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2 Charles Babbage and the Emergence of Automated
Reason
Seth Bullock
Charles Babbage (1791–1871) (figure 2.1) is known for his invention of the
first automatic computing machinery, the Difference Engine and later
the Analytical Engine, thereby prompting some of the first discussions of
machine intelligence (Hyman 1982). Babbage’s efforts were driven by the
need to efficiently generate tables of logarithms—the very word ‘‘com-
puter’’ having originally referred to people employed to calculate the values
for such tables laboriously by hand. Recently, however, historians have
started to describe the wider historical context within which Babbage was
operating, revealing how he, his contemporaries, and their students were
influential in altering our conception of the workforce, the workplace, and
the economics of industrial production in a Britain increasingly concerned
with the automation of labor (Schaffer 1994).
While it was clear that all manner of unskilled manual labour could be
achieved by cleverly designed mechanical devices, the potential for the
same kind of machinery to replicate mental labor was far more controver-
sial. Were reasoning machines possible? Would they be useful? Even if they
were, was their use perhaps less than moral? Babbage’s contribution to this
debate was typically robust. In demonstrating how computing machinery
could take part in (and thereby partially automate) academic debate, he
challenged the limits of what could be achieved with mere automata, and
stimulated the next generation of ‘‘machine analysts’’ to conceive and de-
sign devices capable of moving beyond mere mechanical calculation in an
attempt to achieve full-fledged automated reason.
In this chapter, some of the historical research that has focused on
Babbage’s early machine intelligence and its ramifications will be brought
together and summarized. First, Babbage’s use of computing within
academic research will be presented. The implications of this activity on
the wider question of machine intelligence will then be discussed, and the
relationship between automation and intelligibility will be explored.
Intermittently throughout these considerations, connections between the
concerns of Babbage and his contemporaries and those of modern artificial
intelligence (AI) will be noted. However, examining historical activity
through modern lenses risks doing violence to the attitudes and significan-
ces of the agents involved and the complex causal relationships between
them and their works. In order to guard against the overinterpretation of
what is presented here as a ‘‘history’’ of machine intelligence, the paper
concludes with some caveats and cautions.
The Ninth Bridgewater Treatise
In 1837, twenty-two years before the publication of Darwin’s On the Origin
of Species and over a century before the advent of the first modern com-
puter, Babbage published a piece of speculative work as an uninvited Ninth
Figure 2.1
Charles Babbage in 1847. Source: http://www.kevryr.net/pioneers/gallery/ns_
babbage2.htm (in public domain).
20 Seth Bullock
Bridgewater Treatise (Babbage 1837; see also Babbage 1864, chapter 29,
‘‘Miracles,’’ for a rather whimsical account of the model’s development).
The previous eight works in the series had been sponsored by the will of
Francis Henry Egerton, the Earl of Bridgewater and a member of the English
clergy. The will’s instructions were to make money available to commission
and publish an encyclopedia of natural theology describing ‘‘the Power,
Wisdom, and Goodness of God, as manifested in the Creation’’ (Brock
1966; Robson 1990; Topham 1992).
In attempting such a description, natural theologists tended to draw at-
tention to states of affairs that were highly unlikely to have come about by
chance and could therefore be argued to be the work of a divine hand. For
instance, the length of the terrestrial day and seasons seem miraculously
suited to the needs and habits of plants, man, and other animals. Natural
theologists also sought to reconcile scientific findings with a literal reading
of the Old Testament, disputing evidence that suggested an alarmingly
ancient earth, or accounting for the existence of dinosaur bones, or pro-
moting evidence for the occurrence of the great flood. However, as Simon
Schaffer (1994) points out, natural theology was also ‘‘the indispensable
medium through which early Victorian savants broadcast their messages’’
(p. 224).
Babbage’s contribution to the Bridgewater series was prompted by what
he took to be a personal slight that appeared in the first published and per-
haps most popular Bridgewater Treatise. In it, the author, Reverend William
Whewell, denied ‘‘the mechanical philosophers and mathematicians of re-
cent times any authority with regard to their views of the administration of
the universe’’ (Whewell 1834, p. 334, cited in Schaffer 1994, p. 225). In
reply, Babbage demonstrated a role for computing machinery in the at-
tempt to understand the universe and our relationship to it, presenting
the first published example of a simulation model.
In 1837, Babbage was one of perhaps a handful of scientists capable of
carrying out research involving computational modeling. In bringing his
computational resources to bear on a live scientific and theological ques-
tion, he not only rebutted Whewell and advanced claims for his machines
as academic as well as industrial tools, but also sparked interest in the ex-
tent to which more sophisticated machines might be further involved in
full-blown reasoning and argument.
