This all came crashing down with the quartz crisis of the 1970′s, when Seiko and other companies started to produce electronic timepieces that were both much cheaper and more reliable than mechanicals. For the kids today, of course, with their smartphones and iThings, wristwatches are seemingly going the way of the cassette tape. The Swiss watchmaking industry nearly collapsed, before the surviving companies were able to re-position themselves by appealing to horological connoisseurs and elitist yuppies who would like to think they are.

As someone who thinks about time as a full-time occupation (as well as a bit of an elitist yuppie myself), it was inevitable that I would become fascinated by watches. I don’t have nearly the financial wherewithal to splurge on the latest masterpieces out of Geneva, and my watch-snob credentials are ruined by the fact that I don’t mind wearing a well-designed quartz. But there’s a fascinating little sub-culture there, which you can experience at the WatchUSeek or TimeZone watch forums.

A reasonable argument could be made that we the Golden Age of mechanical watches is right now. As a luxury niche market, watchmakers at the high end have some freedom to experiment and innovate. There are some hits and some misses, of course. At some point I may find the time and energy to post something substantive about watchmaking, but right now I’ll just offer up this cool video for the Urwerk UR-110. (If you can find one for under $80,000, consider it a bargain.) It features a clever design in which a series of rotating barrels display the hour, and move by a dial on the side to indicate the minutes. There’s no attempt to explain what’s going on — this is pure glitz. Still — pretty compelling glitz.

Researchers at George Whiteside’s lab explain that the idea came from observing squid and worms. Well, that’s comforting.

The thing is, I’m pretty sure that my body will eventually overcome this cold virus. That’s one of the great things about living organisms — they can, in a wide variety of circumstances, repair themselves. From fighting off germs to healing broken bones, the body is pretty darn resilient.

Which brings up something that has always worried me about nanotechnology — the fact that the tiny machines that have been heroically constructed by the scientists working in this field just seem so darn fragile. It’s amazingly impressive what modern nano-engineers can do by way of manipulating matter at the atomic and molecular level, creating new materials and tiny machines and motors. But surely one has to worry about the little buggers breaking down. My macroscopic car is also an impressive feat of engineering, but it’s no good if a crucial component breaks.

So what you really want is microscopic machinery that is robust enough to repair itself. Fortunately, this problem has already been solved at least once: it’s called “life.” Even at relatively tiny scales, living organisms are sufficiently loose and redundant to be able to fix themselves when something small goes wrong, greatly extending their useful lifespan.

This is why my utterly underinformed opinion is that the biggest advances will come not from nanotechnology, but from synthetic biology. Once we get to the point that we can truly create new organisms from scratch, not simply modifying existing stock, many of the biggest dreams of nanotech will become much more real.

Some time ago John von Neumann proposed the idea of self-replicating machines. Not everyone believed that such a thing was possible — after all, the machine would have to include blueprints for another version of itself, including the self-replication mechanism, and how do you fit a copy of a machine into itself? (You might think that living organisms are an obvious counterexample to this argument, but some people used it as an argument against the idea that organisms are “just” machines.) But von Neumann figured it out, and immediately proposed the obvious plan: sending self-replicating spacecraft to seed the galaxy.

But if the machine breaks, it defeats the whole purpose. So you really want a self-repairing self-replicating machine. Which is awfully close to a working definition of “life.” It might not be human beings who eventually fill up the galaxy, but my suspicion is that it will be life in some form or another.

But just between you and me, while staring at the intimidating whiteboard full of interesting possibilities for what to do next, I was struck by a depressing insight: I am tired of data.

This isn’t to say that I am tired of experiments. We can’t learn anything about the world without looking at it, and my favorite areas of physics are bubbling along with provocative new results (or at least hints thereof). When data is taken by an experiment in the cause of deciding some scientific question, that’s fine.

It’s the fetishization of data for its own sake that I find fatiguing. It’s hardly surprising that, surrounded by sci-tech folks at the Googleplex, one would be overwhelmed by talk of data collection, data visualization, data analysis, and so on. And good for them! We are being swamped by data in unprecedented forms and quantities, and it’s a crucially important task to sort it all out and understand how we can use it.

I’m just personally kind of exhausted by it all. (And it’s my blog, so if I want to bust out the occasional irrational rant, who will stop me?) Data — like theory! — is a tool we use in the quest for a higher goal — understanding. If people want to show me that they understand some unanticipated new phenomenon on the basis of some data that they collected and analyzed, I am as enthusiastic as ever. But my standards are rising for simply being impressed by new ways of gathering or visualizing data for its own sake.

At least, for the moment. Next time I see a really pretty picture, I’ll undoubtedly forget I said any of this.

