How to get there from here and What to do afterwards

Hans P. Moravec
Computer Science Dept.
Stanford University
September 3, 1977


The following  entities provided inspiration,  encouragement,
suggestions, proofreading:

(in carefully randomized order) HAL-9000, Bruce Baumgart, Marc Le
Brun, Andy Moorer, Bill Gosper, PDP-KA10, Scientific American, Don
Gennery, Mike Farmwald, Erik Gilbert, John McCarthy, Macsyma, Pierre
van Nypelseer, Robert Maas, Ed Mcguire, Electronics magazine, Russ
Taylor, Elaine Kant, Les Earnest, Arthur Thomas, Cart Project, Polle
Zellweger, Jeff Rubin, Tom Binford, Clem Smith, Tom Gafford, Brian
Harvey, ...

This essay is an amalgam of
"The Role of Raw Power in Intelligence" and
"Todays Computers, Intelligent Machines and Our Future".


	The unprecedented opportunities for experiments in complexity
presented by the first modern computers in the late 1940's raised
hopes in early computer scientists (eg. John von Neumann and Alan
Turing) that the ability to think, our greatest asset in our dealings
with the world, might soon be understood well enough to be
duplicated. Success in such an endeavor would extend mankind's mind in
the same way that the development of energy machinery extended his

	In the thirty years since then computers have become vastly
more capable, but the goal of human performance in most areas seems as
elusive as ever, in spite of a great deal of effort.  The last ten
years, in particular, has seen thousands of people years devoted
directly to the problem, referred to as Artificial Intelligence or
AI. Attempts have been made to develop computer programs which do
mathematics, computer programming and common sense reasoning, are able
to understand natural languages and interpret scenes seen through
cameras and spoken language heard through microphones and to play
games humans find challenging.

	There has been some progress.  Samuel's checker program can
occasionally beat checker champions. Chess programs regularly play at
good amateur level, and in March 1977 a chess program from
Northwestern University, running on a CDC Cyber-176 (which is about 20
times as fast as previous computers used to play chess) won the
Minnesota Open Championship, against a slate of class A and expert
players.  A ten year effort at MIT has produced a system, Macsyma,
capable of doing symbolic algebra, trigonometry and calculus
operations better in many ways than most humans experienced in those
fields.  Programs exist which can understand English sentences with
restricted grammar and vocabulary, given the letter sequence, or
interpret spoken commands from hundred word vocabularies.  Some can do
very simple visual inspection tasks, such as deciding whether or not a
screw is at the end of a shaft.  The most difficult tasks to automate,
for which computer performance to date has been most disappointing,
are those that humans do most naturally, such as seeing, hearing and
common sense reasoning.

	A major reason for the difficulty has become very clear to me
in the course of my work on computer vision. It is simply that the
machines with which we are working are still a hundred thousand to a
million times too slow to match the performance of human nervous
systems in those functions for which humans are specially wired.  This
enormous discrepancy is distorting our work, creating problems where
there are none, making others impossibly difficult, and generally
causing effort to be misdirected.

	In the early days of AI the thought that existing machines
might be much too small was widespread, but people hoped that clever
mathematics and advancing computer technology could soon make up the
difference.  The idea that available compute power might still be
vastly inadequate has since been swept under the rug, due to wishful
thinking and a feeling that there was nothing to be done about it
anyway and that voicing such an opinion could cause AI to be
considered impractical, resulting in reduced funding.  This attitude
has had some bad effects, one of them being that AI research has been
centered on computers less powerful than absolutely necessary.

	The first section of this essay discusses natural
intelligence. It notes two major branches of the animal kingdom in
which intelligence evolved independently, and suggests that it is
easier to construct than is sometimes assumed.

	The second part compares the information processing ability of
present computers with intelligent nervous systems. The factor of one
million is derived in two different ways.

	Section three examines the development of electronics, and
concludes the state of the art can provide more power than is now
available, and that the one million gap could be closed in ten years.

	Parts four and five introduce some hardware and software
aspects of a system which would be able to make use of the advancing
technology, providing a means for achieving human equivalence, perhaps
by the next decade.

	Part six considers the implications of the emergence of
intelligent machines, and concludes that they are the final step in a
revolution in the nature of life. Classical evolution based on DNA,
random mutations and natural selection may be completely replaced by
the much faster process of intelligence mediated cultural and
technological evolution.

Section 1:  The Natural History of Intelligence

Product lines:

	Natural evolution has produced a continuum of complexities of
behavior, from the mechanical simplicity of viruses to the magic of
mammals. In the higher animals most of the complexity resides in the
nervous system.

	Evolution of the brain began in early multi-celled animals a
billion years ago with the development of cells capable of
transmitting electrochemical signals.  Because neurons are more
localized than hormones they allow a greater variety of signals in a
given volume.  They also provide evolution with a more uniform medium
for experiments in complexity.

	The advantages of implementing behavioral complexity in neural
nets seem to have been overwhelming, since all modern animals more
than a few cells in size have them [animal refs.].

	Two major branches in the animal kingdom, vertebrates and
mollusks, contain species which can be considered intelligent.  Both
stem from one of the earliest multi-celled organisms, an animal
something like a hydra made of a double layer of cells and possessing
a primitive nerve net.

	Most mollusks are intellectually unimpressive sessile
shellfish, but one branch, the cephalopods, possesses high mobility,
large brains and imaging eyes. These structures evolved independently
of the corresponding equipment in vertebrates and there are
fascinating differences.  The optic nerve connects to the back of the
retina, so there is no blind spot.  The brain is annular, forming a
ring encircling the esophagus.  The circulatory system, also
independently evolved, has three blood pumps, a systemic heart pumping
oxygenated blood to the tissues and two gill hearts, each pumping
venous blood to one gill. The oxygen carrier is a green copper
compound called hemocyanin, evolved from an earlier protein that also
became hemoglobin.

	These animals have some unique abilities.  Shallow water
octopus and squid are covered by a million individually controlled
color changing effectors called chromatophores, whose functions are
camouflage and communication.  The capabilities of this arrangement
have been demonstrated by a cuttlefish accurately imitating a
checkerboard it was placed upon, and an octopus in flight which
produced a pattern like the seaweed it was traversing, coruscating
backward along the length of its body, diverting the eye from the true
motion.  Deep sea squid have photophores capable of generating large
quantities of multicolored light.  Some are as complex as eyes,
containing irises and lenses [squid].  The light show is modulated by
emotions in major and subtle ways. There has been little study of
these matters, but this must provide means of social interaction.
Since they also have good vision, there is the potential for high
bandwidth communication.

	Cephalopod intelligence has not been extensively investigated,
but a few controlled experiments indicate rapid learning in small
octopus [Boycott].  The Cousteau film in the references shows an
octopus' response to a problem requiring a two stage solution.  A
fishbowl containing a lobster is sealed with a cork and dropped into
the water near it.  The octopus is attracted, and spends a long while
alternately probing the container in various ways and returning to its
lair in iridescent frustration.  On the final iteration it exits its
little hole in the ground and unhesitatingly wraps three tentacles
around the bowl, and one about the cork, and pulls.  The cork shoots
to the surface and the octopus eats.  The Time-Life film contains a
similar sequence, with a screw top instead of a cork!  If small
octopus have almost mammalian behavior, what might giant deep sea
squid be capable of?  The behavior of these large brained, apparently
shy, animals has virtually never been observed.

	Birds are more closely related to humans than are cephalopods,
their common ancestor with us being a 300 million year old early
reptile.  Size-limited by the dynamics of flying, some birds have
reached an intellectual level comparable to the highest mammals.

	Crows and ravens are notable for frequently outwitting people.
Their intuitive number sense (ability to perceive the cardinality of a
set without counting) extends to seven, as opposed to three or four in
us.  Such a sense is useful for keeping track of the number of eggs in
a nest. Experiments have shown [Stettner] that most birds are more
capable of high order "reversal" and "learning set" learning than all
mammals except the higher primates.  In mammals these abilities
increase with increasing cerebral cortex size.  In birds the same
functions depend on areas not present in mammalian brains, forebrain
regions called the "Wulst" and the hyperstriatum.  The cortex is small
and relatively unimportant.  Clearly this is another case of
independent evolution of similar mental functions.  Penguins, now
similar to seals in behavior and habitat, might be expected to become
fully aquatic, and evolve analogously to the great whales.

	The cetaceans are related to us through a small 30 million
year old primitive mammal. Some species of dolphin have body and brain
masses identical to ours, and archaeology reveals they have been this
way several times as long.  They are as good as us at many kinds of
problem solving, and perhaps at language.  The references contain many
anecdotes, and describe a few controlled experiments, showing that
dolphins can grasp and communicate complex ideas.  Killer whales have
brains seven times human size, and their ability to formulate plans is
better than the dolphins', on whom they occasionally feed. Sperm
whales, though not the largest animals, have the world's largest
brains.  There may be intelligence mediated conflict with large squid,
their main food supply.

	Elephants have brains about five times human size, matriarchal
tribal societies, and complex behavior. Indian domestic elephants
usually learn 500 commands, limited by the range of tractor-like tasks
their owners need done, and form voluntary mutual benefit
relationships with their trainers, exchanging labor for baths.  They
can solve problems such as how to sneak into a plantation at night to
steal bananas, after having been belled (answer: stuff mud into the
bells).  And they remember for decades.  Inconvenience and cost has
prevented more elephant research.

	The apes are our cousins.  Chimps and gorillas can learn to
use tools and to communicate with human sign languages at a retarded
level.  As chimps have one third, and gorillas one half, human
brainsize, similar results should be achievable with the larger
brained, but less human-like animals.  Though no other species has
managed to develop a technological culture, it may be that some of
them can be made partners in ours, accelerating its evolution with
their unique capabilities.

Time before present       Representative Creatures                Significant events

0 (you are here)   |       |          |       |     | computers   massive technology
2.5 million years  |       |          |       |     |     |
10                 |       |          |       | elephants |                 tool use
                   |       |          |    whales   |  primates
40                 |       |          |       |     |     |
                   |       |          |       |     |     |
90              octopus  squid        |       |     |     |
                   |       |          |       +-----+-----+
160                +---+---+        birds        mammals 
                       |              |             |               learned behavior
250               early squid         +------+------+               warm bloodedness
                       |                  reptiles
360                    |                     |
                  cephalopods     fish       |
490                    |            |    amphibians                 land vertebrates
                       +---+        +----+---+
640                     mollusks    vertebrates
                           |             |
810			   |             |                     complex nerve centers
1 billion years			  |                          invention of the neuron
				  |                                    old age death
1.21				  |                         sex in animals perfected
1.44				  |                           multi-cellular animals
1.69	 		          |
		          plants  |
1.96			     |    |			   oxygen to support animals
2.25				  |
2.56                 blue-green   |                                  nucleated cells
                        algae     |
2.89			  +-------+
				  |			               DNA genetics?
3.24				  |                                   photosynthesis
			    earliest cells                     reliable reproduction
3.61				  |                            invention of the cell
				  |                   inorganic protein microspheres
4 billion years	           non-living chemicals                 amino acid formation

FIGURE: Highlights in the evolution of terrestrial intelligence.
             The distance along the edge of the tree is proportional
             to the square root of the time from the present. This
             seems to space things nicely.

