Research Funding Insight recently asked me to write a piece on the “future of physics”, to accompany a critique of string theory and its offshoots by Jim Baggott (see below). I wanted to take the opportunity to explain that, whatever the shortcomings of string theory might be, they most certainly do not leave physics as a whole in crisis. It is doing very nicely, because it is much, much broader than both string theory in particular and what gets called “fundamental physics” in general. So here it is. The article first appeared in Funding Insight on 7 January 2014, and I’m reproducing it here with kind permission of Research Professional. For more articles like this (including Jim Baggott’s), visit www.researchprofessional.com.
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Is physics at risk of disappearing up its own foundations? To read some of the recent criticisms of work on string theory, which seeks a fundamental explanation of all known forces and particles, you might think so. After about three decades of work, the theory is no closer to a solution – or rather, the number of possible solutions has mushroomed astronomically, while none of them is testable and they all rest on a base of untried speculations.
But while scepticism about the prospects for this alleged Theory of Everything may be justified, it would be mistaken to imagine that the difficulties are gnawing away at the roots of physics. They are the concern of only a tiny fraction of physicists, while many others consider them esoteric at best and perhaps totally irrelevant.
Don’t imagine either that the entire physics community has been on tenterhooks to see what the Large Hadron Collider at CERN in Geneva will come up with, or whether, now that it seems to have found the Higgs boson, the particle accelerator will open up a new chapter in fundamental physics that takes in such mysterious or speculative concepts as dark matter and supersymmetry (a hitherto unseen connection between different classes of particles).
Strings and the LHC are the usual media face of physics: what most non-physicists think physicists do. This sometimes frustrates other physicists intensely. “High-energy physics experiments are over-rated, and are not as significant as they were decades ago”, says one, based in the US. “Now it is tiny increments in knowledge, at excessive costs – yet these things dominate the science news.”
Given the jamboree that has surrounded the work at the LHC, especially after the award of the 2013 Nobel prize in physics to Peter Higgs (with François Englert, who also proposed the particle now known by Higgs’ name), it is tempting to dismiss this as sour grapes. But there’s more to it than the resentment of one group of researchers at seeing the limelight grabbed by another. For the perception that the centre of gravity of physics lies with fundamental particles and string theory reflects a deep misunderstanding about the whole nature of the discipline. The danger is that this misunderstanding might move beyond the general public and media and start to infect funders, policy-makers and educationalists.
The fact is that physics is not a quest for isolated explanations of this or that phenomenon (and string theory, for all its vaunted status as a Theory of Everything, is equally parochial in what it might ‘explain’). Physics attempts to discover how common principles apply to many different aspects of the physical world. It would be foolish to suppose that we know what all these principles are, but we certainly know some of them. In a recent article in Physics World, Peter Main and Charles Tracy from the Institute of Physics’ education section made a decent stab at compiling a list of what constitutes “physics thinking”. It included the notions of Reductionism, Causality, Universality, Mathematical Modelling, Conservation, Equilibrium, the idea that differences cause change, Dissipation and Irreversibility, and Symmetry and Broken Symmetry. There’s no space to explain all of these, but one might sum up many of them in the idea that things change for identifiable reasons; often those reasons are the same in different kinds of system; we can develop simplified maths-based descriptions of them; and when change occurs, some things (like total energy) stay the same before and after.
Many of these notions are older than is sometimes supposed. Particle physicists, for example, have been known to imply that the concept of symmetry-breaking – whereby a system with particular symmetry properties appears spontaneously from one with more symmetry – was devised in the 1950s and 60s to answer some problems in their field. The truth is that this principle was already inherent in the work of the Dutch scientist Johannes Diderik van der Waals in 1873. Van der Waals wasn’t thinking about particle physics, which didn’t even exist then; he was exploring the way that matter interconverts between liquid and gas states, in what is called a phase transition. Phase transitions and symmetry breaking have since proved to be fundamental to all areas of physics, ranging from the cosmological theory of the Big Bang to superconductivity. Looked at one way, the Higgs boson is the product of just another phase transition, and indeed some of the ideas found in Higgs’ theory were anticipated by earlier work on the low-temperature transition that leads to resistance-free electrical conductivity in some materials (called superconductivity).
