An overview of models of technological singularity
Anders Sandberg
Future of Humanity Institute, Oxford University
Suite 8, Littlegate House, 16/17 St Ebbe’s Street
Oxford, OX1 1PT
Tel: +44 (0)1865 286279
Fax: +44 (0)1865 286983
anders.sandberg@philosophy.ox.ac.uk
Abstract
This paper reviews different definitions and models of
technological singularity. The models range from con-
ceptual sketches to detailled endogenous growth mod-
els, as well as attempts to fit empirical data to quan-
titative models. Such models are useful for examining
the dynamics of the world-system and possible types of
future crisis points where fundamental transitions are
likely to occur. Current models suggest that, generi-
cally, even small increasing returns tends to produce
radical growth.
If mental capital becomes copyable
(such as would be the case for AI or brain emulation)
extremely rapid growth would also become likely.
Introduction
The set of concepts today commonly referred to as
“technological singularity” has a long history in the
computer science community, with early examples such
as:
One conversation centered on the ever accel-
erating progress of technology and changes in the
mode of human life, which gives the appearance of
approaching some essential singularity in the his-
tory of the race beyond which human affairs, as we
know them, could not continue. Ulam, S., Tribute
to John von Neumann, Bulletin of the American
Mathematical Society, vol 64, nr 3, part 2, May,
1958, p1-49.
and
Let an ultraintelligent machine be defined as
a machine that can far surpass all the intellectual
activities of any man however clever. Since the de-
sign of machines is one of these intellectual activi-
ties, an ultraintelligent machine could design even
better machines; there would then unquestionably
be an ”intelligence explosion,” and the intelligence
of man would be left far behind. Thus the first
ultraintelligent machine is the last invention that
man need ever make. I.J. Good, (Goo65)
The unifying theme of these two examples is accelerated
technological change leading to a rapid transition to
a state where the current human condition would be
challenged.
Technological singularity is of increasing interest among
futurists both as a predicted possibility in the mid-
term future and as subject for methodological de-
bate.
The concept is used in a variety of contexts,
and has acquired an unfortunately large number of
meanings.
Some versions stress the role of artifical
intelligence, others refer to more general technologi-
cal change. These multiple meanings can overlap, and
many writers use combinations of meanings: even Ver-
nor Vinge’s seminal essay (Vin93) that coined the term
uses several meanings. Some of these meanings may
imply each other but often there is a conflation of dif-
ferent elements that likely (but not necessarily) occur
in parallel. This causes confusion and misunderstand-
ing to the extent that some critics argue that the term
should be avoided altogether (Tyl). At the very least
the term “singularity” has led to many unfortunate as-
sumptions that technological singularity involves some
form of mathematical singularity and can hence be ig-
nored as unphysical.
This paper is attempting a simple taxonomy of models
of technological singularity, hopefully helping to dis-
ambiguate the different meanings of the word. It also
aims at a brief review of formal quantitative models of
singularity-like phenomena, in the hope of promoting a
more stringent discussion of these possibilities.
Definitions of technological singularity
A brief list of meanings of the term “technological sin-
gularity” found in the literature and some of their pro-
ponents:
A. Accelerating change Exponential or superexpo-
nential technological growth (with linked economical
growth and social change) (Ray Kurzweil (Kur05),
John Smart (Smang))
B. Self improving technology Better
technol-
ogy allows faster development of new and better
technology. (Flake (Fla06))
C. Intelligence explosion Smarter systems can im-
prove themselves, producing even more intelligence in
a strong feedback loop. (I.J. Good (Goo65), Eliezer
Yudkowsky)
D. Emergence of superintelligence (Singularity
Institute)
1
E. Prediction horizon Rapid change or the emer-
gence of superhuman intelligence makes the future
impossible to predict from our current limited knowl-
edge and experience. (Vinge, (Vin93))
F. Phase transition The singularity represents a
shift to new forms of organisation. This could be a
fundamental difference in kind such as humanity be-
ing succeeded by posthuman or artificial intelligences,
a punctuated equilibrium transition or the emergence
of a new metasystem level. (Teilhard de Chardin,
Valentin Turchin (Tur77), Heylighen (Hey07))
G. Complexity disaster Increasing complexity and
interconnectedness causes increasing payoffs, but in-
creases instability. Eventually this produces a crisis,
beyond which point the dynamics must be different.
(Sornette (JS01), West (BLH
+
07))
H. Inflexion point Large-scale growth of technology
or economy follows a logistic growth curve. The sin-
gularity represents the inflexion point where change
shifts from acceleration to deacceleration. (Extropian
FAQ, T. Modis (Mod02))
I. Infinite progress The rate of progress in some do-
main goes to infinity in finite time. (Few, if any, hold
this to be plausible
2
)
The three major groupings appear to be accelerat-
ing change, prediction horizon and intelligence explo-
sion leading to superintelligence (as originally noted by
(Bos98) and discussed in (Yud07)).
In addition to the general meaning(s), the singularity
might be local or global (capability takeoff of an entity
or small group, or broad evolution of the whole econ-
omy), fast or slow (occuring on computer timescales,
hardware development timescales, human timescales,
or historical timescales). There is also confusion over
whether the salient issue is the point/eventlike charac-
1
“The Singularity is the technological creation of
smarter-than-human intelligence”, http://singinst.org/
overview/whatisthesingularity
2
The exception may be the Omega Point theory of
Frank J. Tipler, which predicts an infinite information state
reached at the future timelike singularity of a collapsing uni-
verse. However, while this (physical) singularity is reached
in finite proper time, it is not reached in finite subjective
time and there is literally no “after” (BT86).
ter, the historical uniqueness, the nature of the overall
process or the big historical trend.
For the purpose of this paper I will focus on the growth
aspect:
accelerating change, self-improving technol-
ogy, intelligence explosions and the complexity disas-
ter (and to some extent the inflexion point) all involve
the growth of technological or cognitive capability. The
nature and speed of this growth is important both for
understanding what claims are actually made and for
considering the implications of such a process, if it takes
place.
As noted in (Hey97) models for the technological singu-
larity, whether qualitative or quantitative, should not
be taken too literally. Models include what is consid-
ered to be the important and relevant features of a sys-
tem while abstracting away from minor, obscuring fea-
tures. They are useful because they force us to show
our assumptions, bringing them into the open. Their
predictions may be less important than demonstrating
how changes in assumptions affect outcomes.
Models
Linear takeover (Type D, F)
Singular events can occur when one form of growth
outpaces another.
This form of “linear singular-
ity” does not necessarily involve any acceleration of
progress.