Nervous System Size and Intelligence

	A feature shared by all living organisms whose behavior is
complex enough to indicate near-human intelligence is a nervous system
of a hundred billion neurons.  Imaging vision requires a billion
neurons. A million brain cells usually permits fast and interesting,
but stereotyped, behavior as in a bee. A thousand is adequate for slow
moving animals with minimal sensory input, such as slugs and worms.  A
hundred runs most sessile animals.  The portions of nervous systems
for which tentative wiring diagrams have been obtained (eg.  much of
the brain of the large neuroned sea slug, Aplysia, the flight
controller of the locust and the early stages of some vertebrate
visual systems) reveal that the neurons are configured into efficient,
clever, assemblies.  This should not be surprising, as unnecessary
redundancy means unnecessary metabolic load, a distinct selective

	Evolution has stumbled on many ways of speeding up its own
progress, since species that adapt more quickly have a selective
advantage. Most of these speedups, such as sex and dying of old age,
are refinements of one of the oldest, the encoding of genetic
information in the easily mutated and modular DNA molecule.  In the
last few million years the genetically evolved ability of animals,
especially mammals, to learn a significant fraction of their behavior
after birth has provided a new medium for growth of complexity.
Modern man, though perhaps not the most individually intelligent
animal on the planet, is the species in which this cultural evolution
seems to have had the greatest effect, making human culture the most
potent force on the earth's surface.

	Our cultural and technological evolution has proceeded by
massive interchange of ideas and information, trial and error guided
by the ability to predict the outcome of simple situations, and other
techniques mediated by our intelligence.  The process is self
reinforcing because its consequences, such as improved communication
methods and increased wealth and population, allow more experiments
and faster cross fertilization among different lines of inquiry.  Many
of its techniques have not been available to biological evolution.
The effect is that present day global civilization is developing
capabilities orders of magnitude faster.  Of course biological
evolution has had a massive head start.

	Although cultural evolution has developed methods beyond those
of its genetic counterpart, the overall process is essentially the
same.  It involves trying large numbers of possibilities, selecting
the best ones, and combining successes from different lines of
investigation.  This requires time and other finite resources.

	Finding the optimum assembly of particular type of component
which achieves a desired function usually requires examination of a
number of possibilities exponential in the number of components in the
solution.  With fixed resources this implies a design time rising
exponentially with complexity.  Alternatively the resources can be
used in stages, to design subassemblies, which are then combined into
larger units, and so on, until the desired result is achieved.  This
can be much faster since the effort rises exponentially with the
incremental size of each stage and linearly with the number of stages,
with an additional small term, for overall planning, exponential in
the number of stages.  The resulting construct will probably use more
of the basic component and be less efficient than an optimal design.

	Biological evolution is affected by these considerations as
much as our technology. If a device is so difficult to design that our
technology cannot build it, then neither should we expect to find it
in the biological world. Conversely, if we find some naturally evolved
thing, we can rest assured that designing an equally good one one is
not an impossibly difficult task.  Presumably there is a way of using
the physics of the universe to construct entities functionally
equivalent to human beings, but vastly smaller and more efficient.
Terrestrial evolution has not had the time or space to develop such
things.  But by building within the sequence atoms, amino acids,
proteins, cells, organs, animal (often concurrently), it produced a
technological civilization out of inanimate matter in only two billion


	The existence of several examples of intelligence designed
under these constraints should give us great confidence that we can
achieve the same in a time span similar to that of other technological

	The situation is analogous to the history of heavier than air
flight, where birds, bats and insects clearly demonstrated the
possibility before our culture mastered it. Flight without adequate
power to weight ratio is heartbreakingly difficult (vis. Langley's
steam powered aircraft or current attempts at man powered flight),
whereas with enough power (on good authority!) a shingle will fly.
Refinement of the aerodynamics of lift and turbulence is most
effectively tackled after some experience with suboptimal aeroplanes.
After the initial successes our culture was able to far surpass
biological flight in a few decades.

	Although there are known brute force solutions to most AI
problems, current machinery makes their implementation impractical.
Instead we are forced to expend our human resources trying to find
computationally less intensive answers, even where there is no
evidence that they exist.  This is honorable scientific endeavor, but,
like trying to design optimal airplanes from first principles, a slow
way to get the job done.

	With more processing power, competing presently impractical
schemes could be compared by experiment, with the outcomes often
suggesting incremental or revolutionary improvements. Computationally
expensive highly optimizing compilers would permit efficient code
generation at less human cost.  The expanded abilities of existing
systems such as Macsyma, the symbolic mathematics system from MIT,
which can be used as a desk calculator for doing algebra and
trigonometry as well as arithmetic, along with new experimental
results, would accelerate theoretical development. Gains made this way
would improve the very systems being used, causing more speedup.  The
intermediate results would be inefficient kludges busily contributing
to their own improvement.  The end result is systems as efficient and
clever as any designed by more theoretical approaches, but sooner,
because more of the labor has been done by machines.

	With enough power anything will fly. The next section examines
how much is needed.

Section 2:  Measuring Processing Power

	During the past ten years Digital Equipment Corporation's
PDP-10 has become the standard computer for AI and related research,
partly because it was designed with advanced techniques, such as time
sharing and unusual computer languages, in mind.

	When first introduced, the PDP-10 was considered a large
machine.  By today's standards it is medium size.  The PDP-10 dealt
with in this section is the KA model, the standard until very
recently. The very largest scientific computers, heavily used in
physics, chemistry and other fields, made by companies such as Control
Data Corp. and IBM, are about 100 times the speed of the KA.  When it
was new a KA system cost about half a million dollars.  Large
computers sell for around 10 million.

Low level vision:

	The visual system of a few animals has been studied in some
detail, especially the layers of the optic nerve near the retina.  The
neurons comprising these structures are used efficiently to compute
local operations like high pass filtering and edge, curvature,
orientation and motion detection.

	Assuming the visual cortex (and possibly the optic nerve
itself) is as computationally intensive as the retina, successive
layers producing increasingly abstracted representations, we can
estimate the total capability.  There are a million separate fibers in
a cross section of the human optic nerve. The thickness of the optical
cortex is a thousand times the depth occupied by the neurons which
apply a single simple operation. The eye is capable of processing
images at the rate of ten per second (flicker at higher frequencies is
detected by special operators).  This means that the human visual
system evaluates 10,000 million pixel simple operators each second.

      14|					          sperm whale *
    10  |
	|						    human *
      13|						  chimp *
    10  |
	|				       human vision *
    10  |
      11|		  proposed NASA wind +
    10  |                  tunnel simulator
    10  |
      9 |			     Cray II +
    10  |                               bee *
	|	  CDC 7600, IBM 360/195 +
      8 |
    10  |                      KL-10 +
 CP	|
      7 |                      KA-10 +
bit 10  |
---	|
sec   6 |
    10  |		    slug * 
      5 |
    10  |                 * sponge (alive)
      4 |
    10  |
      3 |
    10  |       + pocket calculator
            2    3    4    5    6    7    8    9    10   11   12   13   14   15   16
          10   10   10   10   10   10   10   10   10   10   10   10   10   10   10

                                  CE (bits)

FIGURE:  Compute Power and Energy of various devices. Scales are logarithmic.
The Cray machine is an extremely fast and large scientific computer.
The NASA simulator would probably be a general purpose computer
100 times as powerful as the biggest existing machines. It has not
been designed yet.

	A tightly hand coded simple operator, like high pass filtering
by subtraction of a local average, applied to a million pixel picture
takes at least 160 seconds when executed on a PDP-10, not counting
timesharing.  Since the computer can evaluate only one at a time, the
effective rate is 1/160 million pixel simple operators per second.

	Thus a hand coded PDP-10 falls short of being the equal of the
human visual system by a speed factor of 1.6 million.

	It may not be necessary to apply every operator to every
portion of every picture, and a general purpose computer, being more
versatile than the optic nerve, can take advantage of this.  I grant
an order of magnitude for this effect, reducing the optic nerve to a
mere 100,000 PDP-10 equivalents.

	The size of this factor is related to having chosen to
implement our algorithms in machine language. If we had opted to
disassemble a number of PDP-10's and reconfigure the components to do
the computation, far fewer (perhaps only one!) would have been
required.  On the other hand if we had run our algorithms in
interpreted Lisp, 10 to 100 times as many would be needed.  The
tradeoff is that the design time varies inversely with the execution
efficiency.  A good Lisp program to compute a given function is much
easier to produce than an efficient machine language program, or an
equivalent piece of hardware.

	As a practical example of the kind of problem this gap poses
in current research, consider my work. The task is to construct a
program which can drive a vehicle sensing the world with a TV camera
through terrain cluttered with obstacles, avoiding the obstacles and
getting to a desired place. The programs are written efficiently and
in the spirit of computing only as much as is actually required to
track objects from one image to the next, and to judge their distance
from the parallax caused by vehicle motion. In spite of this it takes
a large program several minutes of computing to process each frame.
Differences in performance caused by changes in the program can often
be determined only after tens of images have been processed, implying
a run time of hours. This greatly limits experimentation. Also, many
ideas on how to significantly improve performance cannot reasonably be
tried because they slow down the computation by another factor of 10
or 100, increasing typical runs to days and weeks! Many (such as
taking pictures at much smaller intervals than the current two foot
motions) require very little additional programming, and would be
almost certain to improve things.

Entropy measurement:

	Is there a quantitative way in which the processing power of a
system, independent of its detailed nature, can be measured?  A
feature of things which compute massively is that they change state in
complicated and unexpected ways.  The reason for believing that, say,
a stationary rock does little computing is its high predictability.
By this criterion the amount of computing done by a device is in the
mind of the beholder. A machine with a digital display which flashed
1, 2, 3, 4 etc., at fixed intervals would seem highly predictable to
an adult of our culture, but might be justifiably considered to be
doing an interesting, nontrivial and informative computation by a
young child.  Information theory provides a measure for this idea. If
a system is in a given state and can change to one of a number of next
states with equal probability, the information in the transition,
which I will call the Compute Energy (CE), is given by

             CE  =  Sum(i=1,N) -p[i] log p[i]

where N is the number of next states.  The measure is in binary
digits, bits.  If we consider the system in the long run, considering
all the states it might ever eventually be in, then this measure
expresses the total potential variety of the system.

	A machine which can accomplish a given thing faster is more
powerful than a slower one.  A measure for Compute Power is obtained
by dividing each term in the above sum by the time required for a
transition.  Thus: CP = Sum(i=1,N) -p[i] log p[i])/t[i] The units are

	Slightly more complicated formulas, which give lower values,
apply if the transitions probabilities and times are not all equal.

	These measures are highly analogous to the energy and power
capacities of a battery.  Some properties follow:

They are linear,  i.e.  the compute power  and energy of a  system of
two or  more independent machines is the  sum of the individual power
and energies;

Speeding up a machine by a factor  of n increases the CP by the  same

A completely  predictable system has a  CP and CE of  zero;

A machine  with a  high short  term CP,  which can  reach a  moderate
number of states in a short time, can yet have a low CE, if the total
number of states attainable in the long run is not high.

