From NanoWerk: Rice scientists argue nanotubes can be treated like polymers
Wade Adams, Matteo Pasquali, Micah Green and Natnael Behabtu at Rice pick up that thread in their discussion of what we know — or think we know — about carbon nanotubes.
Their review in the journal Polymer (”Nanotubes as polymers”) makes the argument that single-walled carbon nanotubes (SWNTs) are polymers and should be treated as such.
The point is to remind the nano community that decades of research into polymers can be applied to their work and hasten the development of novel materials for all kinds of uses.
“In one of his earliest lectures about nanotubes, (late Rice professor and Nobel laureate) Rick Smalley said they’re the ultimate polymer molecule, with every atom in its place, just like a polymer chain would have,” said Adams, director of the Richard E. Smalley Institute for Nanoscale Science and Technology, who focused on polymers for many years at the Air Force Research Laboratory. “I really didn’t believe him initially.”
…
Adams said the goal is to change the mindset of a generation of scientists who have come to think of carbon nanotubes as special when, in a very important way, they’re not special at all.
“We were seeing a lot of literature out there about nanocomposites that were totally ignorant of the 15-, 20- and 30-year-old literature that explored a lot of these areas and had already clarified some of the things you need to think about if you’re going to use these materials,” he said.
Rice University scientists today unveiled a method for the industrial-scale processing of pure carbon-nanotube fibers that could lead to revolutionary advances in materials science, power distribution and nanoelectronics. The result of a nine-year program, the method builds upon tried-and-true processes that chemical firms have used for decades to produce plastics. The research is available online in the journal Nature Nanotechnology.
“Plastics is a $300 billion U.S. industry because of the massive throughput that’s possible with fluid processing,” said Rice’s Matteo Pasquali, a paper co-author and professor in chemical and biomolecular engineering and in chemistry. “The reason grocery stores use plastic bags instead of paper and the reason polyester shirts are cheaper than cotton is that polymers can be melted or dissolved and processed as fluids by the train-car load. Processing nanotubes as fluids opens up all of the fluid-processing technology that has been developed for polymers.”
This is something of a halfway-point to true industrial-scale nanotube use, though, since nanotubes still can’t be made with purity of the types that have the kinds of properties (e.g. conductivity) one would like:
But a final breakthrough remains before the true potential of high-quality carbon nanotubes can be realized. That’s because HiPco and all other methods of making high-end, “single-walled” nanotubes generate a hodgepodge of nanotubes with different diameters, lengths and molecular structures. Scientists worldwide are scrambling to find a process that will generate just one kind of nanotube in bulk, like the best-conducting metallic varieties, for instance.
“One good thing about the process that we have right now is that if anybody could give us one gram of pure metallic nanotubes, we could give them one gram of fiber within a few days,” Pasquali said.
(me:) It seems to me that one obvious way to ameliorate the impact of the AI/robotics revolution in the economic world, then, is simple: build robots whose cognitive architectures are enough different from humans that their relative skillfullness at various tasks will differ from ours. Then, even after they are actually better at everything than we are, the law of comparative advantage will still hold.
Boom, friendliness problem solved. Build robots with different cognitive architectures than us, and they will be forced to keep us around, due to Ricardo’s law of comparative advantage. Sounds wildly naive to me.
All I can say is thanks for noticing I’ve solved the most important problem of the 21st century with a single paragraph! I’m confidently expecting my Nobel Peace Prize.
But seriously, I would like to argue that the concept of the “friendliness problem” is a dangerous misreading of the real difficulties and problems we will face as a result of the development of artificial intellegence over the next few decades. It seems to me that one could characterize the people working on “Friendly AI” as essentially trying to redo moral philosophy, from scratch, and get it right this time. There’s nothing wrong with this; moral philosophy is a valuable intellectual tradition and worthwhile human activity. But the notion that the whole business, with the addition of the new insight that there can be intelligent machines as well as humans among the class of moral agents, could be solved in any useful sense, just strikes me as silly. Indeed, the new insight makes moral philosophy a lot harder, rather than bringing it any closer to any kind of closure.
