Foresight Update 4
page 2
A publication of the Foresight Institute
 Manufacturing
with Nanotechnology
Viewpoint by Jerry Fass
Nanotechnology-based manufacturing techniques should yield
great increases in productivity and wealth. Improvements in two
techniques in particular will greatly decrease resource
requirements: the incorporation of voids, and wearproofing.
Voids
Whenever possible, objects can incorporate carefully shaped
voids to save cost and mass. Generally, voids are more useful for
large systems or those under low loads. They can range in size
from arbitrarily large down to a fraction of a nanometer wide;
the upper limit is set by device size, the lower by the scale of
atoms. For structures under light compressive loads, voids formed
in fractal patterns can yield maximum efficiency.
Today's bulk manufacturing can produce large, irregular voids at
reasonable cost, as in foam rubber and insulation. Nanomachines
should be able to produce uniform voids down to one atom across,
thereby cutting the mass, cost, energy, and time needed for
production. The biggest gains will be for objects with structural
loads in pure compression or mixed compression and tension;
fortunately this includes the majority of objects we use, such as
furniture, doors, most walls, and appliances. The void fraction
of these could be very high, perhaps 99% or more. Highly loaded
objects (e.g., engine parts) will benefit less, and highly loaded
tension systems (e.g., cables and pressure vessels) will benefit
little.
Incorporating voids, combined with scavenging heavy
pre-nanotechnology parts, will allow us to recycle old systems
into multiple new ones without new material resources, reducing
the need for mining and refining.
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Van der Waals cylinder-and-sleeve bearing
© K. Eric Drexler |
Wearproofing
Wear limits the lives of mechanical and structural systems,
which often attain a reasonable lifetime only by having worn-out
parts replaced. (An annoying example is the modern automobile).
Wear is cumulative and can seem exponential, as worn parts
increase wear on other parts. The aim of wearproofing is to head
off the wear process, with the increasingly ambitious goals of
longer-lasting parts, zero-wear parts, and finally self-repair.
Using nanotechnology, we can expect improvements in:
- Tribology--the science of why and how objects wear.
Nanomachines should greatly aid collection of data needed
to further advance this field.
- Hardness--surfaces of harder materials wear more slowly.
Surfaces of ceramic or diamond, or perhaps the new form
of carbon, "C8", reported by Soviet
researchers, will last much longer. Nanomachines could
apply such coatings, and powerful computers may allow us
to design new ones.
- Friction control--lubricants and bearings. All three
means of lubrication--solids (Teflon, graphite), liquids
(oil), and gases (air)--are improving rapidly thanks to
improved data and computer analysis. Contact bearings
will benefit from ever-tighter tolerances and more rugged
materials. A revolutionary non-contact bearing, the
electromagnetic bearing, repulsively or attractively
levitates moving parts in a magnetic or electric field,
with zero friction; wear can often be practically
eliminated by having such a bearing gently
"land" a part after it stops moving. The new
high-temperature superconductors will make such bearings
smaller, stronger, and more precise; since they are often
computer-controlled, nanocomputers will be helpful too.
And of course, Drexler's suggested atomically-precise
sigma bond and van der Waals bearings will not wear in
any conventional sense.
Wear on tools can be reduced even for bulk processes by
forming parts using non-contact methods such as explosives,
lasers, electron beams, plasma torches, water jets, and
electromagnetic forming instead of drill bits, grinding wheels,
and the like. There may be uses where such macro-tools forever
outperform nano-tools: perhaps in well drilling, tunneling, and
excavating.
Synergies between the above techniques can be expected; for
example, making an object with voids but covering the surface
with diamond. And besides saving energy in manufacturing, we can
expect to do so in transport as well: objects will last longer
and so need to be delivered less often, they will weigh less when
they do need transport, and with nanoproduction systems--quiet,
small, flexible, and clean--manufacturing on-site becomes a
possibility.
But eventually, we can expect self-repair to solve the wear
problem.
Jerry Fass is a part-time science writer based in Wisconsin.