The question that Babbage’s model addressed was situated within what
was then a controversial debate between what Whewell had dubbed cata-
strophists and uniformitarians. Prima facie, this dispute was internal to ge-
ology, since it concerned the geological record’s potential to show evidence
Charles Babbage and the Emergence of Automated Reason 21
of divine intervention. According to the best field geologists of the day,
geological change ‘‘seemed to have taken place in giant steps: one geo-
logical environment contained a fossil world adapted to it, yet the next
stratum showed a different fossil world, adapted to its own environment
but not obviously derivable from the previous fossil world’’ (Cannon
1960, p. 7). Catastrophists argued for an interventionist interpretation of
this evidence, taking discontinuities in the record to be indicators of the
occurrence of miracles—violations of laws of nature. In contrast, uniformi-
tarians argued that allowing a role for sporadic divine miracles interrupting
the action of natural processes was to cast various sorts of aspersions on the
Deity, suggesting that His original work was less than perfect, and that He
was constantly required to tinker with his Creation in a manner that
seemed less than glorious. Moreover, they insisted that a precondition of
scientific inquiry was the assumption that the entire geological record
must be assumed to be the result of unchanging processes. Miracles would
render competing explanations of nature equally valid. No theory could be
claimed to be more parsimonious or coherent than a competing theory
that invoked necessarily inexplicable exogenous influences. As such, the
debate was central to understanding whether and how science and religion
might legitimately coexist.
W. Cannon (1960) argues that it is important to recognize that this de-
bate was not a simple confrontation between secular scientists and reli-
gious reactionaries that was ultimately ‘‘won’’ by the uniformitarians.
Rather, it was an arena within which genuine scientific argument and prog-
ress took place. For example, in identifying and articulating the degree to
which the natural and physical world fitted each other, both currently and
historically, and the startling improbability that brute processes of contin-
gent chance could have brought this about, authors such as Whewell laid a
foundation upon which Darwin’s evolutionary theory sat naturally.
Babbage’s response to the catastrophist position that apparent disconti-
nuities were evidence of divine intervention was to construct what can
now be recognized as a simple simulation model (see figure 2.2). He pro-
posed that his suitably programmed Difference Engine could be made to
output a series of numbers according to some law (for example, the inte-
gers, in order, from 0 onward), but then at some predefined point (say
100,000) begin to output a series of numbers according to some different
law such as the integers, in order, from 200,000 onward. Although the
output of such a Difference Engine (an analogue of the geological record)
would feature a discontinuity (in our example the jump from 100,000 to
200,000), the underlying process responsible for this output would have
22 Seth Bullock
remained constant—the general law, or program, that the machine was
obeying would not have changed. The discontinuity would have been the
result of the naturally unfolding mechanical and computational process.
No external tinkering analogous to the intervention of a providential deity
would have taken place.
Babbage not only described such a program in print but demonstrated a
working portion of his Difference Engine carrying out the calculations
described (see figure 2.3). At his Marylebone residence, he surprised a
stream of guests drawn from society and academia with machine behavior
that suggested a new way of thinking about both automata and miracles.
Figure 2.2
Babbage’s (1836) evolutionary simulation model represented the empirically
observed history of geological change as evidenced by the geological record (upper
panel) as the output of a computing machine following a program (lower panel). A
suitably programmed computing machine could generate sequences of output that
exhibited surprising discontinuities without requiring external influence. Hence dis-
continuities in the actual geological record did not require ‘‘catastrophic’’ divine in-
tervention, but could be the result of ‘‘gradualist’’ processes. Source: Seth Bullock.
Charles Babbage and the Emergence of Automated Reason 23
Figure 2.3
Difference Engine. Source: http://www.kevryr.net/pioneers/gallery/ns_babbage5.htm
(in public domain).
24 Seth Bullock
D. Swade (1996) describes how Darwin, recently returned from his voyages
on the Beagle, was urged by Charles Lyell, the leading geologist, to attend
one of Babbage’s ‘‘soire
´es where he would meet fashionable intelligentsia
and, moreover, ‘pretty women’ ’’ ( p. 44). Schaffer (1994) casts Babbage’s
surprising machine as providing Darwin with ‘‘an analogue for the origin
of species by natural law without divine intervention’’ (pp. 225–26).