Instead let’s look at the kind of problems we’re really going to face. There is not — I guarantee it — going to be any single overarching solution to them; there will be a host of minor things we can do to ameliorate the problems as they arise, and we’ll just have to keep coming up with them as problems arise.
We know what it will be like should we manage to invent and implement a giant, powerful decision-making system that takes over the world. We know because we’ve already done it. Some people have observed this system in action and seem to think that it has a “friendliness problem”:
We’re Governed by Callous Children
…
When I see those in government, both locally and in Washington, spend and tax and come up each day with new ways to spend and tax—health care, cap and trade, etc.—I think: Why aren’t they worried about the impact of what they’re doing? Why do they think America is so strong it can take endless abuse?
I think I know part of the answer. It is that they’ve never seen things go dark. They came of age during the great abundance, circa 1980-2008 (or 1950-2008, take your pick), and they don’t have the habit of worry. They talk about their “concerns”—they’re big on that word. But they’re not really concerned. They think America is the goose that lays the golden egg. Why not? She laid it in their laps. She laid it in grandpa’s lap.
They don’t feel anxious, because they never had anything to be anxious about.
Peggy Noonan thinks the government is screwing us up because it’s made of people who don’t care. But I beg to differ. There’s a classic fallacy in the philosophy of mind that shows up in places ranging from Leibniz’ story of the “magnified mill” to Searle’s Chinese Room, which is that for a system to have some property, the property must be present among the parts. This is just as false for caring as it is for understanding or consciousness. In fact the existing system is a perfect example, although in reverse — it’s composed of people who do care, but they interact in a structure that results in an evil bureaucracy.
Instead, what’s happened is that we made a blunder in designing the system that is exactly equivalent to a favorite example of Eliezer Yudkowsky: instead of building a paperclip-maximizing machine, we built a vote-maximizing machine.
I claim that the problem is much more productively looked at from another point of view: the system as a whole is incompetent. It doesn’t do what it was built to do (”… promote the general welfare, secure the blessings of liberty …”). The designers simply assumed a vote-maximizer would do the things they wanted, but they were wrong. Similarly, no human wants the universe to be converted into paperclips, so if he built a machine with that goal, he would have designed incompetently. I claim we should be spending our time on is figuring out how to build competent AI.
First principle of competent AI design: Build a machine that understands what you want. The paperclip maximizer is a study in amazing contrasts — presumably an intelligence powerful enough to take over the world would be capable of understanding human motivations even better than we do, so as to manipulate us effectively. Yet it’s built with a complete cognitive deficit of appropriate motivations, goals, and values for itself. Incompetent.
Second principle: build machines that know their limitations. This basically means that it should confine its activities to those areas where it does understand the effects of its actions.
But in order to do that, we first have to be able to build a machine that can actually understand something — anything — in the full human-level meaning of understanding. And that is the necessary first step to a future of useful and beneficial AI, and it’s what anyone concerned about such things should be working on.
One of the species of early hominids is named Homo habilis, meaning “handy man,” after their significant advancement in tool use over previous hominids. One of the goals of the AGI Roadmap is to chart paths to full human intelligence, and one of the paths might follow the one that evolution took. The Wozniak Test, i.e. being able to make coffee in any randomly-chosen home, is a case of tool use competence. It is a special case of what we might call the Nilsson Test, as outlined in a paper in 2005 by Nils Nilsson, one of the leading figures in AI:
Machines exhibiting true human-level intelligence should be able to do many of the things humans are able to do. Among these activities are the tasks or “jobs” at which people are employed. I suggest we replace the Turing test by something I will call the “employment test.” To pass the employment test, AI programs must be able to perform the jobs ordinarily performed by humans. Progress toward human-level AI could then be measured by the fraction of these jobs that can be acceptably performed by machines.