Caltech researchers Lulu Qian and Erik Winfree have managed to coax 130 strands of DNA into performing what is unquestionably a calculation: taking the square root of a number. (Ars Technica post; Science paper behind paywall; open-access background paper.) Not a big number: we’re talking about four-digit binary numbers, so 15 at the biggest. And not very efficiently: with prodding, the calculation took eight hours. Moore’s Law isn’t really in danger here.

Still, pretty cool stuff. Mostly it’s interesting because it seems scalable: the authors claim that this kind of circuit architecture could be made much larger. It’s not the first biochemical circuit; RNA and bacterial colonies have been made into logic gates. But it’s the first to do something as elaborate as taking a square root.

Best of all, the authors decided to illustrate their method for a wide audience by means of a … whimsical YouTube video! Let’s hope this idea catches on.

Michael Nielsen gave a great talk at TEDxWaterloo about the idea of “open science”:

There’s a great deal of buzz about “openness” in certain sectors of the science community; largely this has passed physics and astronomy by, because we’re already pretty darn open. It’s hard to image something more open than arxiv, where everyone puts their papers up for free even before they’re published in a journal.

But Michael’s talking about something much more ambitious: opening the process of creating science, not just publishing it. For experimentalists this would be difficult, for obvious reasons. (You think people who sweat to build an experiment are going to invite the public in to take a whirl?) For theory it is also hard, but the reasons are more subtle.

The point is that credit in science is given out on the basis of getting your name on published papers. In the arxiv era, the papers don’t necessarily have to appear in a traditional journal — but that’s a topic for another day. The model is set in stone: you have an idea, you work out its consequences to the point where it’s publishable, and you write a paper. Without that last step, you’re not going to get any credit. (Very occasionally you will see references to “unpublished work” or “private communication,” but it’s rare and not really for big-ticket ideas.)

So if I had an idea, I would either work it out myself or start working with students or collaborators. I certainly would not go around publicizing an undeveloped idea; I wouldn’t get any credit for it, and someone else could take it and develop it themselves. I might give seminars in which I mention the idea, but that’s only recommended once it’s to the point where a paper is on the horizon.

Michael and others want to overthrow that model. Their shining example is this blog post by Tim Gowers. Gowers is a mathematician who proposed attacking an open math problem right there on his blog, by asking for comments from the crowd. If they succeeded, they could publish a paper under a collective pseudonym. He next chose a problem — developing a combinatorial approach to the Hales-Jewett theorem — and, several hundred comments later, announced that they had succeeded. Here’s the paper. Buoyed by this success, people have set up a Polymath Wiki to expedite tackling other problems in this way.

Could this work for theoretical physics? I don’t see why not. But note that Michael spends a lot of his time in the talk pointing out the obvious — crowdsourcing doesn’t always work. I could easily imagine ways in which such a project could fail; too much noise and not enough signal, everyone with good ideas deciding they would rather work on them by themselves rather than sharing openly, etc.

Might be worth a shot, though. I’m thinking of suggesting some ideas here on this blog and seeing whether we get any useful input. Let me sleep on it.

Check it out, they have a great animated reconstruction of Spitz’s daily movings, combined with a sidebar display saying how many phone conversations he was having and how many text messages. There’s even a spreadsheet so you can play with the data yourself if you are so inclined. They removed the actual phone numbers with which he was communicating, but of course the phone company has those.

People can decide for themselves whether this is intrusive or benign; more than a few people put nearly as much information online anyway, without thinking twice. But you should know that it’s out there.

In the modern world, there are an awful lot of devices out there communicating by shooting electromagnetic waves at each other. In particular in the radio frequency range (roughly between 10 kHz and 300 GHz), which has the nice property that its waves aren’t blocked by annoying things like walls or air. This means that everyone building such devices wants to produce waves at some part of the spectrum, and that in turn means that the right to do so is an extraordinarily valuable commodity. In the US, the Federal Communications Commission gets to decide who can do what at various different radio frequencies.

This state of affairs has come into the news once again, as wireless carrier AT&T has swallowed competitor T-Mobile; many people would be unsurprised if Verizon counters by swallowing Sprint, leaving us with a duopoly and possibly giving consumers the squeeze. Currently big chunk of spectrum is allocated to broadcast TV, which some are arguing is a waste, since you could stick a lot of mobile data devices in there and everyone has cable anyway.

All very fascinating, but somewhat over my head. I’m more of a theoretical kind of guy. I just wanted an excuse to link to this gorgeous chart (pdf), showing how the spectrum is currently allocated.

Click for much bigger and more legible pdf version. There’s a lot going on here; see the zoom-in of a tiny region near 30 GHz:

Nice to see that there is space carved out for scientific research, including radio astronomy. Those jiggling electrons have a lot of work to do, let’s hope they can keep everything straight.

