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I’m at the AAAI Fall Symposium session on Biologically Inspired Cognitive Architectures, and there was a really interesting talk by Walter Schneider of Pitt about progress in mapping the nerve bundles that are the “information superhighways” between the various parts of the brain. You’ll find his slides from last year’s talk on his home page, and there has apparently been progress amounting to a breakthrough in the interim. This and fMRI together are giving us an understanding of what’s going on in the brain that’s advancing faster than anybody (with the possible exception of Ray Kurzweil) thought it would. Schneider claims that the techniques now being worked on could be pushed to a resolution of 20 microns, with appropriate resources, by 2014 or thereabouts. That’s essentially good enough to have a complete wiring diagram of the brain. In response to my Robo Habilis post, Tim Tyler replied:
This is a fairly widely held view — there were a couple of researchers at the AGI Roadmap meeting expressing the same idea. If I understand him correctly, Minsky feels the same way. I believe, however, that it is not true. To begin with, that was the reigning paradigm of the entire “golden age” of AI from the 50s through the 70s. Even Shakey the Robot had a bicameral control architecture: a body control program written in SAIL, and a cognitive engine written in LISP. It was strongly believed that the parts of thought that were hard for humans would be the hard ones to program, and that once we got those licked, building the lower-level body-controller stuff (or vision, or speech-to-text for the input) would be an afterthought, or at most a clean-up engineering exercise. Over the course of the 60s, classic AI had a tremendous run of success, which is pretty neatly summed up by the work in Minsky’s “Semantic Information Processing.” They had programs that did games, puzzles, intelligence tests, arithmetic word problems, freshman calculus. The hard stuff. They were full of optimism, and predicted that AI would run to a successful conclusion, creating an artificial mind, in another decade or two. They had done the college student; how much more effort should it take to do a toddler? They were wrong. The greatest lesson that came out of the Golden Age was that “the hard stuff is easy, and the easy stuff is hard.” Any toddler could recognize a dog in a picture; it would be three more decades before AI could get even close (and it’s still not really there yet). The mind, it turns out, is like an iceberg — most of it is unseen to consciousness, below the waterline. Perhaps a better analogy would be that consciousness is like the legislature of a country, or the head office of a company. What they perceive is in reality only an executive summary of what’s really happening. What the early AI researchers had done was to build a “company” consisting only of the board of directors and secretaries, but no factories, no sales force, no middle managers, no shop foremen, and no labor force. The brain was evolved as a body controller. Evolution typically takes a structure that works and copies and adapts it to the next task. Consider the increasing intelligence of animals as we work ourselves up the evolutionary tree towards the human: insects, reptiles, mammals, primates. At every level new and improved kinds of control, feedback, discrimination, planning, and learning are built into the structure — and it’s all still there forming the part below the iceberg, the real company outside the boardroom, of human intelligence. The classic AIers at the Roadmap asked me, “Isn’t a blind paraplegic still intelligent?” and of course he is — but only because his brain still contains all the mechanism that was evolved to to the control and interpretation he now lacks. The buzzword in current AI for the reason bodies are important is “symbol grounding.” This refers to philosophical theories of meaning among symbols in symbol-processing machinery, and a simplistic reading of it is that whereas SHRDLU doesn’t “really know” what a red block is, a physical robot that plays with them really does. Unfortunately, the term in common use is often taken as implying that there is some magical transubstantiation of meaning into symbols by virtue of having a physical body, and this isn’t right and obscures the real issue. The paraplegic still has meaning in his mind. What has to be there is not the actual body, but the mental mechanism for controlling it — that allows the mind to imagine, predict, describe, and relate other concepts to the one said to be understood. Most of our higher-level concepts are drawn from, by analogy and blending, the basic (very large) set of concepts we have learned, by experience, on the shop floors of our minds as we interact with the real world over the course of our lives. Could that interpretive, predictive, concept-building, etc, cognitive machinery be built another way than working up a controller for a humanoid robot body? Certainly. But there are two reasons to do it with a body: first, it’s most likely easiest that way. There are a lot of things we don’t know yet about how the mind works. There’s no reason to think that we have no more blind spots like the classic AIers did. Working with real robots will show us the gaps fastest. The second reason is that once we get the brain built, if we’ve put it together in a rough semblance of the phylogenetic/ontogenetic sequence that the human mind is built, there’ll be a much better chance that its meanings will match ours. It will understand things the way we do (of course humans vary a lot in the way we understand things), and do things the way we do, and thus appreciate the way we do them, and vice versa. For example, the parts of the brain that control language and manual manipulation are strongly overlapped. Try to teach your robot sign language without a similar structure and it will never get the “accent” right. Nor, unless it has the same kind of manipulation control to borrow, will it ever be as fluent in English as a human. Separating “intelligence” from the rest of cognitive function is a false dichotomy, and one that has led AI astray — in a big way — before. Nanotechnology devices: Molecular machines shift into gear. An atomically precise gear, rotated by pushing the teeth one at a time with a STM tip. There are at least 4 stages of intelligence levels that AI will have to get through to get to the take-over-the-world level. In Beyond AI I refered to them as hypohuman, diahuman, epihuman, and hyperhuman; but just for fun let’s use fake species names:
