The Singularity Is Near: When Humans Transcend Biology (82 page)

First of all, my thesis includes the idea of combining analog and digital methods in the same way that the human brain does. For example, more advanced neural nets are already using highly detailed models of human neurons, including detailed nonlinear, analog activation functions. There’s a significant efficiency advantage to emulating the brain’s analog methods. Analog methods are also not the exclusive province of biological systems. We used to refer to “digital computers” to distinguish them from the more ubiquitous analog computers widely used during World War II. The work of Carver Mead has shown the ability of silicon circuits to implement digital-controlled analog circuits entirely analogous to, and indeed derived from, mammalian neuronal circuits. Analog methods are readily re-created by conventional transistors, which are essentially analog devices. It is only by adding the mechanism of comparing the transistor’s output to a threshold that it is made into a digital device.

More important, there is nothing that analog methods can accomplish that digital methods are unable to accomplish just as well. Analog processes can be emulated with digital methods (by using floating point representations), whereas the reverse is not necessarily the case.

The Criticism from the Complexity of Neural Processing

Another common criticism is that the fine detail of the brain’s biological design is simply too complex to be modeled and simulated using nonbiological technology. For example, Thomas Ray writes:

The structure and function of the brain or its components cannot be separated. The circulatory system provides life support for the brain, but it also delivers hormones that are an integral part of the chemical information processing function of the brain. The membrane of a neuron is a
structural feature defining the limits and integrity of a neuron, but it is also the surface along which depolarization propagates signals. The structural and life-support functions cannot be separated from the handling of information.
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Ray goes on to describe several of the “broad spectrum of chemical communication mechanisms” that the brain exhibits.

In fact, all of these features can readily be modeled, and a great deal of progress has already been made in this endeavor. The intermediate language is mathematics, and translating the mathematical models into equivalent non-biological mechanisms (examples include computer simulations and circuits using transistors in their native analog mode) is a relatively straightforward process. The delivery of hormones by the circulatory system, for example, is an extremely low-bandwidth phenomenon, which is not difficult to model and replicate. The blood levels of specific hormones and other chemicals influence parameter levels that affect a great many synapses simultaneously.

Thomas Ray concludes that “a metallic computation system operates on fundamentally different dynamic properties and could never precisely and exactly ‘copy’ the function of a brain.” Following closely the progress in the related fields of neurobiology, brain scanning, neuron and neural-region modeling, neuron-electronic communication, neural implants, and related endeavors, we find that our ability to replicate the salient functionality of biological information processing can meet any desired level of precision. In other words the copied functionality can be “close enough” for any conceivable purpose or goal, including satisfying a Turing-test judge. Moreover, we find that efficient implementations of the mathematical models require substantially less computational capacity than the theoretical potential of the biological neuron clusters being modeled. In
chapter 4
, I reviewed a number of brain-region models (Watts’s auditory regions, the cerebellum, and others) that demonstrate this.

Brain Complexity
. Thomas Ray also makes the point that we might have difficulty creating a system equivalent to “billions of lines of code,” which is the level of complexity he attributes to the human brain. This figure, however, is highly inflated, for as we have seen our brains are created from a genome of only about thirty to one hundred million bytes of unique information (eight hundred million bytes without compression, but compression is clearly feasible given the massive redundancy), of which perhaps two thirds describe the principles of operation of the brain. It is self-organizing processes that incorporate significant elements of randomness (as well as exposure to the real world) that
enable so relatively small an amount of design information to be expanded to the thousands of trillions of bytes of information represented in a mature human brain. Similarly, the task of creating human-level intelligence in a non-biological entity will involve creating not a massive expert system comprising billions of rules or lines of code but rather a learning, chaotic, self-organizing system, one that is ultimately biologically inspired.

Ray goes on to write, “The engineers among us might propose nano-molecular devices with fullerene switches, or even DNA-like computers. But I am sure they would never think of neurons. Neurons are astronomically large structures compared to the molecules we are starting with.”

This is exactly my own point. The purpose of reverse engineering the human brain is not to copy the digestive or other unwieldy processes of biological neurons but rather to understand their key information-processing methods. The feasibility of doing this has already been demonstrated in dozens of contemporary projects. The complexity of the neuron clusters being emulated is scaling up by orders of magnitude, along with all of our other technological capabilities.

A Computer’s Inherent Dualism
. Neuroscientist Anthony Bell of Redwood Neuroscience Institute articulates two challenges to our ability to model and simulate the brain with computation. In the first he maintains that

a computer is an intrinsically dualistic entity, with its physical set-up designed not to interfere with its logical set-up, which executes the computation. In empirical investigation, we find that the brain is not a dualistic entity. Computer and program may be two, but mind and brain are one. The brain is thus not a machine, meaning it is not a finite model (or computer) instantiated physically in such a way that the physical instantiation does not interfere with the execution of the model (or program).
¹⁸

This argument is easily dispensed with. The ability to separate in a computer the program from the physical instantiation that performs the computation is an advantage, not a limitation. First of all, we do have electronic devices with dedicated circuitry in which the “computer and program” are not two, but one. Such devices are not programmable but are hardwired for one specific set of algorithms. Note that I am not just referring to computers with software (called “firmware”) in read-only memory, as may be found in a cell phone or pocket computer. In such a system, the electronics and the software may still be considered dualistic even if the program cannot easily be modified.