Let me be explicit about the kinds of jobs I have in mind. Consider, for example, a list of job classifications from “America’s Job Bank.” A
sample of some of them is given in figure 1:
Meeting and Convention Planner
Maid and Housekeeping Cleaner
Receptionist
Financial Examiner
Computer Programmer
Roofer’s Helper
Library Assistant
Procurement and Sales Engineer
Farm, Greenhouse, Nursery Worker
Dishwasher
Home Health Aide
Small Engine Repairer
Paralegal
Lodging Manager
Proofreader
Tour Guide and Escort
Geographer
Engine and Other Machine Assembler
Security Guard
Retail Salesperson
Marriage and Family Counselor
Hand Packer and Packager
Just as objections have been raised to the Turing test, I can anticipate objections to this new, perhaps more stringent, test. Some of my AI colleagues, even those who strive for human-level AI, might say “the employment test is far too difficult—we’ll never be able to automate all of
those jobs!” To them, I can only reply “Just what do you think human-level AI means? After all, humans do all of those things.”
Now some of those jobs require specialized training and years of experience, while some of them are entry-level, accessible immediately to the average human. Most are somewhere in between. Note that “Maid and housekeeping cleaner” is in itself a superset of the Wozniak Test.
The ability of an AGI (= human-level AI) to do most or all of the jobs humans do is cause for a certain amount of concern. This brings us to a recent post by Robin Hanson:
Yes, techies agree on the long term plausibility of machines doing almost all jobs at a cost below human subsistence wages, thereby gaining almost all income, while economists ignore this scenario. …
Economists should listen more to techies on what techs will be feasible at what costs, but techies should also listen more to economists on the social implications of tech costs. Alas, just as economists prefer to rely on their intuitive folk tech forecasts, techies prefer to rely instead on their intuitive folk economics. …
The standard views of techies about what techs will be feasible might be wrong, and the standard views of economists of how to forecast tech consequences might be wrong. And it is fine for contrarians to try to persuade specialists they are in error, though contrarians would be wise to at least understand the standard view before trying to overturn it. But surely what the world needs first and foremost is to see and take seriously the simple combination of the standard views on such important topics.
One of the standard economic laws that applies in this case is Ricardo’s Law of Comparative Advantage. It states basically that it is generally to the advantage of parties of differing productivities to trade. In particular, the counter-intuitive part, it is to the advantage of the more productive party (e.g. the machines) to trade with the less productive (us, in the robot economy scenario). The exception is where the abilities (productivities across goods) are in the same exact proportions, leaving the parties nothing to specialize in.
It seems to me that one obvious way to ameliorate the impact of the AI/robotics revolution in the economic world, then, is simple: build robots whose cognitive architectures are enough different from humans that their relative skillfullness at various tasks will differ from ours. Then, even after they are actually better at everything than we are, the law of comparative advantage will still hold.
Foresight’s mission is essentially an educational one. In simplest terms we are here to point out foreseeable technological developments that not only will make the future different from the past, but make it different in ways that aren’t obvious and which everyone isn’t already planning for. Nanotechnology — true nanotech in Drexler’s original sense of having a thorough control over the structure of matter at the atomic scale and thus being able to build productive machinery — is such a development, even though the word “nanotechnology” is widely used for much more mundane, predictable, linear, and non-revolutionary progress.
Similarly, the term “Artificial Intelligence” is widely used for predictable, linear progress in software engineering. The field has come a long way, so that it is getting close to the point that any well-specified human skill, such as driving a car, can be implemented given an appropriate application of talent and resources. Just like “nanotechnology,” though, it originally meant something more revolutionary:
Some years ago, Ben Goertzel coined the term “AGI” — artificial general intelligence — to distinguish the original, revolutionary goal of AI as originally seen by such pioneers as McCarthy and Minsky, from the more mundane, incremental work that the term AI had come to cover. This was very similar in spirit to the term MNT — molecular nanotechnology — coined by Drexler and Foresight for essentially the same reason.