	If the probabilities and times of all the transitions are
equal the measures simplify to

               CE   =   log2 N

               CP   =   log2 N / t

For N distinct outcomes and equal transition times the maximum CE and
CP is obtained if all the probabilities of occurrence are 1/N, so the
above simplifications  represent an upper  bound in  cases where  the
probabilities are variable or cannot be determined.

A representative computer:

	For the KA-PDP10, considering one instruction time, we have
(roughly) that in one microsecond this machine is able to execute one
of 2^5 different instructions, involving one of 2^4 accumulators and
one of 2^18 memory locations, most of these combinations resulting in
distinct next sates.  This corresponds to a CP of

   log2(2^5 x 2^4 x 2^18) bit / 10^-6 sec  =  27 x 10^6 bit/sec

	This is an extreme upper bound, and represents the most
efficient code possible.  It cannot be maintained for very long,
because many different sequences of instructions have the same
outcome.  Very often, for instance, the order in which a number of
instructions is executed does not matter. Assuming for the moment that
this is always the case, we can calculate the effect on the CP,
measured over a second.  The raw power says there are

                      2^(27 x 10^6)

distinct possible states after a second, or one million instruction
times.  If all permutations result in the same outcome, this must be
reduced by a factor of 1000000!. The log of a quotient is the
difference of the logs, so the adjusted CP is

     27 x 10^6 -  log2(10^6!)  =  27 x 10^6 -  18.5 x 10^6 
                               =  8.5 x 10^6 bit/sec

	The CP is also limited by the total compute energy.  If we
ignore external devices, this is simply the total amount of memory,
about 36x2^18 = 9.4x10^6 bits.  The PDP 10 could execute at its
maximum effectiveness for 9.4/8.5 = 1.1 seconds before reaching a
state which could have been arrived at more quickly another way.

	Large external storage devices such as disks and tapes can
increase the compute energy indefinitely, because they are a channel
through which independently obtained energy may be injected.  On the
other hand, they have only a moderate effect on the power.

	A disk channel connected to our KA-10 has a data rate of
6.4x10^6 bits/sec.  If run at full speed, constantly stuffing new
information into memory, it would add slightly less than that to the
power, because it uses memory cycles the processor might want.  If, as
is usually the case, the disk is used for both reading and writing,
the improvement is reduced by a factor of two.  Further reductions
occur if the use is intermittent.  The overall impact is less than
10^6 bits/sec, about 10% of the power of the raw processor.

	Overall, the processing power of a typical major AI center
computer is at most 10^7 bits/sec.  Time sharing reduces this to about
10^6 b/s per user.  Programming in a moderately efficient high level
language costs another factor of 10, and running under an interpreter
may result in a per user power of a mere 10,000 bits/sec, if the
source code is efficient.  These reductions can be explained in the
light of the CP measure by noting that the a compiler or interpreter
causes the executed code to be far more predictable than optimal code,
eg.  by producing stereotyped sequences of instructions for primitive
high level constructs.

A typical nervous system:

	We now consider the processing ability of animal nervous
systems, using humans as an example.  Since the data is even more
scanty than what we assumed about the PDP-10, some not unassailable
assumptions need to be made.  The first is that the processing power
resides in the neurons and their interconnections, and not in more
compact nucleic acid or other chemical encodings.  There is no
currently widely accepted evidence for the latter, while neural
mechanisms for memory and learning are being slowly revealed.  A
second is that the neurons are used reasonably efficiently, as
detailed analysis of small nervous systems and small parts of large
ones reveals (and common sense applied to evolution suggests).
Thirdly, that neurons are fairly simple, and their state can be
represented by a binary variable, "firing" or "not firing", which can
change about once per millisecond.  Finally we assume that human
nervous systems contain about 40 billion neurons.

	Considering the space of all possible interconnections of
these 40 billion (treating this as the search space available to
natural evolution in its unwitting attempt to produce intelligence, in
the same sense that the space of all possible programs is available to
someone trying to create intelligence in a computer), we note that
there is no particular reason why every neuron should not be able to
change state every millisecond.  The number of combinations thus
reachable from a given state is 2^(40x10^9) the binary log of which
gives CE = 40x10^9.  This leads to a compute power of

   CP  =  40 x 10^9 bit / 10^-3 sec   =    40 x 10^12 bit/sec

which is about a million times the maximum power of the KA-10.

	Keep in mind that much of this difference is due to the high
level of interpretation in the KA, compared to what we assumed for the
nervous system.  Rewiring its gates or transistors for each new task
would greatly increase the CP, but also the programming time.  If the
processor is made of 100,000 devices which can change state in 100 ns,
the potential CP available through reconfiguration is 10^5 bits/10^-7
sec = 10^12 b/s.  The CE would be unaffected.  If automatic design and
fabrication methods result in small quantity integrated circuit
manufacture becoming less expensive and more widely practiced, my
calculations may prove overly pessimistic.

Thermodynamic efficiency:

	Thermodynamics and information theory provide us with a
minimum amount of energy per bit of information generated at a given
background temperature (the energy required to out shout the thermal
noise).  This is approximately the Boltzmann constant,

    1.38 x 10^-16 erg/(deg variable)   =   0.96 x 10^-16 erg/(deg bit)

	The reduction is due to the theoretical fact that a
"variable", also known as a degree of freedom, is worth log2e bits,
about 1.44 bits. This measure allows us to estimate the overall energy
efficiency of computing engines.  For instance, we determined the
computing power of the brain, which operates at 300 degrees K, to be
40x10^12 bits/sec.  This corresponds to a physical power of

    40 x 10^12 bit/sec x 300 deg x 0.96 x 10^-16 erg/(deg bit)
            =  1.15 erg/sec  =  1.15 x 10^-7 watt

	The brain runs on approximately 40 watts, so we conclude that
it is 10^-8 times as efficient as the physical limits allow.

	Doing the same calculation for the KA10, again at 300 deg, we
see that a CP of 8.5x10^6 bit/sec is worth 2.44x10^-14 watts.  Since
this machine needs 10 kilowatts the efficiency is only 10^-18.
Conceivably a ten watt, but otherwise equivalent, KA10 could be
designed today, if care were taken to use the best logic for the
required speed in every assembly.  The efficiency would then still be
only 10^-15.

	As noted previously, there is a large cost inherent in the
organization of a general purpose computer. We might investigate the
computing efficiency of the logic gates of which it is constructed (as
was, in fact, done with the brain measure).  A standard TTL gate can
change state in about 10ns, and consumes 10^-3 watt.  The switching
speed corresponds to a CP of 10^8 bit/sec, or a physical power of
2.87x10^-13 watt.  So the efficiency is 10^-10, only one hundred times
worse than a vertebrate neuron.

	The newer semiconductor logic families are even better. C-MOS
is twice as efficient as TTL, and Integrated Injection Logic is 100
times better, putting it on a par with neurons.

	Experimental superconducting Josephson junction logic operates
at 4 deg K, switches in 10^-11 sec, and uses 10^-7 watts per gate.
This implies a physical compute power of 3.5x10^-12 watt, and an
efficiency of 7x10^-5, 1000 times better than neurons.  At room
temperature it requires a refrigerator that consumes 100 times as much
energy as the logic, to pump the waste heat uphill from 4 degrees to
300.  Since the background temperature of the universe is about 4
degreees, this can probably eventually be done away with.

	It is thus likely that there exist ways of interconnecting
gates made with known techniques which would result in behavior
effectively equivalent to that of human nervous systems.  Using a
million I2L gates, or 10 thousand Josephson junction gates, and a
trillion bits of slower bulk storage, all running at full speed, such
assemblies would consume as little as, or less than, the power needed
to operate a brain of the conventional type.

	Past performance indicates that the amount of human and
electronic compute power available is inadequate to design such an
assembly within the next few years.  The problem is much reduced if
the components used are suitable large subassemblies.  Statements of
good high level computer languages are the most effective such
modularizations yet discovered, and are probably the quickest route to
human equivalence, if the necessary raw processing power can be
accessed through them.  This section has indicated that a million
times the power of typical existing machines is required.  The next
suggests this should be available at reasonable cost in about ten

Section 3:  The Growth of Processing Power

	The references show that the price per electronic switch has
declined by a steady factor of ten every five years, if speed and
reliability gains are included.  Occasionally there is a more
precipitous drop, when a price threshold which opens a mass market is
reached.  This makes for high incentives, stiff price competition and
mass production economies.  It happened in the early sixties with
transistor radios, and is going on now for pocket calculators and
digital wristwatches.  It is begining for microcomputers, as these are
incorporated into consumer products such as stoves, washing machines,
televisions and sewing machines, and soon cars. During such periods
the price can plummet by a factor of 100 in a five year period.  Since
the range of application for cheap processors is larger than for
radios and calculators, the explosion will be more pronounced.

	The pace of these gains is in no danger of slackening in the
forseeable future.  In the next decade the current period may seem to
be merely the flat portion of an exponential rise. On the immediate
horizon are the new semiconductor techniques, I2L, and super fast
D-MOS, CCD for large sensors and fast bulk memory, and magnetic
bubbles for mass storage.  The new 16K RAM designs use a folded
(thicker) cell structure to reduce the area required per bit, which
can be interpreted as the first step towards 3 dimensional
integration, which could vastly increase the density of circuitry.
The use of V-MOS, an IC technique that vertically stacks the elements
of a MOS transistor is expanding.  In the same direction, electron
beam and X-ray lithography will permit smaller circuit elements.

	In the longer run we have ultra fast and efficient Josephson
junction logic, of which small IC's exist in an IBM lab, optical
communication techniques, currently being incorporated into
intermediate distance telephone links, and other things now just
gleams in the eye of some fledgling physicist or engineer.

             Transistor price
  .0001c   .01      1c     $1     $100
+---+---+---+---+---+---+---+---+---+---+   Year
|                                       |
+-                                  O  -+   1950
|                                  #    |   1951   $100 transistor
|                                  #    |   1952   transistor hearing aid
|                                 #     |   1953
|                                 #     |   1954
+-                               #     -+   1955   transistor radios
|                                #      |   1956
|                               O       |   1957   $10 transistor
|                              #        |   1958
|                            #          |   1959
+-                          O          -+   1960   $1 transistor
|                         #             |   1961
|                        #              |   1962   $100,000 small computer (IBM 1620)
|                       #               |   1963
|                      O                |   1964
+-                     #               -+   1965   $0.08 transistor (IC)
|                      #                |   1966   $1000 4 func calculator
|                     #                 |   1967   $6000 scientific calc.
|                    #                  |   1968   $10,000 small computer (PDP 8)
|                   #                   |   1969
+-                  O                  -+   1970   $200 4 func calculator
|                  #                    |   1971
|                 #                     |   1972   1K RAMS (1 c/bit)
|               #                       |   1973
|              #                        |   1974   $1000 small computer (PDP 11)
+-            O                        -+   1975   4K RAMS (.1 c/bit)
|            #                          |   1976   $5 4 func calc (.05 c/trans)
|                                       |   1977
|                                       |   1978
|                                       |   1979

	My favorite fantasies include the "electronics" of super-dense
matter, either made of muonic atoms, where the electrons are replaced
by more massive negative particles or of atoms constructed of magnetic
monopoles which (if they exist) are very massive and affect each other
more strongly than electric charges.  The electronics and chemistry of
such matter, where the "electron" orbitals are extremely close to the
nucleus, would be more energetic, and circuitry built of it should be
astronomically faster and smaller, and probably hotter.  Mechanically
it should exhibit higher strength to weight ratios. The critical
superconducting transition field strengths and temperatures would be
higher.  For monopoles there is the possibility of combination
magnetic electric circuitry which can contain, among many other
goodies, DC transformers, where an electric current induces a monopole
current at right angles to it, which in turn induces another electric
current.  One might also imagine quantum DC transformers, matter
composed of a chainlike mesh of alternating orbiting electric and
magnetic charges.

