The Singularity is Near: When Humans Transcend Biology

by Ray Kurzweil

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. A thousand-bit quantum computer would vastly outperform any conceivable DNA computer, or for that matter any conceivable nonquantum computer. There are two limitations to the process, however. The first is that, like the DNA and optical computers discussed above, only a special set of problems is amenable to being presented to a quantum computer. In essence, we need to be able to test each possible answer in a simple way.

While human neurons are wondrous creations, we wouldn’t (and don’t) design computing circuits using the same slow methods. Despite the ingenuity of the designs evolved through natural selection, they are many orders of magnitude less capable than what we will be able to engineer. As we reverse engineer our bodies and brains, we will be in a position to create comparable systems that are far more durable and that operate thousands to millions of times faster than our naturally evolved systems. Our electronic circuits are already more than one million times faster than a neuron’s electrochemical processes, and this speed is continuing to accelerate.

We’ve already shown that atoms can store information at a density of greater than one bit per atom, such as in computing systems built from nuclear magnetic-resonance devices. University of Oklahoma researchers stored 1,024 bits in the magnetic interactions of the protons of a single molecule containing nineteen hydrogen atoms.51 Thus, the state of the rock at any one moment represents at least 1027 bits of memory. In terms of computation, and just considering the electromagnetic interactions, there are at least 1015 changes in state per bit per second going on inside a 2.2-pound rock, which effectively represents about 1042 (a million trillion trillion trillion) calculations per second. Yet the rock requires no energy input and generates no appreciable heat. Of course, despite all this activity at the atomic level, the rock is not performing any useful work aside from perhaps acting as a paperweight or a decoration.

A more conservative but compelling design for a massively parallel, reversible computer is Eric Drexler’s patented nanocomputer design, which is entirely mechanical.65 Computations are performed by manipulating nanoscale rods, which are effectively spring-loaded. After each calculation, the rods containing intermediate values return to their original positions, thereby implementing the reverse computation. The device has a trillion (1012) processors and provides an overall rate of 1021 cps, enough to simulate one hundred thousand human brains in a cubic centimeter. Setting a Date for the Singularity. A more modest but still profound threshold will be achieved much earlier. In the early 2030s one thousand dollars’ worth of computation will buy about 1017 cps (probably around 1020 cps using ASICs and harvesting distributed computation via the Internet). Today we spend more than $1011 ($100 billion) on computation in a year, which will conservatively rise to $1012 ($1 trillion) by 2030. So we will be producing about 1026 to 1029 cps of nonbiological computation per year in the early 2030s. This is roughly equal to our estimate for the capacity of all living biological human intelligence.

I set the date for the Singularity—representing a profound and disruptive transformation in human capability—as 2045. The nonbiological intelligence created in that year will be one billion times more powerful than all human intelligence today.

Recall that a 2.2-pound rock has on the order of 1027 bits of information encoded in the state of its atoms and about 1042 cps represented by the activity of its particles. Since we are talking about an ordinary stone, assuming that its surface could store about one thousand bits is a perhaps arbitrary but generous estimate.68 This represents 10−24 of its theoretical capacity, or a memory efficiency of 10−24.69 We can also use a stone to do computation. For example, by dropping the stone from a particular height, we can compute the amount of time it takes to drop an object from that height. Of course, this represents very little computation: perhaps 1 cps, meaning its computational efficiency is 10−42.70

Peeling the Onion. The brain is not a single information-processing organ but rather an intricate and intertwined collection of hundreds of specialized regions. The process of “peeling the onion” to understand the functions of these interleaved regions is well under way. As the requisite neuron descriptions and brain-interconnection data become available, detailed and implementable replicas such as the simulation of the auditory regions described below (see “Another Example: Watts’s Model of the Auditory Regions” on p. 183) will be developed for all brain regions.