Eliezer Yudkowsky has presented a simple sketch of why
apparent progress of AI might be deceptive: the rate of
progress is actually high, but starts from a very low
level. This means that the rapid development is not
easily detectable, until it suddenly passes the relatively
narrow human range and goes beyond
3
A simple formalisation of the model would be that hu-
man intelligence X is constant while machine intelli-
gence Y (t) = a + bt grows linearly with time.
At
present X Y (t). We are less sensitive to differences
in less-than-human intelligence than to differences in
human-level intelligence; our subjective experience of
intelligence (and possibly its ability to affect the world)
is a rapidly concave function, for example an expo-
nential curve. The apparent intelligence of machines
would be growing as e
a+bt
, with a “surprising” eventual
jump from an apparently unchanging low state to rapid
growth near human level. This is essentially the same
claim made by Kurzweil in regards to how most peo-
ple do not understand exponential growth, although the
3
“The best answer to the question, ‘Will computers
ever be as smart as humans?’ is probably ‘Yes, but only
briefly” Vernor Vinge. Samuel Butler made a somewhat
similar point in his article ”Darwin among the Machines”
(The Press, Christchurch, 13 June 1863) and novel Erewhon
(1872).
growth here may not be nonlinear (only its detectability
is).
Logistic growth (type H)
It is commonly argued that exponential growth is un-
sustainable, since it requires resources that will eventu-
ally run out. This is true even if space colonization is
available, since the volume of space that can be reached
in time t grows at most as 4πc
3
t
3
/3 ∝ t
3
, where c is
lightspeed. This means that eventually the growth rate
will have to decline to at most a polynomial growth
rate.
However, if the growth is in the domain of knowledge,
culture or pure value it is less obvious that there exists
an upper limit or that the growth is strongly resource
constrained.
There are some physical limits on how
much information can be stored within a region of the
universe but these are very wide; at most they preclude
infinite information storage
4
.
There could exist limiting factors to technology based
on the (possible) finite complexity of the universe: there
would only exist a finite amount of useful facts about
nature that can be discovered. As more and more be-
come known the difficulty of finding new ones increases.
This would be compatible with a logistic growth equa-
tion:
X
0
(t) = r(K − X(t))X(t)
where X(t) is knowledge, r is a growth rate and K is
the upper threshold of what is knowable. Initially X(t)
grows exponentially, but eventually saturates and slows
down, asymptotically approaching X(t) → K. Other
forms of value might still grow after saturation, but the
knowledge will remain bounded.
In this case the singularity would likely denote the
inflexion point, the (historically very brief) transition
from a pre-technological state to a maximally advanced
state.
Metasystem transition (type F)
A metasystem transition is the evolutionary emergence
of a higher level of organisation or control in a sys-
tem (Tur77). A number of systems become integrated
into a higher-order system, producing a multi-level hi-
erarchy of control. Within biology such evolutionary
transitions have occured through the evolution of self-
replication, multicellularity, sexual reproduction, soci-
eties etc. where smaller subsystems merge without los-
ing differentiation yet often become dependent on the
4
The Bekenstein bound on the entropy to energy ratio of
a system with radius R and total energy E constrains the
information stored inside to be less than 2πRE/¯
hc ln(2). For
a 1 kg, 1 meter system the bound is on the order of 10
43
bits (Bek81).
larger entity (SS95). At the beginning of the process the
control mechanism is rudimentary, mainly coordinating
the subsystems. As the whole system develops further
the subsystems specialize and the control systems be-
come more effective. While metasystem transitions in
biology are seen as caused by biological evolution, other
systems might exhibit other forms of evolution (e.g. so-
cial change or deliberate organisation) to cause meta-
system transitions.
Extrapolated to humans, future
transitions might involve parts or the whole of the hu-
man species becoming a super-organism (TJ89).
As a model for technological singularity the metasys-
tem transition is largely qualitative rather than quan-
titative. Without a detailed model of the subsystem
dynamics the transition is undefined. However, the bi-
ological and social examples given by theorists, would
give some analogical data on how future transitions
could work (e.g. as punctated equilibrium models). A
key issue worth exploring may be to what degree such
transitions are convergent or divergent, e.g. whether
under similar circumstances the transitions would pro-
duce similar or different forms of organisation
5
.
Accelerated metasystem transition (type A,B,F)
Heylighen argues (Hey07) that the evolutionary forces
acting on technology (and other systems) tend to lead
to ephemeralization, doing more with less, due to selec-
tive pressures in any resource-constrained environment.
This produces increasing efficiency in the use, process-
ing and transport of matter, energy and information,
reducing “friction” and enabling longer controllable
causal chains.
Similarly innovations in institutions,
mediators and stigmergy (indirect influences on other
agents through this environment, “the medium”) pro-
duces increasingly global coordination. He argues that
stigmergy accelerates evolution, since already found so-
lutions spread to other agents: improvement of the
medium facilitates further innovation, which helps im-
prove and spread the medium. He compares this to the
population growth model of Korotayev (Kor07) where
the population N is controlled by a logistic growth
equation where the carrying capacity is proportional
to the overall productivity T of technology:
N
0
(f ) = a(bT − N )N
Technology grows proportional to population and tech-
nology:
T
0
(t) = cN T
This produces a hyperbolic growth curve with a finite
time singularity. Heylighen notes that the deviation in
population growth from hyperbolic growth in the 1970’s
is still compatible with the general model if we interpret
it as a shift from an r-strategy (fast reproduction, short
life) to a K-strategy (slow reproduction, long life). In
5
This is one of the concerns of the theory in (Smang)
a K-strategy more human capital is invested in the off-
spring, and they will provide more back, making the T
factor of the second equation more important than the
N factor. He predicts that the above trends will lead to
the formation of a global superorganism of some kind
over a relatively short timespan.
Accelerating change
Economic input output models (type A)
Input-output models depict the economy as A, a matrix
denoting the number of units a sector of the economy
needs to buy from another sector to produce a unit of
its own output, and B, a matrix of how many units
from other industries are needed for the production it-
self (e.g equipment, buildings etc). If the output levels
of different sectors at time t are X(t) the amount of
goods delivered to households and other final users will
be (Leo86):
Y (t) = X(t) − AX(t) − B[X(t + 1) − X(t)]
If all the outputs are re-invested into the economy
Y (t) = 0 and the equation produces a dynamical sys-
tem
X(t + 1) = [B
−1
(1 − A − B)]X(t)
The growth will be exponential, with a rate set by the
largest eigenvalue of the bracketed matrix and a pro-
duction vector X tending towards the eigenvector cor-
responding to the value. If the consumption Y (t) is
nonzero the generic case is still exponential growth: ma-
trix recurrences of the form X(t + 1) = CX(t) + d or
differential equations like X
0
(t) = CX(t) + d have solu-
tions tending towards Λe
λt
if C is diagonalizable.