	I interpret these things to mean that the cost of computing
will fall by a factor of 100 during the next 5 years, as a consequence
of the processor explosion, and by the usual factor of 10 in the 5
years after that.  As an approximation to what is available today,
note that in large quantities an LSI-11 sells for under /500.  This
provides a moderately fast 16 bit processor with 4K of memory.
Another /500 could buy an additional 32K of memory, if we bought in
quantity. The result would be a respectable machine, somewhat less
powerful than the KA-10, for /1000.  At the crude level of
approximation employed in the previous section, a million machines of
this type should permit human equivalence. A million dollars would
provide a thousand of them today (a much better buy, in terms of raw
processing power, than a million dollar large processor).  In ten
years a million dollars should provide the equivalent of a million
such machines, in the form of a smaller number of faster processors,
putting human equivalence within reach.

	We now have the problem of what to do with this roomful of
isolated computers.  The next section describes what seems to be the
best known solution to the problem of interconnecting a large number
of processors with maximum generality, at reasonable cost.

Section 4:  Mega Processing

	The highest bandwidth and most flexible way for a number of
computers to interact is by shared memory.  Systems of the size
considered here would require a large, but not unreasonable, address
space of 100 billion words (40 bits of address). They would also
demand memories with a thousand to a million ports.  Although a
variant of the method below could be used to construct such monsters,
they would cost much more than the processors they served.

	Alternatively, we might consider how a mass of the usual kind
of machine, each with a moderate amount of local memory, could
communicate through their bandwidth limited IO busses.  Define a
message period as the minimum interval in which a processor may
transmit and receive one message.  Assuming that messages are
addressed to particular destination machines, an ideal interconnection
system would allow every processor to issue a message during each
message period, and deliver the highest priority message for each
processor to it, providing notices of success or failure to the
originators.  The delay between transmission and reception of a
message would be uniform and short, and the cost of the network would
be reasonable (on the order of the cost of the processors served).

	It so happens that this specification can be met.  The
following construction is a design due to K.E. Batcher [Batcher],
extended considerably to permit pipelining of messages and higher
speed. It has the properties:

Every processor may send a fairly long message to any other processor
about every quarter of a microsecond.  The messages from all the
processors are emitted in synchronized waves. A wave takes one
microsecond to filter through the interconnection net, causing there
to be four waves in the net at one time.

Each  message includes  a priority  number introduced by  the sending

The network delivers to each processor the message with the highest
priority addressed to it, if any.  The processor sending each
delivered message receives an acknowledgement, the processors whose
messages were blocked by higher priority ones receive notices of

The amount of network logic per processor is small, and grows as the
square of the log of the number of processors. This low growth rate
ensures that even in a system of a million processors the cost of the
interconnection is no greater than the cost of the processors.

Log^2 sorting net construction:

	Batcher's major contribution is a method for constructing nets
that can sort N numbers in log(N)^2 time, using only Nxlog^2 N
primitive two element sorters.  Two nets plus a little additional
circuitry can accomplish the message routing task.

	The nets are constructed by wiring together primitive sorting
elements. A primitive sorting element has two inputs, accepting a pair
of numbers, and two outputs on which the numbers appear, sorted by
their magnitude. If the numbers are represented as serial bit streams,
high order bit first, such an element can be built with about 20

	Central to the construction is a method for making a merger
for two ordered lists of 2N numbers (call it a 2N-merger) from two
N-mergers and 2N additional primitive elements.  A primitive sorting
element is itself a 1-merger. A special case application of the method
is shown in fig. 1. Batcher's paper contains proofs of its

	An N-merger can thus be constructed from Nxlog2 N primitive
elements.  A sorter is made of a cascade of ever larger mergers,
forming length two ordered lists from individual inputs with
1-mergers, combining these pairwise into length 4 lists with
2-mergers, and so on, until the entire sorted list of N numbers comes
out of a final N/2-merger. The number of primitive elements involved
is Nxlog2Nx(log2N+1)/4.

         A1 --------------+        +-------------------------
                    +-----+        |               +---+
         A2 ------+ |+----+     3  +---------------+   +-----
                  | ||    |        |  +------------+   +-----
         A3 ------+-+|    +--+  M  +--+----------+ +---+
                  |  |       |  E  |  |          |
         A4 -----+|  |    +--+  R  +--+---------+|
                 ||  |+---+     G  |  |         || +---+
         A5 -----++--+|+--+     E  +--+--------+|+-+   +-----
  I              ||   ||+-+     R  |  |+-------++--+   +-----     O
         A6 ----+||   ||| |        +--++------+||  +---+
  N             |||   ||| +--------+  ||      |||                 U
                |||   |||             ||      |||
  P             |||   |||             ||      |||  +---+          T
                |||   |||             ||      ||+--+   +-----
  U             |||   |||             ||+-----++---+   +-----     P
                |||   |||             |||     ||   +---+
  T      B1 ----+++---+|| +--------+  |||     ||                  U
                ||+----++-+        +--+||     ||
  S             |+-----++-+        |   ||     ||   +---+          T
         B2 ---++------++-+     3  +---+|     |+---+   +-----
               |       || |        |    |+----+----+   +-----
         B3 ---+-------+| +--+  M  +----+|    |    +---+
               |        |    |  E  |     |    |
         B4 --+|        | +--+  R  +-----+    |    +---+
              |+--------+-+     G  |          +----+   +-----
         B5 --+---------+ |     E  +---------------+   +-----
              +-----------+     R  |               +---+
         B6 --------------+        +-------------------------

FIG 1:   An illustration of Batcher's  odd-even merger  construction,
     producing a  6-merger from  two  3-mergers  and  five  primitive
     elements. The first and last outputs undergo less delay than the
     intermediate  ones,  which  is  undesirable.  The  delays can be
     equalized by passing the first and last outputs through an extra
     primitive element.  Batcher provides  an alternate construction,
     which he  calls a  "bitonic sorter"  which merges,  has constant
     delay,  and  uses  the  same  number  of  elements  as the above
     construction with the extra element added.

Communication scheme organization:

              +---------+   +-------+   +---+   +----------+
          1 -+|         |--+|       |--+|   |--+|          |-+ 1
              |         |   |       |   |   |   |          |
          2 -+|         |--+|       |--+|   |--+|          |-+ 2
Processor     |    N    |   |       |   |   |   |          |      Processor
          3 -+|         |--+|       |--+|   |--+|          |-+ 3
Outgoing      |  Input  |   |       |   |   |   |          |     Acknowledge-
              |         |   |       |   |   |   |          |         ment
 Message  .   |  Sorter | . |       | . |   | . |          | .
          .   |         | . |       | . |   | . |          | .      Input
  Ports   .   | (Desti- | . |       | . |   | . |    2N    | .
              |  nation |   |       |   |   |   |          |        Ports
              |  field) |   |       |   | E |   |   Input  |
              |         |   |    N  |   | x |   |          |
          N -+|         |--+|       |--+| c |--+|  Sorter  |-+ N
              +---------+   +-+  M  |   | h |   |          |
                              |  e  |   | a |   | (Source  |   --------------
                            +-+  r  |   | n |   |   field) |
                       1 --+|    g  |--+| g |--+|          |-+ 1
                            |    e  |   | e |   |          |
                       2 --+|    r  |--+| r |--+|          |-+ 2
                            |       |   |   |   |          |      Processor
              Dummy    3 --+|       |--+|   |--+|          |-+ 3
                            |       |   |   |   |          |      Incoming
             Message        |       |   |   |   |          |
                          . |       | . |   | . |          | .     Message
            Injection     . |       | . |   | . |          | .
                          . |       | . |   | . |          | .      Ports
                            |       |   |   |   |          |
                            |       |   |   |   |          |
                       N --+|       |--+|   |--+|          |-+ N
                            +-------+   +---+   +----------+

FIG 2:    Block diagram of the multiprocessor interconnection scheme.
The cost per processor, and the delay in the net, grows as the
square of the log of N, the number of processors.

|         Data         | Source Address  | 0 | Priority | Destination Ad  |

FIG 3:   Processor message format. High order bit is on the right.

|        Unused        | Position Number | 1 |   Zero   | Position Number |

FIG 4:   Dummy message format.

	The interconnection scheme is diagrammed in Figs 2, 3 and 4.
Each processor is assigned a number, its "address", as indicated.  In
the sorters and the merger the smaller numbers come out towards the
top of the diagram.

	Messages are serial bit streams, and consist of a destination
processor address, a priority number (invented by the originating
computer), a one bit "dummy" flag field (set to 0 for actual
messages), the address of the source processor (i.e.  a return
address), and the data to be communicated.  A low priority number
implies high priority. Zero is the highest priority.

	The net is assumed to run at 100% duty cycle, with the
processors emitting successive synchronized waves of messages.  Every
processor emits a message every message interval.  The following
discussion examines a single message wave.

	The first sorting net orders the messages by destination
address, and within a given destination by priority number.  Thus the
upper inputs of the merger receive a list of messages, grouped by
destination, with the highest priority message to each processor
heading its group.

	The lower inputs of the merger receive N dummy messages,
exactly one for each destination processor.  The priority field is the
highest possible (i.e. zero), the dummy flag is 1, the source address
is the same as the destination, and the data portion is unused.

	Merging these dummies with the sorted list of real messages
results in a list still grouped by destination, with each group headed
by a dummy, by virtue of its high priority, followed immediately by
the highest priority real message, if any, for the destination.

	This list is fed to the exchange network, which examines
adjacent pairs of messages (considering overlapping pairs), and
exchanges the data portions of a pair if the first member happens to
be a dummy to a given address, and the second is a real message to the
same address (i.e.  it is the highest priority real message to that

	The sorting network following the exchanger sorts the messages
by the field begining with the dummy flag, which acts as the high
order bit, followed by the source address. Since there were N real
messages, one from each processor, and N dummies, also nominally one
from each processor, and since real messages are sorted ahead of dummy
messages due to the high order bit, (i.e. the dummy flag) being 0, the
second sorter restores the messages into the same order in which they
were introduced.

	Each processor has two input ports, one labeled
"acknowledgement", and the other "incoming message".  The
acknowledgement input of processor i is connected to output i of the
second sorter. The incoming message input is connected to output N+i
(i.e. the i'th output of the lower half).

	In the absence of the exchange network, the i'th processor
would receive its own message back on its acknowledgement input, and
the i'th dummy message on the incoming message input.