First point: One R. googolis can’t take over the world, any more than Google could. You’d have to get to the next stage (R. unclesammus). Any AI in the earlier stages of development that acted antisocial gets stomped on fast (and in early days, they’ll have no rights — so they’ll basically be exterminated). Second point: As Robin Hanson and many economists point out, the complementary effect of machines up through the R. insectis stage has generally been much stronger than the substitution effect, so that improving technology has had a general beneficial effect on incomes even though it put specific people, buggy-whip makers for example, out of work. Complementarity is seen when comparative advantage holds, substitution when it doesn’t:
I don’t think that the “plateau” is really flat, though. There are two reasons. The first is that human capability is a range, with R. habilis at one end and R. sapiens at the other. It’ll take some time to get through — at least a decade, maybe two. The other reason is that the comparative advantage we saw in the Industrial Revolution may just get turned on its head. Right now we have a Moore’s Law for the robot’s brain but not for its body. In other words, we may enter a strange period where white-collar workers are replaced by beige boxes but blue-collar ones are still cheaper — for a little while — than a fully-capable humanoid robot body. (That will disappear soon enough after nanotech manufacturing takes hold, but at the moment, it looks like AI may be a decade earlier than real nanotech.) The key thing to remember when thinking about the economic AI takeover is that it is not something we should be trying to prevent. Why shouldn’t we, the human race as a whole, build machines to do the hard work we need done, and spend our time enjoying the resulting wealth? Why shouldn’t we spend our efforts deciding what needs to be done, and let the machines do it? Questions like unemployment are the result of taking a system that is well-adapted for one economic situation and applying it to a totally different one. What should the economic system look like when robots do all the work? And once we get that figured out, how do we get there from here?
From NanoWerk: Rice scientists argue nanotubes can be treated like polymers
The article coincides with an announcement of a new development in nanotube processing that does, in fact, treat nanotubes as polymers and thus allows for considerably greater industrial use:
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:
My Robo Habilis post was picked up on by Michael Anissimov who wrote:
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”:
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:
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:
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:
From Dexter Johnson at nanoclast:
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:
It covers both bottom-up and top-down approaches and closes:
Accelerating Future » RepRap “Mendel” to be Released Soon!. Nicw round-up with videos of the latest in the Rep-Rap world. The 2-millimeter dash was a nanobot race held as part of the 2009 RoboCup Nanosoccer Demonstration Competition. That was July; typically entry time, as for Robocup 2010 in Singapore, would be year end, but I can’t see any announcement for it on their page. Does anyone know any more details? Here’s a talk happening next Tuesday at UCLA:
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 Interview of Artificial General Intelligence Researcher Itamar Arel by Sander Olson. on Next Big Future 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).
Sounds right to me. Real economic growth based on real technology advances, which take real work. —Chris Peterson Nanoparticle Breakthrough Can Make Higher Efficiency Solar Cells and Speed Development of Nanotechnology. Brian Wang at Next Big Future has the story of a classic case of serendipity in research. 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, Server sky is a proposal to build large dispersed arrays of 7 gram A server-sat is a 100 micron thick, 6 inch solar cell, with A server-sat displaces 25 watts of ground-based electrical generation, Server-sat arrays use unlimited space solar power, and operate outside Earth can return to what it is good at – green and growing things
Who is Keith Lofstrom? Keith is a 56 year old mixed-signal integrated circuit designer in Keith is webmaster for Orcnet, the Oregon IEEE Consultant’s Network. Keith invented the Launch Loop, a space launch system, in 1982. Keith has written for Kluwer Press, various IEEE journals, Researcher honored for experimental work in nanotechnology. — AFOSR via Eurekalert 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.)
Technology Review: Blogs: arXiv blog: Self-Propelling Bacteria Harnessed to Turn Gears. No, it’s not harnessing the flagellar rotory motor to turn nanogears, it’s harnessing the entire beast, statistically, to turn microgears. Still interesting. Don Eigler: Two decades of nanotech – opinion – 14 October 2009 – New Scientist. An interview with Don Eigler of “IBM in 35 xenon atoms” fame.
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