The earthquake happened at 2:26pm. Two minutes later, the Fukushima-Daiichi nuclear plant went into SCRAM mode, and shut down. The control rods were inserted. The diesel generators fired up. Everything worked to plan.

The Fukushima-Daiichi plants are boiling water reactors. In simplest terms, this is just a pile of radioactive material (generally uranium) which gets hot (literally hot, not just radioactive). You run water over it, generate superheated water and steam, drive a turbine, and produce electricity. Instead of burning coal, you use radioactive decay as the source of heat, but otherwise the basic mechanism is surprisingly similar to a conventional power plant. You turn off a nuclear reactor by inserting control rods, which absorb a lot of the neutrons, and inhibit further fission reactions. So, two minutes after the quake, the control rods were inserted, and the reactors were no longer undergoing nuclear fission. However, one of the peculiarities of nuclear power is that even after the reactor is shut “off” there is still a significant amount of residual radioactive material. This material continues to decay, generating significant heat (>10 megawatt; by now [almost a week later] it’s ~1 megawatt, enough to power a thousand homes). Thus, even after turning a reactor off, it still generates significant power for a few weeks, and the resulting heat needs to be removed and the radioactive core kept cool . And to do this, you need to pump in a lot of water (ideally thousands of gallons/min) at high pressure. And this requires a fair amount of power.

The plant was working perfectly for roughly 30 minutes after the earthquake. The tsunami was on its way, but the plant operators were blind to it. Had they known, they could have depressurized the nuclear cores in anticipation. But they were focused on riding out the earthquake, which they did admirably. And then the tsunami hit. Just a few years ago, after the tsunami in the Indian Ocean, the Fukushima-Daiichi plant was upgraded to deal with a worst-case, 5.3 meter tsunami. The wave that hit the plant last Friday was roughly 10 meters high. It swamped the diesel generators, as well as the fuel tanks and the switching station. The system was “live” because of the SCRAM, and the local electrical grid got fried. Fortunately there were backup batteries, which lasted another 9.5 hours. At around midnight the batteries ran out of power, and the plant was no longer able to cool its reactor cores. At this point, the Troubles began.

As the core starts to heat up, it boils off the surrounding water. Eventually the fuel rods are exposed to the air. This causes the core to heat up even faster, and also causes a reaction with the zirconium cladding (which holds the uranium fuel pellets in place), generating hydrogen gas. Without any cooling, the fuel gets hot (> 1500 K/2200 F), and starts to melt. The hydrogen gas collects, and eventually explodes (think Hindenberg). This happened in reactor #1 on Saturday, blowing the roof off of the reactor building, but leaving the containment vessel (which is ~1 meter thick steel) intact. On Monday a similar explosion happened to #3, and on Tuesday there was an explosion at #2. Both of their containment vessels were probably compromised. Rupturing a containment vessel is very bad. So long as most of the radioactive material is contained, the damage to the outside world is similarly contained (modulo venting of various radioactive gas, which has been happening, but not at profoundly dangerous levels). Once a containment vessel is ruptured, the radioactive material can end up anywhere; the sky’s the limit. Chernobyl did not have a containment vessel.

The current situation seems to be that seawater is being pumped into all three broken reactors (#1–3), and they are in thermal control. It seems likely that all three sets of fuel rods are partially melted and damaged. It also seems likely that the containment vessels in #2 and #3 have been compromised, although probably not severely. There are some concerns about spent fuel rods in pools near reactors #3 and #4. So long as the rods are covered in sufficient water, they are stable. If the rods are exposed, they heat up. And when they get hot, they start to burn through their cladding, and emit radioactive material. These pools are not within containment vessels, and therefore they are potentially even more dangerous than the cores of active reactors. Their radioactive emission goes directly into the surroundings. But so long as there is water in the pools, they should be fine. The latest claim (by the Chairman of the United States Nuclear Regulatory Commission) is that the storage pool at the #4 reactor has little to no water. If true, this is a very ominous development.

This is by far the most dire situation on the planet at the moment. It has the world’s attention. We’ve had almost a week. Why can’t we just fix it? There are a number of serious complications. First, there’s the issue of radiation. People are unable to walk up to most of the buildings and see what’s going on, lest they get immediate and severe radiation poisoning. There are remote sensors and cameras, but fundamentally everyone is guessing as to what’s happening inside. Even if we knew exactly how things looked, it’s still a major engineering feat to get the appropriate amount of water running through these highly complex systems to do the cooling. There have been explosions, there are stuck valves, there are broken pumps, there are ongoing fires. The world’s resources are focused on this problem. Millions of lives potentially depend upon the outcome. And, thus far, progress has been haphazard and halting, despite heroic efforts on the part of the Japanese crew. The engineering challenges may simply be too great.