There are a number of other key ways in which the brain differs from a conventional computer: • The brains circuits are very slow. Synaptic-reset and neuron-stabilization times (the amount of time required for a neuron and its synapses to reset themselves after the neuron fires) are so slow that there are very few neuron-firing cycles available to make pattern-recognition decisions. Functional magnetic-resonance imaging (fMRI) and magneto-encephalography (MEG) scans show that judgments that do not require resolving ambiguities appear to be made in a single neuron-firing cycle (less than twenty milliseconds), involving essentially no iterative (repeated) processes. Recognition of objects occurs in about 150 milliseconds, so that even if we “think something over,” the number of cycles of operation is measured in hundreds or thousands at most, not billions, as with a typical computer. • But it’s massively parallel. The brain has on the order of one hundred trillion interneuronal connections, each potentially processing information simultaneously. These two factors (slow cycle time and massive parallelism) result in a certain level of computational capacity for the brain, as we discussed earlier. Today our largest supercomputers are approaching this range. The leading supercomputers (including those used by the most popular search engines) measure over 1014 cps, which matches the lower range of the estimates I discussed in chapter 3 for functional simulation. It is not necessa...

Scanning Using Nanobots. Although these largely noninvasive means of scanning the brain from outside the skull are rapidly improving, the most powerful approach to capturing every salient neural detail will be to scan it from inside. By the 2020s nanobot technology will be viable, and brain scanning will be one of its prominent applications. As described earlier nanobots are robots that will be the size of human blood cells (seven to eight microns) or even smaller.44 Billions of them could travel through every brain capillary, scanning each relevant neural feature from up close. Using high-speed wireless communication, the nanobots would communicate with one another and with computers compiling the brain-scan database. (In other words, the nanobots and computers will all be on a wireless local area network.)

Similarly, biology, which is rooted in chemistry, uses its own models. It is often unnecessary to express higher-level results using the intricacies of the dynamics of the lower-level systems, although one has to thoroughly understand the lower level before moving to the higher one. For example, we can control certain genetic features of an animal by manipulating its fetal DNA without necessarily understanding all of the biochemical mechanisms of DNA, let alone the interactions of the atoms in the DNA molecule.

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 among 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 of firing emerges.

Brain Plasticity In 1861 French neurosurgeon Paul Broca correlated injured or surgically affected regions of the brain with certain lost skills, such as fine motor skills or language ability. For more than a century scientists believed these regions were hardwired for specific tasks. Although certain brain areas do tend to be used for particular types of skills, we now understand that such assignments can be changed in response to brain injury such as a stroke. In a classic 1965 study, D. H. Hubel and T. N. Wiesel showed that extensive and far-reaching reorganization of the brain could take place after damage to the nervous system, such as from a stroke.62 Moreover, the detailed arrangement of connections and synapses in a given region is a direct product of how extensively that region is used. As brain scanning has attained sufficiently high resolution to detect dendritic-spine growth and the formation of new synapses, we can see our brain grow and adapt to literally follow our thoughts. This gives new shades of meaning to Descartes’ dictum “I think therefore I am.”

In one experiment conducted by Michael Merzenich and his colleagues at the University of California at San Francisco, monkeys’ food was placed in such a position that the animals had to dexterously manipulate one finger to obtain it. Brain scans before and after revealed dramatic growth in the interneuronal connections and synapses in the region of the brain responsible for controlling that finger.

An experiment by genetics researchers Fred Gage, G. Kempermann, and Henriette van Praag at the Salk Institute for Biological Studies showed that neurogenesis is actually stimulated by our experience. Moving mice from a sterile, uninteresting cage to a stimulating one approximately doubled the number of dividing cells in their hippocampus regions.72

Common sense is not a simple thing. Instead, it is an immense society of hard-earned practical ideas—of multitudes of life-learned rules and exceptions, dispositions and tendencies, balances and checks. —MARVIN MINSKY

Most of the genome that is devoted to the brain describes the detailed structure of each type of neural cell (including its dendrites, spines, and synapses) and how these structures respond to stimulation and change. Relatively little genomic code is responsible for the actual “wiring.” In the cerebellum, the basic wiring method is repeated billions of times. It is clear that the genome does not provide specific information about each repetition of this cerebellar structure but rather specifies certain constraints as to how this structure is repeated (just as the genome does not specify the exact location of cells in other organs).

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.

Operations of thought are like cavalry charges in a battle—they are strictly limited in number, they require fresh horses, and must only be made at decisive moments. —ALFRED NORTH WHITEHEAD But the big feature of human-level intelligence is not what it does when it works but what it does when it’s stuck. —MARVIN MINSKY

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 of sentences and paragraphs from a limited set of words.

