Superintelligence: Paths, Dangers, Strategies (8 page)

There is good reason to think that the requisite enabling technologies are attainable, though not in the near future. Reasonable computational models of many types of neuron and neuronal processes already exist. Image recognition software has been developed that can trace axons and dendrites through a stack of two-dimensional images (though reliability needs to be improved). And there are imaging tools that provide the necessary resolution—with a scanning tunneling microscope it is possible to “see” individual atoms, which is a far higher resolution than needed. However, although present knowledge and capabilities suggest that there is no in-principle barrier to the development of the requisite enabling technologies, it is clear that a very great deal of incremental technical progress would be needed to bring human whole brain emulation within reach.
²⁴
For example, microscopy technology would need not just sufficient resolution but also sufficient throughput. Using an atomic-resolution scanning tunneling microscope to image the needed surface area would be far too slow to be practicable. It would be more plausible to use a lower-resolution electron microscope, but this would require new methods for preparing and staining cortical tissue to make visible relevant details such as synaptic fine structure. A great expansion of neurocomputational libraries and major improvements in automated image processing and scan interpretation would also be needed.

Table 4
Capabilities needed for whole brain emulation

In general, whole brain emulation relies less on theoretical insight and more on technological capability than artificial intelligence. Just how much technology is required for whole brain emulation depends on the level of abstraction at which the brain is emulated. In this regard there is a tradeoff between insight and technology. In general, the worse our scanning equipment and the feebler our computers, the less we could rely on simulating low-level chemical and electrophysiological brain processes, and the more theoretical understanding would be needed of the computational architecture that we are seeking to emulate in order to create more abstract representations of the relevant functionalities.
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Conversely, with sufficiently advanced scanning technology and abundant computing power, it might be possible to brute-force an emulation even with a fairly limited understanding of the brain. In the unrealistic limiting case, we could imagine emulating a brain at the level of its elementary particles using the quantum mechanical Schrödinger equation. Then one could rely entirely on existing knowledge of physics and not at all on any biological model. This extreme case, however, would place utterly impracticable demands on computational power and data acquisition. A far more plausible level of emulation would be one that incorporates individual neurons and their connectivity matrix, along with some of the structure of their dendritic trees and maybe some state variables of individual synapses. Neurotransmitter molecules would not be simulated individually, but their fluctuating concentrations would be modeled in a coarse-grained manner.

To assess the feasibility of whole brain emulation, one must understand the criterion for success. The aim is not to create a brain simulation so detailed and accurate that one could use it to predict exactly what would have happened in the original brain if it had been subjected to a particular sequence of stimuli. Instead, the aim is to capture enough of the computationally functional properties of the brain to enable the resultant emulation to perform intellectual work. For this purpose, much of the messy biological detail of a real brain is irrelevant.

A more elaborate analysis would distinguish between different levels of emulation success based on the extent to which the information-processing functionality of the emulated brain has been preserved. For example, one could distinguish among (1) a
high-fidelity emulation
that has the full set of knowledge, skills, capacities, and values of the emulated brain; (2) a
distorted emulation
whose dispositions are significantly non-human in some ways but which is mostly able to do the same intellectual labor as the emulated brain; and (3) a
generic emulation
(which might also be distorted) that is somewhat like an infant, lacking the skills or memories that had been acquired by the emulated adult brain but with the capacity to learn most of what a normal human can learn.
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While it appears ultimately feasible to produce a high-fidelity emulation, it seems quite likely that the
first
whole brain emulation that we would achieve if we went down this path would be of a lower grade. Before we would get things to work perfectly, we would probably get things to work imperfectly. It is also possible that a push toward emulation technology would lead to the creation of some kind of neuromorphic AI that would adapt some neurocomputational principles discovered during emulation efforts and hybridize them with synthetic methods, and that this would happen before the completion of a fully functional whole brain emulation. The possibility of such a spillover into neuromorphic AI, as we shall see in a later chapter, complicates the strategic assessment of the desirability of seeking to expedite emulation technology.

How far are we currently from achieving a human whole brain emulation? One recent assessment presented a technical roadmap and concluded that the prerequisite capabilities might be available around mid-century, though with a large uncertainty interval.
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Figure 5
depicts the major milestones in this roadmap. The apparent simplicity of the map may be deceptive, however, and we should be careful not to understate how much work remains to be done. No brain has yet been emulated. Consider the humble model organism
Caenorhabditis elegans
, which is a transparent roundworm, about 1 mm in length, with 302 neurons. The complete connectivity matrix of these neurons has been known since the mid-1980s, when it was laboriously mapped out by means of slicing, electron microscopy, and
hand-labeling of specimens.
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But knowing merely which neurons are connected with which is not enough. To create a brain emulation one would also need to know which synapses are excitatory and which are inhibitory; the strength of the connections; and various dynamical properties of axons, synapses, and dendritic trees. This information is not yet available even for the small nervous system of
C. elegans
(although it may now be within range of a targeted moderately sized research project).
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Success at emulating a tiny brain, such as that of
C. elegans
, would give us a better view of what it would take to emulate larger brains.