Endogenous growth models (type A,B,I)
Endogenous growth theory models the growth of an
economy with improving technology, where the technol-
ogy is assumed to be growing as a function of the econ-
omy and allocation of resources to it. It was developed
as a response to exogenous growth models, where di-
minishing returns predict that growth would stop rather
than continue.
...while the part which nature plays in pro-
duction shows a tendency to diminishing return,
the part which man plays shows a tendency to in-
creasing return. The law of increasing return may
be worded thus:An increase of labour and capital
leads generally to improved organization, which in-
creases the efficiency of the work of labour and
capital. Marshall (1920), Book 4, Chapter XIII,
Paragraph IV.XIII.11.
However, published models usually appear to try to
avoid “unrealistic” explosive growth, by selecting equa-
tions for the growth of knowledge that preclude it. For
example, (HI04) amend their model not only to avoid
negative knowledge but also to preclude superexponen-
tial growth. In a later paper (HI07) they state:
We take the view that a plausible descrip-
tion of the evolution of knowledge should satisfy
two asymptotic conditions. Looking forwards, we
follow Solow (2000) in maintaining that infinite
knowledge in finite time is impossible.
Looking
backwards, we require knowledge to vanish in the
infinite past, but not in finite time. We call an
evolution plausible if it satisfies these criteria.
This paper shows that for knowledge growth
X
0
(t) = gX(t)
φ
(1)
knowledge is either zero at all times, or increasing over
time. In the latter case if φ < 1 the knowledge starts
from zero in the finite past, if φ > 1 knowledge diverges
to infinity in the finite future, or if φ = 1 knowledge
is always finite and grows exponentially without any
singularities. It hence concludes that only the expo-
nential case is possible. Adding (endogenous or exoge-
nous) population growth does not help in the generic
case.
However, a simple model predicting a finite-time sin-
gularity might still be relevant if other, unmodelled,
factors near the potential singularity prevents it from
actually occuring. The assumption that a model must
hold for an infinite timespan (especially when it is a
highly abstracted model of a complex process) seems
unwarranted. Models exhibiting finite-time singulari-
ties can be a quantitatively or qualitatively accurate fit
to current or near-future growth, although they even-
tually leaves the domain of applicability.
As noted in (JS01):
Singularities are always mathematical idealisa-
tions of natural phenomena: they are not present
in reality but foreshadow an important transition
or change of regime. In the present context, they
must be interpreted as a kind of “critical point” sig-
naling a fundamental and abrupt change of regime
similar to what occurs in phase transitions.
The observation that finite-time singularities are
generic when economies of scale in knowledge produc-
tion exist (φ > 1) seems to support further study of su-
perexponential growth, and cast doubt on the assump-
tion that knowledge growth should always be modeled
as exponential.
A widely quoted growth model exhibiting finite-time
singularity is the model of Kremer (M93): the total
population is L(t) and total economic output Y (t),
and their ratio Y (t)/L(t) = ¯
y is set to a subsis-
tence level ¯
y which is fixed.
Output depends on
technology/knowledge A(t) and labour proportional to
L(t):
Y (t) = Y
0
[A(t)L(t)]
1−α
where 0 < α < 1.
The growth rate of technology
is assumed proportional to population and technology
level:
A
0
(t) = BL(t)A(t)
with the explicit assumptions that larger populations
have more talented people that can advance technol-
ogy and that new technology largely is obtained by
leveraging existing technology (hence, it implies a type
B singularity).
Combining the two equations pro-
duces
L
0
(t) = [B(1 − α)/α]L(t)
2
Hence the population (and technology) has a finite time
singularity.
(SS96) further discuss multivariate extensions of the
model with capital K(t) producing output
Y (t) = [(1 − a
K
)K(t)]
α
[A(t)(1 − a
L
)L(t)]
1−α
where a
K
is the fraction of capital stock used in R&D,
a
L
the fraction of labour used in R&D. Innovation is
produced as
A
0
(t) = B[a
K
K(t)]
β
[a
L
L(t)]
γ
[A(t)]
θ
for positive constants B, β and γ. Assuming exogenous
saving rate s and no depreciation gives
K
0
(t) = sY (t) = s[(1 − a
K
)K(t)]
α
[A(t)(1 − a
L
)L(t)]
1−α
This system can achieve finite time singularities even
for fixed population if θ + β > 1. Past innovation and
capital can create explosive growth even when each of
the factors in isolation cannot.
Population-technology model (Type A,F,I)
A population theoretic model similar to endogenous
growth was formulated by Taagepera (Taa79). It links
the population P with technology T and nonrenewable
resources R via a modified logistic growth model:
P
0
(t) = k
0
h
1 − e
−aT
n
i
1 −
(R + C)P
f T
q
R
P
where the first factor represents growth rate (increased
by technology towards a maximal rate k
0
). Technol-
ogy increases as a power of population size for small
populations and independent of it at large sizes:
T
0
(t) = h
P
U + P
m
T
where U is a characteristic population size. The rate of
depletion of resources was either modeled with a com-
plete recycling model, where advanced societies can re-
cycle most material
R
0
(t) = −
f V T P
(V + T )
2
where V is the critical technology level where resource
depletion levels out. The other possibility was merely
a stabilization of per capita depletion rate
R
0
(t) = −
f T P
V + T
For the initial state P U , T V , T
n
1/a and
R C and the equations produce hyperbolic growth.
Depending on whether the first, the middle two or the
last inequalities change sign first there are three out-
comes. The first case, resource depletion, produces an
eventual saturation population with a likely eventual
decrease. In the middle case recycling or per capita
stabilisation becomes significant before population is
large and resources has run out, and hyperbolic growth
continues until one of the other cases occurs. If the
population becomes large but there are still resources
population growth is doubly exponential.
Taagepera notes that the model while avoiding finite
time singularities still has crises where it shifts from
one mode to another, possibly over very short periods
of time.
Law of Accelerating returns (type A,B)
Ray Kurzweil formulates the “law of accelerating re-
turns” as (Kur01):
• Evolution applies positive feedback in that the more
capable methods resulting from one stage of evolu-
tionary progress are used to create the next stage.
