In chapter 3, I mentioned the work of robotics pioneer Hans Moravec, who has been reverse engineering the image processing done by the retina and early visual-processing regions in the brain. For more than thirty years Moravec has been constructing systems to emulate the ability of our visual system to build representations of the world. It has only been recently that sufficient processing power has been available in microprocessors to replicate this human-level feature detection, and Moravec is applying his computer simulations to a new generation of robots that can navigate unplanned, complex environments with human-level vision.105 Carver Mead has been pioneering the use of special neural chips that utilize transistors in their native a.n.a.log mode, which can provide very efficient emulation of the a.n.a.log nature of neural processing. Mead has demonstrated a chip that performs the functions of the retina and early transformations in the optic nerve using this approach.106 A special type of visual recognition is detecting motion, one of the focus areas of the Max Planck Inst.i.tute of Biology in Tubingen, Germany. The basic research model is simple: compare the signal at one receptor with a time-delayed signal at the adjacent receptor.107 This model works for certain speeds but leads to the surprising result that above a certain speed, increases in the I velocity of an observed object will decrease the response of this motion detector. Experimental results on animals (based on behavior and a.n.a.lysis of I, neuronal outputs) and humans (based on reported perceptions) have closely matched the model. This model works for certain speeds but leads to the surprising result that above a certain speed, increases in the I velocity of an observed object will decrease the response of this motion detector. Experimental results on animals (based on behavior and a.n.a.lysis of I, neuronal outputs) and humans (based on reported perceptions) have closely matched the model.

Other Works in Progress: An Artificial Hippocampus and an Artificial Olivocerebellar Region

The hippocampus is vital for learning new information and long-term storage of memories. Ted Berger and his colleagues at the University of Southern California mapped the signal patterns of this region by stimulating slices of rat hippocampus with electrical signals millions of times to determine which input produced a corresponding output.108 They then developed a real-time mathematical model of the transformations performed by layers of the hippocampus and programmed the model onto a chip. They then developed a real-time mathematical model of the transformations performed by layers of the hippocampus and programmed the model onto a chip.109 Their plan is to test the chip in animals by first disabling the corresponding hippocampus region, noting the resulting memory failure, and then determining whether that mental function can be restored by installing their hippocampal chip in place of the disabled region. Their plan is to test the chip in animals by first disabling the corresponding hippocampus region, noting the resulting memory failure, and then determining whether that mental function can be restored by installing their hippocampal chip in place of the disabled region.

Ultimately, this approach could be used to replace the hippocampus in patients affected by strokes, epilepsy, or Alzheimer's disease. The chip would be located on a patient's skull, rather than inside the brain, and would communicate with the brain via two arrays of electrodes, placed on either side of the damaged hippocampal section. One would record the electrical activity coming from the rest of the brain, while the other would send the necessary instructions back to the brain.

Another brain region being modeled and simulated is the olivocerebellar region, which is responsible for balance and coordinating the movement of limbs. The goal of the international research group involved in this effort is to apply their artificial olivocerebellar circuit to military robots as well as to robots that could a.s.sist the disabled.110 One of their reasons for selecting this particular brain region was that "it's present in all vertebrates-it's very much the same from the most simple to the most complex brains," explains Rodolfo Llinas, one of the researchers and a neuroscientist at New York University Medical School. "The a.s.sumption is that it is conserved [in evolution] because it embodies a very intelligent solution. As the system is involved in motor coordination-and we want to have a machine that has sophisticated motor control-then the choice [of the circuit to mimic] was easy." One of their reasons for selecting this particular brain region was that "it's present in all vertebrates-it's very much the same from the most simple to the most complex brains," explains Rodolfo Llinas, one of the researchers and a neuroscientist at New York University Medical School. "The a.s.sumption is that it is conserved [in evolution] because it embodies a very intelligent solution. As the system is involved in motor coordination-and we want to have a machine that has sophisticated motor control-then the choice [of the circuit to mimic] was easy."

One of the unique aspects of their simulator is that it uses a.n.a.log circuits. Similar to Mead's pioneering work on a.n.a.log emulation of brain regions, the researchers found substantially greater performance with far fewer components by using transistors in their native a.n.a.log mode.

One of the team's researchers, Ferdinando Mussa-Ivaldi, a neuroscientist at Northwestern University, commented on the applications of an artificial olivocerebellar circuit for the disabled: "Think of a paralyzed patient. It is possible to imagine that many ordinary tasks-such as getting a gla.s.s of water, dressing, undressing, transferring to a wheelchair-could be carried out by robotic a.s.sistants, thus providing the patient with more independence."