	Because of the exchanger, however, if the message that
processor i had sent happened to be the highest priority message to
the requested destination, then the data portion of the message on the
acknowledgement input would be that of the dummy it had been swapped
with (signaling success).  Also, if any messages had been addressed to
processor i, the data portion of the highest priority one would arrive
on the incoming message port, in place of the dummy message.

	Thus a processor receives the highest priority message
addressed to it on its incoming message port, or a dummy if nobody
wanted to talk to it. It receives a dummy on its acknowledgement port
if its message has gotten through, or the message back if it hasn't,
due to the existence of a higher priority message to the same

	Actually the serial nature of the sorter causes the
destination and priority field to be lost in the source address sorter
(it tails after the previous wave of messages). In the case of
messages that fail to get delivered, this means that the originating
processor must remember to whom it sent the message (about four
message times ago in a typical design, due to the latency of the net),
if it wants to try again.  This is probably undesirable.  Also,
delivered messages contain no indication of who sent them, having had
their source address field exchanged with that of a dummy, unless the
source address is included in the data field.

	These shortcomings can be overcome if the exchanger shuffles
the destination address, source address and priority fields in the
manner suggested by Figs 5, 6 and 7.  Such shuffling can be
accomplished with an amount of storage at each exchanger position
equal to the number of bits in the destination and priority fields.


|        Unused        | Destination Ad  | 1 |   Zero   | Destination Ad  |

|         Data         | Source Address  | 0 | Priority | Destination Ad  |


| Priority | Source Address  |          Data        | Destination Ad  | 1 |

|   Zero   | Destination Ad  |         Unused       | Source Address  | 0 |

FIG 5:   Rearrangements effected by the exchanger in an exchanged pair.


|         Data         | Source Address  | 0 | Priority | Destination Ad  |


| Priority | Destination Ad  |          Data        | Source Address  | 0 |

FIG 6:   Rearrangements in an unsuccessful message.


|        Unused        | Destination Ad  | 1 |   Zero   | Destination Ad  |


|   Zero   | Destination Ad  |         Unused       | Destination Ad  | 1 |

FIG 7:   Rearrangements in an isolated dummy message.

Package counts:

	If the numbers to be sorted are sent into such a net as serial
bit streams, high order bit first, then a primitive sorting element
has two output wires labelled "smaller" and "larger", two input wires
and a reset and a clock line, and works as follows:

	Just before the first bit time the element is reset.  Bits
then stream into the input terminals, and simply stream out of the
output terminals until the first bit position in which the two inputs
differ comes along.  At that instant, the input with the 0 is
connected to the "smaller" output, and the one with the 1 is connected
to "larger". This interconnection is latched by the element and all
subsequent bits stream from the inputs to the outputs on the basis of
it, until the next reset.

	Such a unit can be built with approximately 20 gates, and
introduces one bit time of delay. Careful design should permit an ECL
version with a 100 or 200 MHz bit rate. These could be packed into 48
(say) pin LSI packages, 8 independent elements per package (the clock
and reset lines are common), 16 per package, configured into 4
2-mergers, 24 per package, arranged into two 4-mergers, and 32 per
package containing a single 8-merger.

	Larger mergers can be constructed out of these using an
extension of the bitonic sorter strategies given in [Batcher],
resulting in total package counts (and partial eight merger, four, two
and single element package counts) shown in Fig.  8 (to re-iterate, a
merger size of N refers to one which takes two lists of size N and
produces a list of size 2N).

|  Merger size  |                        Package counts              |
|               |       Total     Eights    Fours    Twos      Ones  |
|               |              |         |        |        |         |
|          1    |         1/8  |         |        |        |    1/8  |
|          2    |         1/4  |         |        |   1/4  |         |
|          4    |         1/2  |         |   1/2  |        |         |
|          8    |           1  |      1  |        |        |         |
|         16    |           4  |      2  |        |        |      2  |
|         32    |           8  |      4  |        |     4  |         |
|         64    |          16  |      8  |     8  |        |         |
|        128    |          32  |     32  |        |        |         |
|        256    |          96  |     64  |        |        |     32  |
|        512    |         192  |    128  |        |    64  |         |
|      1,024    |         384  |    256  |   128  |        |         |
|      2,048    |         768  |    768  |        |        |         |
|      4,096    |       2,048  |   1536  |        |        |    512  |
|      8,192    |       4,096  |   3072  |        |  1024  |         |
|     16,384    |       8,192  |   6144  |  2048  |        |         |
|     32,768    |      16,384  |  16384  |        |        |         |
|     65,536    |      40,960  |  32768  |        |        |   8192  |
|    131,072    |      81,920  |  65536  |        | 16384  |         |
|    262,144    |     163,840  | 131072  | 32768  |        |         |
|    524,288    |     327,680  | 327680  |        |        |         |
|  1,048,576    |     786,432  | 655360  |        |        | 131072  |
|               |              |         |        |        |         |

FIG 8:   Package counts for mergers

	These counts can now be used to calculate the number of
packages required to build sorters of various sizes:

|  Sorter size |                            Package counts                 |
|              |           Total     Eights    Fours       Twos      Ones  |
|              |                 |          |         |         |          |
|         2    |             1/8 |          |         |         |     1/8  |
|         4    |             1/2 |          |         |     1/4 |     1/4  |
|         8    |             3/2 |          |     1/2 |     1/2 |     1/2  |
|        16    |               4 |        1 |       1 |       1 |       1  |
|        32    |              12 |        4 |       2 |       2 |       4  |
|        64    |              32 |       12 |       4 |       8 |       8  |
|       128    |              80 |       32 |      16 |      16 |      16  |
|       256    |             192 |       96 |      32 |      32 |      32  |
|       512    |             480 |      256 |      64 |      64 |      96  |
|     1,024    |           1,152 |      640 |     128 |     192 |     192  |
|     2,048    |           2,688 |     1536 |     384 |     384 |     384  |
|     4,096    |           6,144 |     3840 |     768 |     768 |     768  |
|     8,192    |          14,336 |     9216 |    1536 |    1536 |    2048  |
|    16,384    |          32,768 |    21504 |    3072 |    4096 |    4096  |
|    32,768    |          73,728 |    49152 |    8192 |    8192 |    8192  |
|    65,536    |         163,840 |   114688 |   16384 |   16384 |   16384  |
|   131,072    |         368,640 |   262144 |   32768 |   32768 |   40960  |
|   262,144    |         819,200 |   589824 |   65536 |   81920 |   81920  |
|   524,288    |       1,802,240 |  1310720 |  163840 |  163840 |  163840  |
| 1,048,576    |       3,932,160 |  2949120 |  327680 |  327680 |  327680  |
| 2,097,152    |       8,650,752 |  6553600 |  655360 |  655360 |  786432  |
|              |                 |          |         |         |          |

FIG 9:   Package counts for sorters

	An interconnection net for N processors involves an N sorter,
an N merger and a 2N sorter. Thus a 128 processor system would require
304 48 pin sorter packages, 2.3 for each processor. A 1024 processor
needs 4224, roughly four per processor.  A size 16,384 system needs 7
for each computer.  A million processor would have 12.75 per machine.

	It is likely that the biggest versions of this system will
require denser packaging.  Remember, though, that a thousand processor
system is sufficient for human equivalence if each machine is big and
fast enough. It will probably not be necessary to build a
megaprocessor in the decade envisioned here.

Speed calculations:

	As outlined, a message consists of a destination address, a
priority, a bit, a source address and a data portion.  The two
addresses must be at least large enough to uniquely specify each
machine. Considering the case of a thousand and a million machine
system, we note that the address lengths are 10 and 20 bits. Let's
make the priority field the same length, leaving room for considerable
flexibility in priority assignment schemes. The message portion should
be fairly long, to permit messages like memory write requests, which
require both a memory address and the data.  Four address lengths,
say.  This gives us a message length 7 addresses long, 70 bits in a
1000 processor, 140 bits in a megaprocessor.

	The full time taken by a message from start of transmission to
completion of reception, in bit times, is the message length, plus the
depth of the net (in primitive elements), plus an address and a
priority time due to the buffering at the exchanger.

	The depth of a 1024 processor net is 110 elements.  This
combines with the message and exchanger delays to result in a transit
time of 110+70+20 = 200 bit times. If the bit rate is 100 MHz then
messages are delivered in two microseconds.  If the bit rate is 200
MHz, the time is 1 microsecond.  The net contains about two full
message waves at any instant, and a message time is 700 nanoseconds
for 100 MHz, or 350 nanoseconds for 200 MHz.

	The same calculation for a megaprocessor reveals a depth of
420 and a total transit time of 420+140+40 = 600 bit times. This
corresponds to 6 and 3 microsecond transits for 100 and 200 MHz data
rates.  Corresponding message times are 1.4 microseconds and 700
nanoseconds.  The net contains slightly more than 3 message waves at a

Possible refinements:

	The transit time of the net can be decreased, at the cost of
increasing the message times a little, by running some of the
primitive sorter elements asynchronously, clocking (and introducing
bit time delays) at larger intervals.  For instance, an 8-merger might
accept a bit time worth of inputs, which would then trickle through
four stages of primitive sorters built without shift register delays
between them. When everything had settled down, the entire merger
would be clocked, and those elements which had decided to latch at
this bit time would do so.  The delay of the unit is one bit time
rather than four, which reduces the 110 or the 420 term in the transit
calculations. The settling time is longer, however, so the bit rate
would be slower. Perhaps a factor of two in transit time can be gained
in this way, at a cost of 1.5 in data rate.

	At an increase in gate count, several levels of asynchronous
primitive sorters can be replaced by an equivalent circuit with fewer
gate delays.  This might enable a given data rate to be maintained
while the transit time was reduced.

	[Van Voorhis] offers some slight reductions in primitive
sorter count, essentially by substituting special case solutions
better than the systematic construction wherever possible in a large
net.  Unfortunately these invariably have an uneven amount of delay
along the various paths, making them almost worthless as synchronous
nets. They may be useful as designs for asynchronous subnets.

	I have generalizations of Batcher's constructions using
primitive elements that are M sorters (as opposed to 2 sorters).
These allow building a merger which combines M sorted lists of size
NxM into a single sorted MxMxN length list, out of M mergers each
capable of merging M size N lists, and some layers of extra M sorter
primitive elements to combine the output of the mergers (these are
analogous to the single layer of 2-sorters following the mergers in
the odd-even merger of Fig 1.  The number of such layers grows
(empirically) roughly as log(M)).  Although the number of gates needed
for a given size sorter grows slowly with M, the number packages used
shrinks.  This is because each package, being a sorter rather than a
merger, contains more logic.  My constructions are complete for M<=8,
and partially complete for general M.

	Well, now we have a room full of not only processors, but a
massive switching system as well. Can it be made to pay for its keep?
The next section examines some programming implications and

Section 5:  Programming Interconnected Processors

	A major feature of this scheme is its flexibility.  It can
function as any of the fixed interconnection patterns of current
experimental multiprocessors, or as a hexagonal mesh, or a 7
dimensional cubic lattice, should that be desired, or the tree
organization being considered in a Stanford proposal. It can simulate
programmed pipeline machines, where numbers stream between units that
combine and transform them.  What is more, it can do all of these
things simultaneously, since messages within one isolated subset of
the processors have no effect on messages in a disjoint subset.  This
permits a very convenient kind of "time" sharing, where individual
users get and return processors as their demands change.