In trying to show that discontinuities were not necessarily the result of
meddling, but could be the natural result of unchanging processes, Babbage
cultivated the image of God as a programmer, engineer, or industrialist, ca-
pable of setting a process in motion that would accomplish His intentions
without His intervening repeatedly. In Victorian Britain, the notion of God
as draughtsman of an ‘‘automatic’’ universe, one that would run unassisted,
without individual acts of creation, destruction, and so forth, proved attrac-
tive. This conception was subsequently reiterated by several other natural
philosophers, including Darwin, Lyell, and Robert Chambers, who argued
that it implied ‘‘a grander view of the Creator—One who operated by gen-
eral laws’’ (Young 1985, p. 148). However, here we are less interested in the
theological implications of Babbage’s work, and more concerned with
the manner in which he exploited his computational machinery in order
to achieve an academic goal.
Babbage clearly does not attempt to capture the full complexity of nat-
ural geology in his machine’s behavior. Indeed, the analogy between the
Difference Engine’s program and the relevant geological processes is a crude
one. However, the formal resemblance between the two was sufficient to
enable Babbage’s point to be made. His computing machine is thus clearly
being employed as a model, and a model of a particular kind—an idealized
conceptual tool rather than a realistic facsimile intended to ‘‘stand in’’ for
the real thing.
Moreover, the model’s goal is not to shed light directly on geological dis-
continuity per se. Its primary function is to force an audience to reflect on
their own reasoning processes (and on those of the authors of the preced-
ing eight legitimate Bridgewater Treatises). More specifically, the experi-
ment encourages viewers to (re)consider the grounds upon which one
might legitimately identify a miracle, suggesting that a mere inability to
understand some phenomenon as resulting from the continuous action of
natural law is not sufficient, for the continuous action of some ‘‘higher
law,’’ one discernible only from a more systemic perspective, could always
be responsible. Thus, Babbage’s is an ‘‘experiment’’ that brings no new data
to light, it generates no geological facts for its audience, but seeks to re-
arrange their theoretical commitments.
1
Charles Babbage and the Emergence of Automated Reason 25
Babbage approached the task of challenging his audiences’ assumptions
as a stage magician might have done (Babbage 1837, p. 35):
Now, reader, let me ask how long you will have counted before you are firmly con-
vinced that the engine, supposing its adjustments to remain unaltered, will continue
whilst its motion is maintained, to produce the same series of natural numbers?
Some minds perhaps are so constituted, that after passing the first hundred terms
they will be satisfied that they are acquainted with the law. After seeing five hundred
terms, few will doubt; and after the fifty-thousandth term the propensity to believe
that the succeeding term will be fifty thousand and one, will be almost irresistible.
Key to his argument was the surprise generated by mechanical disconti-
nuity. That a process unfolding ‘‘like clockwork’’ could nevertheless con-
found expectation simultaneously challenged the assumed nature of both
mechanical and natural processes and the power of rational scientific in-
duction. In this respect, Babbage’s argument resonates with some modern
treatments of ‘‘emergent behavior.’’ Here, nonlinearities in the interactions
between a system’s components give rise to unexpected (and possibly
irreducible, that is, quasi-miraculous) global phenomena, as when, for in-
stance, the presumably simple rules followed by insects generate complex
self-regulating nest architectures (Ladley and Bullock 2005), or, indeed, the
way in which novel forms can emerge from shape grammars (March 1996a,
1996b). For Babbage, however, any current inability on our part to recon-
cile some aggregate property with the constitution and organization of the
system that gives rise to it is no reason to award the phenomenon special
status. His presumption is that for some more sophisticated observer, rec-
onciling the levels of description will be both possible and straightforward,
nonlinearity or no nonlinearity.
Additionally, there is a superficial resemblance between the catastrophist
debate of the nineteenth century and the more recent dispute over the
theory of punctuated equilibria introduced by Niles Eldredge and Stephen
Jay Gould (1973). Both arguments revolved around the significance of
what appear to be abrupt changes on geological time scales. However,
where Babbage’s dispute centered on whether change could be explained
by one continuously operating process or must involve two different
mechanisms—the first being geological processes, the second Divine
intervention—Gould and Eldredge did not dispute that a single evolution-
ary process was at work. They take pains to point out that their theory does
not supersede phylogenetic gradualism, but augments it. They wish to
account for the two apparent modes of action evidenced by the fossil
record—long periods of stasis, short bursts of change—not by invoking
26 Seth Bullock
two processes but by explaining the unevenness of evolutionary change.
