Simulation Essay Research Paper Eric FingermanBy a

СОДЕРЖАНИЕ: Simulation Essay, Research Paper Eric Fingerman By a “superintelligence” we mean an intellect that is much smarter than the best human brains in practically every field, including scientific creativity,

Simulation Essay, Research Paper

Eric Fingerman

By a “superintelligence” we mean an intellect that is much smarter than the

best human brains in practically every field, including scientific creativity,

general wisdom and social skills. This definition leaves open how the

superintelligence is implemented: it could be a digital computer, an

ensemble of networked computers, cultured cortical tissue or what have

you. It also leaves open whether the superintelligence is conscious and has

subjective experiences.

Entities such as companies or the scientific community are not

superintelligences according to this definition. Although they can perform a

number of tasks of which no individual human is capable, they are not

intellects and there are many fields in which they perform much worse than

a human brain – for example, you can’t have real-time conversation with

“the scientific community”.

Superintelligence requires software as well as hardware. There are several

approaches to the software problem, varying in the amount of top-down

direction they require. At the one extreme we have systems like CYC which

is a very large encyclopedia-like knowledge-base and inference-engine. It

has been spoon-fed facts, rules of thumb and heuristics for over a decade by

a team of human knowledge enterers. While systems like CYC might be

good for certain practical tasks, this hardly seems like an approach that will

convince AI-skeptics that superintelligence might well happen in the

foreseeable future. We have to look at paradigms that require less human

input, ones that make more use of bottom-up methods.

Given sufficient hardware and the right sort of programming, we could

make the machines learn in the same way a child does, i.e. by interacting

with human adults and other objects in the environment. The learning

mechanisms used by the brain are currently not completely understood.

Artificial neural networks in real-world applications today are usually

trained through some variant of the Backpropagation algorithm (which is

known to be biologically unrealistic). The Backpropagation algorithm

works fine for smallish networks (of up to a few thousand neurons) but it

doesn’t scale well. The time it takes to train a network tends to increase

dramatically with the number of neurons it contains. Another limitation of

backpropagation is that it is a form of supervised learning, requiring that

signed error terms for each output neuron are specified during learning. It’s

not clear how such detailed performance feedback on the level of

individual neurons could be provided in real-world situations except for

certain well-defined specialized tasks.

A biologically more realistic learning mode is the Hebbian algorithm.

Hebbian learning is unsupervised and it might also have better scaling

properties than Backpropagation. However, it has yet to be explained how

Hebbian learning by itself could produce all the forms of learning and

adaptation of which the human brain is capable (such the storage of

structured representation in long-term memory – Bostrom 1996).

Presumably, Hebb’s rule would at least need to be supplemented with

reward-induced learning (Morillo 1992) and maybe with other learning

modes that are yet to be discovered. It does seems plausible, though, to

assume that only a very limited set of different learning rules (maybe as few

as two or three) are operating in the human brain. And we are not very far

from knowing what these rules are.

Creating superintelligence through imitating the functioning of the human

brain requires two more things in addition to appropriate learning rules

(and sufficiently powerful hardware): it requires having an adequate initial

architecture and providing a rich flux of sensory input.

The latter prerequisite is easily provided even with present technology.

Using video cameras, microphones and tactile sensors, it is possible to

ensure a steady flow of real-world information to the artificial neural

network. An interactive element could be arranged by connecting the system

to robot limbs and a speaker.

Developing an adequate initial network structure is a more serious problem.

It might turn out to be necessary to do a considerable amount of hand-coding

in order to get the cortical architecture right. In biological organisms, the

brain does not start out at birth as a homogenous tabula rasa; it has an

initial structure that is coded genetically. Neuroscience cannot, at its present

stage, say exactly what this structure is or how much of it needs be

preserved in a simulation that is eventually to match the cognitive

competencies of a human adult. One way for it to be unexpectedly difficult

to achieve human-level AI through the neural network approach would be if

it turned out that the human brain relies on a colossal amount of genetic

hardwiring, so that each cognitive function depends on a unique and

hopelessly complicated inborn architecture, acquired over aeons in the

evolutionary learning process of our species.