As a result, the rate of progress of an evolutionary
process increases exponentially over time. Over time,
the “order” of the information embedded in the evo-
lutionary process (i.e., the measure of how well the
information fits a purpose, which in evolution is sur-
vival) increases.
• A correlate of the above observation is that the “re-
turns” of an evolutionary process (e.g., the speed,
cost-effectiveness, or overall “power” of a process) in-
crease exponentially over time.
• In another positive feedback loop, as a particular evo-
lutionary process (e.g., computation) becomes more
effective (e.g., cost effective), greater resources are
deployed toward the further progress of that process.
This results in a second level of exponential growth
(i.e., the rate of exponential growth itself grows ex-
ponentially).
• Biological evolution is one such evolutionary process.
• Technological evolution is another such evolutionary
process. Indeed, the emergence of the first technology
creating species resulted in the new evolutionary pro-
cess of technology. Therefore, technological evolution
is an outgrowth of–and a continuation of–biological
evolution.
• A specific paradigm (a method or approach to solv-
ing a problem, e.g., shrinking transistors on an inte-
grated circuit as an approach to making more power-
ful computers) provides exponential growth until the
method exhausts its potential. When this happens,
a paradigm shift (i.e., a fundamental change in the
approach) occurs, which enables exponential growth
to continue.
Kurzweil models the growth as composed of a velocity
of technology growth V (t) = c
1
W (t) driven by and driv-
ing the total world knowledge W
0
(t) = c
2
V (t) (where
c
1
and c
2
are constants). This has an exponential so-
lution with growth rate c
1
c
2
. He then assumes that
there are exponentially increasing resources for com-
putation N (t) = c
c
4
t
3
and that world knowledge grows
proportional to the product of V (t) and N (t): W
0
(t) =
c
1
V (t)N (t) = c
1
c
2
c
c
4
t
3
W (t) which has a double expo-
nential solution of the form W (t) ∝ exp(Ct
2
).
It is not clear why the exogenous computational re-
sources N (t) are not increasing proportional to W (t),
for example if the economy and population providing
them were positively influenced by knowledge growth,
rather than just growing by a steady exponential. If
there were any positive feedback with W (t) the equa-
tions would show essentially the same behavior as equa-
tion 1 and have a finite time singularity.
Vinge/Moravec model (type A,B,I)
The original singularity essay by Vinge (Vin93) does
not describe any quantitative model. It says:
“When greater-than-human intelligence drives
progress, that progress will be much more rapid.
In fact, there seems no reason why progress itself
would not involve the creation of still more intel-
ligent entities – on a still-shorter time scale. The
best analogy that I see is with the evolutionary
past: Animals can adapt to problems and make in-
ventions, but often no faster than natural selection
can do its work – the world acts as its own simu-
lator in the case of natural selection. We humans
have the ability to internalize the world and con-
duct “what if’s” in our heads; we can solve many
problems thousands of times faster than natural
selection. Now, by creating the means to execute
those simulations at much higher speeds, we are
entering a regime as radically different from our
human past as we humans are from the lower ani-
mals.”
This is a re-statement of the intelligence explosion
model of Good (Goo65). Vinge also argues that cur-
rent technological progress (especially in computing) is
influenced by automation of design, producing strong
incentives to further automate it. This leads to a strong
feedback where either AI, intelligence amplification or
distributed intelligence produces an intelligence explo-
sion. A formalisation of this would be to model techno-
logical improvement as an exponential process, where
new technology reduces the doubling time. However,
whether this produces a finite time singularity or merely
accelerating growth depends on the exact functional
form.
Hans Moravec explored a model based on Vinge (Mora;
Morb). He initially assumed that “world knowledge”
X(t) produces an exponential speedup of computer per-
formance V (t) = e
X(t)
. In the case of a constant num-
ber of humans working unassisted the growth of knowl-
edge would be linear, X
0
(t) = 1, producing X(t) = t
and V (t) = e
t
(standard Moore’s law).
If knowledge growth is instead driven directly by com-
puters X
0
(t) = V (t) = e
X(t)
, producing the solu-
tion X(t) = log(−1/t) (t < 0).
This corresponds
to a slow growth reaching a singularity.
A further
model added computer power to human power, giving
X(t) = log(1/(e
−t
− 1)) which has linear growth for
t 0, becomes roughly exponential near t = −1 and
has a singularity at t = 0.
Observing that V (t) = e
X(t)
was likely far too op-
timistic he then went on to show a variety of mod-
els where V (t) was a concave increasing function of
X(t) produce finite time singularities.
These results
are substantially the same as the observation about
equation 1. Slightly superlinear growth, e.g. X
0
(t) =
(1 + log(X(t)))X(t) merely produces fast double expo-
nential growth, X(t) = exp(e
t
− 1), but squared log-
arithmic growth (X
0
(t) = (1 + log
2
(X(t)))X(t)) gives
rise to a true singularity (X(t) = exp(tan(t))). In fact,
if the logarithm has exponent > 1 there is an eventual
mathematical singularity.
In a subsequent discussion between Moravec, Kurzweil,
and Vinge (KVM) Kurzweil noted:
My sense is that it is difficult to justify on theo-
retical grounds having the rate of increase of knowl-
edge be equal to the “size” of knowledge raised to
a power greater than 1.
However, I do think it is feasible to justify sepa-
rate components of growth, some (or one) of which
have an exponent of one, and some (or one) of
which have a log exponent. The analysis above
points to a log exponent.
I believe we can jus-
tify that the “value” of a typical network or of a
database does not expand proportional to its size,
but to the log of its size. For example, the value of
the Internet to me does not double if the number of
users double. Rather, its value to me increases in a
logarithmic fashion. The exponent of 1 comes from
the number of users themselves. Having twice as
many users means it is serving twice as many peo-
ple. So the overall value of the network = n ∗ log n
(n people served times the value of the network to
each person = log n). This varies from Metcalfe’s
formula that the value of a network = n
2
.
He suggested that empirical data might be enough to
justify a double exponential, but found the unlikelihood
of infinite information strong enough to tentatively rule
out faster increments.
Solomonoff (type A,B,I)
Ray J. Solomonoff presented a model of progress in AI
based on the idea that once a machine is built that has
general problem solving capacity near that of a human
in an area such as industry or science such machines
will be used for in those areas, speeding up technolog-
ical progress. This would eventually lead to construc-
tion of machines with capacities near or beyond the
original computer science community (Sol85). In par-
ticular, he notes that the key factors is that machine
intelligence will eventually become cheaper than human
intelligence, and that high initial training costs can be
offset by copying already trained machines.
