An article in New Scientist with the optimistic title “Artificial life forms evolve basic intelligence” gives an update on how two specific examples of computational artificial life is doing in terms of evolving to have more interesting behavior.  An excerpt:

Brains that have been evolved with HyperNEAT have millions of connections, yet still perform a task well, and that number could be pushed higher yet,” he says. “This is a sea change for the field. Being able to evolve functional brains at this scale allows us to begin pushing the capabilities of artificial neural networks up, and opens up a path to evolving artificial brains that rival their natural counterparts.

See the comments after the article for useful discussion.  A field to keep an eye on.  —Chris Peterson

Modern computers operate at enormous speeds—capable of executing in excess of 10¹³ instructions per second—but their sequential approach to processing, by which logical operations are performed one after another, has remained unchanged since the 1950s. In contrast, although individual neurons of the human brain fire at around just 10³times per second, the simultaneous collective action of millions of neurons enables them to complete certain tasks more efficiently than even the fastest supercomputer. Here we demonstrate an assembly of molecular switches that simultaneously interact to perform a variety of computational tasks including conventional digital logic, calculating Voronoi diagrams, and simulating natural phenomena such as heat diffusion and cancer growth. As well as representing a conceptual shift from serial-processing with static architectures, our parallel, dynamically reconfigurable approach could provide a means to solve otherwise intractable computational problems.

He explains:

…we have realized 700 bits parallel processing using cellular automaton for the first time in the world. This is a significant advancement from our 16 bit parallel processing which you highlighted in your website (http://www.foresight.org/nanodot/?p=2687)…This invention may be in coherence with the Feynman’s vision…We can solve some problems which computers will take more than the age of this universe. We did it in 6-10 minutes (in the Nature Physics paper).

Some coverage:

http://www.msnbc.msn.com/id/36788441/ns/technology_and_science-innovation/

http://www.natureasia.com/asia-materials/highlight.php?id=708&utm_source=NPG+Asia+Materials&utm_content=Research+Highlights
Anirban writes, “Hope you may like this.”  We do indeed!  —Christine Peterson

Industrial robots are getting precise enough that they’re less like dumb machines and more like automated sculptors producing artwork. Case in point: Daishin’s Seki5-axis mill. The Japanese company celebrated its 50th anniversary last year by using this machine to carve out a full scale motorcycle helmet out of one piece of aluminum. No breaks, no joints, the 5-Axis mill simply pivots and rotates to carve metal at some absurd angles. Every cut is guided by sophisticated 3D design software (Openmind’s HyperMill). While the Daishin helmet made a nice showpiece for a biannual meeting of machiningcompanies (EMO), this level of production is becoming the new standard. Your average industrial company got hi-tech in a hurry and now we have machines that can transform computer designs into the highest quality professional metal objects, seemingly at a push of a button.

More like science fiction every year!  —Chris Peterson

http://hplusmagazine.com/articles/ai/synapse-chip

In a video, HP researcher R. Stanley Williams explains how his team created the first memristor in 2008, while the article also explains how U.C. researchers made an even more startling discovery: the memristor “already existed in nature.” It matches the electrical activity controlling the flux of potassium and sodium ions across a cell membrane, suggesting memristors could ultimately function like a human synapse, providing the “missing link” of memory technology.

HP believes memristors “could one day lead to computer systems that can remember and associate patterns in a way similar to how people do.” But DARPA’s SyNAPSE project already appears committed to scaling memristor technology to perform like a human synapse.  —Chris Peterson

Victoria Stodden is currently at Yale Law School, and she gave a short talk at the recent Science Online meeting in which she discussed the legal aspects of ensuring that the code behind computational tools is accessible enough for reproducibility. The obvious answer is some sort of Creative Commons or open source license, and Stodden is exploring the legal possibilities in that regard. But she makes a forceful argument that some form of code sharing will be essential.

“You need the code to see what was done,” she told Ars. “The myriad computational steps taken to achieve the results are essentially unguessable—parameter settings, function invocation sequences—so the standard for revealing it needs to be raised to that of when the science was, say, lab-based experiment.” This sort of openness is also in keeping with the scientific standards for sharing of more traditional materials and results. “It adheres to the scientific norm of transparency but also to the core practice of building on each other’s work in scientific research,” she said. But the same worries that apply to more traditional data sharing—researchers may have a competitor use that data to publish first—also apply here. In the slides from her talk, she notes that a survey she conducted of computational scientists indicates that many are concerned about attribution and the potential loss of publications in addition to legal issues. (The biggest worry is the effort involved to clean up and document existing code.)