Is this the case? A number of considerations that suggest otherwise. We

have to contend ourselves with a very brief review here. For a more

comprehensive discussion, the reader may consult Phillips & Singer


Quartz & Sejnowski (1997) argue from recent neurobiological data that the

developing human cortex is largely free of domain-specific structures. The

representational properties of the specialized circuits that we find in the

mature cortex are not generally genetically prespecified. Rather, they are

developed through interaction with the problem domains on which the

circuits operate. There are genetically coded tendencies for certain brain

areas to specialize on certain tasks (for example primary visual processing

is usually performed in the primary visual cortex) but this does not mean

that other cortical areas couldn’t have learnt to perform the same function. In

fact, the human neocortex seems to start out as a fairly flexible and

general-purpose mechanism; specific modules arise later through

self-organizing and through interacting with the environment.

Strongly supporting this view is the fact that cortical lesions, even sizeable

ones, can often be compensated for if they occur at an early age. Other

cortical areas take over the functions that would normally have been

developed in the destroyed region. In one study, sensitivity to visual

features was developed in the auditory cortex of neonatal ferrets, after that

region’s normal auditory input channel had been replaced by visual

projections (Sur et al. 1988). Similarly, it has been shown that the visual

cortex can take over functions normally performed by the somatosensory

cortex (Schlaggar & O’Leary 1991). A recent experiment (Cohen et al.

1997) showed that people who have been blind from an early age can use

their visual cortex to process tactile stimulation when reading Braille.

There are some more primitive regions of the brain whose functions cannot

be taken over by any other area. For example, people who have their

hippocampus removed, lose their ability to learn new episodic or semantic

facts. But the neocortex tends to be highly plastic and that is where most of

the high-level processing is executed that makes us intellectually superior to

other animals. (It would be interesting to examine in more detail to what

extent this holds true for all of neocortex. Are there small neocortical

regions such that, if excised at birth, the subject will never obtain certain

high-level competencies, not even to a limited degree?)

Another consideration that seems to indicate that innate architectural

differentiation plays a relatively small part in accounting for the

performance of the mature brain is the that neocortical architecture,

especially in infants, is remarkably homogeneous over different cortical

regions and even over different species:

Laminations and vertical connections between lamina are

hallmarks of all cortical systems, the morphological and

physiological characteristics of cortical neurons are equivalent in

different species, as are the kinds of synaptic interactions

involving cortical neurons. This similarity in the organization of

the cerebral cortex extends even to the specific details of cortical

circuitry. (White 1989, p. 179).

In the seventies and eighties the AI field suffered some stagnation as the

exaggerated expectations from the early heydays failed to materialize and

progress nearly ground to a halt. The lesson to draw from this episode is not

that strong AI is dead and that superintelligent machines will never be built.

It shows that AI is more difficult than some of the early pioneers might have

thought, but it goes no way towards showing that AI will forever remain


In retrospect we know that the AI project couldn’t possibly have succeeded

at that stage. The hardware was simply not powerful enough. It seems that at

least about 100 Tops is required for human-like performance, and possibly

as much as 10^17 ops is needed. The computers in the seventies had a

computing power comparable to that of insects. They also achieved

approximately insect-level intelligence. Now, on the other hand, we can

foresee the arrival of human-equivalent hardware, so the cause of AI’s past

failure will then no longer be present.

There is also an explanation for the relative absence even of noticeable

progress during this period. As Hans Moravec points out:

[F]or several decades the computing power found in advanced

Artificial Intelligence and Robotics systems has been stuck at

insect brain power of 1 MIPS. While computer power per dollar

fell [should be: rose] rapidly during this period, the money

available fell just as fast. The earliest days of AI, in the mid

1960s, were fuelled by lavish post-Sputnik defence funding,

which gave access to $10,000,000 supercomputers of the time. In

the post Vietnam war days of the 1970s, funding declined and

only $1,000,000 machines were available. By the early 1980s, AI

research had to settle for $100,000 minicomputers. In the late

1980s, the available machines were $10,000 workstations. By the

1990s, much work was done on personal computers costing only

a few thousand dollars. Since then AI and robot brain power has

risen with improvements in computer efficiency. By 1993

personal computers provided 10 MIPS, by 1995 it was 30 MIPS,

and in 1997 it is over 100 MIPS. Suddenly machines are reading

text, recognizing speech, and robots are driving themselves cross

country. (Moravec 1997)

In general, there seems to be a new-found sense of optimism and excitement

among people working in AI, especially among those taking a bottom-up

approach, such as researchers in genetic algorithms, neuromorphic

engineering and in neural networks hardware implementations. Many

experts who have been around, though, are wary not again to underestimate

the difficulties ahead.