Understanding Higher-Level Functions: Imitation, Prediction, and Emotion

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 If love is the answer, could you please rephrase the question?-LILY TOMLIN

Because it sits at the top of the neural hierarchy, the part of the brain least well understood is the cerebral cortex. This region, which consists of six thin layers in the outermost areas of the cerebral hemispheres, contains billions of neurons. According to Thomas M. Bartol Jr. of the Computational Neurobiology Laboratory of the Salk Inst.i.tute of Biological Studies, "A single cubic millimeter of cerebral cortex may contain on the order of 5 billion ... synapses of different shapes and sizes." The cortex is responsible for perception, planning, decision making and most of what we regard as conscious thinking.

Our ability to use language, another unique attribute of our species, appears to be located in this region. An intriguing hint about the origin of language and a key evolutionary change that enabled the formation of this distinguishing skill is the observation that only a few primates, including humans and monkeys, are able to use an (actual) mirror to master skills. Theorists Giacomo Rizzolatti and Michael Arbib hypothesized that language emerged from manual gestures (which monkeys-and, of course, humans-are capable of). Performing manual gestures requires the ability to mentally correlate the performance and observation of one's own hand movements.111 Their "mirror system hypothesis" is that the key to the evolution of language is a property called "parity," which is the understanding that the gesture (or utterance) has the same meaning for the party making the gesture as for the party receiving it; that is, the understanding that what you see in a mirror is the same (although reversed left-to-right) as what is seen by someone else watching you. Other animals are unable to understand the image in a mirror in this fashion, and it is believed that they are missing this key ability to deploy parity. Their "mirror system hypothesis" is that the key to the evolution of language is a property called "parity," which is the understanding that the gesture (or utterance) has the same meaning for the party making the gesture as for the party receiving it; that is, the understanding that what you see in a mirror is the same (although reversed left-to-right) as what is seen by someone else watching you. Other animals are unable to understand the image in a mirror in this fashion, and it is believed that they are missing this key ability to deploy parity.

A closely related concept is that the ability to imitate the movements (or, in the case of human babies, vocal sounds) of others is critical to developing language.112 Imitation requires the ability to break down an observed presentation into parts, each of which can then be mastered through recursive and iterative refinement. Imitation requires the ability to break down an observed presentation into parts, each of which can then be mastered through recursive and iterative refinement.

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 Tec.u.mseh 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. 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.

Another key feature of the human brain is the ability to make predictions, including predictions about the results of its own decisions and actions. Some scientists believe that prediction is the primary function of the cerebral cortex, although the cerebellum also plays a major role in the prediction of movement.

Interestingly, we are able to predict or antic.i.p.ate 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 b.u.mbling 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.

The most complex capability of the human brain-what I would regard as its cutting edge-is our emotional intelligence. Sitting uneasily at the top of our brain's complex and interconnected hierarchy is our ability to perceive and respond appropriately to emotion, to interact in social situations, to have a moral sense, to get the joke, and to respond emotionally to art and music, among other high-level functions. Obviously, lower-level functions of perception and a.n.a.lysis feed into our brain's emotional processing, but we are beginning to understand the regions of the brain and even to model the specific types of neurons that handle such issues.

These recent insights have been the result of our attempts to understand how human brains differ from those of other mammals. The answer is that the differences are slight but critical, and they help us discern how the brain processes emotion and related feelings. One difference is that humans have a larger cortex, reflecting our stronger capability for planning, decision making, and other forms of a.n.a.lytic thinking. Another key distinguishing feature is that emotionally charged situations appear to be handled by special cells called spindle cells, which are found only in humans and some great apes. These neural cells are large, with long neural filaments called apical dendrites that connect extensive signals from many other brain regions. This type of "deep" interconnectedness, in which certain neurons provide connections across numerous regions, is a feature that occurs increasingly as we go up the evolutionary ladder. It is not surprising that the spindle cells, involved as they are in handling emotion and moral judgment, would have this form of deep interconnectedness, given the complexity of our emotional reactions.

What is startling, however, is how few spindle cells there are in this tiny region: only about 80,000 in the human brain (about 45,000 in the right hemisphere and 35,000 in the left hemisphere). This disparity appears to account for the perception that emotional intelligence is the province of the right brain, although the disproportion is modest. Gorillas have about 16,000 of these cells, bon.o.bos about 2,100, and chimpanzees about 1,800. Other mammals lack them completely.