His formal model consists of the growth of the effective
computer science community (humans plus AI, humans
assumed to be constant) C(t) as
C
0
(t) = Rx(t)
where R is the rate of money spent on AI and x(t)
is the amount of computing power per dollar. x(t) is
assumed to grow exponentially with a doubling time in-
versely proportional to the size of the computer science
community:
(log(x(t)))
0
= AC(t)
where A is a constant. Combining equations and as-
suming c = x = 1 at t = 0 produces
C
0
(t) = A(C(t)
2
− 1)/2 + R
which has a finite time singularity for some t. Assuming
a doubling time of x every four years produces A =
log(2)/4 ≈ 0.1733; with R = 1 the singularity occurs in
4.62 years, for R = 0.1 in 11.11 years and R = 0.01 in
21.51 years.
The model implicitly sets t = 0 at the point where the
milestone of near-human intelligence is reached; before
this computers are not assumed to expand the effective
computer science community very much. It is hence
a model of just a particular technological community
rather than general technological growth, although it is
plausible that the rapid increase of x(t) will influence
society profoundly.
Hamacher (Type E)
Hamacher commented on the Moravec/Vinge/Kurzweil
model, noting that its macroeconomic coarseness ig-
nored issues of coordination problems, competition, re-
source allocation and sociological issues. Instead he in-
troduced a cobweb-model of price determination, where
supply and demand iteratively determine quantity and
price of a product. If S(q) is the supply and D(q) the
demand at quantity q, the available price p
t
and quan-
tity q
t
are updated iteratively
p
t+1
= D(q)
q
t+1
= S
−1
(p
t+1
)
Such models have a rich dynamics corresponding to iter-
ated 1-dimensional maps, including stable fixed points,
limit cycles and chaotic attractors depending on the
choice of S(q) and D(q). In particular the entropy or
Lyapunov exponent of the time series q
t
produces a pre-
diction horizon: initial uncertainties about the system
state grow exponentially at a rate set by these values.
As parameters are varied to mimic substitution dynam-
ics the probability of encountering a prediction horizon
increase (Ham06).
City economics (Type A,G)
(BLH
+
07) analysed the economies of scale of cities,
showing that many quantities reflecting wealth creation
and innovation scale with population with an expo-
nent β > 1, implying increasing returns. Infrastructure
quantities on the other hand scale with β < 1, showing
economies of scale. A larger city will hence produce
more per capita but have lower maintenance costs per
capita. This is in direct contrast to biological organ-
isms, where the “economy” (heart rate, metabolism,
pace of life) decreases with size.
The authors suggested an urban growth equation.
Growth is constrained by the availability of resources
and their rate of consumption. The resources Y are
used both for maintaining the existing infrastructure
(at a cost of R per unit time and unit of infrastructure)
and to expand it (at a cost of E to get one unit). The
allocation can be expressed as Y = RX(t) + EX
0
(t)
where X
0
(t) is the growth rate of the city. The total
growth is
X
0
(t) = (Y
0
/E)X(t)
β
− (R/E)X(t)
which has solution (β 6= 1; for β = 1 the solution is
exponential):
X(t) =
h
Y
0
/R + (X(0)
1−β
− Y
0
/R)e
−R(1−β)t/E
i
1/(1−β)
For β < 1 growth is eventually limited and approaches
an asymptotic level, very similar to biological growth.
For β > 1 growth becomes superexponential and even-
tually reaches a finite time singularity if there are
enough resources.
However, the authors do not consider this singularity
to be reachable. They argue that eventually there is
a transition to a resource-limited state and instead the
growth reverses and eventually collapses. In order to
avoid this crisis the system must “innovate”, chang-
ing the constraining resource limits to allow further
growth. By repeating this in multiple cycles collapse
can be postponed. The model predicts that the time
between cycles decreases as 1/X(0)
β−1
where X(0) is
the size at the start of a cycle. This produces superex-
ponential average growth as long as sufficiently strong
new innovations can be supplied at an ever accelerating
rate - until a finite time singularity is reached.
While the model describes cities, the general structure
seems applicable to economic systems with increasing
returns.
Hanson (Type A)
Robin
Hanson
has
examined
the
economics
of
technological
singularity
using
standard
economic
tools.
He analysed a simple model of investment in the context
of technological singularity (Han98b). He found that
the curve of supply of capital has two distinct parts,
producing two modes of economic growth. In the nor-
mal slow growth mode rates of return are limited by
human discount rates.
In the fast mode investment
is limited by the total amount of wealth - the returns
are so great that the savings rate become very high.
As technology develops the demand for capital slowly
increases, nudging the system towards the fast mode
(which, given past growth of savings rate, might occur
somewhere “near the year 2150”). However, this mode
requires fine-tuning the savings rate and the fraction of
investment return.
In most senses of technological singularity the amount
of available “mental capital” (humans or machines able
to do skilled work) increases significantly. Hanson mod-
els this in an exogenous growth model that examines
economic growth given machine intelligence (Han98a).
As machines become more capable they no longer just
complement human labor (which tends to increase the
demand for skilled labor) but can substitute for it.
In the model, human-substituting machines have a
falling price, but originally computer investments only
buy non-intelligent complementary computers since the
price of AI is too high. As the price falls AI starts to
replace humans, and human wages will fall along with
the computer price.
Introducing machine intelligence makes annual growth
rate at least an order of magnitude larger than the
current growth rate, with doubling times measured in
months rather than years. Partially this is due to the
assumption that computer/AI technology grows faster
than other technology, partially because machines can
be manufactured to meet production demands while
humans have to grow up. This also produces a very
fast increase in the population of intelligences, which
may rapidly reach Malthusian resource limits: Hanson
predicts that per-intelligence consumption will tend to
fall. Whether this would lead to increasing per-capita
income for humans depends on whether they retain a
constant fraction of capital.
Model variations such as endogenous growth, changing
work hours, a continuum of job types where some are
more suitable for humans than others, and distinguish-
ing human capital, hardware, and software from other
capital do not change the essential conclusions.
Hanson found that the transition from a human-
dominated to a machine dominated economy could be
fast (on the order of a few years, assuming Moore’s
law-like computer cost development). This model is es-
sentially an economic model of a “linear singularity”
(section ) where AI passes the human intelligence (or
economic efficiency) range. It does not assume tech-
nological progress to speed up as the economy grows
faster, which, if included, would speed up the transi-
tion.
Similar economic effects appear likely to occur if brain
emulations (simulations of human brains with suffi-
cient resolution to produce human-equivalent behavior
and problem-solving (SB08)) could be created (Han94;
Han08a). The presence of copyable human capital leads
to a software population explosion limited by the cost
and availability of the hardware necessary to run the
emulations, producing a high growth rate but dimin-
ishing individual wages.