Within the past couple of years, the Productive Nanosystems Roadmap was organized and published, under the names of a wide sampling of people from academia, industry, and the national laboratories. This had the effect of making it clear that the ultimate goal of nanotechnology research is indeed “MNT”-style capabilities, and is one that is ultimately feasible and worth working toward.
While the “diaspora” in AI may have been deeper than the one in nanotech, it was also longer ago — there was no need for the AGI Roadmap to re-establish the possibility of an artificial intelligence in the full sense, but to try and make some sense of the state of the art with respect to it, figure out some milestones and metrics that might be used to judge progress, and so forth.
The meeting last weekend at the University of Tennessee, organized by Ben Goertzel and Itamar Arel, served to bootstrap the process and begin to work out what kind of roadmap might be possible. The main problem, of course, is that we don’t really know how intelligence works, which pieces are essential and which ancillary, or indeed whether there are a few powerful underlying principles or a huge kludge of random techniques.
To that end we began by trying to define the kind of tasks that we felt a general intelligence could do but that no hand-coded “narrow AI” could do. The classic such task, or course, is the Turing Test, which has many points in its favor but is also considered (a) too high a bar, and (b) a test of the wrong thing, since it requires fooling a judge as well as exhibiting basic intelligence.
To give some of the flavor of the scenarios, here’s the one I proposed:
The Wozniak Test
In an interview a few years ago, Steve Wozniak of Apple fame opined that there would never be a robot that could walk into an unfamiliar house and make a cup of coffee. I feel that the task is demanding enough to stand as a pons asinorum for embodied AGI.
A robot is placed at the door of a typical house or apartment. It must find a doorbell or knocker, or simply knock on the door. When the door is answered, it must explain itself to the householder and enter once it has been invited in. (We will assume that the householder has agreed to allow the test in her house, but is otherwise completely unconnected with the team doing the experiment, and indeed has no special knowledge of AI or robotics at all.) The robot must enter the house, find the kitchen, locate coffee-making supplies and equipment, make coffee to the householder’s taste, and serve it in some other room. It is allowed, indeed required by some of the specifics, for the robot to ask questions of the householder, but it may not be physically assisted in any way.
The state of the robotics art falls short of this capability in a number of ways. The robot will need to use vision to navigate, identify objects, possibly identify gestures (“the coffee’s in that cabinet over there”), and to coordinate complex manipulations. Manipulation and physical modelling in a tight feedback learning loop may be necessary, for example, to pour coffee from an unfamiliar pot into an unfamiliar cup. Speech recognition and natural language understanding and generation will be necessary. Planning must be done at a host of levels ranging from manipulator paths to coffee-brewing sequences.
But the major advance for a coffee-making robot is that all of these capabilities must be coordinated and used appropriately and coherently in aid of the overall goal. The usual set-up, task definition, and so forth are gone from standard narrow AI formulations of problems in all these areas; the robot has to find the problems as well as to solve them. That makes coffee-making a strenuous test of a system’s adaptiveness and ability to deploy common sense.
I claim that this test addresses the bulk of the aspects of general intelligence that are missing from AI today. Although standard shortcuts might be used, such as having a database of every manufactured coffeemaker built in, it would be prohibitive to have the actual manipulation sequences for each one pre-programmed, especially given the variability in workspace geometry, dispensers and containers of coffee grounds, and so forth. Transfer learning, generalization, reasoning by analogy, and in particular learning from example and practice are almost certain to be necessary for the system to be practical.
Coffee-making is a good test of generality because, although it would be possible to hand-code most of the skills needed, it would be much cheaper simply to build a coffeemaker into the robot! Thus the only economical way to approach the task is to build general learning skills and have a robot that is capable of learning not only to make coffee but any similar domestic chore.
Coffee-making is a task that most 10-year-old humans can do reliably with a modicum of experience. I would guess that a week’s worth of being shown and practicing coffeemaking in a variety of homes with a variety of methods would provide the grounding for enough generality that a 10-year-old could make coffee in the vast majority of homes in a Wozniak test.