In this respect, the theory that Eldredge and Gould supply attempts to
meet a modern challenge: that of explaining nonlinearity, rather than
merely accommodating it. Whereas Babbage’s aim was merely to demon-
strate that a certain kind of nonlinearity was logically possible in the
absence of exogenous interference, Gould and Eldredge exemplify the at-
tempt to discover how and why nonlinearities arise from the homogeneous
action of low-level entities.
Babbage, too, spent some time developing theories with which he sought
to explain how specific examples of geological discontinuity could have
arisen as the result of unchanging and continuously acting physical geolog-
ical processes. One example of apparently rapid geological change that had
figured prominently in geological debate since being depicted on the fron-
tispiece of Lyell’s Principles of Geology (1830) was the appearance of the
Temple of Serapis on the edge of the Bay of Baiae in Pozzuoli, Italy (see fig-
ure 2.4). The surfaces of the forty-two-foot pillars of the temple are charac-
terized by three regimes. The lower portions of the pillars are smooth, their
central portions have been attacked by marine creatures, and above this
region the pillars are weathered but otherwise undamaged. These abrupt
changes in the character of the surfaces of the pillars were taken by geolo-
gists to be evidence that the temple had been partially submerged for a
considerable period of time.
For Lyell (1830), an explanation could be found in the considerable seis-
mic activity that had characterized the area historically. It was well known
that eruptions could cover land in considerable amounts of volcanic mate-
rial and that earthquakes could suddenly raise or lower tracts of land. Lyell
reasoned that a volcanic eruption could have buried the lower portion of
the pillars before an earthquake lowered the land upon which the temple
stood into the sea. Thus the lower portion would have been preserved
from erosion, while a middle portion would have been subjected to marine
perforations and an upper section to the weathering associated with wind
and rain.
Recent work by B. P. Dolan (1998) has uncovered the impact that Bab-
bage’s own thoughts on the puzzle of the pillars had on this debate.
Babbage, while visiting the temple, noted an aspect of the pillars that had
hitherto gone undetected: a patch of calciated stone located between the
central perforated section and the lower smooth portion. He inferred that
this calciation had been caused, over considerable time, by calcium-bearing
spring waters that had gradually flooded the temple, as the land upon
which it stood sank lower and lower. Eventually this subsidence caused
Charles Babbage and the Emergence of Automated Reason 27
the temple pillars to sink below sea level and resulted in the marine erosion
evident on the middle portion of the columns.
Thus Babbage’s explanation invoked gradual processes of cumulative
change, rather than abrupt episodes of discontinuous change, despite the
fact that the evidence presented by the pillars is that of sharply separated
regimes. Babbage’s account of this gradual change relied on the notion
that a central, variable source of heat, below the earth’s crust, caused ex-
pansion and contraction of the land masses above it. This expansion or
contraction would lead to subsidence or elevation of the land masses
involved. Babbage exploited the power of his new calculating machine in
attempting to prove his theory, but not in the form of a simulation model.
Instead, he used the engine to calculate tables of values that represented
the expansion of granite under various temperature regimes, extrapolated
from empirical measurements carried out with the use of furnaces. With
Figure 2.4
The Temple of Serapis. The frontispiece for the first six volumes of Lyell’s Principles
of Geology. By permission of the Syndics of Cambridge University.
28 Seth Bullock
these tables, Babbage could estimate the temperature changes that would
have been necessary to cause the effects manifested by the Temple of Sera-
pis (see Dolan 1998, for an extensive account of Babbage’s work on this
subject).
Here, Babbage is using a computer, and is moving beyond a gradualist ac-
count that merely tolerates discontinuities, such as that in his Bridgewater
Treatise, to one that attempts to explain them. In this case his engine is not
being employed as a simulation model but as a prosthetic calculating
device. The complex, repetitive computations involved in producing and
compiling his tables of thermal expansion figures might normally have
been carried out by ‘‘computers,’’ people hired to make calculations manu-
ally. Babbage was able to replace these error-prone, slow, and costly manual
calculations with the action of his mechanical reckoning device.
