The Singularity is Near: When Humans Transcend Biology

by Ray Kurzweil

When scientists become a million times more intelligent and operate a million times faster, an hour would result in a century of progress (in today’s terms).

Not only is each chip doubling in power each year for the same unit cost, but the number of chips being manufactured is also growing exponentially; thus, computer research budgets have grown dramatically over the decades.

Homo sapiens evolved over the course of a few hundred thousand years, and early stages of humanoid-created technology (such as the wheel, fire, and stone tools) progressed barely faster, requiring tens of thousands of years to evolve and be widely deployed. A half millennium ago, the product of a paradigm shift such as the printing press took about a century to be widely deployed. 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.

The Life Cycle of a Paradigm. Each paradigm develops in three stages: 1. Slow growth (the early phase of exponential growth) 2. Rapid growth (the late, explosive phase of exponential growth), as seen in the S-curve figure below 3. A leveling off as the particular paradigm matures

S-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.

During this third or maturing phase in the life cycle of a paradigm, pressure begins to build for the next paradigm shift. In the case of technology, research dollars are invested to create the next paradigm. We can see this in the extensive research being conducted today toward three-dimensional molecular computing, despite the fact that we still have at least a decade left for the paradigm of shrinking transistors on a flat integrated circuit using photolithography.

The explosion of the Internet appears to be a surprise from the linear chart but was perfectly predictable from the logarithmic one.

Wolfram makes the following point repeatedly: “Whenever a phenomenon is encountered that seems complex it is taken almost for granted that the phenomenon must be the result of some underlying mechanism that is itself complex. But my discovery that simple programs can produce great complexity makes it clear that this is not in fact correct.”

If we add another simple concept–an evolutionary algorithm–to that of Wolfram’s simple cellular automata, we start to get far more exciting and more intelligent results.

Contemporary economic theory and policy are based on outdated models that emphasize energy costs, commodity prices, and capital investment in plant and equipment as key driving factors, while largely overlooking computational capacity, memory, bandwidth, the size of technology, intellectual property, knowledge, and other increasingly vital (and increasingly increasing) constituents that are driving the economy.

Virtually all of the economic models taught in economics classes and 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 in their view of long-term trends. That’s because they are based on the “intuitive linear” view of history (the assumption that the pace of change will continue at the current rate) rather than the historically based exponential view.

As this book is being written, the country is debating changing the Social Security program based on projections that go out to 2042, approximately the time frame I’ve estimated for the Singularity (see the next chapter). This economic policy review is unusual in the very long time frames involved. The predictions are based on linear models of longevity increases and economic growth that are highly unrealistic. On the one hand, longevity increases will vastly outstrip the government’s modest expectations. On the other hand, people won’t be seeking to retire at sixty-five when they have the bodies and brains of thirty-year-olds. Most important, the economic growth from the “GNR” technologies (see chapter 5) will greatly outstrip the 1.7 percent per year estimates being used (which understate by half even our experience over the past fifteen years).

In the East Asian and Pacific region, the number of people living in extreme poverty went from 470 million in 1990 to 270 million in 2001, and is projected by the World Bank to be under 20 million by 2015.

Products ordered in five minutes on the Web and delivered to your door are worth more than products that you have to fetch yourself. Clothes custom-manufactured for your unique body are worth more than clothes you happen to find on a store rack. These sorts of improvements are taking place in most product categories, and none of them is reflected in the productivity statistics.

The economists’ concern is that if consumers can buy what they need and want with fewer dollars, the economy will shrink (as measured in dollars). This ignores, however, the inherently insatiable needs and desires of human consumers. The revenues of the semiconductor industry, which “suffers” 40 to 50 percent deflation per year, have nonetheless grown by 17 percent each year over the past half century.88 Since the economy is in fact expanding, this theoretical implication of deflation should not cause concern.

BP Amoco’s cost for finding oil in 2000 was less than one dollar per barrel, down from nearly ten dollars in 1991. Processing an Internet transaction costs a bank one penny, compared to more than one dollar using a teller.