Or take quantum theory, which began to acquire its modern form when in 1926 Erwin Schrödinger wrote down a ‘wavefunction’ to describe the behaviour of quantum particles. Schrödinger didn’t just pluck his equation from nowhere: he adapted it from the centuries-old discipline of wave mechanics, which describes what ordinary waves do.
This is not to say that physicists are always stealing old ideas without attribution. Quite the opposite: it is precisely because they were so thoroughly immersed in the traditions and ideas of classical physics, going back to Isaac Newton and Galileo, that the physicists of the early twentieth century such as Einstein, Max Planck and Niels Bohr were able to instigate the revolutionary new ideas of quantum theory and relativity. All the best contemporary physicists, such as Richard Feynman and the Soviet Lev Landau, have had a deep appreciation of the connections between old and new ideas. Feynman’s so-called path-integral formulation of quantum electrodynamics, which supplied a quantum theory of how light interacts with matter, drew on the eighteenth-century classical mechanics of Joseph Louis Lagrange. It is partly because they point out these connections that Feynman’s famous Lectures on Physics are so revered; the links are also to be found, in more forbidding Soviet style, in the equally influential textbooks by Landau and his colleague Evgeny Lifshitz.
The truly profound aspect of central concepts like those proposed by Main and Tracy is that they don’t recognize any distinctions of time and space. They apply at all scales: to collisions of atoms and bumper cars, to nuclear reactions and solar cells. It seems absurd to imagine that the burst of ultrafast cosmic expansion called inflation, thought to be responsible for the large-scale structure of the universe we see today, has any connection with the condensation of water on the windowpane – but it does. Equally, that condensation is likely to find analogies in the appearance of dense knots and jams in moving traffic. Looked at this way, what is traditionally called fundamental physics – theories of the subatomic nature of matter – is no more fundamental than is the physics of sand or sound. It merely applies the same concepts at smaller scales.
This, then, is one important message for physics education: don’t teach it as a series of subdisciplines with their own unique set of concepts. Or if you must parcel it up in this way, keep the connections at the forefront. It’s also a message for students: always consider how the subject you’re working on finds analogues elsewhere.
All this remains true even while – one might even say especially as – physics ventures into applied fields. It’s possible (honestly) to see something almost sublime in the way quantum theory describes the behaviour of electrons in solids such as the semiconductors of transistors. On one level it’s obvious that it should: quantum theory describes very small things, and electrons are very small. But the beauty is that, under the auspices of quantum rules, electrons can get marshalled into states that mirror those in quite different and more exotic systems. They can acquire ‘orbits’ like those in atoms, so that blobs of semiconductor can act as artificial atoms. They can get bunched into pairs or other groups that travel in unison, giving us superconductivity, itself analogous to the weird frictionless superfluid behaviour of liquid helium. One of the most interesting features of the atom-thick carbon sheets called graphene is not that they will provide new kinds of touch-screen (we have those already) but that their electrons, partly by virtue of being trapped in two dimensions, can collectively behave like particles called Dirac fermions, which have no mass and move at the speed of light. The electrons don’t actually do this – they just ‘look’ like particles that do. In such ways, graphene enables experiments that seem to come from the nether reaches of particle physics, all in a flake of pencil lead on a desktop.
As graphene promises to show, these exotic properties can feed back into real applications. Other electronic ‘quasiparticles’ called excitons (a pairing of an electron with a gap or ‘hole’ in a pervasive electron ‘sea’) are responsible for the light emission from polymers that is bringing flexible plastics to screens and display technology. In one recent example, an exotic form of quantum-mechanical behaviour called Bose-Einstein condensation, which has attracted Nobel prizes after being seen in clouds of electromagnetically trapped ultracold gas, has been achieved in the electronic quasiparticles of an easily handled plastic material at room temperature, making it possible that this once arcane phenomenon could be harnessed cheaply to make new kinds of laser and other light-based devices.
There is a clear corollary to all this for allocating research priorities in physics: you never know. However odd or recondite a phenomenon or the system required to produce it, you never know where else it might crop up and turn out to have uses. That of course is the cliché attached to the laser: the embodiment of a quirky idea of Einstein’s in 1917, it has come to be almost as central to information technology as the transistor.