	Such mimicry fails to take advantage of the ability to
reconfigure the interconnection totally every message wave.  There are
many applications, such as searching a tree of possibilities in
reasoning or game playing where this could be used very effectively.
Several existing programming languages can be extended to make this
capability conveniently available to programmers.

A little Lisp:

	Take an existing Lisp and purify it to something closer to its
lambda calculus foundations.  Flush RPLACA and RPLACD and even SETQ (a
pseudo SETQ can be introduced later), and discourage PROGs, which are
inherently sequential and do things that could have been stated more
clearly as recursive functions.  In this system recursive programs are
also more efficient.

	An evaluation is handled as follows.  A free processor (one
not currently doing an evaluation) receives a message demanding the
value of Expr with variable bindings given in ALIST.  Call this the
task [Expr,ALIST].  If the top level of Expr is F(exprA,exprB,exprC),
it generates the tasks [exprA,ALIST], [exprB,ALIST] and [exprC,ALIST],
and passes them to three other free processors. These evaluate them
and sooner or later return valA (the value of exprA), valB and valC to
the original processor.  This processor then refers to the definition
of F, and in particular to the names of the dummy arguments in the
definition.  Suppose these were A, B and C.  It combines the ALIST it
was given with the (name, value) pairs (A, valA), (B, valB), (C, valC)
to form a new ALIST'.  Then it generates the task [bodyF,ALIST'],
where bodyF is the body of the definition of F, which is passed to
another free processor. On receiving the value of this expression, it
passes it back to whoever had given it the original task, and then
declares itself free.

	If Expr had happened to be an atom, the processor would simply
have looked it up in ALIST, and returned its value. If F had been a
predefined system function (such as CAR or CONS) the sequence of
actions would have been whatever the machine code for those functions
(of which each processor would have a copy) specified.

	The parallelism comes from the fact that the arguments in a
multi-argument function can be evaluated simultaneously.  This causes
moderate parallelism when functions are nested, and can cause
explosive parallelism when a function which at some level uses a
multi-argument function, invokes itself recursively (as in a tree
search).  Most non-trivial programs stated as recursive functions do

	The description above implies that the processor waits for the
results of tasks it has farmed out to other machines.  These waits can
be arbitrarily long. Also, the number of tasks spawned can easily
become more than the number of processors.  In that case, presumably,
a processor with a task to farm out would have to wait until a machine
becomes free.  Keeping a processor idle under these conditions is
clearly undesirable.

	If each processor were time shared, pretending to be many
machines, then when the job being run becomes temporarily idle there
may be another to switch to.  When a message pertaining to a given
waiting job arrives, the processor deals with it, and if it provides
the information necessary to allow that job to resume, it is resumed.
This scheme makes the number of processors available seem larger,
perhaps enough to make it possible to acquire a processor for a new
task simply by picking a machine at random and asking if it has a free
job slot.  This works well if the answer is usually yes, (i.e.  if
there reasonably more slots than tasks), and replaces a more
complicated free processor pool method that must be able to deal with
many requests simultaneously.

	Alternatively instead of each processor having its own little
pool of running jobs, the whole Lisp system can maintain a communal
pool, which processors refer to as they become free.  In this
organization a task that must pause is placed into the pool (freeing
the processor that was running it), to be taken up again by a free
processor when its requirements are met.  Moderate processing power is
required to manage the pool.

	The list structure is spread out among all the processors in
the system. Pointers consist of a processor number and address within
processor field.  A machine evaluating an expression whose top level
function is CONS creates the new cell in its own memory. A CAR or CDR
involves sending a message to the processor which owns the cell
involved, and waiting for a reply (thus a CONS is usually cheaper than
a CAR!).

	Since RPLACA and RPLACD are eliminated, circular lists cannot
be created.  This permits garbage collection to be mediated by
reference counts.  A small fixed reference count field (three bits
wide, say) is part of each cell.  A processor doing a CONS sends
messages to the owners of the cells that the new cell will be pointing
to, indicating that their reference count should be incremented. If a
cell getting a message of this type has a reference count that has
already reached maximum (7 in our example), it sends back an indignant
message to the CONS'ing processor saying, effectively, "I'm full, you
can't point to me. But here are my CAR and my CDR, roll your own and
point to that". This not only makes the reference count field size
manageable, but reduces message traffic jams to processors containing
very popular cells.  It does make it mandatory to use EQUAL rather
than EQ when comparing lists.  When a processor no longer requires a
cell to which it had a pointer it sends a message to the owner saying
so.  The reference count of the cell is decreased by one, and if it
reaches zero, it is freed, and similar messages are sent to the cells
pointed to by its CAR and CDR.  This garbage collection process goes
on continuously and independently of the main computations.

	How is the A-list of bindings to be handled?  The canonical
representation, using a simple list of dotted pairs, is very
inefficient, since it forces a sequential search. A hash table, as in
most current Lisp implementations is also undesirable, partly because
it requires an incompatible data type (a relatively large contiguous
block of memory), but primarily because in this parallel system there
are many different contexts (in different branches of the computation)
active at one time.  The entire hash table would need to be duplicated
whenever new bindings are made (i.e.  at every level of evaluation),
since the older context is in use in another branch.

	A nice alternative is to use essentially an A-list, but to
structure it as a balanced tree, with each node containing a binding,
a left subtree for those variables with names lexicographically (say)
less than it, and a right subtree for those greater.  An entry in such
a structure can be found in time proportional to the logarithm of the
number of nodes, and a version of the list with an entry added,
deleted or modified can also be constructed in log time without
affecting the original in any way, by building a new path down to the
element (and sometimes a few side paths, to keep the tree reasonably
balanced), which points to many of the subtrees of the original.
Since this structure can be built of standard list elements, it can be
managed via the normal allocation and garbage collection
mechanisms. Call this structure an A-tree.

	In general applications a balanced tree is capable of being
used in this system more effectively than a simple list, because a
parallel process can get to all the nodes in log time, whereas the
last node takes linear time in a linear list.  This suggests that
programmers wanting maximum efficiency will be coerced into using them
much more frequently than is now the case. This is similar to the
pressure on present day Lisp programmers to use PROGS rather than
recursive functions, because compiled PROGS run faster.

	The [expr,ALIST] task description is conveyed simply as a pair
of pointers, expr to an S expression version of expr, and ALIST to the
head of the balanced A-tree.  The result returned in an evaluation is
likewise a pointer.

	Since the list structure is only built upon, and never
altered, it is possible to speed up the operation of the system by
having individual processors maintain software managed caches of
frequently referred to cells that reside in remote machines. This cuts
down on the message traffic, and generally speeds things up.

A little Algol:

	Although recursive functions provide an excellent way of
exploiting a very parallel architecture, there are other ways. An
algorithmic (iterative) language can be made to serve, if a few
features are present.

	Existing Algol unfortunately has few of these.  Consider the
following pair of statements excerpted from an imaginary program:

	A := 3 ;
	B := 4 ;

These say that first A is set to 3, then B is assigned the value 4.
There is an implied sequentialness. Presumably the programmer knew
that the order of the assignments was unimportant, and that they could
be done simultaneously. The syntax of the language provided no way for
him to indicate this.

	A simple construct which permits information of this type to
be conveyed is the "parallel semicolon", which we will indicate by a
vertical bar "|". Statements seperated by parallel semicolons may be
executed simultaneously. The following is an example of its use:

	A := 3 |
	B := 4 |
	C := 6 ;
	D := A+B+C ;

The first three statements may be executed together, the fourth must
wait for their completion.  It is implied that compound statements,
bracketed by BEGIN END pairs can be similarly separated by parallel as
well as sequential semicolons.

	If compound statements are permitted to return as a value the
value of their last component statement, then the structure

         T :=  BEGIN
               INTEGER A,B,C ;
               A := expressionA |
               B := expressionB |
               C := expressionC ;

               expression involving A, B and C


is equivalent to a lambda expression construct in Lisp. A, B and C are
the dummy arguments, evaluated in parallel, and the fourth expression,
which must wait for A, B and C, is the body of the lambda.  The
constructs involved can be used in many other ways, unlike a real
lambda expression.

	If the Algol also has recursion, then it is possible to obtain
massive parallelism in the same way it was achieved in Lisp, namely by
writing functions which recurse deeply, and invoke themselves a few
times in parallel at each level.

	A minor form of parallelism already automatically available
resides in arithmetic expressions. Different subexpressions of larger
formulas can be evaluated simultaneously, and then combined.  This is
analogous to the evaluation of Lisp expressions.

	More massive concurrency can be obtained if the data types of
Algol are expanded to include a complete set of array operators and
genuine dynamic allocation of arrays. It is this type of data and
operator set that makes APL an extremely powerful language in spite of
an execrable control structure.

	The idea is that operators such as addition and multiplication
work not only on simple variables representing single numbers, but on
whole multi-dimensional arrays. Since arrays are more complex objects
a much larger range of operators is required.  Included are such
things as array restructuring, subscript permutation (generalized
transpose), element shuffling, subarray extraction, generalized cross
and dot products, etc..  In general an operator combines one or more
arrays and produces a value which is a new array, often of a different
size and shape.

	Compounding of such operators substitutes for a large amount
of explicit loop control, and results in substantially more compact
source code.  On conventional computers such code also runs much
faster because most the run time is spent in carefully hand coded
implementations of the basic operators, which do a great deal at each
invokation if the arrays are large.  Typical equivalent Algol programs
execute second rate compiler produced code almost exclusively.  An
operator set of this kind provides the same capabilities as a
hypothetical parallel FOR loop construct, with greater clarity.
Conditionals can be handled by first selecting out subarrays according
to the condition, and then applying the appropriate operations.

	It is desirable in our system for large arrays to be spread
out among many machines, so that array operations can be carried out
in parallel where possible.  An often occurring process is the
application of an arithmetic or other operator to one or more arrays
of a given size, producing a result of the same size.  This could be
made maximally efficient if corresponding elements of the arrays
resided in the same processor.  Assigning a processor to an array
element by means of a hash function of both the array dimensions and
the indices of the element will cause arrays of different size to be
stored in different places, but will put corresponding elements of
arrays of the same size in the same place.

	Now comes the question of how a gaggle of processors managing
a given array gets word that an operation concerning the array is to
be executed. The initiator of the operation is the single processor
running the code requesting it.  The fastest way to propagate such
information is to initiate a "chain letter".  The originating
processor can consider the array as two equal (or almost equal)
pieces, and send a message to a member of each piece. Each recipient
then divides the piece of which it is a member into two, and sends a
message to a representative of each of those smaller subsets, etc.
When the subset size becomes one, the operation is performed.

	It may be more efficient to store larger pieces of arrays in
individual processors.  This adds a slight serial component to the run
times, but saves message handling time.