All of the technologies exhibiting exponential growth shown in the above charts are continuing without losing a beat through recent economic slowdowns. Market acceptance also shows no evidence of boom and bust.

So 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. The ladder has been moving up, and thus we have been exponentially increasing investments in education at all levels (see the figure below).93

Since stock prices reflect the consensus of a buyer-seller market, the prices reflect the underlying linear assumption that most people share 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.

Intermediate steps are already under way: new technologies that will lead to the sixth paradigm of molecular three-dimensional computing include nanotubes and nanotube circuitry, molecular computing, self-assembly in nanotube circuits, biological systems emulating circuit assembly, computing with DNA, spintronics (computing with the spin of electrons), computing with light, and quantum computing.

Nanotubes Are Still the Best Bet. In The Age of Spiritual Machines, I cited nanotubes—using molecules organized in three dimensions to store memory bits and to act as logic gates—as the most likely technology to usher in the era of three-dimensional molecular computing.

One cubic inch of nanotube circuitry, once fully developed, would be up to one hundred million times more powerful than the human brain.9

Nanotube circuitry was controversial when I discussed it in 1999, but there has been dramatic progress in the technology over the past six years. Two major strides were made in 2001. A nanotube-based transistor (with dimensions of one by twenty nanometers), operating at room temperature and using only a single electron to switch between on and off states, was reported in the July 6, 2001, issue of Science.10 Around the same time, IBM also demonstrated an integrated circuit with one thousand nanotube-based transistors.11 More recently, we have seen the first working models of nanotube-based circuitry. In January 2004 researchers at the University of California at Berkeley and Stanford University created an integrated memory circuit based on nanotubes.

The Nantero design provides random access as well as nonvolatility (data is retained when the power is off), meaning that it could potentially replace all of the primary forms of memory: RAM, flash, and disk.

One type of molecule that researchers have found to have desirable properties for computing is called a “rotaxane,” which can switch states by changing the energy level of a ringlike structure contained within the molecule. Rotaxane memory and electronic switching devices have been demonstrated, and they show the potential of storing one hundred gigabits (1011 bits) per square inch. The potential would be even greater if organized in three dimensions.

Self-assembling of nanoscale circuits is another key enabling technique for effective nanoelectronics. Self-assembly allows improperly formed components to be discarded automatically and makes it possible for the potentially trillions of circuit components to organize themselves, rather than be painstakingly assembled in a top-down process.

As with the DNA computer described above, a key to successful quantum computing is a careful statement of the problem, including a precise way to test possible answers. The quantum computer effectively tests every possible combination of values for the qubits. So a quantum computer with one thousand qubits would test 21,000 (a number approximately equal to one followed by 301 zeroes) potential solutions simultaneously.

The retina, according to Moravec’s analysis, performs ten million of these edge and motion detections each second. Based on his several decades of experience in creating robotic vision systems, he estimates that the execution of about one hundred computer instructions is required to re-create each such detection at human levels of performance, meaning that replicating the image-processing functionality of this portion of the retina requires 1,000 MIPS. The human brain is about 75,000 times heavier than the 0.02 grams of neurons in this portion of the retina, resulting in an estimate of about 1014 (100 trillion) instructions per second for the entire brain.

Brain scientist Allan Snyder has reported that about 40 percent of his test subjects hooked up to TMS display significant new skills, many of which are remarkable, such as drawing abilities.38

Once we have a sufficient number of particles to call something a gas rather than a bunch of particles, solving equations for each particle interaction becomes impractical, whereas the laws of thermodynamics work extremely well. The interactions of a single molecule within the gas are hopelessly complex and unpredictable, but the gas itself, comprising trillions of molecules, has many predictable properties.

The reason memories can remain intact even if three quarters of the connections have disappeared is that the coding method used appears to have properties similar to those of a hologram. In a hologram, information is stored in a diffuse pattern throughout an extensive region. If you destroy three quarters of the hologram, the entire image remains intact, although with only one quarter of the resolution.