The worst-case scenario for the Daiichi reactors plays out something like this: 1. the storage pool at #4 is indeed dry. Because it’s uncontained, the radiation levels in the area get very high. Everyone needs to evacuate the complex. 2. Without anyone manning the cooling systems, the cooling stops. Everything overheats. 3. There are various explosions, resulting in a breach to a containment vessel. 4. There is a subsequent steam explosion, and a plume of radioactive material is generated. 5. Wind carries the plume in the direction of Tokyo (world’s largest metropolis), a mere 140 miles (225 km) away. We can’t even contemplate trying to evacuate and treat a city of 35 million people. As far as I can tell, things do not appear to be headed in this direction. But such an outcome is unfortunately not outside the realm of possibility, and just contemplating this should freak you out. But, to reiterate, it’s very unlikely, and a lot of things would have to go catastrophically wrong. I’d love to quantify just how unlikely, but cannot. My guess is that nobody can, since there are too many uncertainties, and we’re fundamentally in uncharted territory.

The best-case scenario, and probably most likely, is that the Fukushima-Daiichi plant will limp along, but without any catastrophic events (such as a major Chernobyl-style radioactive explosion and fire). The fuel will continue to cool, the fires will be put out, the amount of radiation will subside, and eventually the entire site will be entombed and become a testament to human hubris.

The dream for a superconducting proton synchrotron at Fermilab goes back to at least 1976, when it began to become clear that the interesting mass range to explore in order to understand the weak interaction would be around 100 GeV. The lab was engaged in a wide range of fixed target experiments, using the Fermilab Main Ring proton synchrotron as its workhorse, and in 1977 the b (or bottom) quark was discovered there. This meant there had to be a top quark, as well as very massive (80-100 GeV) W and Z bosons.

But Europe pulled ahead – it already had the Super Proton Synchrotron, and plans to convert it into a proton-antiproton collider. Whoever did so first would have the energy to produce W’s and Z’s directly, and nail down their masses. And maybe, whoever managed to create the first high energy proton-antiproton collider would be able to find the top quark, whose mass could be, well, just about anything above the b quark mass of 5 GeV, but probably at least 20 GeV.

Fermilab designed a new generation of superconducting magnets to operate at 3 Tesla, and began construction of the new ring, then dubbed the Saver (short for Energy Saver, since Energy Doubler, the initial name, sounded too expensive). At the same time CERN developed its antiproton source and the techniques for cooling the antiproton bunches for injection to the new SppS, the Super Proton-Antiproton Synchrotron (there’s a bar over the second p).

CERN won the race for the W and Z. In early 1983, not long after commissioning the new complex, handfuls of these massive carriers of the weak force were observed by the UA1 experiment, and confirmed by UA2. Carlo Rubbia, who lead UA1, and Simon van der Meer, who designed the antiproton source, shared the Nobel the next year. Would the top quark soon follow?

But in late 1983, the Fermilab Saver turned on, running extracted proton beams to the new raft of fixed target experiments awaiting them. My graduate thesis experiment, E615, was among them, and I count it among some of the most thrilling science moments in my career to have had my face plastered to the viewer of an oscilloscope, eyes dark-adapted, waiting for the first pulses from the counters in our experiment to tell me the secondary pion beam had arrived to us. The chain of injectors, a thousand new superconducting magnets, RF kickers, beam separators…it all worked! There were the pions! We went on, over the next year or so, to collect tons (for us) of data on the structure of the pion, still the best measurement to date.

UA1 and UA2 continued on, and CERN commenced deep tunnel drilling for LEP, the Large Electron Positron collider, slated to begin in 1988. It was designed to directly produce thousands of Z bosons by colliding electrons and positrons (antielectrons), and measure the Z as precisely as posible. At SLAC at Stanford, construction was underway to build the SLC, the SLAC Linear Collider, which would challenge LEP in the race for the Z. The age of the great colliders was in full blossom.

Fermilab commissioned its antiproton source in 1985 and 1986, and began collisions, redubbing the machine the Tevatron, as the ultimate goal was to reach 1 TeV per beam of energy – a trillion volts! The first engineering run was 800 GeV per beam, and much work remained to be done to get the beam intensity high enough to produce useful numbers of W’s and Z’s and surpass the SppS results.

SLC and LEP began operating in 1989 (a year late for LEP) but the Mark II experiment at the SLAC and the four (!) LEP experiments immediately made two important discoveries. First, by measuring the Z production rate, there appeared to be only three species of neutrino to which the Z could decay. This meant a fourth generation of quarks and leptons did not appear to be ready to be discovered. The second important SLC/LEP discovery was that the top quark must be rather massive, 150 GeV or more. This put it out of range of the electron positron machines, and meant that the top could be discovered soon at the Tevatron, if it could collide enough protons and antiprotons.