Like simulation modeling, this use of computers has become widespread
across modern academia. Numerical and iterative techniques for calculat-
ing, or at least approximating, the results of what would be extremely
taxing or tedious problems have become scientific mainstays. However,
this kind of automated extrapolation differs significantly from the simula-
tion described above. Just as the word ‘‘intelligence’’ itself can signify, first,
the possession or exercise of superior cognitive faculties and, second, the
obtainment or delivery of useful information, such as military intelligence,
for Babbage, machine intelligence could either refer to some degree of auto-
mated reasoning or (less impressively) the ‘‘manufacture’’ of information
(Schaffer 1994). While Babbage’s model of miracles and his automatic gen-
eration of thermal expansion tables were both examples of ‘‘mechanized
intelligence,’’ they differed significantly in that the first was intended to
take part in and thereby partially automate thought processes directed at
understanding, whereas the second exemplified his ability to ‘‘manufacture
numbers’’ (Babbage 1837, p. 208). This subtle but important difference was
not lost upon Babbage’s contemporaries, and was central to unfolding dis-
cussions and categorizations of mental labor.
Automating Reason
For his contemporaries and their students, the reality of Babbage’s machine
intelligence and the prospect of further advances brought to the foreground
questions concerning the extent to which mental activity could and should
be automated. The position that no such activity could be achieved ‘‘me-
chanically’’ had already been somewhat undermined by the success of un-
skilled human calculators and computers, who were able to efficiently
Charles Babbage and the Emergence of Automated Reason 29
generate correct mathematical results while lacking an understanding of
the routines that they were executing.
National programs to generate navigational and astronomical tables of
logarithmic and trigonometric values (calculated up to twenty-nine deci-
mal places!) would not have been possible in practice without this redistri-
bution of mental effort. Babbage himself was strongly influenced by Baron
Gaspard De Prony’s work on massive decimal tables in France from 1792,
where he had employed a division of mathematical labor apparently
inspired by his reading of Adam Smith’s Wealth of Nations (see Maas 1999,
pp. 591–92).
[De Prony] immediately realised the importance of the principle of the division of
labour and split up the work into three different levels of task. In the first, ‘‘five or
six’’ eminent mathematicians were asked to simplify the mathematical formulae. In
the second, a similar group of persons ‘‘of considerable acquaintance with mathe-
matics’’ adapted these formulae so that one could calculate outcomes by simply add-
ing and subtracting numbers. This last task was then executed by some eighty
predominantly unskilled individuals. These individuals were referred to as the com-
puters or calculators.
Babbage’s Difference Engine was named after this ‘‘method of differ-
ences,’’ reducing formulae to combinations of addition and subtraction.
However, there was a clear gulf separating true thinking from the mindless
rote activity of computers, whether human or mechanical. For commenta-
tors such as the Italian mathematician and engineer Luigi Federico Mene-
brea, whose account of a lecture Babbage gave in Turin was translated into
English by Ada Lovelace (Lovelace 1843), there appeared little chance that
machinery would ever achieve more than the automation of this lowest
level of mental activity. In making this judgment, Menebrea ‘‘pinpointed
the frontiers of the engine’s capacities. The machine was able to calculate,
but the mechanization of our ‘reasoning faculties’ was beyond its reach,
unless, Menebrea implicitly qualified, the rules of reasoning themselves
could be algebraised’’ (Maas 1999, p. 594–95).
For Menebrea it was apparently clear that such a mental calculus would
never be achieved. But within half a century, just such algebras were being
successfully constructed by George Boole and John Venn. For some, the po-
tential for mechanizing such schemes seemed to put reasoning machines
within reach, but for others, including Venn himself, the objections raised
by Menebrea still applied.
Simon Cook (2005) describes how Venn, in his ‘‘On the Diagrammatic
and Mechanical Representation of Propositions and Reasonings’’ of 1880,
clearly recognized considerable potential for the automation of his logical
30 Seth Bullock
formalisms but went on to identify a strictly limited role for such ma-
chinery. The nature of the labor involved in logical work, Venn stated (p.
340),
involves four ‘‘tolerably distinct steps’’: the statement of the data in accurate logical
language, the putting of these statements into a form fit for an ‘‘engine to work
with,’’ thirdly the combination or further treatment of our premises after such a re-
duction, and finally interpretation of the results. In Venn’s view only the third of
these steps could be aided by an engine.
For Venn, then, computing machinery would only ever be useful for
automating the routine process of thoughtlessly combining and processing
logical terms that had to be carefully prepared beforehand and the resulting
products analyzed afterward.
This account not only echoes De Prony’s division of labor, but, to modern
computer scientists, also bears a striking similarity to the theory developed
by David Marr (1982) to describe the levels of description involved in cog-
nitive science and artificial intelligence. For Marr, any attempt to build a
cognitive system within an information-processing paradigm involves first
a statement of the cognitive task in information-processing terms, then the
development of an algorithmic representation of the task, before an imple-
mentation couched in an appropriate computational language is finally for-
mulated. Venn’s steps also capture this march from formal conception to
computational implementation. Rather than stressing the representational
form employed at each stage, Venn concentrates on the associated activity,
and, perhaps as a result, considers a fourth step not included by Marr: the
interpretation of the resulting behavior, or output, of the computational pro-
cess. We will return to the importance of this final step.
