The reason is that as of even date, you just can’t put enough processing in a mobile robot to be doing the kind of processing a human is doing with its sensory and motor streams.

There’s also of course Kismet:

and various other research robots ranging from the simple –

Babybot

Ludwig

They get more complex, e.g. Berti:

,

to the extremely anthrobiomimetic CRONOS2:

to some serious engineering:

Domo:

iCub:

Saika:

Nasa’s Robonaut:

These use commercial arms and manipulators:

The UMASS Torso robot:

The Iowa torsobot:

And finally, these appear to be commercially available:

Hiro:

Motoman SDA10:

and the Meka (a descendent of Domo in commercial form):

so … the bodies are there.

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.

An intelligence challenge should not involve building mechanical robot controllers – IMO. That’s a bit of a different problem – and a rather difficult one – because of the long build-test cycle involved in such
projects.
There are plenty of purer tests of intelligence that use more abstract ideas – games, puzzles, and other classical intelligence test fodder.
If you want to measure the abilities of mechanical robots, then fine, but let’s not pretend that it’s the same thing as measuring intelligence.

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.

An atomically precise gear, rotated by pushing the teeth one at a time with a STM tip.

• Robo insectis: rote, mechanical gadgets (or thinkers) with hand-coded skills, such as Roomba or industrial robots or automated call-center systems or dictation programs.
• Robo habilis: Rosie the housemaid robot level intelligence, able to handle service level jobs in the real world but not a rocket scientist.
• Robo sapiens: up to and including rocket scientists, AI researchers, corporate executives, any human capability.
• Robo googolis: a collection of top R. sapiens wired together in a box running at accelerated speed, equivalent to, say, Google (the company and the search engine together).

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:

So far, machines have displaced relatively few human workers, and when they have done so, they have in most cases greatly raised the incomes of other workers. That is, the complementary effect has outweighed the substitution effect–but this trend need not continue.
In our graph of machines and humans, imagine that the ocean of machine tasks reached a wide plateau. This would happen if, for instance, machines were almost capable enough to take on a vast array of human jobs. For example, it might occur if machines were on the very cusp of human-level cognition. In this situation, a small additional rise in sea level would flood that plateau and push the shoreline so far inland that a huge number of important tasks formerly in the human realm were now achievable with machines. We’d expect such a wide plateau if the cheapest smart machines were whole-brain emulations whose relative abilities on most tasks should be close to those of human beings.

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

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.

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:

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.

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.

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.

From Dexter Johnson at nanoclast:

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. …