Once artificial intelligence reaches human level, there will be a positive

feedback loop that will give the development a further boost. AIs would

help constructing better AIs, which in turn would help building better AIs,

and so forth.

Even if no further software development took place and the AIs did not

accumulate new skills through self-learning, the AIs would still get smarter

if processor speed continued to increase. If after 18 months the hardware

were upgraded to double the speed, we would have an AI that could think

twice as fast as its original implementation. After a few more doublings this

would directly lead to what has been called “weak superintelligence”, i.e.

an intellect that has about the same abilities as a human brain but is much


Also, the marginal utility of improvements in AI when AI reaches

human-level would also seem to skyrocket, causing funding to increase. We

can therefore make the prediction that once there is human-level artificial

intelligence then it will not be long before superintelligence is

technologically feasible.

A further point can be made in support of this prediction. In contrast to

what’s possible for biological intellects, it might be possible to copy skills

or cognitive modules from one artificial intellect to another. If one AI has

achieved eminence in some field, then subsequent AIs can upload the

pioneer’s program or synaptic weight-matrix and immediately achieve the

same level of performance. It would not be necessary to again go through

the training process. Whether it will also be possible to copy the best parts

of several AIs and combine them into one will depend on details of

implementation and the degree to which the AIs are modularized in a

standardized fashion. But as a general rule, the intellectual achievements of

artificial intellects are additive in a way that human achievements are not,

or only to a much less degree.

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 technofobics

could plausibly argue “hither but not further”.

It therefore seems that up to human-equivalence, the driving-forces behind

improvements in AI will easily overpower whatever resistance might be

present. When the question is about human-level or greater intelligence then

it is conceivable that there might be strong political forces opposing further

development. Superintelligence might be seen to pose a threat to the

supremacy, and even to the survival, of the human species. Whether by

suitable programming we can arrange the motivation systems of the

superintelligences in such a way as to guarantee perpetual obedience and

subservience, or at least non-harmfulness, to humans is a contentious topic.

If future policy-makers can be sure that AIs would not endanger human

interests then the development of artificial intelligence will continue. If they

can’t be sure that there would be no danger, then the development might

well continue anyway, either because people don’t regard the gradual

displacement of biological humans with machines as necessarily a bad

outcome, or because such strong forces (motivated by short-term profit,

curiosity, ideology, or desire for the capabilities that superintelligences

might bring to its creators) are active that a collective decision to ban new

research in this field can not be reached and successfully implemented.

Depending on degree of optimization assumed, human-level intelligence

probably requires between 10^14 and 10^17 ops. It seems quite possible

that very advanced optimization could reduce this figure further, but the

entrance level would probably not be less than about 10^14 ops. If Moore’s

law continues to hold then the lower bound will be reached sometime

between 2004 and 2008, and the upper bound between 2015 and 2024. The

past success of Moore’s law gives some inductive reason to believe that it

will hold another ten, fifteen years or so; and this prediction is supported by

the fact that there are many promising new technologies currently under

development which hold great potential to increase procurable computing

power. There is no direct reason to suppose that Moore’s law will not hold

longer than 15 years. It thus seems likely that the requisite hardware for

human-level artificial intelligence will be assembled in the first quarter of

the next century, possibly within the first few years.

There are several approaches to developing the software. One is to emulate

the basic principles of biological brains. It is not implausible to suppose

that these principles will be well enough known within 15 years for this

approach to succeed, given adequate hardware.

The stagnation of AI during the seventies and eighties does not have much

bearing on the likelihood of AI to succeed in the future since we know that

the cause responsible for the stagnation (namely, that the hardware

available to AI researchers was stuck at about 10^6 ops) is no longer


There will be a strong and increasing pressure to improve AI up to

human-level. If there is a way of guaranteeing that superior artificial

intellects will never harm human beings then such intellects will be created.

If there is no way to have such a guarantee then they will probably be

created nevertheless.


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