Germany’s Federal Environment Agency (UBA) last week made a background paper available on their website, which they now concede contained no new research and none that their organization had actually performed, entitled “Nanotechnology for Humans and the Environment: Increasing Chances, Minimizing Risks,” that got the German and international press to generate frightening headlines like “Germany warns over dangers of nanotechnology”.
This wasn’t the reaction they were expecting so the the UBA authorities wanted to make clear in a new article that they don’t think nanotechnology is all bad. …
Nanotechnology Enables Real Atomic Precision is the title of a piece by Susan Smith in Desktop Engineering, which includes comments by longtime Foresight Senior Associates Steve Vetter and Tihamer Toth-Fejel:
While nanotechology might mean different things to different people, the term was originally coined to describe the building of things from the bottom up with atomic precision. That, says Steve Vetter, CEO of Molecular Manufacturing Enterprises of Saint Paul, MN, means “a place for every atom and every atom in its place.”
It covers both bottom-up and top-down approaches and closes:
Many different nanotechnologies are converging on the same basic concept—to control not just trillions, but kilogram quantities of atoms—and make them atomically precise.
Abstract: High-speed AFM is important for following processes occurring on short time scales inaccessible to conventional AFM. We are working on two versions: one is capable of extremely high imaging rates and can image over relatively large areas on samples with relatively large height variations, and the other is a noncontact version which is more appropriate for studying single molecular bio processes in liquid. Both are also capable of writing structure,s e.g., by electrochemical oxidation, at high-speed. The majority of our examples are biological. At the same time, we have been developing a holographic optical tweezers instrument capable of assembling, sometimes automatically, structures which go from individual nanotools to photonic bandgap crystals. The nanotools can be used, e.g., to manipulate living cells or can become an independent AFM probes operating with all degrees of freedom (see http://HoloAssembler.com). We are now interfacing to both of these instruments via a multitouch table which greatly increases their versatility and accessibility to the non-expert user. (Emphasis added)
The holographic assembler site states, “The dynamic holographic assembler (DHA) is being developed principally as a new technology for the assembly of functional devices using components from the micrometer scale to the tens of nanometers scale.”
Sounds interesting! But note that the term Assembler here is used differently from the way Foresight uses it. —Chris Peterson
This is particularly apropos, since as I write I’m heading off to the AGI Roadmap meeting which Itamar has organized (and of which Foresight is a sponsor).
Nouriel Roubini, the New York University economist who accurately forecast the bursting of the housing bubble and the resulting economic contraction, has become famous for his pessimism—he has been the gloomiest of the doomsayers…
“The question is, can the U.S. grow in a non-bubble way?” [Roubini] asked the question rhetorically, so I turned it back on him. Can it?
“I think we have to …” He paused. “You know, the potential for our future growth is going to be lower, because of the excesses we’ve had. Sustainable growth may mean investing slowly in infrastructures for the future, and rebuilding our human capital. Renewable resources. Maybe nanotechnology? We don’t know what it’s going to be. There are parts of the economy we can expect to lead to a more sustainable and less bubble-like growth. But it’s going to be a challenge to find a new growth model. It’s not going to be simple.” I took this not as pessimism but as realism. [Boldface added]
Sounds right to me. Real economic growth based on real technology advances, which take real work. —Chris Peterson
The yellow is what the sun puts out that hits the top of the atmosphere (what a solar power satellite would see, for example). The red is what gets through to the ground. The green vertical bar represents a photovoltaic’s optimal response (but it actually has a tail that covers the visible range at a reduced efficiency). The blue bar is the tunable response area of the nanoparticles used as quantum dots — you have to make a lot of different sizes to cover the range.
Once we get real nanotech, of course, we’ll just make yagi antennas in the appropriate frequencies.