He also coordinates FI's journal monitoring project.
How
Many Bytes in Human Memory?
[Note: A related article on the computational
limits of the human brain is available in Update 6.]
Today it is commonplace to compare the human brain to a computer,
and the human mind to a program running on that computer. Once
seen as just a poetic metaphor, this viewpoint is now supported
by most philosophers of human consciousness and most researchers
in artificial intelligence. If we take this view literally, then
just as we can ask how many megabytes of RAM a PC has we should
be able to ask how many megabytes (or gigabytes, or terabytes, or
whatever) of memory the human brain has.
Several approximations to this number have already appeared in
the literature based on 'hardware' considerations (though in the
case of the human brain perhaps the term 'wetware' is more
appropriate). One estimate of 1020 bits is actually an
early estimate (by Von Neumann
in The Computer and the Brain) of all the neural
impulses conducted in the brain during a lifetime. This number is
almost certainly larger than the true answer. Another method is
to estimate the total number of synapses, and then presume that
each synapse can hold a few bits. Estimates of the number of
synapses have been made in the range from 1013 to 1015,
with corresponding estimates of memory capacity.
A fundamental problem with these approaches is that they rely on
rather poor estimates of the raw hardware in the system. The
brain is highly redundant and not well understood: the mere fact
that a great mass of synapses exists does not imply that they are
in fact all contributing to memory capacity. This makes the work
of Thomas
K. Landauer very interesting, for he has entirely avoided
this hardware guessing game by measuring the actual functional
capacity of human memory directly (See "How Much Do People
Remember? Some Estimates of the Quantity of Learned Information
in Long-term Memory", in Cognitive Science 10,
477-493, 1986).
Landauer works at Bell Communications Research--closely
affiliated with Bell Labs where the modern study of information
theory was begun by C.
E. Shannon to analyze the information carrying capacity of
telephone lines (a subject of great interest to a telephone
company). Landauer naturally used these tools by viewing human
memory as a novel 'telephone line' that carries information from
the past to the future. The capacity of this 'telephone line' can
be determined by measuring the information that goes in and the
information that comes out, and then applying the great power of
modern information theory.
Landauer reviewed and quantitatively analyzed experiments by
himself and others in which people were asked to read text, look
at pictures, and hear words, short passages of music, sentences,
and nonsense syllables. After delays ranging from minutes to days
the subjects were tested to determine how much they had retained.
The tests were quite sensitive--they did not merely ask 'What do
you remember?' but often used true/false or multiple choice
questions, in which even a vague memory of the material would
allow selection of the correct choice. Often, the differential
abilities of a group that had been exposed to the material and
another group that had not been exposed to the material were
used. The difference in the scores between the two groups was
used to estimate the amount actually remembered (to control for
the number of correct answers an intelligent human could guess
without ever having seen the material). Because experiments by
many different experimenters were summarized and analyzed, the
results of the analysis are fairly robust; they are insensitive
to fine details or specific conditions of one or another
experiment. Finally, the amount remembered was divided by the
time allotted to memorization to determine the number of bits
remembered per second.
The remarkable result of this work was that human beings
remembered very nearly two bits per second under all the
experimental conditions. Visual, verbal, musical, or
whatever--two bits per second. Continued over a lifetime, this
rate of memorization would produce somewhat over 109
bits, or a few hundred megabytes.
While this estimate is probably only accurate to within an order
of magnitude, Landauer says "We need answers at this level
of accuracy to think about such questions as: What sort of
storage and retrieval capacities will computers need to mimic
human performance? What sort of physical unit should we expect to
constitute the elements of information storage in the brain:
molecular parts, synaptic junctions, whole cells, or
cell-circuits? What kinds of coding and storage methods are
reasonable to postulate for the neural support of human
capabilities? In modeling or mimicking human intelligence, what
size of memory and what efficiencies of use should we imagine we
are copying? How much would a robot need to know to match a
person?"
What is interesting about Landauer's estimate is its small size.