Dr. Arthur Craig of the Barrow Neurological Inst.i.tute in Phoenix has recently provided a description of the architecture of the spindle cells.115 Inputs from the body (estimated at hundreds of megabits per second), including nerves from the skin, muscles, organs, and other areas, stream into the upper spinal cord. These carry messages about touch, temperature, acid levels (for example, lactic acid in muscles), the movement of food through the gastrointestinal tract, and many other types of information. This data is processed through the brain stem and midbrain. Key cells called Lamina 1 neurons create a map of the body representing its current state, not unlike the displays used by flight controllers to track airplanes. Inputs from the body (estimated at hundreds of megabits per second), including nerves from the skin, muscles, organs, and other areas, stream into the upper spinal cord. These carry messages about touch, temperature, acid levels (for example, lactic acid in muscles), the movement of food through the gastrointestinal tract, and many other types of information. This data is processed through the brain stem and midbrain. Key cells called Lamina 1 neurons create a map of the body representing its current state, not unlike the displays used by flight controllers to track airplanes.

The information then flows through a nut-size region called the posterior ventromedial nucleus (VMpo), which apparently computes complex reactions to bodily states such as "this tastes terrible," "what a stench," or "that light touch is stimulating." The increasingly sophisticated information ends up at two regions of the cortex called the insula. These structures, the size of small fingers, are located on the left and right sides of the cortex. Craig describes the VMpo and the two insula regions as "a system that represents the material me."

Although the mechanisms are not yet understood, these regions are critical to self-awareness and complicated emotions. They are also much smaller in other animals. For example, the VMpo is about the size of a grain of sand in macaque monkeys and even smaller in lower-level animals. These findings are consistent with a growing consensus that our emotions are closely linked to areas of the brain that contain maps of the body, a view promoted by Dr. Antonio Damasio at the University of Iowa.116 They are also consistent with the view that a great deal of our thinking is directed toward our bodies: protecting and enhancing them, as well as attending to their myriad needs and desires. They are also consistent with the view that a great deal of our thinking is directed toward our bodies: protecting and enhancing them, as well as attending to their myriad needs and desires.

Very recently yet another level of processing of what started out as sensory information from the body has been discovered. Data from the two insula regions goes on to a tiny area at the front of the right insula called the frontoinsular cortex. This is the region containing the spindle cells, and tMRI scans have revealed that it is particularly active when a person is dealing with high-level emotions such as love, anger, sadness, and s.e.xual desire. Situations that strongly activate the spindle cells include when a subject looks at her romantic partner or hears her child crying.

Anthropologists believe that spindle cells made their first appearance ten to fifteen million years ago in the as-yet-undiscovered common ancestor to apes and early hominids (the family of humans) and rapidly increased in numbers around one hundred thousand years ago. Interestingly, spindle cells do not exist in newborn humans but begin to appear only at around the age of four months and increase significantly from ages one to three. Children's ability to deal with moral issues and perceive such higher-level emotions as love develop during this same time period.

The spindle cells gain their power from the deep interconnectedness of their long apical dendrites with many other brain regions. The high-level emotions that the spindle cells process are affected, thereby, by all of our perceptual and cognitive regions. It will be difficult, therefore, to reverse engineer the exact methods of the spindle cells until we have better models of the many other regions to which they connect. However, 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 is important to point out that the spindle cells are not doing rational problem solving, which is why we don't have rational control over our responses to music or over falling in love. The rest of the brain is heavily engaged, however, in trying to make sense of our mysterious high-level emotions.

Interfacing the Brain and Machines

I want to do something with my life; I want to be a cyborg.-KEVIN WARWICK

Understanding the methods of the human brain will help us to design similar biologically inspired machines. Another important application will be to actually interface our brains with computers, which I believe will become an increasingly intimate merger in the decades ahead.

Already the Defense Advanced Research Projects Agency is spending $24 million per year on investigating direct interfaces between brain and computer. As described above (see the section "The Visual System" on p. 185), Tomaso Poggio and James DiCarlo at MIT, along with Christof Koch at the California Inst.i.tute of Technology (Caltech), are attempting to develop models of the recognition of visual objects and how this information is encoded. These could eventually be used to transmit images directly into our brains.