While Europe put its hopes of discovery in the electron positron machine, the US community was already building the biggest machine of all, the Superconducting Supercollider, or SSC, in Waxahachie, Texas. This mammoth proton-proton collider with 54 miles of superconducting magnets would begin operation in 1999 at an energy 20 times that of the Tevatron. “Throw deep” was Ronald Reagan’s exhortation to the physicists when presented with the project, and deep they threw. Too deep perhaps – by 1993, the cost of the project had grown too rapidly, and, in a cost-cutting mood not unlike the present day, in October of that year the US Congress killed the project, which had spent about $2 billion, and dug almost 15 miles of tunnel (now backfilled).

The demise of the SSC was a serious blow to the US high energy community, and left one option for staying on the high energy frontier: an enhanced Tevatron complex. But it took another half decade after the LEP startup to amass the data sample necessary to get the first glimpse of top-antitop production in the CDF and D0 experiments at Fermilab. In early 1995, during “Run 1″ of the Tevatron, both experiments announced the discovery of excess production of events consistent with that expected from top production. The mass of the top turned out to be a whopping 175 GeV. Pinning it down, and also measuring the mass of the W would answer the ultimate question: where is the Higgs boson lurking? Can it be seen at the Tevatron? Or would LEP2, with ever-increasing energy in the late 1990′s, get it first? Or would the Large Hadron Collider, scheduled to begin colliding protons in the LEP tunnel at CERN in 2004, be the first to see it?

Fermilab completed construction of the new Main Injector and Antiproton Recycler as the millennium turned, and cast its eye upon the Higgs boson. The two new additions to the Fermilab accelerator complex would allow a huge increase in the beam intensities, possibly enough to produce the very weakly interacting Higgs. I helped lead a study which, in late 2000, published the prediction: it would take somewhere around 15 inverse femtobarns of integrated luminosity, plus major upgrades to the experiments, to find the Higgs boson, at least the vanilla Standard Model one.

LEP2, in late 2000, was facing its own imminent shutdown to make way for the LHC, having reached 206 GeV energy and a sensitivity to a Higgs boson of at least 114.5 GeV, but had no evidence for the Higgs boson. The accelerator and detectors began to be removed the next year, 2001, just as the Tevatron began Run 2.

The first years of Run 2 at the Tevatron were plagued by problems, some due to the then-aging infrastructure, some from the years of downtime since 1996, and some due the fact that, well, colliding matter and antimatter in large quantities is just damned hard to do. There was a learning curve, and by 2003 the lab was on track to meet the design goals for the collider. Large new samples began to roll in, and physics papers began to roll out. But getting to even 10 inverse femtobarns before the LHC turned on seemed to be a receding hope.

The LHC was having difficulties of its own. The plan to operate in 2004, became 2005, then 2006. A somewhat snide plot of the projected start date as a function of time showed that the machine would start in late 2008.

Which it did. But then, as faithful readers of CV know, the LHC had its rather spectacular failure triggered by a faulty magnet interconnect, and propagated by a detection/avoidance system that failed to detect the condition and was insufficient to avoid the massive boil-off of six tons of liquid helium. The resulting damage took a year and 35 million Euros to repair.

In these years, the Tevatron marched on, reaching its first inverse femtobarn by mid 2005, then setting new records almost routinely in the following years. The huge new sample provided a treasure trove for us data-starved particle hunters. And it became clear that with the slow turn-on of the LHC, the Tevatron had a chance, albeit a slim one, to discover the Higgs or some other new physics before the LHC. And so in 2009 the run of the Tevatron was extended until late 2011. The machine has kept running, kept improving, and we now hope to have 10 fb^-1 by the time we shut down.

Will the Tevatron experiments see the SM Higgs boson before the LHC? It is looking quite doubtful, but there is some chance the run will end with a tantalizing excess. How long will it take the LHC? With the new data collected last year, only a tiny fraction of the Tevatron sample but at much higher energy, we are beginning to see the physics power of the new machine, and I owe you all a lot of posts on the picture beginning to emerge. But the Higgs boson could turn out to be at the hardest-to-discover mass of all at the LHC, around 120 GeV, in which case the LHC will almost certainly need to run well beyond the end of 2012 to get enough data. It may take as much as 10 inverse femtobarns at the LHC to see it at the golden five-sigma level. But of course we’ll get excited a long time before that.

And we know that the Higgs boson cannot be the one of the Standard Model, right? More on that tantalizing prospect later. (And three years ago from the Tevatron.)