Although Venn’s line on automated thought was perhaps the dominant
position at that time, for some scholars Babbage’s partially automated argu-
ment against miracles had begun to undermine it. Here a computer took
part in scientific work not by automating calculation, but in a wholly differ-
ent way. The engine was not used to compute a result. Rather, the substan-
tive element of Babbage’s model was the manner in which it changed over
time. In the scenario that Babbage presented to his audience, his suitably
programmed Difference Engine will, in principle, run forever. Its calcula-
tion is not intended to produce some end product; rather, the ongoing cal-
culation is itself the object of interest. In employing a machine in this way,
as a model and an aid to reasoning, Babbage ‘‘dealt a severe blow to the tra-
ditional categories of mental philosophy, without positively proving that
our higher reasoning faculties could be mechanized’’ (Maas 1999, p. 593).
Charles Babbage and the Emergence of Automated Reason 31
Recent historical papers have revealed how the promise of Babbage’s sim-
ulation model, coupled with the new logics of Boole and Venn, inspired
two of the fathers of economic science to design and build automated rea-
soning machines (Maas 1999; Cook 2005). Unlike Babbage and Lovelace,
the names Stanley Jevons (1835–1882) and Alfred Marshall (1842–1924)
are not well known to students of computing or artificial intelligence. How-
ever, from the 1860s onward, first Jevons and then Marshall brought about
a revolution in the way that economies were studied, effectively establish-
ing modern economics. It was economic rather than biological or cognitive
drivers that pushed both men to consider the role that machinery might
play in automating logical thought processes.
Jevons pursued a mathematical approach to economics, exploring ques-
tions of production, currency, supply and demand, and so forth and devel-
oping his own system of logic (the ‘‘substitution of similars’’) after studying
and extending Boole’s logic. His conviction that his system could be auto-
mated such that the logical consequences of known states of affairs could
be generated efficiently led him to the design of a ‘‘logical piano . . . capable
of replacing for the most part the action of thought required in the perfor-
mance of logical deduction’’ ( Jevons 1870, p. 517). But problems persisted,
again limiting the extent to which thought could be automated. Jevons’s
logical extrapolations relied upon the substitution of like terms, such as
‘‘London’’ and ‘‘capital of England.’’ The capacity to decide which terms
could be validly substituted appeared to resist automation, becoming for
Jevons ‘‘a dark and inexplicable gift which was starkly to be contrasted
with calculative, mechanical rationality’’ (Maas 1999, p. 613). Jevons’s
piano, then, would not have inclined Venn to alter his opinion on the lim-
itations of machine logic.
Cook (2005) has recently revealed that Marshall (who, upon Jevons’s
early death by drowning in 1882, would eventually come to head the mar-
ginalist revolution within economics) also considered the question of
machine intelligence. In ‘‘Ye Machine,’’ the third of four manuscripts
thought to have been written in the late 1860s to be presented to Cam-
bridge Grote Club, he described his own version of a machine capable of
automatically following the rules of logic. However, in his paper he moves
beyond previous proponents of machine intelligence in identifying a
mechanism capable of elevating his engine above mere calculation, to the
realm of creative reason. Menebrea himself had identified the relevant re-
spect in which these calculating machines were significantly lacking in his
original discussion of Babbage’s engines. ‘‘[They] could not come to any
32 Seth Bullock
correct results by ‘trial and guess-work’, but only by fully written-out proce-
dures’’ (Maas 1999, p. 593). It was introducing this kind of exploratory
behavior that Marshall imagined. What was required were the kinds of sur-
prising mechanical jumps staged by Babbage in his drawing room. Marshall
(Cook 2005, p. 343) describes a machine with the ability to process logical
rules that,
‘‘like Paley’s watch’’, might make others like itself, thus giving rise to ‘‘hereditary and
accumulated instincts.’’ Due to accidental circumstances the ‘‘descendents,’’ how-
ever, would vary slightly, and those most suited to their environment would survive
longer: ‘‘The principle of natural selection, which involves only purely mechanical
agencies, would thus be in full operation.’’