Miguel Nicolelis and his colleagues at Duke University implanted sensors in the brains of monkeys, enabling the animals to control a robot through thought alone. The first step in the experiment involved teaching the monkeys to control a cursor on a screen with a joystick. The scientists collected a pattern of signals from EEGs (brain sensors) and subsequently caused the cursor to respond to the appropriate patterns rather than physical movements of the joystick. The monkeys quickly learned that the joystick was no longer operative and that they could control the cursor just by thinking. This "thought detection" system was then hooked up to a robot, and the monkeys were able to learn how to control the robot's movements with their thoughts alone. By getting visual feedback on the robot's performance, the monkeys were able to perfect their thought control over the robot. The goal of this research is to provide a similar system for paralyzed humans that will enable them to control their limbs and environment.

A key challenge in connecting neural implants to biological neurons is that the neurons generate glial cells, which surround a "foreign" object in an attempt to protect the brain. Ted Berger and his colleagues are developing special coatings that will appear to be biological and therefore attract rather than repel nearby neurons.

Another approach being pursued by the Max Planck Inst.i.tute 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 We have already learned how to interface surgically installed neural implants. In cochlear (inner-ear) implants it has been found that the auditory nerve reorganizes itself to correctly interpret the multichannel signal from the implant. A similar process appears to take place with 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 implant from outside the patient.

The Accelerating Pace of Reverse Engineering the Brain

h.o.m.o 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: Tobe able at any moment to sacrifice what we are for what we could become.-CHARLES DUBOIS

Some observers have expressed concern that as we develop models, simulations, and extensions to the human brain we risk not really understanding what we are tinkering with and the delicate balances involved. Author W. French Anderson writes:

We may be like the young boy who loves to take things apart. He is bright enough to disa.s.semble a watch, and maybe even bright enough to get it back together so that it works. But what if he tries to "improve" it? ... The boy can understand what is visible, but he cannot understand the precise engineering calculations that determine exactly how strong each spring should be....Attempts on his part to improve the watch will probably only harm it....I fear ... we, too, do not really understand what makes the [lives] we are tinkering with tick.118

Anderson's concern, however, does not reflect the scope of the broad and painstaking effort by tens of thousands of brain and computer scientists to methodically test out the limits and capabilities of models and simulations before taking them to the next step. We are not attempting to disa.s.semble and reconfigure the brain's trillions of parts without a detailed a.n.a.lysis at each stage. The process of understanding the principles of operation of the brain is proceeding through a series of increasingly sophisticated models derived from increasingly accurate and high-resolution data.

As the computational power to emulate the human brain approaches-we're almost there with supercomputers-the efforts to scan and sense the human brain and to build working models and simulations of it are accelerating. As with every other projection in this book, it is critical to understand the exponential nature of progress in this field. I frequently encounter colleagues who argue that it will be a century or longer before we can understand in detail the methods of the brain. As with so many long-term scientific projections, this one is based on a linear view of the future and ignores the inherent acceleration of progress, as well as the exponential growth of each underlying technology. Such overly conservative views are also frequently based on an underestimation of the breadth of contemporary accomplishments, even by pract.i.tioners in the field.

Scanning and sensing tools are doubling their overall spatial and temporal resolution each year. Scanning-bandwidth, price-performance, and image-reconstruction times are also seeing comparable exponential growth. These trends hold true for all of the forms of scanning: fully noninvasive scanning, in vivo scanning with an exposed skull, and destructive scanning. Databases of brain-scanning information and model building are also doubling in size about once per year.

We have demonstrated that our ability to build detailed models and working simulations of subcellular portions, neurons, and extensive neural regions follows closely upon the availability of the requisite tools and data. The performance of neurons and subcellular portions of neurons often involves substantial complexity and numerous nonlinearities, but the performance of neural cl.u.s.ters and neuronal regions is often simpler than their const.i.tuent 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. Our success to date in effectively modeling several important regions of the brain shows the effectiveness of this approach.

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 nan.o.bot 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 nan.o.bots through its capillaries will enable us to noninvasively scan an entire working brain in real time. We have already created effective (although still incomplete) models of extensive regions of the brain with today's relatively crude tools. 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. We will also benefit from the inherent strength of machines in storing, retrieving, and quickly sharing ma.s.sive amounts of information. We will then be in a position to implement these powerful hybrid systems on computational platforms that greatly exceed the capabilities of the human brain's relatively fixed architecture.