Video via Steinn.

However, in a recent blog post I included a video of stars orbiting the supermassive black hole at our galactic center (not Hollywood effects; this is real data, of real stars orbiting our neighborhood supermassive black hole). The movie comes from Andrea Ghez‘s group at UCLA; I put it up on YouTube so I could trivially embed it in the post. Within 24 hours, the video had received over 50,000 views. I find this number staggering, and immensely encouraging. I love the idea that 50,000 people, from all walks of life and from across the globe, are brought together to watch a movie of stellar orbits around a black hole.

It’s increasingly apparent that these social media tools aren’t just mindless fads. They represent something radically new and empowering. Although I’m still somewhat unclear as to how to harness their power for good.

The plot shows what we call “integrated luminosity” which is simply a measure of the number of collisions of protons in the interaction regions at the four experiments. In this case, it’s my own experiment, CMS, the Compact Muon Solenoid experiment. CMS and ATLAS are the two large general-purpose detectors, each with thousands of physicists eager for real physics data.

As you can see, the vertical axis of the plot is labelled in units of “nb^-1” or inverse nanobarns. The unit “barn” is a unit of area, a kind of joke from Enrico Fermi and friends who, despite the tiny size of a nucleus, said it was “as big as a barn” even though in cross sectional area it’s on the order of 10^-28 m² (which is in fact the definition of one barn). If we think about the cross sectional area of the protons colliding in the LHC, they have a cross sectional area (or simply a total collision cross section) of about 0.12 barns.

So what’s an inverse nanobarn? Well, if we try to collide lots of protons, we might ask “how many collisions per barn or cross sectional area did we make?” It’s like throwing little paint blobs at a wall, one at a time. Eventually the wall is covered, and then covered again, and then covered many times over. We can ask “how many paint blobs per unit area of the wall did we cover?” The nano in nanobarns means one billionth of a barn, and so, now, the LHC has managed to produce its first inverse nanobarn: one collision per every billionth of a barn of cross section.

It’s just a unit – all that matters is “how many collision events of my favorite kind should have been produced?” To get this, you multiply the number of inverse nanobarns by the production cross section for that kind of event, and also by the probability that you actually detect it. So for Z boson production, for example, the cross section is about 30 nanobarns, so we should have a few by now. (I am not at liberty to say whether we do or not…)

The plot has stair steps – the horizontal axis is real time, and the LHC machine is filled with protons, then brought to full energy, then collimators put in, then the experiment turns on and records data for some time until the accelerator folks decide to dump the beam out and refill. As you can see this cycle has been going like clockwork, with fill after fill of the machine. And the experiment has been recording a very large fraction of the delivered collisions, the losses being quite normal and due to end effects and the occasional glitch.

But then came the LHC baby’s first real step last weekend: squeezing the beam. By raising the quadrupole beam focusing magnets to high field, the transverse size of proton bunches in the machine shrinks down and the probability of collisions goes up. In this case, the luminosity went up by an order of magnitude – it was a stunning success. Any imperfection in the focusing fields can send the beam right out of the machine, and, clearly, that did not happen.

The goal in the next year is to get to one inverse femtobarn – a million times more data. In the next week or so the plan, if all goes well, is to achieve another couple orders of magnitude in luminosity. Shit’s about to get real, folks…

With this varied background, Malcolm studies connections between biomechanics and neuroscience — how do brains and bodies interact? This unique expertise helped land him a gig as the science advisor on Caprica, the SyFy Channel’s prequel show to Battlestar Galactica. He also blogs at Northwestern’s Science and Society blog. It’s a pleasure to welcome him to Cosmic Variance, where he’ll tell us about robots, artificial intelligence, and war.

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It’s a pleasure to guest blog for CV and Sean Carroll, a friend of some years now. In my last posting back at Northwestern University’s Science and Society Blog, I introduced some issues at the intersection of robotics, artificial intelligence (AI), and morality. While I’ve long been interested in this nexus, the most immediate impetus for the posting was meeting Peter Singer, author of the excellent book ‘Wired for War’ about the rise of unmanned warfare, while simultaneously working for the TV show Caprica and a U.S. military research agency that funds some of the work in my laboratory on bio-inspired robotics. Caprica, for those who don’t know it, is a show about a time when humans invent sentient robotic warriors. Caprica is a prequel to Battlestar Galactica, and as we know from that show, these warriors rise up against humans and nearly drive them to extinction.

Here, I’d like to push the idea that as interesting as the technical challenges in making sentient robots like those on Caprica are, an equally interesting area is the moral challenges of making such machines. But “interesting” is too dispassionate—I believe that we need to begin the conversation on these moral challenges. Roboticist Ron Arkin has been making this point for some time, and has written a book on how we may integrate ethical decision making into autonomous robots.