Empirical estimates
Empirical estimates of technological singularity consists
of attempts to collate historical (sometimes even pale-
ontological and cosmological) data to estimate whether
the “rate of change” is increasing exponentially or su-
perexponentially. This presupposes that the singularity
is due to an ongoing, large-scale process in the present
or earlier. Intelligence explosion and prediction horizon
models can likely not be supported or disputed using
this kind of data.
Technological growth (Type A,B,H)
There is a sizeable literature on the exponential growth
of scientific resources and output, as well as growth
within many technological fields. Similarly there exist
various forecasts of imminent ends of progress, either
due to resource depletion or more subtle issues.
Rescher’s law states that the cumulative output of first-
rate findings in science is proportional to the logarithm
of how much has been investment, producing merely lin-
ear returns as investment grows exponentially (second-
rate findings, on the other hand, may grow proportion-
ally to investment and does not necessarily have neglig-
ble utility) (WD01).
On the other hand, Meyer and Vallee argued that tech-
nological growth in many domains (such as power,
manufacturing, speed, typesetting etc.)
has been
superexponential, likely hyperbolic and composed of
smaller logistic growth curves for individual technolo-
gies. Since the authors did not assume infinite growth
to be feasible, they predicted that the shift from hy-
perbolic growth to some other regimen would either be
through some form of “soft regulation” or a catastrophe
(MV75).
Population (Type A,G,I)
Various authors have fitted human population to a hy-
perbolic growth curve N (t) = C/(T − t), where pop-
ulation reaches infinity at some time T .
This was
claimed to be a good empirical fit (vMA60; Taa79;
JS01; Kor07), but since 1970 the observed population
no longer fits the growth curve. (Kap06) argues that
the model is still essentially valid (growth is driven by
the square of population) but can be extended and the
singularity removed by taking into account finite hu-
man reproduction; the changed model instead predicts
the year 2000 as the inflexion point of global population
growth.
Kremer tested the prediction of his economic growth
model, finding that population growth rate historically
has been proportional to population. Since the number
of technological innovations (which increase the max-
imum possible population) is assumed to be propor-
tional to population this would give some evidence for
hyperexponential growth (M93).
Sequence of economical growth modes (Type A, F,
H)
Robin Hanson examined long-term growth of the hu-
man economy (Han98c), based on empirical estimations
of the past world product. He found that the world
product time series over the last two million years could
be fit well with a small number of exponential growth
modes. One interpretation was as a sum of four ex-
ponentials added together
6
, but a better fit (with the
same number of parameters) was a model with con-
stant elasticity of substitution betwee three exponen-
tial growth modes. These modes appear to correspond
to the hunting, farming and industrial eras, and had
doubling times of 224,000, 909 and 6.3 years. There
might also be some evidence for an earlier mode based
on brain evolution.
6
A hyperbolic fit produced a worse result.
During each time period the economy tends to be dom-
inated by only one mode, and the rate of growth is
roughly constant
7
. Between these modes a rapid (faster
than the typical doubling time of the previous mode)
shift occurs to a mode with a growth rate about two
orders of magnitude larger. This is driven by the ap-
pearance of new technologies that change the structure
and scale of the economy (Han08b).
Given the past appearance of such new modes Hanson
proposed that a new mode might occur in the relatively
near future. If the number of doubling times during a
mode is roughly constant this would place the transition
somewhere during the 21st century.
This model involves type A, F and H singularities.
Technically, it does not involve any logistic limitation on
growth, but rather a logistic-like limitation of increase
of growth rate within each mode. It is also unique in
predicting multiple past singularities (in the sense of
type F radical phase transitions).
Sornette (Type A,F,G)
(JS01) fit power laws of the form (T −t)
β
to world popu-
lation, GDP and financial data. β is allowed to be com-
plex, implying not only a superexponential growth as
time T is approached (due to the real part of the expo-
nent) but also increasingly faster oscillations (due to the
imaginary part). The use of this form is motivated with
analogy with physics, for example cascades of Rayleigh-
Taylor instabilities, black hole formation, phase sepa-
ration and material failure which all show log-periodic
oscillations before the final singularity. Theoretically
this has been motivated by considerations of discrete
scale invariance and complex critical exponents in non-
unitary field theories (SS96).
Their conclusion is:
The main message of this study is that, what-
ever the answer and irrespective of one’s opti-
mistic or pessimistic view of the world sustainabil-
ity, these important pieces of data all point to the
existence of an end to the present era, which will be
irreversible and cannot be overcome by any novel
innovation of the preceding kind, e.g., a new tech-
nology that makes the final conquest of the Oceans
and the vast mineral resources there possible. This,
since any new innovation is deeply embedded in the
very existence of a singularity, in fact it feeds it. As
a result, a future transition of mankind towards a
qualitatively new level is quite possible.
7
This contradicts the claim that innovation and growth
have been accelerating recently.
Paradigm shifts (type A,F)
Various more or less empirical attempts to predict the
rate of paradigm shifts have been made. The level of ev-
idence and modeling is variable. Some examples:
Gerald S. Hawkins proposed the “mindstep equa-
tion”
M
n
= −30, 629 + 98, 048(1/4 + 1/4
2
+ 1/4
3
+ . . . + 1/4
n
)
for the years where a “mindstep” (a fundamental hu-
man paradigm shift) would occur. It converges towards
2053, and is based on 5 (!) data points (Haw02).
Nottale et al.
have argued that the main economic
crises of western civilization show a log-periodic ac-
celerating law with a finite time singularity ≈ 2080
(NCG01).
Theodore Modis collected a number of evolutionary
turning points from a variety of sources, estimating
their “complexity jumps” (based on the assumption
that the change in complexity would be inversely pro-
portional to the distance to the next) (Mod02).
He
found that the best fit to complexity growth was a
large-scale logistic curve, placing the present about
halfway through the history of the universe.
Ray
Kurzweil re-plotted the same data and concluded that
the paradigm shift rate is doubling every decade, pro-
ducing an extremely rapid rate of acceleration (Kur01).
This led to a spirited response critiquing the indepen-
dence of the data, Kurzweil’s methodology and the gen-
eral assumption of accelerating technological growth
(Mod06).