	How are programs communicated from processor to processor, as
the number of executing instruction streams grows?  It would be an
extravagant waste of memory to duplicate the entire source program in
every processor (besides, it might not fit).  A software caching
scheme is called for.  When a processor initiates a subtask, it
obtains a free processor and passes to it a moderate amount of the
code needed for the task.  When the new processor runs out, it sends a
message back to the first machine for more.  This machine trys to
obtain it from its own cache, and if it too is out, sends a message
further up in the hierarchy, to the machine which had initiated the
task which it is running, and so on until the requested code is
obtained. At top level the entire runnable program is assumed to be
available from some sort of file, maintained by the combined storage
of a number of processors.

A little operating systems:

	Assume that most input/output devices are connected to
individual processors, and that, to maximize the bandwidth, each of
these processors has only one, or a most a very small number, of
devices associated with it. Included among these are communications
lines to user terminals, so that each console talks directly to a
dedicated processor with sorting net access to the entire system.

	If we expect to support more than one user on such a system,
it will be necessary to have some type of protection scheme, to
prevent one user's processes from accidentally interfering with

	If each processor contains a monitor, then message sending can
handled by system calls. The monitor can then check for validity,
testing if the requested destination is within the set of processors
assigned to the user.  This monitor, which every machine assigned to a
user must run, can be flexible enough to time share the machine it
runs on, to provide multiple simulated processors.

	Controlling the state of the individual machines' monitors is
the task of a global system monitor, operated by several machines,
which maintains a pool of free processors, and parcels them out on
request, and which also handles file system requests (bulk storage
would be connected to a handful of the processors), and allocation of
other devices.

	Processes belonging to a single user will be initiated by a
particular master machine, probably the one connected to his console.
This master can create a tree of subprocesses, possibly
intercommunicating, running on different machines.  It should be
possible, for example, to have one subset configured as an array
processor for efficient implementation of low level vision operations,
while another is running an Algol/APL for the less structured analytic
geometry needed to interpret the image, and yet a third is operating a
Lisp system doing abstract reasoning about the scene.  Many existing
systems permit this kind of organization, but they are hampered by
having an absurdly small amount of computing power.

	How is a system of this kind initialized, and how does one
abort an out of control process taking place in part of it without
affecting the rest?  A possibility is to have an "executive" class of
messages (perhaps signalled by a particular bit in the data portion),
which user jobs are not permitted to emit.  Reception of such messages
might cause resetting of the processor, loading of memory locations
within it, and starting execution at a requested locations.  A single
externally controllable machine can be used to get things going,
fairly quickly if it emits a self replicating "chain letter".

	Now consider reliability.  The system can obviously tolerate
any reasonable number of inoperable processors, by simply declaring
them unavailable for use. Failures in the communication net are much
more serious, and under most situations will require the system to
stop operating normally. It is possible to write diagnostic programs
which can track down defective comparator elements or broken data
wires. If something should happen to the clock signals to a given
level it would be necessary to wheel out an oscilloscope.  If
reliability were a critical issue it would be possible to include a
duplicate net, to run things the while other was being debugged.


	The software outlines are obviously only partly baked. This is
mostly due to the limited amount of thought and work that has gone
into them. On the other hand, it is my belief that even well thought
out designs at this point will look naive in the light of experience
with a working version of such a machine.  Many of the fundamental
decisions depend on things difficult to estimate, including the number
of processors, communication speed compared with processor speed,
memory size, and most importantly the kinds, sizes and mix of
operations that people will tend to run (these will surely differ from
what is being done now, the limitations being so different).

	This is not to say that more thought isn't called for.  The
nature of the memory protection, interrupt scheme, word size,
instruction set, I/O structure, etc.  of the individual machines
should be tailored to permit convenient implementation of the
individual processor operating systems, and typical message types.
What these are can best be determined by trying to write some
monitors, interpreters, compilers and array crunchers.

	Simulation of the system on a conventional machine is next to
useless.  Since the total amount of memory envisaged is larger than
conventional machines carry, and since a simulation will have to jump
around from simulated core image to simulated core image, in order to
keep a realistic message synchrony, there would be an enormous amount
of disk accessing. The slowness of this, combined with the inherent
speed difference, would allow only the most trivial (and therefore
unrepresentative) things to be tried, and not many of these.

	Construction of a moderate size version is a better use of
manpower.  Undoubtedly there will be mistakes made in the first (or
first few) models, which will become apparent after a bit of

	The flexibility offered should make this design much more
attractive to a large class of programmers than current essentially
special purpose architectures such as ILLIAC IV. Making it out of
existing processors with proven machine languages will help too.  I,
for one, can hardly wait to start programming even a flawed version of
a machine that can process and generate real time television with
programs written in Algol, and simultaneously jump over tall game and
proof trees in a single bound.

Section 6:  The Future

	Suppose my projections are correct, and the hardware
requirements for human equivalence are available in 10 years for about
the current price of a medium large computer.  Suppose further that
software development keeps pace (and it should be increasingly easy,
because big computers are great programming aids), and machines able
to think as well as humans begin to appear in 10 years.  If the cost
of electronics continues to plummet beyond then (and the existence of
increasingly cheaper and better robot labor, in addition to scientific
and engineering improvements, should ensure that), an additional 15
years should bring human equivalence into the pocket calculator price
range.  I also assume that sensors and effectors for such devices will
be able to match human performance, since even today's technology is
able to supercede it in many areas. What then?

	Well, even if these machines are only as clever as human
beings, they will have enormous advantages over humans in competitive
situations.  Their production and upkeep is vastly less expensive, so
more of them can be put to work with given resources.  They can be
easily specialized for given tasks, and be programmed to work
tirelessly. Because we are not constrained to use any particular type
of component in building them, versions can be designed to work
efficiently in environments in which sustaining humans is very
expensive, such as deep in the oceans, and more importantly in
boundless outer space. Most significantly of all, they can be put to
work as programmers and engineers, with the task of optimizing the
software and hardware which make them what they are. The successive
generations of machines produced this way will be increasingly smarter
and more cost effective. Of course, there is no reason to assume that
human equivalence represents any sort of upper bound.  When pocket
calculators can out-think humans, what will a really big computer be
like?  Regardless of how benevolent these machines are made, homo
sapiens will simply be outclassed.

	Societies and economies are as surely subject to evolutionary
pressures as biological organisms.  Failing social systems eventually
wither and die, and are replaced by more successful competitors, and
those that can sustain the most rapid expansion dominate sooner or

	I expect the human race to expand into space in the near
future, and O'Neill's habitats for people will be part of this.  But
as soon as machines are able to match human performance, the economics
against human colonies become very persuasive.  Just as it was much
cheaper to send Pioneer to Jupiter and Viking to Mars than men to the
Moon, so it will be cheaper to build orbiting power stations with
robot rather than human labor.  A machine can be designed to live in
free space and love it, drinking in unattenuated sunlight and
tolerating hard radiation.  And instead of expensive pressurized,
gravitied, decorated human colonies, the machines could be put to work
converting lunar material into orbiting automatic factories. The
doubling time for a machine society of this type would be much shorter
than for human habitats, and the productive capability would expand
correspondingly faster.

	The first societies in space will be composed of co-operating
humans and machines, but as the capabilities of the self-improving
machine component grow, the human portion will function more and more
as a parasitic drag.  Communities with a higher ratio of machines to
people will be able to expand faster, and will become the bulk of the
intelligent activity in the solar system.  In the long run the sheer
physical inability of humans to keep up with these rapidly evolving
progeny of our minds will ensure that the ratio of people to machines
approaches zero, and that a direct descendant of our culture, but not
our genes, inherits the universe.

	This may not be as bad as it sounds, since the machine society
can, and for its own benefit probably should, take along with it
everything we consider important, up to and including the information
in our minds and genes.  Real live human beings, and a whole human
community, could then be reconstituted if an appropriate circumstance
ever arose.  Since biology has committed us to personal death anyway,
with whatever immortality we can hope for residing only in our
children and our culture, shouldn't we be happy to see that culture
become as capable as possible?  In fact, attempting to hobble its
growth is an almost certain recipe for long term suicide. The universe
is one random event after another.  Sooner or later an unstoppable
virus deadly to humans will evolve, or a major asteroid will collide
with the earth, or the sun will go nova, or we will be invaded from
the stars by a culture that didn't try to slow down its own evolution,
or any number of other things. The bigger, more diverse and competent
our offspring are, the more capable they will be of detecting and
dealing with the problems that arise.

	For the egomaniacs among us there is another possibility.  The
main problem in keeping up with the machines is that we evolve by the
old DNA + nucleated cell + sex + personal death method, while our
machines evolve by the new improved intelligence + language + culture
+ science + technology technique, which is so very much faster that
our biology seems to stand still in comparison. If we could somehow
transfer our evolution to the faster form, we should be able hold our

	At first thought genetic engineering might seem to be the key.
Successive generations of human beings could be designed by
engineering mathematics and on the basis of computer simulations just
like airplanes and computers are now. But this is just like building
robots out of proteins instead of metal and plastic.  Being made of
protein is in fact a major drawback. That stuff is stable only in a
narrow temperature and pressure range, sensitive to all sorts of high
energy disturbances, and so on, and rules out many construction
techniques and components.  Is there some way to retain our essential
humanness, at least temporarily until we think of something better,
while transferring ourselves to a more malleable form?

	Imagine the following process (meant to suggest a variety of
ways such a thing could be done).  You are in an operating theater,
and a brain surgeon (probably a machine) is in attendance.  On a table
next to yours is a potentially human equivalent computer, dormant now
for lack of a program to run.  Your skull, but not your brain, is
under the influence of a local anaesthetic.  You are fully conscious.
Your brain case is opened, and the surgeon peers inside.  Its
attention is directed at a small clump of about 100 neurons somewhere
near the surface. It examines, non-destructively, the three
dimensional structure and chemical makeup of that clump with neutron
tomography, phased array radio encephalography, etc., and derives all
the relevant parameters.  It then writes a program which can simulate
the behavior of the clump as a whole, and starts it running on a small
portion of the computer next to you. It then carefully runs very fine
wires from the computer to the edges of the neuron assembly, to
provide the simulation with the same inputs the neurons are getting.
You and it both check out the accuracy of the simulation. After you
are satisfied, it carefully inserts tiny relays between the edges of
the clump and the rest of the brain, and runs another set of wires
from the relays to the computer. Initially these simply transmit the
clump's signals through to the brain, but on command they can connect
the simulation instead.  A button which activates the relays when
pressed is placed in your hand.  You press it, release it and press it
again.  There should be no difference. As soon as you are satisfied,
the simulation connection is established firmly, and the now
unconnected clump of neurons is removed.

	The process is repeated over and over for adjoining clumps,
until the entire brain has been dealt with.  Occasionally several
clump simulations are combined into a single equivalent but more
efficient program.  Though you have not lost consciousness, or even
your train of thought, your mind has been removed from the brain and
transferred to the machine.  A final step is the disconnection of the
your old sensory and motor system, to be replaced by higher quality
ones in your new home.  This last part is no different than the
installation of functioning artificial arms, legs, pacemakers,
kidneys, ears and hearts and eyes being done or contemplated now.