Still, this sort of disclosure, as with other open source software, should provide a key benefit: more interested parties able to evaluate and improve the code. “Not only will we clearly publish better science, but redesigned and updated code bases will be valuable scientific contributions,” Stodden said. “Over time, we won’t stagnate forever on one set of published code.”

via Ars Technica: Nobel Intent: Keeping computers from ending science’s reproducibility.

Nanotechnology, the revolutionary technology, was always about the power of self-replication and never only about the very small.

The ability of a machine system to make more of itself, or more generally, make its own parts and be able to assemble or replace them as needed, is called autogeny.  There’s a very related concept in wider use called autopoiesis, which is essentially a description of certain biological or ecological systems.

An autopoietic machine is a machine organized (defined as a unity) as a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network.

There’s a key difference.  I defined autogeny (it’s a real word, or at least “autogenous” is, and I merely specialized it to this technical meaning) as a subset of what autopoiesis means.  An autopoietic system is a process,not an object.  It can not only make its own parts but does so constantly, replacing the ones it’s made of (“continuously regenerate”).  It has an identity that is more like that of the Ship of Theseus than a simple object.  You and I are autopoietic on a number of different levels: Our cells constantly rebuild and replace themselves on the molecular level; our minds constantly learn and re-integrate the ideas they’re made of — memories not regularly used and re-remembered tend to fade.

Auytogeny takes half of that idea in a more mechanistic sense and can be used to describe a permanent physical object.  My nano-manufacturing system, for example, is autopoietic in its growing phase where early early-generation inefficient machines are replaced by late-generation ones, but merely atuogenous thereafter.  Utility Fog in use would constitute an autopoietic system — individual foglets would be constantly failing and being replaced. But a simple nanofactory is merely autogenous. An autogenous system makes its own parts but doesn’t (necessarily) constantly replace them.  Like most machines, it needs to be fixed from the outside if it breaks.

But in early engineering stages, autogeny is enough.  It’s a good simplification as a halfway point in engineering toward advanced, autopoietic, nanosystems with the kind of complexity and robustness that life has.

Dunbar knew that scientists often don’t think the way the textbooks say they are supposed to. He suspected that all those philosophers of science — from Aristotle to Karl Popper — had missed something important about what goes on in the lab. (As Richard Feynman famously quipped, “Philosophy of science is about as useful to scientists as ornithology is to birds.”) …

Dunbar brought tape recorders into meeting rooms and loitered in the hallway; he read grant proposals and the rough drafts of papers; he peeked at notebooks, attended lab meetings, and videotaped interview after interview. …

Dunbar came away from his in vivo studies with an unsettling insight: Science is a deeply frustrating pursuit. Although the researchers were mostly using established techniques, more than 50 percent of their data was unexpected. (In some labs, the figure exceeded 75 percent.) “The scientists had these elaborate theories about what was supposed to happen,” Dunbar says. “But the results kept contradicting their theories. It wasn’t uncommon for someone to spend a month on a project and then just discard all their data because the data didn’t make sense.”

The real world, it turns out, is a messy place, even in a completely controlled laboratory.  The job of a scientist, after all, is to abstract clean understandable rules and regularities away from the messiness.

But consider: information theory tells us that the amount of information a signal contains depends on how unexpected it is.  A signal consisting of a string of 0′s that we know is going to be a string of 0′s tells us nothing at all, and conveys no information.  So we should hope that the data from an experiment will be unexpected.

The import of the article, and of Dunbar’s research, lies in how the new information is used.

“The scientists were trying to explain away what they didn’t understand,” Dunbar says. “It’s as if they didn’t want to believe it.”
The experiment would then be carefully repeated. Sometimes, the weird blip would disappear, in which case the problem was solved. But the weirdness usually remained, an anomaly that wouldn’t go away.
This is when things get interesting. According to Dunbar, even after scientists had generated their “error” multiple times — it was a consistent inconsistency — they might fail to follow it up. “Given the amount of unexpected data in science, it’s just not feasible to pursue everything,” Dunbar says. “People have to pick and choose what’s interesting and what’s not, but they often choose badly.” And so the result was tossed aside, filed in a quickly forgotten notebook. The scientists had discovered a new fact, but they called it a failure.