Perhaps more interesting is the trend--from Von Neumann's early
and very high estimate, to the high estimates based on rough
synapse counts, to a better supported and more modest estimate
based on information theoretic considerations. While Landauer
doesn't measure everything (he did not measure, for example, the
bit rate in learning to ride a bicycle, nor does his estimate
even consider the size of 'working memory') his estimate of
memory capacity suggests that the capabilities of the human brain
are more approachable than we had thought. While this might come
as a blow to our egos, it suggests that we could build a device
with the skills and abilities of a human being with little more
hardware than we now have--if only we knew the correct way to
organize that hardware.
This article is
also available on Dr. Merkle's Web site.
Dr. Merkle's interests range from neurophysiology to
computer security. He recently spoke on nanotechnology and
biostasis at the Life Against Death Conference in San Francisco.
The
Road to Nanomachine Design
by Thomas Donaldson
One of the contributions by K. Eric Drexler to nanotechnology
was his success with estimating the behavior of nanomachines by
using simple mechanical calculations. Ultimately, however, these
exploratory engineering calculations remain approximations only.
Serious nanomachine design will require much more. Almost
certainly it needs very powerful computers able to carry out
dynamic calculations on large molecules. These calculations need
lots of computer power. Specialized chemical workstations with
prices in the range of $200,000 already exist.
To speed nanotechnology along what we really want is lower price
computers, ideally costing no more than a Mac II. There is a wide
open road to just such a computer. Technology for chemical design
workstations costing about $40,000 exists right now, for the
trouble of assembling a system from standard boards
(unfortunately not done yet). The same parts will cost far less
in a few years (so Popular NanoMechanics may start
publication soon).
The technology depends on the Transputer, a chip specially
designed for parallel processing. Computer System Architects
sells IBM PC boards with 16 Transputer chips and 16 megabytes of
memory for $28,000.
Chemical
Design Ltd, a British company, already sells a chemical
design workstation, the MITIE 1000, which can contain as many as
36 independent Transputers. The smallest MITIE 1000 sells for
$170,000. The MITIE calculates as much as 72 times faster than a
VAX 8600, analyzing the conformation and dynamics of large
molecules at supercomputer speeds.
The MITIE contains a microVAX as a host machine; the remaining
modules run on the VAX. Chemical Design has about 250 customers
around the world, including Glaxo, Rhone-Poulenc, Fisons, Dupont,
American Cyanamid, Merck, and Hoffmann-LaRoche. The program ChemX
contains specific modules for building and displaying the
molecule (ChemCore), modelling molecules (fitting, analyzing the
conformation: ChemModel), designing proteins (ChemProtein), and
carrying out calculations to find minimum energy states (ChemQM).
There are also library modules to maintain a large database
(ChemLib: the recommended size of hard disk for a single-user
system is 70 Megabytes).
Any molecular machine we design must be chemically stable in the
environment for which we design it. We must therefore make sure
not just that the molecule would be stable if isolated from all
other chemicals but also that the system will withstand likely
chemical attack. Molecules will try to attain minimum energy
states, and their excited states are also of interest. To resolve
all of these issues will require very fast chemical design
software. Ultimately software for nanomachine design will do much
more, but even existing chemical design software running on an
affordable workstation puts us far ahead.
What about the software? Unfortunately, porting software to a
parallel computer usually requires a total rewrite of any modules
in which you expect to use parallelism. Porting should be a
cooperative effort between someone versed in parallel computing
and someone versed in chemical design software. When someone will
get a chemical design show on the road, porting software to a MAC
II-transputer system, isn't clear to me. My own expertise lies in
parallel computing. Anyone interested can reach me through Foresight
Update.
Dr. Thomas Donaldson currently writes software for a
transputer machine for the FEM market. He pioneered the idea of
artificial enzyme systems as an approach to cell repair.
From Foresight Update 4, originally
published 15 October 1988.
Foresight thanks Dave Kilbridge for converting Update 4 to
html for this web page.
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