Interestingly, we are able to predict or anticipate our own decisions. Work by physiology professor Benjamin Libet at the University of California at Davis shows that neural activity to initiate an action actually occurs about a third of a second before the brain has made the decision to take the action. The implication, according to Libet, is that the decision is really an illusion, that “consciousness is out of the loop.” The cognitive scientist and philosopher Daniel Dennett describes the phenomenon as follows: “The action is originally precipitated in some part of the brain, and off fly the signals to muscles, pausing en route to tell you, the conscious agent, what is going on (but like all good officials letting you, the bumbling president, maintain the illusion that you started it all).”114 A related experiment was conducted recently in which neurophysiologists electronically stimulated points in the brain to induce particular emotional feelings. The subjects immediately came up with a rationale for experiencing those emotions. It has been known for many years that in patients whose left and right brains are no longer connected, one side of the brain (usually the more verbal left side) will create elaborate explanations (“confabulations”) for actions initiated by the other side, as if the left side were the public-relations agent for the right side.

Interfacing the Brain and Machines I want to do something with my life; I want to be a cyborg. —KEVIN WARWICK

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

Homo sapiens, the first truly free species, is about to decommission natural selection, the force that made us…. [S]oon we must look deep within ourselves and decide what we wish to become. —E. O. WILSON, CONSILIENCE: THE UNITY OF KNOWLEDGE, 1998 We know what we are, but know not what we may be. —WILLIAM SHAKESPEARE The most important thing is this: To be able at any moment to sacrifice what we are for what we could become. —CHARLES DUBOIS

The generation of scanning tools now emerging will for the first time provide spatial and temporal resolution capable of observing in real time the performance of individual dendrites, spines, and synapses. These tools will quickly lead to a new generation of higher-resolution models and simulations. Once the nanobot era arrives in the 2020s we will be able to observe all of the relevant features of neural performance with very high resolution from inside the brain itself. Sending billions of nanobots through its capillaries will enable us to noninvasively scan an entire working brain in real time.

Uploading the Human Brain To become a figment of your computer’s imagination. —DAVID VICTOR DE TRANSEND, GODLING’S GLOSSARY, DEFINITION OF “UPLOAD”

Although I estimated that 1016 cps of computation and 1013 bits of memory are sufficient to emulate human levels of intelligence, my estimates for the requirements of uploading were higher: 1019 cps and 1018 bits, respectively. The reason for the higher estimates is that the lower ones are based on the requirements to re-create regions of the brain at human levels of performance, whereas the higher ones are based on capturing the salient details of each of our approximately 1011 neurons and 1014 interneuronal connections. Once uploading is feasible, we are likely to find that hybrid solutions are adequate. For example, we will probably find that it is sufficient to simulate certain basic support functions such as the signal processing of sensory data on a functional basis (by plugging in standard modules) and reserve the capture of subneuron details only for those regions that are truly responsible for individual personality and skills.

To capture this level of detail will require scanning from within the brain using nanobots, the technology for which will be available by the late 2020s. Thus, the early 2030s is a reasonable time frame for the computational performance, memory, and brain-scanning prerequisites of uploading. Like any other technology, it will take some iterative refinement to perfect this capability, so the end of the 2030s is a conservative projection for successful uploading. We should point out that a person’s personality and skills do not reside only in the brain, although that is their principal location. Our nervous system extends throughout the body, and the endocrine (hormonal) system has an influence, as well. The vast majority of the complexity, however, resides in the brain, which is the location of the bulk of the nervous system. The bandwidth of information from the endocrine system is quite low, because the determining factor is overall levels of hormones, not the precise location of each hormone molecule.

Perhaps the most important question will be whether or not an uploaded human brain is really you. Even if the upload passes a personalized Turing test and is deemed indistinguishable from you, one could still reasonably ask whether the upload is the same person or a new person. After all, the original person may still exist. I’ll defer these essential questions until chapter 7. In my view the most important element in uploading will be our gradual transfer of our intelligence, personality, and skills to the nonbiological portion of our intelligence. We already have a variety of neural implants. In the 2020s we will use nanobots to begin augmenting our brains with nonbiological intelligence, starting with the “routine” functions of sensory processing and memory, moving on to skill formation, pattern recognition, and logical analysis. By the 2030s the nonbiological portion of our intelligence will predominate, and by the 2040s, as I pointed out in chapter 3, the nonbiological portion will be billions of times more capable. Although we are likely to retain the biological portion for a period of time, it will become of increasingly little consequence.