Now of course most of the time the new fact is something like “the particular bleach processing used to make this particular filter paper produces surface irregularities on the fibers that have an unexpected interaction with this particular protein when prepared this particular way”, a fact that will never be of use to anyone who’s not trying that particular experiment.  So most of the time it is the right thing for scientists to do to ignore the anomalous results and redo the experiment with different but “equivalent” equipment (or whatever).

75% anomalous results represents a huge information stream in information-theoretic terms.  But it’s mostly noise.  So scientists have filters in their minds to deal with it — as do we all (read the rest of the article for a little neuroscience about that).  The filters explain why “normal science” can proceed so long in the face of anomalies before a Kuhnian paradigm shift occurs.  It’s a perfectly reasonable bias to assume that your existing theory, that has worked in the past, is right and the contradictory evidence is noise.  It usually is.

But if the bias of your filters somehow gets set by something else — a political belief, for example — the fact that the filters control so much of what you see can steer you wrong a lot faster than you think.

In a perfect world, whenever someone did an experiment, all the data would be put online, accessible to anyone who cared to look, instead of filed away in a “quickly forgotten notebook”.  In the 20th century that would have been a utopian dream; but today, it’s possible, and tomorrow, it should be relatively easy.

Imagine a world where, say, just 1% of today’s MMORPG players spent their time and efforts crawling through lab records, analysis programs, and satellite feeds — gleaning not virtual gold, but scientific truth.

Techno-rapturists among our reading audience might be quick to respond with glib answers about miraculous nanotechnology solutions that are just around the corner, or the promise of a superintelligent friendly AI who can take over everything and solve all our troubles just like Daddy would.

So I feel that it would be appropriate to set the record straight on a few points. First and simplest, the Dead Sea is a completely natural phenomenon — it is dead because it has ten times the salinity of the oceans, and the salinity is from exactly the same reason — rivers run into it, bearing dissolved minerals, but water leaves by evaporation, leaving and thus concentrating the solutes. The idea of using a picture of the Dead Sea to illustrate a paragraph about ocean acidification makes me quite skeptical of the scientific reasoning behind the rest of the article.

Let us suppose that, for example, we had been monitoring the oceanic biosphere by satellite for a decade and over that time the levels of life had fallen by 6%. That would be a clear cause for alarm and one would have to worry about acidification and other deleterious human influences. But in fact the results of such studies show that the opposite is the case. Both in the ocean and on land, plant growth, which is what can be measured directly by satellite, has increased, and this appears largely due to increased CO2 and warming. (After all, we build greenhouses for a reason.)

Mike quotes a Green angst blog that quotes a Green angst opinion piece in Nature as follows:

The Earth has nine biophysical thresholds beyond which it cannot be pushed without disastrous consequences.
Ominously, we have already moved past three of these tipping points.

There is really no scientific basis for this kind of statement. James Hansen, one of the authors of the Nature piece in question, has been making this kind of noise about runaway warming feedbacks for years. But the actual physical greenhouse effect is logarithmic. You have to postulate some completely different positive feedback to talk about “tipping points”, and no such thing has been demonstrated in the real climate system (although there is no lack of them in the computer models). In fact the icecore paleothermometry reconstructions show that there have been fairly rapid rises at the beginning of each interglacial, as if there were a positive feedback operating, but that they stop uniformly when they get to temperatures a little warmer than current, as if there were some very strong negative feedback in that part of the phase space.

Furthermore, there’s not much sign of a positive feedback acceleration in the current temperature record, either:

cubic fit to UAH temp record

(This is a simple least-squares cubic fit to the satellite temperature record (UAH global monthly averages) to date.)  For there to be a dominant positive feedback (there are plenty of minor ones), it would have to be something that operates on longer timescales than the PDO/ENSO oscillations (and thus longer current GCMs can model accurately).  Regarding which this pithy remark in one of the climategate emails:

Without trying to prejudice this work, but also because of what I
almost think I know to be the case, the results of this study will
show that we can probably say a fair bit about <100 year
extra-tropical NH temperature variability (at least as far as we
believe the proxy estimates), but honestly know fuck-all about what
the >100 year variability was like with any certainty (i.e. we know
with certainty that we know fuck-all).