D.J. LePoire attempted to estimate the progress of
physics by assigning events to different subfields (e.g.
optics, wave theory, quantum mechanics etc.) and plot-
ting curves of the fraction of results in the subfield
that has been completed at a certain time; these were
roughly logistic. Using their median times, the assump-
tion that overall physics progress overall follows a logis-
tic curve and that some subfields form natural “stages”
produced a fit with center on 1925 (LeP05). His model
suggest that the singularity is already past, at least in
physics (a strong type H claim).
Rober Aunger has argued that thermodynamics repre-
sents a key factor in changing the organization of sys-
tems across history, and focused on the emergence of
new mechanisms of control of energy flow within sys-
tems. Using a dataset of candidates he found an in-
creasing trend of energy flow density and a power law
decrease of gap length between transitions. Although
he predicted the next transition to start near 2010 and
to last 20-25 years, he argued that there has been a pla-
teu in transition lengths for the last century that would
preclude a technological singulariy (Aun07)
8
.
8
Eric Chaisson makes a similar claim over the past for
the rise in free energy rate density (Joules of free energy pro-
Discussion
Generically, mathematical models that exhibit growth
tend to exhibit at least exponential growth since this is
the signature of linear self-coupling terms. If there ex-
ist efficiencies of scale introducing nonlinearities super-
exponential growth or finite time singularities appear
to be generic. Hence it is unsurprising that there is a
plethora of models producing rapid or singular growth.
The real issue is whether models with a strong mapping
to reality can exhibit such phenomena.
It should also be stressed that even if a model admits
mathematical singularities its applicability or plausibil-
ity may not diminish. Consider Newtonian and Ein-
steinian gravity, where mathematical singularities oc-
cur, yet merely indicate the limits of the applicability
of the theory. Mathematical singularities in a growth
model are likely indicators for transitions to other do-
mains of growth or that other, unmodeled factors will
become relevant close to the point.
The plausibility
(but obviously not the applicability) of a model is more
dependent on its fit to non-singular dynamics in the
past and present than its potential misbehavior in the
future.
Empirical fits based on “paradigm shifts” or other dis-
crete events suffer from limited data as well as bias-
ing memory effects - we have more available data for
recent times, and events close to us may appear more
dramatic. It is also easy to cherry-pick data points con-
sciously or unconsciously, fitting expectations (Sch06).
In addition there is a risk of biased model selection:
people inclined to believe in a singularity of a par-
ticular type are more likely to attempt to fit models
with that kind of behavior than people resistant to the
idea.
Population models may be less biased but do not nec-
essarily track the technological factors of interest di-
rectly and are obviously limited by biological limita-
tions. Still, the intersection between population mod-
eling, empirical studies of technological and economic
growth and growth models appears to afford much em-
pirically constrained modeling. The fact that long-term
(at least) exponential growth can occur and that many
not too implausible endogenous growth models can pro-
duce radical growth appears to support some forms of
the singularity concept.
A common criticism is that technological singularity as-
sumes technological determinism. This appears untrue:
several if not all of the singularity concepts in the in-
troduction could apply even if technology just exhib-
ited trends driven by non-deterministic (but not com-
cessed per second per kilogram of matter) (Cha98). How-
ever, his data on free energy rate does not seem to suggest a
curve steadily convergent to a near-future singularity, just a
universal trend towards higher rates over time where human
technology has increased the slope significantly.
pletely random) microlevel decisions. After all, popula-
tion models are not criticised as being “biologically de-
terministic” if they do not model microscale dynamics
– ignoring contingency and statistical spread are merely
part of the normal modeling process, and further mod-
els may if needed include such subtle details. Future
models of technological singularity may very well in-
clude the multiform microscale or cultural details that
shape large-scale progress, but understanding the ma-
jor structure of the phenomenon needs to start with
simple and robust models where the impact of different
assumptions is laid bare. It might turn out that tech-
nological progress has sufficient degrees of freedom to
deviate from any simple model in unpredictable ways,
but past trends appear to suggest that large-scale tech-
nological trends often are stable.
The most solid finding given the above models and fits
is that even small increasing returns in a growth model
(be it a model of economics, information or system size)
can produce radical growth. Hence identifying feedback
loops with increasing returns may be a way of detecting
emerging type A singularities.
Endogenous growth models and Robin Hanson’s mod-
els also strongly support the conclusion that if men-
tal capital (embodied in humans, artificial intelligence
or posthumans) becomes relatively cheaply copyable,
extremely rapid growth is likely to follow. Hence ob-
serving progress towards artificial intelligence, brain
emulation or other ways of increasing human capital
might provide evidence for or against type A singulari-
ties.
There is a notable lack of models of how an intelligence
explosion could occur. This might be the most impor-
tant and hardest problem to crack in the domain of sin-
gularity studies. Most important since the emergence
of superintelligence has the greatest potential of being
fundamentally game-changing for humanity (for good
or ill). Hardest, since it appears to require an under-
standing of the general nature of super-human minds
or at least a way to bound their capacities and growth
rates.
The dearth of models of future predictability is less sur-
prising: available evidence show that human experts
are usually weak at long-term forecasting even without
singularities.
Acknowledgments
I would like to thank Nick Bostrom, Toby Ord, Stuart
Armstrong, Carl Schulman and Roko Mijic for useful
comments and additions.
References
Robert Aunger. A rigorous periodization of ’big’ his-
tory.
Technological Forecasting and Social Change,
74:1164–1178, 2007.
Jacob D. Bekenstein. Universal upper bound on the
entropy-to-energy ratio for bounded systems. Phys.
Rev. D, 23(2):287–298, Jan 1981.
Lu´ıs M. A. Bettencourt, Jos´
e Lobo, Dirk Helbing,
Christian K¨
uhnert, and Geoffrey B. West. Growth,
innovation, scaling, and the pace of life in cities.
Proceedings of the National Academy of Sciences,
104(17):7301–7306, April 2007.
Nick Bostrom. Singularity and predictability. http:
//hanson.gmu.edu/vc.html#bostrom, 1998.
J.D. Barrow and F.J. Tipler. The Anthropic Cosmo-
logical Principle. Oxford University Press, 1986.
Eric J. Chaisson.
The cosmic environment for the
growth of complexity.
Biosystems, 46(1-2):13 – 19,
1998.
Gary William Flake. How i learned to stop worrying
and love the imminent internet singularity. In Proceed-
ings of the 15th ACM international conference on In-
formation and knowledge management, page 2, 2006.
Arlington, Virginia, USA.
I.J. Good. Speculations concerning the first ultrain-
telligent machine. Advances in Computers, 6, 1965.
Kay Hammacher. Accelerating changes in our epoch
and the role of time-horizons. In Vladimir Burdyuzha,
editor, The Future of Life and the Future of our Civi-
lization, volume III. Springer, 2006.