Figure 5
Whole brain emulation roadmap. Schematic of inputs, activities, and milestones.
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At some point in the technology development process, once techniques are available for automatically emulating small quantities of brain tissue, the problem reduces to one of scaling. Notice “the ladder” at the right side of
Figure 5
. This ascending series of boxes represents a final sequence of advances which can commence after preliminary hurdles have been cleared. The stages in this sequence correspond to whole brain emulations of successively more neurologically sophisticated model organisms—for example,
C. elegans → honeybee
→
mouse
→
rhesus monkey
→
human
. Because the gaps between these rungs—at least after the first step—are mostly quantitative in nature and due mainly (though not entirely) to the differences in size of the brains to be emulated, they should be tractable through a relatively straightforward scale-up of scanning and simulation capacity.
³¹

Once we start ascending this final ladder, the
eventual
attainment of human whole brain emulation becomes more clearly foreseeable.
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We can thus expect to get some advance warning before arrival at human-level machine intelligence along the whole brain emulation path, at least if the last among the requisite enabling technologies to reach sufficient maturity is either high-throughput scanning or the computational power needed for real-time simulation. If, however, the last enabling technology to fall into place is neurocomputational modeling, then the transition from unimpressive prototypes to a working human emulation could be more abrupt. One could imagine a scenario in which, despite abundant scanning data and fast computers, it is proving difficult to get our neuronal models to work right. When finally the last glitch is ironed out, what was previously a completely dysfunctional system—analogous perhaps to an unconscious brain undergoing a grand mal seizure—might snap into a coherent wakeful state. In this case, the key advance would not be heralded by a series of functioning animal emulations of increasing magnitude (provoking newspaper headlines of correspondingly escalating font size). Even for those paying attention it might be difficult to tell in advance of success just how many flaws remained in the neurocomputational models at any point and how long it would take to fix them, even up to the eve of the critical breakthrough. (Once a human whole brain emulation has been achieved, further potentially explosive developments would take place; but we postpone discussion of this until
Chapter 4
.)

Surprise scenarios are thus imaginable for whole brain emulation even if all the relevant research were conducted in the open. Nevertheless, compared with the AI path to machine intelligence, whole brain emulation is more likely to be preceded
by clear omens since it relies more on concrete observable technologies and is not wholly based on theoretical insight. We can also say, with greater confidence than for the AI path, that the emulation path will not succeed in the near future (within the next fifteen years, say) because we know that several challenging precursor technologies have not yet been developed. By contrast, it seems likely that somebody could
in principle
sit down and code a seed AI on an ordinary present-day personal computer; and it is conceivable—though unlikely—that somebody somewhere will get the right insight for how to do this in the near future.

A third path to greater-than-current-human intelligence is to enhance the functioning of biological brains. In principle, this could be achieved without technology, through selective breeding. Any attempt to initiate a classical large-scale eugenics program, however, would confront major political and moral hurdles. Moreover, unless the selection were extremely strong, many generations would be required to produce substantial results. Long before such an initiative would bear fruit, advances in biotechnology will allow much more direct control of human genetics and neurobiology, rendering otiose any human breeding program. We will therefore focus on methods that hold the potential to deliver results faster, on the timescale of a few generations or less.

Our individual cognitive capacities can be strengthened in various ways, including by such traditional methods as education and training. Neurological development can be promoted by low-tech interventions such as optimizing maternal and infant nutrition, removing lead and other neurotoxic pollutants from the environment, eradicating parasites, ensuring adequate sleep and exercise, and preventing diseases that affect the brain.
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Improvements in cognition can certainly be obtained through each of these means, though the magnitudes of the gains are likely to be modest, especially in populations that are already reasonably well-nourished and -schooled. We will certainly not achieve superintelligence by any of these means, but they might help on the margin, particularly by lifting up the deprived and expanding the catchment of global talent. (Lifelong depression of intelligence due to iodine deficiency remains widespread in many impoverished inland areas of the world—an outrage given that the condition can be prevented by fortifying table salt at a cost of a few cents per person and year.
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)