Does this mean that physics, by virtue of its universality, can in fact have no priorities, but must let a thousand flowers bloom? Probably the truth is somewhere in between: it makes sense, in any field of science, to put some emphasis on areas that look particularly technologically promising or conceptually enriching, as well as curbing areas that seem to have run their course. But it would be a mistake to imagine that physics, any more than Darwinian evolution, has any direction – that somehow the objective is to work down from the largest scales towards the smaller and more ‘fundamental’.
Another reason to doubt the overly reductive approach is supplied by Michael Berry, a distinguished physicists at the University of Bristol whose influential work has ranged from classical optics and mechanics to quantum chaos. “There are different kinds of fundamentality”, says Berry. “As well as high-energy and cosmology, there are the asymptotic regimes of existing theories, where new phenomena emerge, or lurk as borderland phenomena between the theories.” Berry has pointed out that an ‘asymptotic regime’ in which some parameter in a theory is shrunk to precisely zero (as opposed to being merely made very small), the outcomes of the theory can change discontinuously: you might find some entirely new, emergent behaviour.
As a result, these ‘singular limits’ can lead to new physics, making it not just unwise but impossible to try to derive the behaviour of a system at one level from that at a more ‘fundamental’ level. That’s a reason to be careful about Main and Tracy’s emphasis on reductionism. Some problems can be solved by breaking them down into simpler ones, but sometimes that will lose the very behaviour you’re interested in. “If you don’t think emergence is important too, you won't get far as a condensed matter physicist”, says physicist Richard Jones, Pro-Vice-Chancellor for Research and Innovation at the University of Sheffield.
It’s important to recognize too that the biggest mysteries, however alluring they seem, may not be the most pressing, nor indeed the most intellectually demanding or enriching. The search for dark matter is certainly exciting, well motivated, and worth pursuing. But at present it is only rather tenuously linked to the mainstream of ideas in physics – we have so few clues, either observationally or theoretically, about how to look or what we hope to find, that it is largely a matter of blind empiricism. It is usually wise not to spend too much of your time stumbling around in the dark.
With all this in mind, here are a few suggestions for where what we might call ‘small physics’ might usefully devote some of its energies in the coming years:
- quantum information and quantum optics: even if quantum computers aren’t going to be a universal game-changer any time soon, the implications of pursuing quantum theory as an information science are vast, ranging from new secure communications technologies to deeper insights into the principles that really underpin the quantum world.
- the physics of biology: this can mean many things, from understanding how the mechanics of cells determine their fate (stem cells sometimes select their eventual tissue type from how the individual cells are pulled and tugged) to the question of whether phase transitions underpin cancer, brain activity and even natural selection. This one needs handling with care: physicists are likely to go badly astray unless they talk to biologists.
- materials physics: from new strong materials to energy generation and conversion, it is essential to develop an understanding of how materials systems behave over a wide range of size scales (and that’s not necessarily a problem to tackle from the bottom up). Such knowhow is likely to be central to a scientific basis for sustainability.
- new optical technologies: you’ve probably heard about invisibility cloaks, and while some of those claims need to be taken with a pinch of salt, the general idea that light can be moulded, manipulated and directed by controlling the microstructure of materials (such as so-called photonic band-gap materials and metamaterials) is already leading to new possibilities in display technologies, telecommunications and computing.
- electronics: this one kind of goes without saying, perhaps, but the breadth and depth of the topic is phenomenal, going way beyond ways to make transistors ever smaller. There is a wealth of weird and wonderful behaviour in new and unusual materials, ranging from spintronics (electronics that uses the quantum spins of electrons), molecular and polymer electronics, and unusual electronic behaviour on the surfaces of insulators (check out “topological insulators”).
None of this is to deny the value of Big Physics: new accelerators, telescopes, satellites and particle detectors will surely continue to reveal profound insights into our universe. But they are only part of a bigger picture.