As such, Marshall had imagined the first example of an explicitly evolu-
tionary algorithm, a machine that would surprise its user by generating and
testing new ‘‘mutant’’ algorithmic tendencies. In terms of De Prony’s tri-
partite division of labor, such a machine would transcend the role of mere
calculator, taking part in the ‘‘adapting of formulae’’ function heretofore
carried out by only a handful of persons ‘‘of considerable acquaintance
with mathematics.’’ Likewise, Marshall’s machine broke free of Venn’s
restrictions on machine intelligence. In addition to the task of mechani-
cally combining premises according to explicitly stated logics, Marshall’s
machine takes on the more elevated task of generating new, superior logics
and their potentially unexpected results.
Andy Clark (1990) has described the explanatory complications intro-
duced by this move from artificial intelligences that employ explicit, man-
ually derived logic to those reliant on some automatic process of design
or adaptation. Although the descent through Marr’s ‘‘classical cascade’’
involved in the manual design of intelligent computational systems
delivers, as a welcome side effect, an understanding of how the system’s be-
havior derives from its algorithmic properties, no such understanding is
guaranteed where this design process is partially automated. For instance,
Marr’s computational algorithms for machine vision, once constructed,
were understood by their designer largely as a result of his gradual progres-
sion from computational to algorithmic and implementational representa-
tions. The manual design process left him with a grasp of the manner in
which his algorithms achieved their performance. By contrast, when one
employs artificial neural networks that learn how to behave or evolutionary
algorithms that evolve their behavior, a completed working system
demands further interpretation—Venn’s fourth step—before the way it
works can be understood.
Charles Babbage and the Emergence of Automated Reason 33
The involvement of automatic adaptive processes thus demands a partial
inversion of Marr’s cascade. In order to understand an adaptive machine
intelligence, effort must be expended recovering a higher, algorithmic-level
representation of how the system achieves its performance from a working
implementation-level representation. The scale and connectivity of the ele-
ments making up these kinds of adaptive computational system can make
achieving this algorithmic understanding extremely challenging.
For at least one commentator on machine intelligence, it was exactly the
suspect intelligibility of automatic machine intelligence that was objection-
able. The Rev. William Whewell was a significant Victorian figure, having
carved out a role for himself as historian, philosopher, and critic (see figure
2.5). His principal interest was in the scientific method and the role of
induction within it. For Whewell, the means with which scientific ques-
tions were addressed had a moral dimension. We have already heard how
Whewell’s dismissal of atheist mathematicians in his Bridgewater Treatise
seems to have stimulated Babbage’s work on simulating miracles (though
Whewell was likely to have been targeting the mathematician Pierre-Simon
Laplace rather than Babbage). He subsequently made much more explicit
Figure 2.5
The Rev. William Whewell in 1835.
34 Seth Bullock
attacks on the use of machinery by scientists—a term he had coined in
1833.
Whewell brutally denied that mechanised analytical calculation was proper to
the formation of the academic and clerical elite. In classical geometry ‘‘we tread the
ground ourselves at every step feeling ourselves firm,’’ but in machine analysis ‘‘we
are carried along as in a rail-road carriage, entering it at one station, and coming our
of it at another. . . . It is plain that the latter is not a mode of exercising our own loco-
motive powers. . . . It may be the best way for men of business to travel, but it cannot
fitly be made a part of the gymnastics of education. (Schaffer 1994, pp. 224–25)
The first point to note is that Whewell’s objection sidesteps the issues of
performance that have occupied us so far. Here, it was irrelevant to Whe-
well that machine intelligence might generate commercial gain through
accurate and efficient calculation or reasoning. A legitimate role within
science would be predicated not only on the ability of computing
machines to replicate human mental labor but also on their capacity to
aid in the revelation of nature’s workings. Such revelation could only be
achieved via diligent work. Shortcuts would simply not do. For Whewell it
was the journey, not the destination, that was revelatory. Whewell’s objec-
tion is mirrored by the assertion sometimes made within artificial intelli-
gence that if complex but inscrutable adaptive algorithms are required in
order to obtain excellent performance, it may be necessary to sacrifice a
complete understanding of how exactly this performance is achieved—
‘‘We are engineers, we just need it to work.’’ Presumably, Whewell would
have considered such an attitude alien to academia.
More prosaically, the manner in which academics increasingly rely upon
automatic ‘‘smart’’ algorithms to aid them in their work would have wor-
ried Whewell. Machine intelligence as typically imagined within modern
AI (for example, the smart robot) may yet be a distant dream, but for
Whewell and Babbage, it is already upon us in the automatically executed
statistical test, the facts, figures, opinions, and arguments instantaneously
harvested from the Internet by search engines, and so forth. Where these
shortcuts are employed without understanding, Whewell would argue, aca-
demic integrity is compromised.