The Scalability of Human Intelligence. In response to Hofstadter's concern as to whether human intelligence is just above or below the threshold necessary for "self-understanding," the accelerating pace of brain reverse engineering makes it clear that there are no limits to our ability to understand ourselves-or anything else, for that matter. 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. For example, a model of a biological cell can include models of the nucleus, ribosomes, and other cellular systems. In turn, the model of the ribosome may include models of its submolecular components, and then down to the atoms and subatomic particles and forces that it comprises. In response to Hofstadter's concern as to whether human intelligence is just above or below the threshold necessary for "self-understanding," the accelerating pace of brain reverse engineering makes it clear that there are no limits to our ability to understand ourselves-or anything else, for that matter. 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. For example, a model of a biological cell can include models of the nucleus, ribosomes, and other cellular systems. In turn, the model of the ribosome may include models of its submolecular components, and then down to the atoms and subatomic particles and forces that it comprises.

Our ability to understand complex systems is not necessarily hierarchical. A complex system like a cell or the human brain cannot be understood simply by breaking it down into const.i.tuent subsystems and their components. We have increasingly sophisticated mathematical tools for understanding systems that combine both order and chaos-and there is plenty of both in a cell and in the brain-and for understanding the complex interactions that defy logical breakdown.

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 a.s.sist 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.

Uploading the Human Brain

To become a figment of your computer's imagination.-DAVID VICTOR DE TRANSCEND, G.o.dLING'S GLOSSARY G.o.dLING'S GLOSSARY, DEFINITION OF "UPLOAD"

A more controversial application than the scanning-the-brain-to-understand-it scenario is scanning the brain to upload it scanning the brain to upload it. Uploading a human brain means scanning all of its salient details and then reinstantiating those details into a suitably powerful computational substrate. This process would capture a person's entire personality, memory, skills, and history.

If we are truly capturing a particular person's mental processes, then the reinstantiated mind will need a body, since so much of our thinking is directed toward physical needs and desires. As I will discuss in chapter 5, by the time we have the tools to capture and re-create a human brain with all of its subtleties, we will have plenty of options for twenty-first-century bodies for both nonbiological humans and biological humans who avail themselves of extensions to our intelligence. The human body version 2.0 will include virtual bodies in completely realistic virtual environments, nanotechnology-based physical bodies, and more.

In chapter 3 I discussed my estimates for the memory and computational requirements to simulate the human brain. Although I estimated that 1016 cps of computation and 10 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: 10 bits of memory are sufficient to emulate human levels of intelligence, my estimates for the requirements of uploading were higher: 1019 cps and 10 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 10 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 10 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. Nonetheless, we will use our higher estimates for this discussion. 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. Nonetheless, we will use our higher estimates for this discussion.

The basic computational resources (1019 cps and 10 cps and 1018 bits) will be available for one thousand dollars in the early 2030s, about a decade later than the resources needed for functional simulation. The scanning requirements for uploading are also more daunting than for "merely" re-creating the overall powers of human intelligence. In theory one could upload a human brain by capturing all the necessary details without necessarily comprehending the brain's overall plan. In practice, however, this is unlikely to work. Understanding the principles of operation of the human brain will reveal which details are essential and which details are intended to be disordered. We need to know, for example, which molecules in the neurotransmitters are critical, and whether we need to capture overall levels, position and location, and/or molecular shape. As I discussed above, we are just learning, for example, that it is the position of actin molecules and the shape of ePEB molecules in the synapse that are key for memory. It will not be possible to confirm which details are crucial without having confirmed our understanding of the theory of operation. That confirmation will be in the form of a functional simulation of human intelligence that pa.s.ses the Turing test, which I believe will take place by 2029. bits) will be available for one thousand dollars in the early 2030s, about a decade later than the resources needed for functional simulation. The scanning requirements for uploading are also more daunting than for "merely" re-creating the overall powers of human intelligence. In theory one could upload a human brain by capturing all the necessary details without necessarily comprehending the brain's overall plan. In practice, however, this is unlikely to work. Understanding the principles of operation of the human brain will reveal which details are essential and which details are intended to be disordered. We need to know, for example, which molecules in the neurotransmitters are critical, and whether we need to capture overall levels, position and location, and/or molecular shape. As I discussed above, we are just learning, for example, that it is the position of actin molecules and the shape of ePEB molecules in the synapse that are key for memory. It will not be possible to confirm which details are crucial without having confirmed our understanding of the theory of operation. That confirmation will be in the form of a functional simulation of human intelligence that pa.s.ses the Turing test, which I believe will take place by 2029.119 To capture this level of detail will require scanning from within the brain using nan.o.bots, 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 princ.i.p.al 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.