Most of all, it isn’t a matter of training physicists to be experts in any of these (or other) areas. Rather, they need to know how to adapt the powerful tools of physics to whatever problem is at hand. The common notion (or is it just in physics?) that a physicist can turn his or her hand to anything is a bit too complacent for comfort, but it is nonetheless true that a ‘physics way of thinking’ is a potential asset for any science.
Tuesday, January 14, 2014
Monday, January 13, 2014
A prize for Max von Laue
In my book Serving the Reich, I make some remarks about the potential pitfalls of naming institutions, prizes and so forth after “great” scientists, and I say that, while my three main subjects Max Planck, Werner Heisenberg and Peter Debye are commemorated in this way, Max von Laue is not (“to my knowledge”). This seemed ironic, given that during the Nazi era Laue much more obviously and courageously resisted the regime than did these others.
Crystallographer Udo Heinemann of the Max Delbrück Centre for Molecular Medicine in Berlin has pointed out to me that a Max von Laue prize does in fact exist. It is awarded by the German Crystallographic Society (Deutsche Gesellschaft für Kristallographie, DGK) annually to junior scientists for “outstanding work in the field of crystallography in the broadest sense”, and is worth 1500 euros. I have discussed elsewhere the perils of this “name game”, but given that everyone plays it, I am pleased to see that Laue has not been overlooked. It seems all the more fitting to have this pointed out during the International Year of Crystallography.
Crystallographer Udo Heinemann of the Max Delbrück Centre for Molecular Medicine in Berlin has pointed out to me that a Max von Laue prize does in fact exist. It is awarded by the German Crystallographic Society (Deutsche Gesellschaft für Kristallographie, DGK) annually to junior scientists for “outstanding work in the field of crystallography in the broadest sense”, and is worth 1500 euros. I have discussed elsewhere the perils of this “name game”, but given that everyone plays it, I am pleased to see that Laue has not been overlooked. It seems all the more fitting to have this pointed out during the International Year of Crystallography.
Thursday, January 09, 2014
The cult of the instrument
I have a piece in Aeon about instruments in science. Here’s how it looked at the outset.
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Whenever I visit scientists to discuss their research, there always comes a moment when they say, with pride they can barely conceal, “Do you want a tour of the lab?” It is invariably slightly touching – like Willy Wonka dying to show off his factory. I’m always glad to accept, knowing what lies in store: shelves bright with bottles of coloured liquid and powders, webs of gleaming glass tubing, slabs of perforated steel holding lasers and lenses, cryogenic chambers like ornate bathyspheres whose quartz windows protect slivers of material about to be raked by electron beams.
It’s rarely less than impressive. Even if the kit is off-the-shelf, it will doubtless be wired into a makeshift salmagundi of wires, tubes, cladding, computer-controlled valves and rotors and components with more mysterious functions. Much of the gear, however, is likely to be home-made, custom-built for the research at hand. The typical lab set-up is, among other things, a masterpiece of impromptu engineering – you’d need degrees in electronics and mechanics just to put it all together, never mind how you make sense of the graphs and numbers it produces.
All this usually stays behind the scene in science. Headlines announcing “Scientists have found…” rarely bother to tell you how those discoveries were made. And would you care? The instrumentation of science is so highly specialized that it must often be accepted as a kind of occult machinery for producing knowledge. We figure they must know how it all works.
It makes sense in a way that histories of science tend to focus on the ideas and not the methods – surely what matters most is what was discovered about the workings of the world? But most historians of science today recognize that the relationship of scientists to their instruments is an essential part of the story. It is not simply that the science is dependent on the devices; rather, the devices determine what is known. You explore the things that you have the means to explore, and you plan your questions accordingly. That’s why, when a new instrument comes along – the telescope and the microscope are the most thoroughly investigated examples, but this applies as much today as it did in the seventeenth century – entirely new fields of science can be opened up. Less obviously, such developments demand a fresh negotiation between the scientists and their machines, and it’s not fanciful to see there some of the same characteristics as are found in human relationships. Can you be trusted? What are you trying to tell me? You’ve changed my life! Look, isn’t she beautiful? I’m bored with you, you don’t tell me anything new any more. Sorry, I’m swapping you for a newer model.
That’s why it is possible to speak of interactions between scientists and their instruments that are healthy or dysfunctional. How do we tell one from the other?
