For starters, you should realize that as soon as a computer achieves a level of intelligence comparable to human intelligence, it will necessarily soar past it. A key advantage of nonbiological intelligence is that machines can easily share their knowledge. If I learn French, I can't readily download that learning to you. My knowledge, skills and memories are embedded in a vast pattern of neurotransmitter concentrations and interneuronal connections and cannot be quickly accessed or transmitted.

But when we construct the nonbiological equivalents of human neuron clusters, we will almost certainly include built-in, quick-downloading ports. When one computer learns a skill or gains an insight, it will be able to share that wisdom immediately with billions of other machines.

Let's consider the requirements for a computer to exhibit human-level intelligence, by which I include all the diverse and subtle ways in which humans are intelligent--including musical and artistic aptitude, creativity, the ability to physically move through the world and even to respond to emotion. A necessary (but not sufficient) condition is the requisite processing power, which I estimate at about 20 million billion calculations per sec. (we have on the order of 100 billion neurons, each with some 1,000 connections to other neurons, with each connection capable of performing about 200 calculations per sec.). As Moore's law reaches its limit and computing power no longer doubles roughly every 12 to 18 months (by my reckoning, around 2019), conventional silicon chips may not be able to deliver that kind of performance. But each time one computing technology has reached its limit, a new approach has stepped in to continue exponential growth (see "What Will Replace Silicon?" in this issue). Nanotubes, for example, which are already functioning in laboratories, could be fashioned into three-dimensional circuits made of hexagonal arrays of carbon atoms. One cubic inch of nanotube circuitry would be 1 million times more powerful than the human brain, at least in raw processing power.

More important, however, is the software of intelligence. The most compelling scenario for mastering that software is to tap into the blueprint of the best example we can get our hands on: the brain. There is no reason why we cannot reverse-engineer the human brain and copy its design. We can peer inside someone's brain today with noninvasive scanners, which are increasing their resolution with each new generation. To capture the salient neural details of the human brain, the most practical approach would be to scan it from inside. By 2030, "nanobot" technology should be available for brain scanning. Nanobots are robots that are the size of human blood cells or even smaller (see "Will Tiny Robots Build Diamonds One Atom at a Time?"). Billions of them could travel through every brain capillary and scan neural details up close. Using high-speed wireless connections, the nanobots would communicate with one another and with other computers that are compiling the brain-scan database.

Armed with this information, we can design biologically inspired re-creations of the methods used by the human brain. After the algorithms of a region are understood, they can be refined and extended before being implemented in synthetic neural equivalents. For one thing, they can be run on computational systems that are more than 10 million times faster than the electrochemical processes used in the brain. We can also throw in the methods for building intelligent machines that we already understand. The computationally relevant aspects of individual neurons and neural structures are complicated but not beyond our ability to model accurately. Scientists at several laboratories around the world have built integrated circuits that match the digital and analog information-processing characteristics of biological neurons, including clusters of hundreds of neurons.

By the third decade of the 21st century, we will be in a position to create highly detailed maps of the pertinent features of neurons, neural connections and synapses in the human brain--including all the neural details that play a role in the behavior and functionality of the brain--and to re-create these designs in suitably advanced neural computers. By that time, computers will greatly exceed the basic computational power of the human brain. The result will be machines that combine the complex and rich skills of humans with the speed, accuracy and knowledge-sharing ability that machines excel in.

How will we apply technology that is more intelligent than its creators? One might be tempted to respond, "Carefully!" But let's take a look at some examples.

The same nanobots that will scan our brains will also be able to expand our thinking and our experiences. Nanobot technology will provide fully immersive, totally convincing virtual reality. By taking up positions in close physical proximity to every interneuronal connection coming from all our sense organs (e.g., eyes, ears, skin), the nanobots can suppress all the inputs coming from the real senses and replace them with the signals that would be appropriate for a virtual environment. By 2030, "going to a website" will mean entering a virtual-reality environment. The implant will generate the streams of sensory input that would otherwise come from our real senses, thus creating an all-encompassing virtual environment that will respond to the behavior of our own virtual body (and those of others) in that environment.

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