	Advantages become apparent as soon as the process is complete.
Somewhere in your machine is a control labelled "speed".  It was
initially set to "slow", to enable the simulations to remain
synchronized with the rest of your old brain, but now the setting is
changed to "fast". You can communicate, react and think at a thousand
times your former rate. But this is only a minor first step.

	Major possibilities stem from the fact that the machine has a
port which enables the changing program that is you to be read out,
non-destructively, and also permits new portions of program to be read
in.  This allows you to conveniently examine, modify, improve and
extend yourself in ways currently completely out of the question.  Or,
your entire program can be copied into a similar machine, resulting in
two thinking, feeling versions of you. Or a thousand, if you want. And
your mind can be moved to computers better suited for given
environments, or simply technologically improved, far more
conveniently than the difficult first transfer.  The program can also
be copied to a dormant information storage medium, such as magnetic
tape.  In case the machine you inhabit is fatally clobbered, a copy of
this kind can be read into an unprogrammed computer, resulting in
another you, minus the memories accumulated since the copy was made.
By making frequent copies, the concept of personal death could be made
virtually meaningless. Another plus is that since the essence of you
is an information packet, it can be sent over information
channels. Your program can be read out, radioed to the moon, say, and
infused there into a waiting computer. This is travel at the speed of
light.  The copy that is left behind could be shut down until the trip
is over, at which time the program representing you with lunar
experiences is radioed back, and transfused into the old body.  But
what if the original were not shut down during the trip? There would
then be two separate versions of you, with different memories for the
trip interval.

	When the organization of the programs making up humans is
adequately understood, it should become possible to merge two sets of
memories. To avoid confusion, they would be carefully labelled as to
which had happened where, just as our current memories are usually
labelled with the time of the events they record.  This technique
opens another vast realm of possibilities.  Merging should be possible
not only between two versions of the same individual but also between
different persons.  And there is no particular reason why mergings
cannot be selective, involving some of the other person's memories,
and not others.  This is a very superior form of communication, in
which memories, skills, attitudes and personalities can be rapidly and
effectively shared.

	The amount of memory storage an individual will typically
carry will certainly be greater than humans make do with today, but
the growth of knowledge will insure the impracticability of everybody
lugging around all the world's knowledge.  This implies that
individuals will have to pick and choose what their minds contain at
any one time. There will often be knowledge and skills available from
others superior to a person's own.  The incentive to substitute those
talents for native ones will be overwhelming most of the time.  This
will result in a gradual erosion of individuality, and formation of an
incredibly potent community mind.

	A pleasant possibility presents itself.  Why should the mind
transferral process be limited to human beings? Earthly life contains
several species with brains as large as or larger than man's, from
dolphins, our cephalic equals, to elephants and the large whales, and
perhaps giant squid, with brains up to twenty times as big.  If the
technical problem of translation can be overcome, and it may be quite
difficult for squid, in particular, since their minds are evolved
entirely independently, then our culture could be fused with theirs,
with each component used according to its value. In fact, a synthesis
of all terrestrial life is desirable with the simpler organisms
contributing only the information in their DNA, if that's all they
have.  In this way all the knowledge generated by terrestrial
biological and cultural evolution will be retained in the data banks,
available whenever needed. This is a far more secure form of storage
than the present one, where genes and ideas are lost as species become
extinct and individuals die.

	We now have a picture of a super-consciousness, the synthesis
of terrestrial life, and perhaps jovian and martian life as well,
constantly improving and extending itself, spreading outwards from the
solar system, converting non-life into mind. There may be other such
bubbles expanding from elsewhere.  What happens when we meet another?
Well, it's presumptuous of me to say at this tender stage of the
evolution, but fusion of us with them is certainly a possibility,
requiring only a translation scheme between the data representations.
This process, possibly occuring now elsewhere, might convert the
entire universe into an extended thinking entity.


Section 1:  The Natural History of Intelligence

   JERISON, Harry J.,                            RIOPELLE, A.J., ed.
   "Paleoneurology and the Evolution of Mind",   "Animal Problem Solving",
   Scientific American, Vol. 234, No. 1,         Penguin Books, 1967.
   January 1976, 90-101.
                                              GOODRICH, Edwin S.,
   BITTERMAN, M. E.,                          "Studies on the Structure and Development of Vertebrates",
   "The Evolution of Intelligence",           Dover Publications Inc., New York, 1958.
   Scientific American, Vol. 212, No. 1,
   January 1965, 92-100.                      BUCHSBAUM, Ralph,
                                              "Animals without Backbones",
   GRIFFIN, Donald R., ed.                    The University of Chicago Press, 1948.
   "Animal Engineering",
   W.H. Freeman and Company,                  FARAGO, Peter, and Lagnado, John,
   San Francisco, June 1974.                  "Life in Action"
                                              Alfred A. Knopf, New York, 1972.
   BURIAN, Z. and Spinar, Z.V.,
   "Life Before Man",                         BONNER, John Tyler,
   American Heritage Press, 1972.             "Cells and Societies",
                                              Princeton University Press, Princeton, 1955.

   COUSTEAU, Jacques-Yves and Diole, Philippe,    BOYCOTT, Brian B.,
   "Octopus and Squid",                           "Learning in the Octopus",
   Doubleday & Company, Garden City, N.Y., 1973.  Scientific American, Vol. 212, No. 3,
   (also a televised film of the same name)       March 1965, 42-50.

   "The Octopus",                                 LANE, Frank W.,
   a televised film, Time-Life films.             "The Kingdom of the Octopus",
                                                  Worlds of Science Book, Pyramid Publications Inc.
                                                  October, 1962.
   BAKKER, Robert T.,                             STETTNER, Laurence Jay and Matyniak, Kenneth A.
   "Dinosaur Renaissance",                        "The Brain of Birds",
   Scientific American, Vol. 232, No. 4,          Scientific American, Vol. 218, No. 6,
   April 1975.                                    June 1968, 64-76.

   LILLY, John. C.,                               STENUIT, Robert,
   "The Mind of the Dolphin" &                    "The Dolphin, Cousin to Man",
   "Man and Dolphin",                             Bantam Books, New York, 1972.
   Doubleday and Company, New York, 1967.

   COUSTEAU, Jacques-Yves and Diole, Philippe,    "Whales and Dolphins",
   "The Whale", Doubleday & Company,              A BBC produced film shown
   Garden City, N.Y., 1972.                       in the NOVA television series

   FICHTELIUS, Karl-Erik and Sjolander, Sverre,   McINTYRE, Joan, ed.
   "Smarter than Man?",                           "Mind in the Waters",
   Ballantine Books, New York, 1974.              Charles Scribner's Sons, San Francisco, 1974.

   RENSCH, Bernhard,
   "The Intelligence of Elephants",
   Scientific American,
   February 1957, 44.

   "The First Words of Washoe",                 PFEIFFER, John,
   Televised film shown in the NOVA series      "The Human Brain", Pyramid, N.Y. 1962.

   LeGROS CLARK, W.E.,                          SAGAN, Carl,
   "History of the Primates",			"The Dragons of Eden",
   The University of Chicago Press,             Random House, N.Y. 1977.
   Chicago 1966.

Section 2:  Measuring Processing Power

   WILLOWS, A.O.D.,                        HUBEL, David H.,
   "Giant Brain Cells in Mollusks",        "The Visual Cortex of the Brain",
   Scientific American, Vol. 224, No. 2,   Scientific American,
   February 1971, 69-75.                   November 1963, 54-62.

   KANDEL, Eric R.,                        WILLIAMS, Peter L., Warwick, Roger
   "Nerve Cells and Behavior",             "Functional Neuroanatomy of Man",
   Scientific American, Vol. 223, No. 1,   W.B. Saunders Company, Philadelphia, 1975.
   July 1970, 57-70.
                                           "PDP-10 Reference Handbook",
   AGRANOFF, Bernard W.,                   Digital Equipment Corporation, Maynard Mass., 1971.
   "Memory and Protein Synthesis",
   Scientific American, Vol. 216, No. 6,   TRIBUS, Myron and McIrvine, Edward C.,
   June 1967, 115-122.                     "Energy and Information",
                                           Scientific American, Vol. 224, No. 3,
   KENNEDY, Donald,                        September 1971, 179-188.
   "Small Systems of Nerve Cells",
   Scientific American, Vol. 216, No. 5,   GLASSTONE, Samuel, Lewis, David,
   May 1967, 44-52.                        "Elements of Physical Chemistry",
                                           D. Van Nostrand Co. Inc., New York, 1960.
   BAKER, Peter F.,
   "The Nerve Axon",                       MILLER, Richard T.,
   Scientific American, Vol. 214, No. 3,   "Super Switch", 284, in Science Year annual 1975,
   March 1966, 74-82.                      Field Enterprises Educational Corp., 1975.

					   LANDAUER, Rolf,
					   "Fundamental Limitations in the Computational
					   Process", IBM, Yorktown Heights N.Y. 1976

Section 3:  The Growth of Processing Power

   McWHORTER, Eugene W.                         TIEN, P. K.
   "The Small Electronic Calculator",           "Integrated Optics",
   Scientific American, Vol. 234, No. 3,        Scientific American, Vol. 230, No. 4,
   March 1976, 88-98.                           April 1974, 28-35.

   HODGES, David A.,                            HITTINGER, William C.
   "Trends  in  Computer  Hardware Technology", Metal-Oxide-Semiconductor Technology",
   Computer Design, Vol. 15, No. 2,             Scientific American, Vol. 229, No. 2,
   February 1976, 77-85.                        August 1973, 48-57.

   SCRUPSKI, Stephen E., et al.,                BOBECK, Andrew H. and Scovil, H. E. D.
   "Technology Update",                         "Magnetic Bubbles",
   Electronics, Vol. 48, No. 21, McGraw-Hill,   Scientific American, Vol. 224, No. 6,
   October 16, 1975, 74-127.                    June 1971, 78-90.

   VACROUX, Andre G.                            HEATH, F. G.
   "Microcomputers",                            "Large-Scale Integration in Electronics",
   Scientific American, Vol. 232, No. 5,        Scientific American, Vol. 222, No. 3,
   May 1975, 32-40.                             February 1970, 22-31.

   TURN, Rien                                   RAJCHMAN, Jan A.
   "Computers in the 1980's",                   "Integrated Computer Memories",
   Columbia University Press,                   Scientific American, Vol. 217, No. 1,
   Rand Corporation, 1974.                      July 1967, 18-31.

   HITTINGER, William C. and Sparks, Morgan
   Scientific American, Vol. 213, No. 5,
   November 1965, 56-70.

Section 4:  Mega Processing

   BATCHER, K.E.                                    KNUTH, D.E.
   "Sorting Networks and their Applications",       "Sorting and Searching",
   1968 Spring Joint Computer Conf. Proceedings     The Art of Computer Programming, Vol. 3
   April 1968, 307-314.                             Addison-Wesley, 1973.

   VAN VOORHIS, David C.		            BAKER, Henry G. and Hewitt, Carl
   "An Economical Construction for Sorting "        The Incremental Garbage Collection
   Networks", 1974 NCC Proceedings                  of Processes", AI*PL proceedings,
   April 1974, 921-927.   		            Sigplan Notices, Aug 1977, 55-59.