So, what’s the difference between a Gaian take on Sinners in the Hands of an Angry Earth, and a reasoned scientific stance?  One main difference is the degree of certainty which is attached to the statements.  Another is the moral coloration given to any human influence.  The main reason I object to that is the attempted disguise of the coloration as scientific projections of physical effects.  It would be perfectly fine to say, as other religions do, that men are born evil and we should all take vows of poverty because it is harder for a camel to go through the eye of a needle than a rich American to enter the kingdom of heaven.

There are, of course, deleterious effects to human operations and expansion over the Earth’s surface.  If we pave over the whole planet, it would be a pretty bad thing.  The satellite biosphere inventories show that, indeed, life is contracting in some areas while expanding in others.  There are limits to virtually every form of human impact, from emitting CO2 to building suburban homes and mowing the grass.  But by and large the effects are proportional to the cause: we do more stuff, things get worse in some regard (polluted waters, for example), the effects are noticed, and the causes are backed off from.  But there’s very little reason to believe that we’re sitting in a circle of traps where one false step in any direction will result in certain doom.

Would it be nice to save the Earth as a park and wildlife preserve?  I personally happen to think so.  Indeed, I live in the remote mountains   where I can’t see another human habitation from my windows, putting up with considerable inconvenience to do so because I like the natural environment more than cities or suburbs.  But the only way that will happen in a realistic projection, is for the substantial mass of humanity and industry to move into space (or cyberspace or the equivalent).  And that will require nanotech.

Anyway, here’s what Feynman had to say about how to recognize cargo cult science:

There is one feature I notice that is generally missing in “cargo cult science.” It’s a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty — a kind of leaning over backwards. For example, if you’re doing an experiment, you should report everything that you think might make it invalid — not only what you think is right about it; other causes that could possibly explain your results; and things you thought of that you’ve eliminated by some other experiment, and how they worked — to make sure the other fellow can tell they have been eliminated.
Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can — if you know anything at all wrong, or possibly wrong — to explain it. If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it. There is also a more subtle problem. When you have put a lot of ideas together to make an elaborate theory, you want to make sure, when explaining what it fits, that those things it fits are not just the things that gave you the idea for the theory; but that the finished theory makes something else come out right, in addition.
In summary, the idea is to try to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgment in one particular direction or another.

Sounds pretty tame, doesn’t it?  Nothing about trickery, deceit, or other malfeasance. Nothing about whether the cargo cult scientist does or doesn’t earnestly believe the results. The thing that makes science different, and in my opinion better, than all other forms of human inquiry to date, is that bending-over-backwards honesty. Because in the end, without it, the results are not to be trusted.

xkcd: Abstraction

… which underlines yet again how amazing the technology is beneath the abilities we take for granted everyday (and put to very ordinary uses).

Back in this post: The heavily-loaded takeoff I pointed out that that was likely to be the fate of most of the to-us astounding capabilities of nanotech:

The machine I’m writing this essay on could do the pure bit-flipping work of a million of those micros (e.g. in doing a quantum mechanics simulation of a molecule or a fluid flow simulation of a wind tunnel). It does not, unfortunately, let me be a million times as productive.

…There’s a phenomenon that is implicit in almost every economic analysis: the law of diminishing returns. It says, simply, that each dollar you spend is going to get you something worth less than what you got from earlier dollars. It’s simple because all it says is that if there were something more valuable to get, you would have gotten it first, and put off the less valuable thing.
The same thing is true of computing cycles. The text editor I used on 1980’s micro gave me a significant fraction of the value of the one I’m using now — say, at least 10 percent — for probably one hundred thousandth of the cost in instructions. The first instructions in the editing program allow you to type text onto the screen. The billionth ones animate specular highlights on the simulated button-press as you select between nearly identical typefaces.
The parallel, I hope, is clear. As AI and nanotech pervade the economy through the middle of the century, each additional unit of productive work will be put to a less valuable use, since we’re already doing the most valuable uses with the effort and resources we can presently bring to bear.

The capabilities of nanotech are almost unimaginable in current terms. The capabilities of copyable, self-improving AI are similarly staggering. The term “weakly godlike” is sometimes thrown about as an indication of the kind of capabilities we might expect.  But consider: suppose I gave you a tool that would let you examine an enzyme or structural protein in a cell.  And fix it if something were wrong with it. Now how long would it take you to check the proteins in all the trillions of cells in your body?  You need the equivalent of more AI than the intelligence of the world’s population today, just to make cell repair machines work.  But instead of screen-savers, they’ll be gene-savers.