Imperative The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man. —GEORGE BERNARD SHAW, “MAXIMS FOR REVOLUTIONISTS,” MAN AND SUPERMAN, 1903 All progress is based upon a universal innate desire on the part of every organism to live beyond its income. —SAMUEL BUTLER, NOTEBOOKS, 1912

sizes (such as transistors) as small as ten nanometers.6 NTT demonstrated the technology by creating a high-resolution model of the Earth sixty microns in size with ten-nanometer features.

In comparison, what can we say about the efficiency of the human brain? Earlier in this chapter we discussed how each of the approximately 1014 interneuronal connections can store an estimated 104 bits in the connection’s neurotransmitter concentrations and synaptic and dendritic nonlinearities (specific shapes), for a total of 1018 bits. The human brain weighs about the same as our stone (actually closer to 3 pounds than 2.2, but since we’re dealing with orders of magnitude, the measurements are close enough). It runs warmer than a cold stone, but we can still use the same estimate of about 1027 bits of theoretical memory capacity (estimating that we can store one bit

Is the Human Brain Different from a Computer? The answer to this question depends on what we mean by

Recursion is the key capability identified in a new theory of linguistic competence. In Noam Chomsky’s early theories of language in humans, he cited many common attributes that account for the similarities in human languages. In a 2002 paper by Marc Hauser, Noam Chomsky, and Tecumseh Fitch, the authors cite the single attribution of “recursion” as accounting for the unique language faculty of the human species.113 Recursion is the ability to put together small parts into a larger chunk, and then use that chunk as a part in yet another structure and to continue this process iteratively. In this way, we are able to build the elaborate structures

it is remarkable how few neurons appear to be exclusively involved with these emotions. We have fifty billion neurons in the cerebellum that deal with skill formation, billions in the cortex that perform the transformations for perception and rational planning, but only about eighty thousand spindle cells dealing with high-level emotions. It

Another approach being pursued by the Max Planck Institute for Human Cognitive and Brain Sciences in Munich is directly interfacing nerves and electronic devices. A chip created by Infineon allows neurons to grow on a special substrate that provides direct contact between nerves and electronic sensors and stimulators. Similar work on a “neurochip” at Caltech has demonstrated two-way, noninvasive communication between neurons and electronics.117

the deep-brain stimulation implant used for Parkinson’s patients.

The biological neurons in the vicinity of this FDA-approved brain implant receive signals from the electronic device and respond just as if they had received signals from the biological neurons that were once functional.

Recent versions of the Parkinson’s-disease implant provide the ability to download upgraded software directly to the ...

the performance of neural clusters and neuronal regions is often simpler than their constituent parts. We have increasingly powerful mathematical tools, implemented in effective computer software, that are able to accurately model these types of complex hierarchical, adaptive, semirandom, self-organizing, highly nonlinear systems.

Once the nanobot era arrives in the 2020s

Within twenty years, we will have at least a millionfold increase in computational power and vastly improved scanning resolution and bandwidth. So we can have confidence that we will have the data-gathering and computational tools needed by the 2020s to model and simulate the entire brain, which will make it possible to combine the principles of operation of human intelligence with the forms of intelligent information processing that we have derived from other AI research.

The key to the scalability of human intelligence is our ability to build models of reality in our mind. These models can be recursive, meaning that one model can include other models, which can include yet finer models, without limit.

Our computers, which are themselves accelerating, have been a critical tool in enabling us to handle increasingly complex models, which we would otherwise be unable to envision with our brains alone. Clearly, Hofstadter’s concern would be correct if we were limited just to models that we could keep in our minds without technology to assist us. That our intelligence is just above the threshold necessary to understand itself results from our native ability, combined with the tools of our own making, to envision, refine, extend, and alter abstract—and increasingly subtle—models of our own observations.

While each revolution will solve the problems from earlier transformations, it will also introduce new perils. G will overcome the age-old difficulties of disease and aging but establish the potential for new bioengineered viral threats. Once N is fully developed we will be able to apply it to protect ourselves from all biological hazards, but it will create the possibility of its own self-replicating dangers, which will be far more powerful than anything biological. We can protect ourselves from these hazards with fully developed R, but what will protect us from pathological intelligence that exceeds our own? I do have a strategy for dealing with these issues, which I discuss at the end of chapter 8. In this chapter, however, we will examine how the Singularity will unfold through these three overlapping revolutions: G, N, and R.

postulated immediately suggests a possible copying mechanism for the genetic material. —JAMES WATSON AND FRANCIS CRICK3 After three billion years of evolution, we have before us the instruction set that carries each of us from the one-cell egg through adulthood to the grave. —DR. ROBERT WATERSTON, INTERNATIONAL HUMAN GENOME SEQUENCING CONSORTIUM4 Underlying all of the wonders of life and misery of disease are information processes, essentially software programs, that are surprisingly compact. The entire human genome is a sequential binary code containing only about eight hundred million bytes of information. As I mentioned earlier, when its massive redundancies are removed using conventional compression techniques, we are left with only thirty to one hundred million bytes, equivalent to the size of an average contemporary software program.5 This code is supported by a set of biochemical machines that translate these linear (one-dimensional) sequences of DNA “letters” into strings of simple building blocks called amino acids, which are in turn folded into three-dimensional proteins, which make up all living creatures from bacteria to humans. (Viruses occupy a niche in between living and nonliving matter but are also composed of fragments of DNA or RNA.) This machinery is essentially a self-replicating nanoscale replicator that builds the elaborate hierarchy of structures and increasingly complex systems that a living creature comprises.

based not on definitive logic rules but rather