The telescope and microscope were celebrated even by their first users as examples of the value of enhancing the powers of human perception. But the most effective, not to mention elegant, scientific instruments serve also as a kind of prosthesis for the mind: they emerge as an extension of the experimenter’s thinking. That is exemplified in the work of the New Zealand physicist Ernest Rutherford, perhaps the finest experimental scientist of the twentieth century. Rutherford famously preferred the sealing-wax-and-string approach to science: it was at a humble benchtop with cheap, improvised and homespun equipment that he discovered the structure of the atom and then split it. This meant that Rutherford would devise his apparatus to tell him precisely what he wanted to know, rather than being limited by someone else’s view of what one needed to know. His experiments thus emerged organically from his ideas: they could almost be seen as theories constructed out of glass and metal foil.
Ernest Rutherford’s working space in the Cavendish Laboratory, Cambridge, in the 1920s.
In one of the finest instances, at Manchester University in 1908 Rutherford and his coworkers figured out that the alpha particles of radioactive decay are the nuclei of helium atoms. If that’s so, then one needs to collect the particles and see if they behave like helium. Rutherford ordered from his glassblower Otto Baumbach a glass capillary tube with extraordinarily thin walls, so that alpha particles emitted from radium could pass right through. Once they had accumulated in an outer chamber, Rutherford connected it up to become a gas-discharge tube, revealing the helium from the fingerprint wavelength of its glow. It was an exceedingly rare example of a piece of apparatus that answers a well defined question – are alpha particles helium? – with a simple yes/no answer, almost literally by whether or not a light switches on.
A more recent example of an instrument embodying the thought behind it is the scanning tunnelling microscope, invented by the late Heinrich Rohrer and Gerd Binnig at IBM’s Zurich research lab in the 1980s. They knew that electrons within the surface of an electrically conducting sample should be able to cross a tiny gap to reach another electrode held just above the surface, thanks to a quantum-mechanical effect called tunnelling. Because tunnelling is acutely sensitive to the width of the gap, a needle-like metal tip moving across the sample, just out of contact, could trace out the sample’s topography. If the movement was fine enough, the map might even show individual atoms and molecules. And so it did.
A ring of iron atoms on the surface of copper, as shown by the scanning tunnelling microscope. The ripples on the surface are electron waves. Image: IBM Almaden Research Center.
Between the basic idea and a working device, however, lay an incredible amount of practical expertise – of sheer craft – allied to rigorous thought. Against all expectation (they were often told the instrument “should not work” on principle), Rohrer and Binnig got it going, invented perhaps the central tool of nanotechnology, and won a Nobel prize in 1986 for their efforts.
So that’s when it goes right. What about when it doesn’t?
Scientific instruments have always been devices of power: those who possess the best can find out more than the others. Galileo recognized this: he conducted a cordial correspondence with Johannes Kepler in Prague, but when Kepler requested the loan of one of Galileo’s telescopes the Italian found excuses, knowing that with one of these instruments Kepler would be an even more serious rival. Instruments, Galileo already knew, confer authority.
But now instruments – newer, bigger, better – have become symbols of prestige as never before. I have several times been invited to admire the most state-of-the-art device in a laboratory purely for its own sake, as though I am being shown a Lamborghini. Historian of medical technology Stuart Blume of the University of Amsterdam has argued that, as science has started to operate according to the rules of a quasi-market, the latest equipment serves as a token of institutional might that enhances one’s competitive position in the marketplace. When I spoke to several chemists recently about their use of second-hand equipment, often acquired from the scientific equivalent of eBay, they all asked to remain anonymous, as though this would mark them out as second-rate scientists.
One of the dysfunctional consequences of this sort of relationship with an instrument is that the machine becomes its own justification, its own measure of worth – a kind of totem rather than a means to and end. A result is then “important” not because of what it tells us but because of how it was obtained. The Hubble Space Telescope is (despite its initial myopia) one of the most glorious instruments ever made, a genuinely new window on the universe. But when it first began to send back images of the cosmos in the mid 1990s, Nature would regularly receive submissions reporting the first “Hubble image” of this or that astrophysical object. The authors would be bemused and affronted when told that what the journal wanted was not the latest pretty picture, but some insight into the process it was observing – a matter that required rather more thought and research.