Confirmation of the uploading milestone will be in the form of a "Ray Kurzweil" or "Jane Smith" Turing test, in other words convincing a human judge that the uploaded re-creation is indistinguishable from the original specific person. By that time we'll face some complications in devising the rules of any Turing test. Since nonbiological intelligence will have pa.s.sed the original Turing test years earlier (around 2029), should we allow a nonbiological human equivalent to be a judge? How about an enhanced human? Unenhanced humans may become increasingly hard to find. In any event, it will be a slippery slope to define enhancement, as many different levels of extending biological intelligence will be available by the time we have purported uploads. Another issue will be that the humans we seek to upload will not be limited to their biological intelligence. However, uploading the nonbiological portion of intelligence will be relatively straightforward, since the ease of copying computer intelligence has always represented one of the strengths of computers.

One question that arises is, How quickly do we need to scan a person's nervous system? It clearly cannot be done instantaneously, and even if we did provide a nan.o.bot for each neuron, it would take time to gather the data. One might therefore object that because a person's state is changing during the data-gathering process, the upload information does not accurately reflect that person at an instant in time but rather over a period of time, even if only a fraction of a second.120 Consider, however, that this issue will not interfere with an upload's pa.s.sing a "Jane Smith" Turing test. When we encounter one another on a day-to-day basis, we are recognized as ourselves even though it may have been days or weeks since the last such encounter. If an upload is sufficiently accurate to re-create a person's state within the amount of natural change that a person undergoes in a fraction of a second or even a few minutes, that will be sufficient for any conceivable purpose. Some observers have interpreted Roger Penrose's theory of the link between quantum computing and consciousness (see chapter 9) to mean that uploading is impossible because a person's "quantum state" will have changed many times during the scanning period. But I would point out that my quantum state has changed many times in the time it took me to write this sentence, and I still consider myself to be the same person (and no one seems to be objecting). Consider, however, that this issue will not interfere with an upload's pa.s.sing a "Jane Smith" Turing test. When we encounter one another on a day-to-day basis, we are recognized as ourselves even though it may have been days or weeks since the last such encounter. If an upload is sufficiently accurate to re-create a person's state within the amount of natural change that a person undergoes in a fraction of a second or even a few minutes, that will be sufficient for any conceivable purpose. Some observers have interpreted Roger Penrose's theory of the link between quantum computing and consciousness (see chapter 9) to mean that uploading is impossible because a person's "quantum state" will have changed many times during the scanning period. But I would point out that my quantum state has changed many times in the time it took me to write this sentence, and I still consider myself to be the same person (and no one seems to be objecting).

n.o.bel Prize winner Gerald Edelman points out that there is a difference between a capability and a description of that capability. A photograph of a person is different from the person herself, even if the "photograph" is very high resolution and three-dimensional. However, the concept of uploading goes beyond the extremely high-resolution scan, which we can consider the "photograph" in Edelman's a.n.a.logy. The scan does need to capture all of the salient details, but it also needs to be instantiated into a working computational medium that has the capabilities of the original (albeit that the new nonbiological platforms are certain to be far more capable). The neural details need to interact with one another (and with the outside world) in the same ways that they do in the original. A comparable a.n.a.logy is the comparison between a computer program that resides on a computer disk (a static picture) and a program that is actively running on a suitable computer (a dynamic, interacting ent.i.ty). Both the data capture and the reinstantiation of a dynamic ent.i.ty const.i.tute the uploading scenario.

Perhaps the most important question will be whether or not an uploaded human brain is really you. Even if the upload pa.s.ses 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 nan.o.bots 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 a.n.a.lysis. 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. So we will have effectively uploaded ourselves, albeit gradually, never quite noticing the transfer. There will be no "old Ray" and "new Ray," just an increasingly capable Ray. Although I believe that uploading as in the sudden scan-and-transfer scenario discussed in this section will be a feature of our future world, it is this gradual but inexorable progression to vastly superior nonbiological thinking that will profoundly transform human civilization.