Special thanks to longtime Foresight member Monica Anderson for setting up this November 4 Bay Area talk by another longtime Foresight member, Keith Lofstrom:
Server-Sky: Solar powered server and communication arrays in Earth orbit.
The EPA predicts US data center power consumption in 2011 will be
120 billion kilowatt hours, or 3% of total US power consumption,
doubling every 5 years thereafter. Our work as programmers and
technologists will continue this exponential growth. This will
have huge environmental, social, and economic consequences unless
we find alternative ways to power the digital economy.
Server sky is a proposal to build large dispersed arrays of 7 gram
paper-thin solar-powered computer satellites and launch them into
6400km earth orbit.
A server-sat is a 100 micron thick, 6 inch solar cell, with
processor memory, and radio chips around the edges. Server-sats
use light pressure for thrust and electrochromic light shutters
for steering. <!–more–> Thousands of server-sats position themselves in three
dimensional arrays, about 100 meters on a side. An array acts as
a large phased array antenna, permitting it to transmit thousands
of communication beams simultaneously to ground receivers and other
arrays in space.
A server-sat displaces 25 watts of ground-based electrical generation,
cooling, and power conversion. A server-sat does not need the racks,
cabling, power converters, land, buildings, and other infrastructure
needed to build a ground-based server farm. These savings alone may
pay for launch.
Server-sat arrays use unlimited space solar power, and operate outside
the biosphere. The environmental impact of power generation and heat
disposal is tiny. In time, new launch techniques, and solar cells made
from lunar rock, can further reduce the environmental and economic
costs of manufacturing and launch.
Earth can return to what it is good at – green and growing things
– while space can be filled with gray and computing things.
Keith is a 56 year old mixed-signal integrated circuit designer in
Beaverton, Oregon. Keith is CEO of SiidTech, which licenses silicon
identification technology to semiconductor manufacturers. Keith is
also an integrated circuit design consultant.
Keith is webmaster for Orcnet, the Oregon IEEE Consultant’s Network.
Keith is active in open source and the Portland Linux Unix Group.
Keith’s server hosts the dirvish disk-to-disk backup program, based
on rsync and written in Perl. Keith has a special interest in low
power, high efficiency computing.
Keith invented the Launch Loop, a space launch system, in 1982.
This speculative space launch system can be built with existing
technologies and launch thousands of tons into orbit per day at
costs below $1/pound.
Keith has written for Kluwer Press, various IEEE journals,
SysAdmin magazine, Liberty magazine, aerospace journals, and Analog.
This is a re-announcement of Custance, Sugimoto, and Abe’ Feynman Prize from the Air Force Office of Scientific Research. (I have a personal fondness for AFOSR since they funded some of my optical computing research back in the 80’s.)
The Feynman Prizes in Nanotechnology recognize researchers whose recent work has most advanced the field toward the achievement of Feynman’s vision for nanotechnology: molecular manufacturing — the construction of atomically-precise products through the use of molecular machine systems.
For the past two years, the Asian Office of Aerospace Research and Development (AOARD), an international detachment of the Air Force Office of Scientific Research, has been supporting Custance’s research to develop catalysts that use an atomic-scale-precision technique to place active gold atoms at an exact location on or near the surface of a model system. For the purpose of this research, Custance is studying the system of gold on cerium dioxide, or ceria.
“Gold has become an exciting element to study for its catalytic properties,” explains Dr. Thomas Erstfeld, AOARD program manager. “It was once thought of as relatively inert, but in the past couple of years, it has been discovered that nano-sized gold particles are excellent catalysts.”
Custance will share the award with Professors Yoshiaki Sugimoto and Masayuki Abe of Osaka University in recognition of their pioneering experimental demonstrations of mechanosynthesis for vertical and lateral manipulation of single atoms on semiconductor surfaces.
No, it’s not harnessing the flagellar rotory motor to turn nanogears, it’s harnessing the entire beast, statistically, to turn microgears. Still interesting.