Although we have the illusion of receiving high-resolution images from our eyes, what the optic nerve actually sends to the brain is just outlines and clues about points of interest in our visual field. We then essentially hallucinate the world from cortical memories that interpret a series of extremely low-resolution movies that arrive in parallel channels.

University of California at Berkeley, and doctoral student Boton Roska, M.D., showed that the optic nerve carries ten to twelve output channels, each of which carries only minimal information about a given scene.104 One group of what are called ganglion cells sends information only about edges (changes in contrast). Another group detects only large areas of uniform color, whereas a third group is sensitive only to the backgrounds behind figures of interest.

In 1993 I wrote a health book (The 10% Solution for a Healthy Life)

These results are not accidental; I have been very aggressive about reprogramming my biochemistry. I take 250 supplements (pills) a day and receive a half-dozen intravenous therapies each week (basically nutritional supplements delivered directly into my bloodstream, thereby bypassing my GI tract). As a result, the metabolic reactions in my body are completely different than they would otherwise be.

Energy storage today is highly centralized, which represents a key vulnerability in that liquid-natural-gas tanks and other storage facilities are subject to terrorist attacks, with potentially catastrophic effects. Oil trucks and ships are equally exposed. The emerging paradigm for energy storage will be fuel cells, which will ultimately be widely distributed throughout our infrastructure, another example of the trend from inefficient and vulnerable centralized facilities to an efficient and stable distributed system.

Today solar power costs an estimated $2.75 per watt.136 Several companies are developing nanoscale solar cells and hope to bring the cost of solar power below that of other energy sources. Industry sources indicate that once solar power falls below $1.00 per watt, it will be competitive for directly supplying electricity to the nation’s power grid.

Nanosolar has a design based on titanium oxide nanoparticles that can be mass-produced on very thin flexible films. CEO Martin Roscheisen estimates that his technology has the potential to bring down solar-power costs to around fifty cents per watt by 2006, lower than that of natural gas.

Applying nanotechnology to home and industrial lighting could reduce both the need for electricity and an estimated two hundred million tons of carbon emissions per year.145

With Freitas’s respirocytes (robotic red blood cells) a runner could do an Olympic sprint for fifteen minutes without taking a breath.

For example, a researcher at the University of Illinois at Chicago has cured type 1 diabetes in rats with a nanoengineered device that incorporates pancreatic islet cells.156 The device has seven-nanometer pores that let insulin out but won’t let in the antibodies that destroy these cells.

The premise is that once strong AI is achieved, it will immediately become a runaway phenomenon of rapidly escalating superintelligence.160

Computer science professor and AI entrepreneur Ben Goertzel has written a series of books and articles that describe strategies and architectures for combining the diverse methods underlying intelligence.

the entire history of AI reveals that machines started with the skills of professionals and only gradually moved toward the language skills of a child. Early AI systems demonstrated their prowess initially in professional fields such as proving mathematical theorems and diagnosing medical conditions.

RAY: That’s another benefit of virtual reality circa 2029; you have your choice of millions of artificial people. MOLLY 2104: I understand that you’re back in 2004, but we kind of got rid of that terminology back when the Nonbiological Persons Act was passed in 2052. I mean, we’re a lot more real than … umm, let me rephrase that.

The next several decades will see a major trend toward decentralization. Today we have highly centralized and vulnerable energy plants and use ships and fuel lines to transport energy. The advent of nanoengineered fuel cells and solar power will enable energy resources to be massively distributed and integrated into our infrastructure.

No matter how convincing the behavior of a nonbiological person, some observers will refuse to accept the consciousness of such an entity unless it squirts neurotransmitters, is based on DNA-guided protein synthesis, or has some other specific biologically human attribute.

If we copy me and then destroy the original, that’s the end of me, because as we concluded above the copy is not me. Since the copy will do a convincing job of impersonating me, no one may know the difference, but it’s nonetheless the end of me.