Robin Hanson. If uploads come first: the crack of a
future dawn. Extropy, 6(2), 1994. http://hanson.
gmu.edu/uploads.html.
Robin Hanson. Economic growth given machine intel-
ligence. http://hanson.gmu.edu/aigrow.pdf, 1998.
Robin Hanson. Is a singularity just around the cor-
ner? what it takes to get explosive economic growth.
Journal of Evolution and Technology, 2, 1998. http:
//hanson.gmu.edu/fastgrow.html.
Robin Hanson.
Long-term growth as a sequence
of exponential modes.
http://hanson.gmu.edu/
longgrow.pdf, 1998.
Robin Hanson. Economics of brain emulations. In Pe-
ter Healey and Steve Rayner, editors, Unnatural Se-
lection - The Challenges of Engineering Tomorrow’s
People,, pages 150–158. EarthScan, London, 2008.
Robin Hanson. Economics of the singularity. IEEE
Spectrum, pages 37–42, June 2008.
Gerald S. Hawkins. Mindsteps to the cosmos. World
Scientific, 2002.
Francis Heylighen. The socio-technological singularity.
http://pespmc1.vub.ac.be/SINGULAR.html, 1997.
Francis Heylighen.
Accelerating socio-technological
evolution: from ephemeralization and stigmergy to the
global brain. In George Modelski, Tessaleno Devezas,
and William Thompson, editors, Globalization as an
Evolutionary Process: Modeling Global Change. Rout-
ledge, London, 2007.
Hendrik Hakenes and Andreas Irmen. Airy growth -
was the take-off inevitable?, 2004.
Hendrik Hakenes and Andreas Irmen. On the long-
run evolution of technological knowledge. Economic
Theory, 30:171–180, 2007.
Anders Johansen and Didier Sornette. Finite-time sin-
gularity in the dynamics of the world population, eco-
nomic and financial indices. Physica A, 294:465–502,
2001.
Sergey P. Kapitza. Global population blow-up and af-
ter: the demographic revolution and information soci-
ety. Report to the Club of Rome, 2006.
Andrey Korotayev. Compact mathematical models of
world system development, and how they can help us
to clarify our understanding of globalization processes.
In George Modelski, Tessaleno Devezas, and William
Thompson, editors, Globalization as an Evolution-
ary Process: Modeling Global Change, pages 133–161.
Routledge, London, 2007.
Raymond
Kurzweil.
The
law
of
accelerating
returns.
http://www.kurzweilai.net/articles/
art0134.html, March 7 2001.
Raymond Kurzweil. The Singularity Is Near: When
Humans Transcend Biology. Viking Penguin, 2005.
Ray
Kurzweil,
Vernor
Vinge,
and
Hans
Moravec.
Singularity math trialogue.
http:
//www.kurzweilai.net/meme/frame.html?main=
/articles/art0151.html.
Wassily W. Leontief. Input-output economics. Oxford
University Press, 2nd edition, 1986.
David J. LePoire. Application of logistic analysis to
the history of physics. Technological Forecasting and
Social Change, 72:471–479, 2005.
Kremer M.
Population growth and technological
change: One million b.c. to 1990. Quarterly Journal
of Economics, 108:681–716, 1993.
Theodore Modis. Forecasting the growth of complex-
ity and change. Technological Forecasting and Social
Change, 69:377–404, 2002.
Theodore Modis. Discussion. Technological Forecast-
ing and Social Change, 73(2):104 – 112, 2006.
Hans Moravec.
Simple equations for vinge’s tech-
nological
singularity.
http://www.frc.ri.cmu.
edu/~hpm/project.archive/robot.papers/1999/
singularity.html.
Hans Moravec.
Simpler equations for vinge’s tech-
nological singularity. http://www.frc.ri.cmu.edu/
users/hpm/project.archive/robot.papers/2003/
singularity2.html.
Francois Meyer and Jacques Vallee. The dynamics of
long-term growth. Technological forecasting and social
change, 7:285–300, 1975.
L. Nottale, J. Chaline, and P. Grou. On the fractal
structure of evolutionary trees. In G. Losa, D. Merlini,
T. Nonnenmacher, and E. Weibel, editors, ”Fractals in
Biology and Medicine”, Vol III, Proceedings of Frac-
tal 2000 Third International Symposium, pages 247–.
Birckh¨
auser Verlag, 2001.
Anders Sandberg and Nick Bostrom. Whole brain em-
ulation: A roadmap. Technical Report #2008-3, Fu-
ture of Humanity Institute, Oxford University, 2008.
http://www.fhi.ox.ac.uk/reports/2008-3.pdf.
Juergen Schmidhuber. New millennium ai and the con-
vergence of history. Challenges to Computational In-
telligence, 2006.
John Smart. Evo devo universe? a framework for spec-
ulations on cosmic culture. In Steven J. Dick, editor,
Cosmos and Culture. NASA Press, forthcoming.
Ray J. Solomonoff. The time scale of artificial intelli-
gence: reflections on social effects. Nort-Holland Hu-
man Systems Management, 5:149–153, 1985.
John Maynard Smith and E¨
ors Szathm´
ary. The Ma-
jor Transitions in Evolution. Oxford University Press,
1995.
H. Saleur and D. Sornette. Complex exponents and
log-periodic corrections in frustrated systems. J. Phys.
I France, 6(3):327–355, mar 1996.
R Taagepera. People, skills, and resources: an interac-
tion model for world population growth. Technological
forecasting and social change, 13:13–30, 1979.
Valentin Turchin and Cliff Joslyn.
The cybernetic
manifesto. http://pespmc1.vub.ac.be/MANIFESTO.
html, 1989.
Valentin Turchin. The Phenomenon of Science. A cy-
bernetic approach to human evolution. Columbia Uni-
versity Press, New York, 1977.
Time
Tyler.
The
singularity
is
nonsense.
http://alife.co.uk/essays/the_singularity_
is_nonsense/.
Vernor Vinge. The coming technological singularity:
How to survive in the post-human era. Number NASA
CP-10129, 1993.
Heinz
von
Foerster,
Patricia
M.
Mora,
and
Lawrence W. Amiot. Doomsday: Friday 13 november,
a.d. 2026. Science, 132:1291–1295, 1960.
Roland Wagner-D¨
obler. Rescher’s principle of decreas-
ing marginal returns of scientific research. Scientomet-
rics, 50(3), 2001.
Eliezer S. Yudkowsky.
Three major singular-
ity schools.
http://yudkowsky.net/singularity/
schools, 2007.