SIGMUND FREUD: When you talk about reverse engineering the human brain, just whose brain are you talking about? A mans brain? A woman’s? A child’s? The brain of a genius? A retarded individual? An “idiot savant”? A gifted artist? A serial murderer? RAY: Ultimately, we’re talking about all of the above. There are basic principles of operation that we need to understand about how human intelligence and its varied constituent skills work. Given the human brain’s plasticity, our thoughts literally create our brains through the growth of new spines, synapses, dendrites, and even neurons. As a result, Einstein’s parietal lobes—the region associated with visual imagery and mathematical thinking—became greatly enlarged.121 However, there is only so much room in our skulls, so although Einstein played music he was not a world-class musician. Picasso did not write great poetry, and so on. As we re-create the human brain, we will not be limited in our ability to develop each skill. We will not have to compromise one area to enhance another. We can also gain insight into our differences and an understanding of human dysfunction. What went wrong with the serial murderer?

MARVIN: Keep in mind that not all AIs will need human bodies. RAY: Indeed. As humans, despite some plasticity, both our bodies and brains have a relatively fixed architecture. MOLLY 2004: Yes, it’s called being human, something you seem to have a problem with. RAY: Actually, I often do have a problem with all the limitations and maintenance that my version 1.0 body requires, not to mention all the limitations of my brain. But I do appreciate the joys of the human body. My point is that AIs can and will have the equivalent of human bodies in both real and virtual-reality environments. As Marvin points out, however, they will not be limited just to this. MOLLY 2104: It’s not just AIs that will be liberated from the limitations of version 1.0 bodies. Humans of biological origin will have the same freedom in both real and virtual reality. GEORGE 2048: Keep in mind, there won’t be a clear distinction between AIs and humans. MOLLY 2104: Yes, except for the MOSHs (Mostly Original Substrate Humans) of course.

The first half of the twenty-first century will be characterized by three overlapping revolutions—in Genetics, Nanotechnology, and Robotics. These will usher in what I referred to earlier as Epoch Five, the beginning of the Singularity. We are in the early stages of the “G” revolution today. By understanding the information processes underlying life, we are starting to learn to reprogram our biology to achieve the virtual elimination of disease, dramatic expansion of human potential, and radical life extension. Hans Moravec points out, however, that no matter how successfully we fine-tune our DNA-based biology, humans will remain “second-class robots,” meaning that biology will never be able to match what we will be able to engineer once we fully understand biology’s principles of operation.2 The “N” revolution will enable us to redesign and rebuild—molecule by molecule—our bodies and brains and the world with which we interact, going far beyond the limitations of biology. The most powerful impending revolution is “R”: human-level robots with their intelligence derived from our own but redesigned to far exceed human capabilities. R represents the most significant transformation, because intelligence is the most powerful “force” in the universe. Intelligence, if sufficiently advanced, is, well, smart enough to anticipate and overcome any obstacles that stand in its path.

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.

“Whereas some of my contemporaries may be satisfied to embrace aging gracefully as part of the cycle of life, that is not my view. It may be ‘natural,’ but I don’t see anything positive in losing my mental agility, sensory acuity, physical limberness, sexual desire, or any other human ability. I view disease and death at any age as a calamity, as problems to be overcome.”

A hybrid scenario involving both bio- and nanotechnology contemplates turning biological cells into computers. These “enhanced intelligence” cells can then detect and destroy cancer cells and pathogens or even regrow human body parts. Princeton biochemist Ron Weiss has modified cells to incorporate a variety of logic functions that are used for basic computation.54 Boston University’s Timothy Gardner has developed a cellular logic switch, another basic building block for turning cells into computers.55 Scientists at the MIT Media Lab have developed ways to use wireless communication to send messages, including intricate sequences of instructions, to the computers inside modified cells.56 Weiss points out that “once you have the ability to program cells, you don’t have to be constrained by what the cells know how to do already. You can program them to do new things, in new patterns.”

One of the most powerful methods of applying life’s machinery involves harnessing biology’s own reproductive mechanisms in the form of cloning. Cloning will be a key technology—not for cloning actual humans but for life-extension purposes, in the form of “therapeutic cloning.” This process creates new tissues with “young” telomere-extended and DNA-corrected cells to replace without surgery defective tissues or organs. All responsible ethicists, including myself, consider human cloning at the present time to be unethical.