SIGMUND FREUD: When you talk about reverse engineering the human brain, just whose brain are you talking about? A man's brain? A woman's? A child's? The brain of a genius? A r.e.t.a.r.ded individual? An "idiot savant"? A gifted artist? A serial murderer? When you talk about reverse engineering the human brain, just whose brain are you talking about? A man's brain? A woman's? A child's? The brain of a genius? A r.e.t.a.r.ded 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 const.i.tuent 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 a.s.sociated with visual imagery and mathematical thinking-became greatly enlarged. 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 const.i.tuent 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 a.s.sociated with visual imagery and mathematical thinking-became greatly enlarged. However, there is only so much room in our skulls, so although Einstein played music he was not a world-cla.s.s musician. Pica.s.so 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. However, there is only so much room in our skulls, so although Einstein played music he was not a world-cla.s.s musician. Pica.s.so 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? It must, after all, have something to do with his brain. This type of disastrous behavior is clearly not the result of indigestion.

MOLLY 2004: You know, I doubt it's just the brains we're born with that account for our differences. What about our struggles through life, and all this stuff I'm trying to learn? You know, I doubt it's just the brains we're born with that account for our differences. What about our struggles through life, and all this stuff I'm trying to learn?

RAY: Yes, well, that's part of the paradigm, too, isn't it? We have brains that can learn, starting from when we learn to walk and talk to when we study college chemistry. Yes, well, that's part of the paradigm, too, isn't it? We have brains that can learn, starting from when we learn to walk and talk to when we study college chemistry.

MARVIN MINSKY: It's true that educating our AIs will be an important part of the process, but we can automate a lot of that and greatly speed it up. Also, keep in mind that when one AI learns something, it can quickly share that knowledge with many other AIs. It's true that educating our AIs will be an important part of the process, but we can automate a lot of that and greatly speed it up. Also, keep in mind that when one AI learns something, it can quickly share that knowledge with many other AIs.

RAY: They'll have access to all of our exponentially growing knowledge on the Web, which will include habitable, full-immersion virtual-reality environments where they can interact with one another and with biological humans who are projecting themselves into these environments. They'll have access to all of our exponentially growing knowledge on the Web, which will include habitable, full-immersion virtual-reality environments where they can interact with one another and with biological humans who are projecting themselves into these environments.

SIGMUND: These AIs don't have bodies yet. As we have both pointed out, human emotion and much of our thinking are directed at our bodies and to meeting their sensual and s.e.xual needs. These AIs don't have bodies yet. As we have both pointed out, human emotion and much of our thinking are directed at our bodies and to meeting their sensual and s.e.xual needs.

RAY: Who says they won't have bodies? As I will discuss in the human body version 2.0 section in chapter 6, we'll have the means of creating nonbiological yet humanlike bodies, as well as virtual bodies in virtual reality. Who says they won't have bodies? As I will discuss in the human body version 2.0 section in chapter 6, we'll have the means of creating nonbiological yet humanlike bodies, as well as virtual bodies in virtual reality.

SIGMUND: But a virtual body is not a real body. But a virtual body is not a real body.

RAY: The word "virtual" is somewhat unfortunate. It implies "not real," but the reality will be that a virtual body is just as real as a physical body in all the ways that matter. Consider that the telephone is auditory virtual reality. No one feels that his voice in this virtual-reality environment is not a "real" voice. With my physical body today, I don't directly experience someone's touch on my arm. My brain receives processed signals initiated by nerve endings in my arm, which wind their way through the spinal cord, through the brain stem, and up to the insula regions. If my brain-or an AI's brain-receives comparable signals of someone's virtual touch on a virtual arm, there's no discernible difference. The word "virtual" is somewhat unfortunate. It implies "not real," but the reality will be that a virtual body is just as real as a physical body in all the ways that matter. Consider that the telephone is auditory virtual reality. No one feels that his voice in this virtual-reality environment is not a "real" voice. With my physical body today, I don't directly experience someone's touch on my arm. My brain receives processed signals initiated by nerve endings in my arm, which wind their way through the spinal cord, through the brain stem, and up to the insula regions. If my brain-or an AI's brain-receives comparable signals of someone's virtual touch on a virtual arm, there's no discernible difference.

MARVIN: Keep in mind that not all AIs will need human bodies. 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. 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. 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. 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.a bodies. Humans of biological origin will have the same freedom in both real and virtual reality. It's not just AIs that will be liberated from the limitations of version 1.a 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. 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. Yes, except for the MOSHs (Mostly Original Substrate Humans) of course.