I am referring instead to systems with dedicated logic that cannot be programmed at all—such as application-specific integrated circuits (used, for example, for image and signal processing). There is a cost efficiency in implementing algorithms in this way, and many electronic consumer products use such circuitry. Programmable computers cost more but provide the flexibility of allowing the software to be changed and upgraded. Programmable computers can emulate the functionality of any dedicated system, including the algorithms that we are discovering (through the efforts to reverse engineer the brain) for neural components, neurons, and brain regions.

There is no validity to calling a system in which the logical algorithm is inherently tied to its physical design “not a machine.” If its principles of operation can be understood, modeled in mathematical terms, and then instantiated on another system (whether that other system is a machine with unchangeable dedicated logic or software on a programmable computer), then we can consider it to be a machine and certainly an entity whose capabilities can be re-created in a machine. As I discussed extensively in
chapter 4
, there are no barriers to our discovering the brain’s principles of operation and successfully modeling and simulating them, from its molecular interactions upward.

Bell refers to a computer’s “physical set-up [that is] designed not to interfere with its logical set-up,” implying that the brain does not have this “limitation.” He is correct that our thoughts do help create our brains, and as I pointed out earlier we can observe this phenomenon in dynamic brain scans. But we can readily model and simulate both the physical and logical aspects of the brain’s plasticity in software. The fact that software in a computer is separate from its physical instantiation is an architectural advantage in that it allows the same software to be applied to ever-improving hardware. Computer software, like the brain’s changing circuits, can also modify itself, as well as be upgraded.

Computer hardware can likewise be upgraded without requiring a change in software. It is the brain’s relatively fixed architecture that is severely limited. Although the brain is able to create new connections and neurotransmitter patterns, it is restricted to chemical signaling more than one million times slower than electronics, to the limited number of interneuronal connections that can fit inside our skulls, and to having no ability to be upgraded, other than through the merger with nonbiological intelligence that I’ve been discussing.

Levels and Loops
. Bell also comments on the apparent complexity of the brain:

Molecular and biophysical processes control the sensitivity of neurons to incoming spikes (both synaptic efficiency and post-synaptic responsivity), the excitability of the neuron to produce spikes, the patterns of
spikes it can produce and the likelihood of new synapses forming (dynamic rewiring), to list only four of the most obvious interferences from the subneural level. Furthermore, transneural volume effects such as local electric fields and the transmembrane diffusion of nitric oxide have been seen to influence, respectively, coherent neural firing, and the delivery of energy (blood flow) to cells, the latter of which directly correlates with neural activity.

The list could go on. I believe that anyone who seriously studies neuromodulators, ion channels or synaptic mechanism and is honest, would have to reject the neuron level as a separate computing level, even while finding it to be a useful descriptive level.
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Although Bell makes the point here that the neuron is not the appropriate level at which to simulate the brain, his primary argument here is similar to that of Thomas Ray above: the brain is more complicated than simple logic gates.

He makes this explicit:

To argue that one piece of structured water or one quantum coherence is a necessary detail in the functional description of the brain would clearly be ludicrous. But if, in every cell, molecules derive systematic functionality from these submolecular processes, if these processes are used all the time, all over the brain, to reflect, record and propagate spatio-temporal correlations of molecular fluctuations, to enhance or diminish the probabilities and specificities of reactions, then we have a situation qualitatively different from the logic gate.

At one level he is disputing the simplistic models of neurons and interneuronal connections used in many neural-net projects. Brain-region simulations don’t use these simplified models, however, but rather apply realistic mathematical models based on the results from brain reverse engineering.

The real point that Bell is making is that the brain is immensely complicated, with the consequent implication that it will therefore be very difficult to understand, model, and simulate its functionality. The primary problem with Bell’s perspective is that he fails to account for the self-organizing, chaotic, and fractal nature of the brain’s design. It’s certainly true that the brain is complex, but a lot of the complication is more apparent than real. In other words, the principles of the design of the brain are simpler than they appear.

To understand this, let’s first consider the fractal nature of the brain’s organization,
which I discussed in
chapter 2
. A fractal is a rule that is iteratively applied to create a pattern or design. The rule is often quite simple, but because of the iteration the resulting design can be remarkably complex. A famous example of this is the Mandelbrot set devised by mathematician Benoit Mandelbrot.
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Visual images of the Mandelbrot set are remarkably complex, with endlessly complicated designs within designs. As we look at finer and finer detail in an image of the Mandelbrot set, the complexity never goes away, and we continue to see ever finer complication. Yet the formula underlying all of this complexity is amazingly simple: the Mandelbrot set is characterized by a single formula Z = Z
²
+ C, in which Z is a “complex” (meaning two-dimensional) number and C is a constant. The formula is iteratively applied, and the resulting two-dimensional points are graphed to create the pattern.