There are also clear echoes of Whewell’s opinions in the widespread ten-
dency of modern theoreticians to put more faith in manually constructed
mathematical models than automated simulation models of the same phe-
nomena. While the use of computers to solve mathematical equations nu-
merically (compare Babbage’s thermal expansion calculations) is typically
regarded as unproblematic, there is a sense that the complexity—the
Charles Babbage and the Emergence of Automated Reason 35
impenetrability—of simulation models can undermine their utility as sci-
entific tools (Grimm 1999; Di Paolo et al. 2000).
However, it is in Marshall’s imagined evolving machine intelligence that
the apotheosis of Whewell’s concerns can be found. In the terms of Whe-
well’s metaphor, not only would Marshall be artificially transported from
problem to solution by such a machine, but he would be ferried through
deep, dark, unmapped tunnels in the process. At least the rail tracks leading
from one station to another along which Whewell’s imagined locomotive
must move had been laid by hand in a process involving much planning
and toil. By contrast, Marshall’s machine was free to travel where it pleased,
arriving at a solution via any route possible. While the astonishing jumps
in the behavior of Babbage’s machine were not surprising to Babbage him-
self, even the programmer of Marshall’s machine would be faced with a
significant task in attempting to complete Venn’s ‘‘interpretation’’ of its
behavior.
Conclusion
This chapter has sought to highlight activities relevant to the prehistory of
artificial intelligence that have otherwise been somewhat neglected within
computer science. In gathering together and presenting the examples of
early machine intelligence created by Babbage, Jevons, and Marshall, along
with contemporaneous reflections on these machines and their potential,
the chapter relies heavily on secondary sources from within a history of
science literature that should be of growing importance to computer
science. Although this paper attempts to identify a small number of issues
that link contemporary AI with the work of Babbage and his contempo-
raries, it is by no means a piece of historical research and the author is no
historian. Despite this, in arranging this material here on the page, there is
a risk that it could be taken as such.
Babbage’s life and work have already been the repeated subject of Whig-
gish reinterpretation—the tendency to see history as a steady linear pro-
gression (see Hyman 1990 for a discussion). In simplifying or ignoring the
motivations of our protagonists and the relationships between them, there
is scope here, too, for conveying the impression of an artificially neat causal
chain of action and reaction linking Babbage, Whewell, Jevons, Marshall,
and others in a consensual march toward machine intelligence driven by
the same questions and attitudes that drive modern artificial intelligence.
Such an impression would, of course, be far from the truth. The degree to
which each of these thinkers engaged with questions of machine intelli-
36 Seth Bullock
gence varied wildly: for one it was the life’s work; for another, a brief inter-
est. And even with respect to the output of each individual, the elements
highlighted here range from significant signature works to obscure foot-
notes or passing comments. It will be left to historians of science to provide
an accurate account of the significances of the activities presented here.
This chapter merely seeks to draw some attention to them.
Given the sophistication already evident in the philosophies associated
with machine intelligence in the nineteenth century, it is perhaps surpris-
ing that a full-fledged philosophy of technology, rather than science, has
only recently begun to emerge (Ihde 2004). In the absence of such a disci-
pline, artificial intelligence and cognitive philosophy, especially that influ-
enced by Heidegerrian themes, have played a key role in extending our
understanding of the role that technology has in influencing the way we
think (see, for example, Dreyfus 2001). If we are to cope with the rapidly
expanding societal role of computers in, for instance, complex systems
modeling, adaptive technologies, and the Internet, we must gain a firmer
grasp of the epistemic properties of the engines that occupied Babbage and
his contemporaries.
Unlike an instrument, that might simply be a pencil, engines embody highly differ-
entiated engineering knowledge and skill. They may be described as ‘‘epistemic’’
because they are crucially generative in the practice of making scientific knowl-
edge. . . . Their epistemic quality lies in the way they focus activities, channel re-
search, pose and help solve questions, and generate both objects of knowledge and
strategies for knowing them. (Carroll-Burke 2001, p. 602)
Acknowledgments
This chapter owes a significant debt to the painstaking historical research
of Simon Schaffer, B. P. Dolan, S. Cook, H. Maas, and, less recently, W.
Cannon.
Note
1. See Bullock (2000) and Di Paolo, Noble, and Bullock (2000) for more discussion of
Babbage’s simulation model and simulation models in general.
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