An interview with Don Eigler of “IBM in 35 xenon atoms” fame.
Has nanotechnology trickled down into everyday life yet?
To some extent. It’s showing up in coatings, cosmetics and sunscreens, and it’s starting to show up in electronic devices. The length scales at which we manufacture computing devices are at the lower end of the nanometre scale. My laptop and cellphone are chock full of nanometre-scale technologies. But I think it’s going to evolve to produce new technologies which will have a much broader impact.
What sort of evolution do you have in mind?
I like to differentiate between evolutionary technology and revolutionary technology. My cellphone and laptop contain evolutionary nanotechnology because they can be traced back to larger structures. Revolutionary is still very much in the future, but I’m thinking of things like new forms of drug delivery or new kinds of molecular structures. The bulk of the influence on the person in the street is still to come, but there’s a 16-year-old kid out there now who’s going to come up with something really wonderful.
PD: So today, one of my companies, Space Adventures, sends people into orbit privately. A trip is $40 million. Our next customer goes up in 5 days, Guy Laliberté, the founder of Cirque du Soleil.
If you were to calculate the energy requirement to put you and your space suit into orbit, you can actually calculate the amount of energy, it’s easy to do, it’s a high school physics problem, it’s mass times gravity times height to get the potential energy of the altitude, and then one half mass times velocity squared to get kinetic energy. It’s about 1.6 gigajoules. If you were to buy that over the electric grid at 7 cents a kilowatt hour, and you had an electric winch that could winch you up into space very easily, and you spend the energy over the course of an hour, it turns out that the cost to get you and your space suit into orbit, if you can convert the energy 100% efficiently, is about $100. So the price improvement curve from the cost of going to space today, which is $40 million, to theoretically what it could be in the future, which is $100, is extraordinary. So that’s the future that I’m focused on creating.
Given molecular manufacturing, that (energy) limit can be approached, and using the same kind of advances in technology and construction, the price of the energy could likely be brought down pretty drastically as well.
There’s an excellent round-up over at Next Big Future on the Roadmap for Additive Manufacturing. This is solid freeform fabrication, 3-D printing, stereolithography, rapid prototyping, and so forth.
In the long run, 3-D printing is one of the more straightforward paths to full-fledged nanotech with mechanosynthesis. Mechanosynthesis might be seen simply as the ultimate in precision and range of materials for additive manufacturing.
In the intermediate stages, there is going to be increasing synergy between bottom-up nanotech, biotech, and AM. For example, the Roadmap calls for printing of living organs in 15 years! In general, bottom-up nano will create new materials and precursors that will continue to give the AM process an ever-widening range of products.
The key thing about AM as a pathway to nanotech is that there are useful (and remunerative!) applications at every step.
It isn’t really clear from this story whether the “robots” involved or available were autonomous, teleoperated, or some combination. However, this story wraps up my reaction to a lot of techno-angst in a nutshell:
Speaking at the Association for Unmanned Vehicle Systems International conference, Lynch said that robot systems already in place could have saved 122 of the 155 men who died during his time in Iraq.
Lynch’s concerns hold particular weight, as he has both the combat experience of leading the Army’s Third Infantry Division in Iraq, and the academic experience of earning a Master’s Degree in robotics from MIT.
Echoing similar statements he made in August, Lynch claims that deploying remotely, or autonomously, navigated ground vehicles could have lowered casualties as a result of IEDs, and that robotic infantry could have replaced humans on dangerous surveillance missions.
Some robot infantry had been deployed to Iraq, specifically the SWORD gun platform, but the Army severely restricted their use over safety concerns.
Right. Perhaps the title should be “Safety concerns kill 122.”
Superconductors.ORG herein reports the observation of record high superconductivity near 254 Kelvin (-19C, -2F). This temperature critical (Tc) is believed accurate +/- 2 degrees, making this the first material to enter a superconductive state at temperatures commonly found in household freezers.
In 3 months, it will be colder than that on my front porch.