Given that we are hardly at the threshold of building sentient robots, it may seem overly dramatic to characterize this as an urgent concern, but new developments in the way we wage war should make you think otherwise. I heard a telling sign of how things are changing when I recently tuned in to the live feed of the most popular radio station in Washington DC, WTOP. The station had commercial after commercial from iRobot (of Roomba fame), a leading builder of unmanned military robots, clearly targeting military listeners. These commercials reflect how the use of unmanned robots in the military has gone from close to zero in 2001 to over ten thousand now, with the pace of acquisition still accelerating. For more details on this, see Peter Singer’s ‘Wired for War’, or the March 23 2010 congressional hearing on The Rise of the Drones here.

While we are all aware of these trends to some extent, it’s hardly become a significant issue of concern. We are comforted by the knowledge that the final kill decision is still made by a human. But is this comfort warranted? The importance of such a decision changes as the way in which war is conducted, and the highly processed information supporting the decision, becomes mediated by unmanned military robots. Some of these trends have been helpful to our security. For example, the drones have been effective against the Taliban and Al-Qaeda because they can do long-duration monitoring and attacks of sparsely distributed non-state actors. However, in a military context, unmanned robots are clearly the gateway technology to autonomous robots, where machines can eventually be in the position to make decisions that have moral weight.

“But wait!” many will say, “Isn’t this the business-as-usual-robotics-and-AI-are-just-around-the-corner argument we’ve heard for decades?” Robotics and AI have long been criticized as promising more than they could deliver. Are there signs that this could be changing? While an enormous amount could be said about the reasons for the past difficulties of AI, it is clear that some of its past difficulties stem from having too narrow a conception of what constitutes intelligence, a topic I’ve touched on for the recent Cambridge Handbook of Situated Cognition. This narrow conception revolved around what might loosely be described as cognitive processing or reasoning. Newer types of AI and robotics, such as embodied AI and probabilistic robotics, tries to integrate some of the aspects of what being more than a symbol processor involves: for example, sensing the outside world and dealing with the uncertainty in those signals in order to be highly responsive, and emotional processing. Advanced multi-sensory signal processing techniques such as Bayesian filtering were in fact integral to the success of Stanley, the autonomous robot that won DARPA’s Grand Challenge to drive without human intervention across a challenging desert course.

As these prior technical problems are overcome, autonomous decision making will become more common. Eventually, this will raise moral challenges. One area of challenge will be how we should behave towards artifacts, be they virtual or robotic, which are endowed with such a level of AI that how we treat them becomes an issue. On the other side, how they treat us becomes a problem, most especially in military or police contexts. What happens when an autonomous or semi-autonomous war robot makes an error and kills an innocent? Do we place responsibility on the designers of the decision making systems, the military strategists who placed machines with known limitations into contexts they were not designed for, or some other entity?

Both of these challenges are about morality and ethics. But it is not clear whether our current moral framework, which is a hodgepodge of religious values, moral philosophies, and secular humanist values, is up to responding to these challenges. It is for this reason that the future of AI and robotics will be as much a moral challenge as a technical challenge. But while we have many smart people working on the technical challenges, very few are working on the moral challenges.

How do we meet the moral challenge? One possibility is to look toward science for guidance. In my next posting I’ll discuss some of the efforts in this direction, pushed most recently by a new activist form of atheism which holds that it is incorrect to think that we need religion to ground morality, and even dangerous. We can instead, they claim, look to the new sciences of happiness, empathy, and cooperation for guiding our value system.

Via Tom Levenson:

Let’s assume that the buses are supposed to arrive every 15 minutes. If the buses adhered to their schedule, and I showed up at a random time, I should generally have to wait roughly half the mean bus arrival time: 7.5 minutes. If the buses were totally random, then I would have to wait the average time between bus arrivals: 15 minutes (if you haven’t thought about this before, this statement should sound crazy; perhaps I’ll do a future post on it). So the question is: why did I always end up waiting roughly 30 minutes or more?

I always assumed that the Universe was conspiring against me. This is a common feeling in graduate school. However….

I just stumbled across a blog post of a friend of mine from graduate school, Alex Lobkovsky. In it, he discusses precisely this problem, and presents various reasons for the bunching of buses. I have no doubt that he was inspired from similar suffering. Perhaps at the very same bus stop.

At the end of the day, there’s a fairly straightforward solution. Imagine all of the buses are roughly on time. Now imagine that one bus (call it bus S) happens to fall behind. Because S is running behind, more time has elapsed since the previous bus has passed. This means that more waiting passengers have accumulated, at more bus stops. This in turn means that bus S has to stop more often, and has to pick up more people at each stop. Hence, bus S falls even farther behind. Which means even more people accumulate at each stop. Which means the bus falls even farther behind. And so on. In short: a slow bus gets slower and slower.