Nanotechnology has the potential to enhance human performance, to bring sustainable development for materials, water, energy, and food, to protect against unknown bacteria and viruses, and even to diminish the reasons for breaking the peace [by creating universal abundance]. —NATIONAL SCIENCE FOUNDATION NANOTECHNOLOGY REPORT

However, as Drexler points out, a nanoscale assembler does not necessarily have to be self-replicating.76 Given the inherent dangers in self-replication, the ethical standards proposed by the Foresight Institute (a think tank founded by Eric Drexler and Christine Peterson) contain prohibitions against unrestricted self-replication, especially in a natural environment.

Nature shows that molecules can serve as machines because living things work by means of such machinery. Enzymes are molecular machines that make, break, and rearrange the bonds holding other molecules together. Muscles are driven by molecular machines that haul fibers past one another. DNA serves as a data-storage system, transmitting digital instructions to molecular machines, the ribosomes, that manufacture protein molecules. And these protein molecules, in turn, make up most of the molecular machinery. —ERIC DREXLER

Upgrading the Cell Nucleus with a Nanocomputer and Nanobot. Here’s a conceptually simple proposal to overcome all biological pathogens except for prions (self-replicating pathological proteins). With the advent of full-scale nanotechnology in the 2020s we will have the potential to replace biology’s genetic-information repository in the cell nucleus with a nanoengineered system that would maintain the genetic code and simulate the actions of RNA, the ribosome, and other elements of the computer in biology’s assembler. A nanocomputer would maintain the genetic code and implement the gene-expression algorithms. A nanobot would then construct the amino-acid sequences for the expressed genes.

DNA is proving to be as versatile as nanotubes for building molecular structures. DNA’s proclivity to link up with itself makes it a useful structural component. Future designs may combine this attribute as well as its capacity for storing information. Both nanotubes and DNA have outstanding properties for information storage and logical control, as well as for building strong three-dimensional structures.

A particularly impressive demonstration of a nanoscale device constructed from DNA is a tiny biped robot that can walk on legs that are ten nanometers long.90 Both the legs and the walking track are built from DNA, again chosen for the molecule’s ability to attach and detach itself in a controlled manner. The nanorobot, a project of chemistry professors Nadrian Seeman and William Sherman of New York University, walks by detaching its legs from the track, moving down it, and then reattaching its legs to the track. The project is another impressive demonstration of the ability of nanoscale machines to execute precise maneuvers. An alternate method of designing nanobots is to learn from nature. Nanotechnologist Michael Simpson of Oak Ridge National Laboratory describes the possibility of exploiting bacteria “as ready-made machine[s].” Bacteria, which are natural nanobot-size objects, are able to move, swim, and pump liquids.91 Linda Turner, a scientist at the Rowland Institute at Harvard, has focused on their thread-size arms, called fimbriae, which are able to perform a wide variety of tasks, including carrying other nanoscale objects and mixing fluids. Another approach is to use only parts of bacteria. A research group headed by Viola Vogel at the University of Washington built a system using just the limbs of E. coli bacteria that was able to sort out nanoscale beads of different sizes. Since bacteria are natural nanoscale systems that can perform a wide variety of functio...

A few weeks ago I gave a talk on nanotechnology and energy titled “Be a Scientist, Save the World” to about 700 middle and high school students in the Spring Branch ISD, a large public school system here in the Houston area. Leading up to my visit the students were asked to write an essay on “why I am a Nanogeek”. Hundreds responded, and I had the privilege of reading the top 30 essays, picking my favorite top 5. Of the essays I read, nearly half assumed that self-replicating nanobots were possible, and most were deeply worried about what would happen in their future as these nanobots spread around the world. I did what I could to allay their fears, but there is no question that many of these youngsters have been told a bedtime story that is deeply troubling. You and people around you have scared our children.