Now let us consider the bus behind bus S; we’ll call it bus F. Bus F starts out roughly on schedule. But because bus S is running late, less time than average has elapsed between when bus S last passed and when bus F arrives. This means fewer people have accumulated, at fewer stops. Which means bus F makes fewer stops, and picks up fewer people. Which means that it starts to run faster than average. Which means even fewer people accumulate. Which means it runs even faster. And so on. In short: a fast bus gets faster and faster.

Putting this all together: if a random fluctuation creates a slow bus, then it will get slower and slower, and the bus behind it will get faster and faster, until the two buses meet up. At this point, the buses stick together, and are essentially incapable of separating. Thus, in general, buses will bunch up. This will usually happen in pairs, though on occasion triples and even quads may occur. This argument predicts that the arrival of buses will be random, with pairs of buses arriving more often than not, being separated by on average double the mean bus separation. And this is precisely what I discovered, the hard way, shivering at the corner of 55th St. and Hyde Park Boulevard. (N.B. I spent a year in Berlin. There, the buses are fermions, and always arrive exactly on time. It’s the stereotype, but it turns out to be true.)

After writing this post, I found that wikipedia has already figured it all out. Regardless, it’s nice to know that my suffering was due to statistics, and not because the Universe is out to get me.

McGonigal makes some good points in this short video, especially about how dealing with things in a video-game environment — like failure, or social interactions — can be greatly helpful when one eventually has to deal with them in the real world. She also helped put together Urgent Evoke, a large-scale multiperson game where you collect achievements by performing world-saving tasks.

The kids these days, they love their gaming. So it makes sense to ask how that passion can be put to good use. Personally I’m fascinated by the prospects of using games to teach people science. Not just facts and features of the real world — although those are important — but the scientific method of hypothesis-testing and experiment. Games already feature exactly those features, of course; everyone who figures out the “laws of nature” in the game world is secretly doing science. It wouldn’t be that hard to tweak things here and there so that the techniques they were practicing connected more directly with science in the non-virtual reality.

In November and December the LHC commissioning resumed, and it became the world’s highest energy collider on December 8, eventually delivering about 50,000 collisions at 2.36 TeV to CMS and ATLAS before shutting down for Christmas.

But the question facing the LHC managers this week was whether attempting to operate the LHC at 5 TeV on 5 TeV in 2010 was worth the risk to the machine itself. Clearly another disaster of the scale of the one in 2008 would cripple the program for a long time. In the end the decision is to operate the LHC at 3.5 TeV on 3.5 TeV (7 TeV collision energy, 3.5 times that of the Tevatron) and accumulate a substantial amount of physics-quality data: 1 inverse femtobarn, or stop by the end f 2011, whichever comes first. This corresponds to something like ten trillion proton-proton collisions, of which only a small fraction will yield events interesting enough to record for later analysis by the experiments, and of these, only a tiny fraction yielding data relevant for physics.

After a one to one-and-a-half year shutdown in 2012 to retrofit the rest of the machine and make other preparations, the LHC will attempt to double the energy, to 14 TeV in the center of mass, in 2013 and accumulate substantial physics data. My best guess is that if the Higgs boson is to be discovered, it will be at high energy with this large sample of 14 TeV data. We might be able to rule it out at 95% confidence in certain mass ranges if it’s not there, but we ought not be able to do that if it is, right? Patience, patience!

Nevertheless, there is no question that in a few weeks, when operated at 7 TeV collision energy, the LHC will become an awesome discovery machine. There are many new physics scenarios in which we will be able to see new phenomena with just a fraction of the full 1 fb^-1 sample. Will nature give up her secrets so readily though? She may not – we may spend this year and the next rediscovering the Standard Model, building up understanding of the detector, and sharpening our analysis tools in order to discover quite subtle effects. No matter what happens, this is the most exciting time in particle physics in decades.

But many of the other answers are fascinating. Just to pick some at semi-random, I enjoyed the responses from Danny Hillis, Anthony Aguirre, Frank Wilczek, Victoria Stodden, Martin Rees, Scott Atran, Lisa Randall, Irene Pepperberg, and Clay Shirky. Keep thinking!

Yes, there is a robot that crawls around inside your colon, not to mention a Japanese emobot, but the one I would least like to meet in a dark alley is Chembot. It’s a blob-shaped thing that uses jamming in granular materials to make a robot that can alter its shape.

Still pretty primitive, but you can see where we’re headed here. Don’t say we didn’t warn you.