The net effect of these nanomedical interventions will be the continuing arrest of all biological aging, along with the reduction of current biological age to whatever new biological age is deemed desirable by the patient, severing forever the link between calendar time and biological health. Such interventions may become commonplace several decades from today. Using annual checkups and cleanouts, and some occasional major repairs, your biological age could be restored once a year to the more or less constant physiological age that you select. You might still eventually die of accidental causes, but you’ll live at least ten times longer than you do now. —ROBERT A. FREITAS JR.148

MOLLY 2004: Okay, so I’ll have all these nanobots in my bloodstream. Aside from being able to sit at the bottom of my pool for hours, what else is this going to do for me? RAY: It will keep you healthy. They’ll destroy pathogens such as bacteria, viruses, and cancer cells, and they won’t be subject to the various pitfalls of the immune system, such as autoimmune reactions. Unlike your biological immune system, if you don’t like what the nanobots are doing, you can tell them to do something different. MOLLY 2004: You mean, send my nanobots an e-mail? Like, Hey, nanobots, stop destroying those bacteria in my intestines because they’re actually good for my digestion? RAY: Yes, good example. The nanobots will be under our control. They’ll communicate with one another and with the Internet. Even today we have neural implants (for example, for Parkinson’s disease) that allow the patient to download new software into them.

RAY: When nanotechnology is mature, it’s going to solve the problems of biology by overcoming biological pathogens, removing toxins, correcting DNA errors, and reversing other sources of aging. We will then have to contend with new dangers that it introduces, just as the Internet introduced the danger of software viruses. These new pitfalls will include the potential for self-replicating nanotechnology getting out of control, as well as the integrity of the software controlling these powerful, distributed nanobots.

MOLLY 2104: In the 2040s we developed the means to instantly create new portions of ourselves, either biological or nonbiological. It became apparent that our true nature was a pattern of information, but we still needed to manifest ourselves in some physical form. However, we could quickly change that physical form

Given that superintelligence will one day be technologically feasible, will people choose to develop it? This question can pretty confidently be answered in the affirmative. Associated with every step along the road to superintelligence are enormous economic payoffs. The computer industry invests huge sums in the next generation of hardware and software, and it will continue doing so as long as there is a competitive pressure and profits to be made. People want better computers and smarter software, and they want the benefits these machines can help produce. Better medical drugs; relief for humans from the need to perform boring or dangerous jobs; entertainment—there is no end to the list of consumer-benefits. There is also a strong military motive to develop artificial intelligence. And nowhere on the path is there any natural stopping point where technophobics could plausibly argue “hither but not further.” —NICK BOSTROM, “HOW LONG BEFORE SUPERINTELLIGENCE?” 1997 It is hard to think of any problem that a superintelligence could not either solve or at least help us solve. Disease, poverty, environmental destruction, unnecessary suffering of all kinds: these are things that a superintelligence equipped with advanced nanotechnology would be capable of eliminating. Additionally, a superintelligence could give us indefinite lifespan, either by stopping and reversing the aging process through the use of nanomedicine, or by offering us the option to upload ourselves. A superint...

Machines have exacting memories. Contemporary computers can master billions of facts accurately, a capability that is doubling every year.159 The underlying speed and price-performance of computing itself is doubling every year, and the rate of doubling is itself accelerating. As human knowledge migrates to the Web, machines will be able to read, understand, and synthesize all human-machine information. The last time a biological human was able to grasp all human scientific knowledge was hundreds of years ago. Another advantage of machine intelligence is that it can consistently perform at peak levels and can combine peak skills. Among humans one person may have mastered music composition, while another may have mastered transistor design, but given the fixed architecture of our brains we do not have the capacity (or the time) to develop and utilize the highest level of skill in every increasingly specialized area.

There’s this stupid myth out there that A.I. has failed, but A.I. is everywhere around you every second of the day. People just don’t notice it. You’ve got A.I. systems in cars, tuning the parameters of the fuel injection systems. When you land in an airplane, your gate gets chosen by an A.I. scheduling system. Every time you use a piece of Microsoft software, you’ve got an A.I. system trying to figure out what you’re doing, like writing a letter, and it does a pretty damned good job. Every time you see a movie with computer-generated characters, they’re all little A.I. characters behaving as a group. Every time you play a video game, you’re playing against an A.I. system. —RODNEY BROOKS, DIRECTOR OF THE MIT AI LAB161

Neural nets are also naturally amenable to parallel processing, since that is how the brain works. The human brain does not have a central processor that simulates each neuron. Rather, we can consider each neuron and each interneuronal connection to be an individual slow processor. Extensive work is under way to develop specialized chips that implement neural-net architectures in parallel to provide substantially greater throughput.