This kind of instrument-worship is, however, at least relatively harmless in the long run. More problematic is the notion of instrument as “knowledge machine”, an instrument that will churn out new understanding as long as you keep cranking the handle. The European particle-physics centre CERN has flirted with this image for the Large Hadron Collider, which the former director-general Robert Aymar called a “discovery machine.” This idea harks back (usually without knowing it) to a tradition begun by Francis Bacon in his Novum Organum (1620). Here Bacon drew on Atistotle’s notion of an organon, a mechanism for logical deduction. Bacon’s “new organon” was a new method of analysing facts, a systematic procedure (what we would now call an algorithm) for distilling observations of the world into underlying causes and mechanisms. It was a gigantic logic machine, accepting facts at one end and ejecting theorems at the other.
In the event, Bacon’s “organon” was a system so complex and intricate that he never even finished describing it, let alone ever put it into practice. Even if he had, it would have been to not avail, because it is now generally agreed among philosophers and historians of science that this is now how knowledge comes about. The preference of the early experimental scientists, like those who formed the Royal Society, to pile up facts in a Baconian manner while postponing indefinitely the framing of hypotheses to explain them, will get you nowhere. (It’s precisely because they couldn’t in fact restrain their impulse to interpret that men like Isaac Newton and Robert Boyle made any progess.) Unless you begin with some hypothesis, you don’t know which facts you are looking for, and you’re liable to end up with a welter of data, mostly irrelevant and certainly incomprehensible.
This seems obvious, and most scientists would agree. But that doesn’t mean the Baconian “discovery machine” has vanished. As it happens, the LHC doesn’t have this defect after all: the reams of data it has collected are being funnelled towards a very few extremely well defined (even over-refined) hypotheses, in particular the existence of the Higgs particle. But the Baconian impulse is alive and well elsewhere, driven by the allure of “knowledge machines”. The ability to sequence genomes quickly and cheaply will undoubtedly prove valuable for medicine and fundamental genetics, but these experimental techniques have already far outstripped not only our understanding of how genomes operate but our ability to formulate questions about that. As a result, some gene-sequencing projects seem conspicuously to lack a suite of ideas to test. The hope seems to be that, if you have enough data, understanding will somehow fall out of the bottom of the pile. As a result, biologist Robert Weinberg of the Massachusetts Institute of Technology has said, “the dominant position of hypothesis-driven research is under threat.”
And not just in genomics. The United States and Europe have recently announced two immense projects, costing hundreds of millions of dollars, to use the latest imaging technologies to map out the human brain, tracing out every last one of the billions of neural connections. Some neuroscientists are drooling at the thought of all that data. “Think about it,” said one. “The human brain produces in 30 seconds as much data as the Hubble Space Telescope has produced in its lifetime.”
If, however, one wanted to know how cities function, creating a map of every last brick and kerb would be an odd way to go about it. Quite how these brain projects will turn all their data into understanding remains a mystery. One researcher in the European project, simply called the Human Brain Project, inadvertently revealed the paucity of any theoretical framework for navigating this information glut: “It is a chicken and egg situation. Once we know how the brain works, we'll know how to look at the data.” The fact that the Human Brain Project is not quite that clueless hardly mitigates the enormity of this flippant statement. Science has never worked by shooting first and asking questions later, and it never will.
Biology, in which the profusion of evolutionary contingencies makes it particularly hard to formulate broad hypotheses, has long felt the danger of a Baconian retreat to pure data-gathering, substituting instruments for thinking. Austrian biochemist Erwin Chargaff, whose work helped elucidate how DNA stores genetic information, commented on this tendency as early as 1977:
“Now I go through a laboratory… and there they all sit before the same high speed centrifuges or scintillation counters, producing the same superposable graphs. There has been very little room left for the all important play of scientific imagination.”
Thanks to this, Chargaff said, “a pall of monotony has descended on what used to be the liveliest and most attractive of all scientific professions.” Like Chargaff, the pioneer of molecular biology Walter Gilbert saw in this reduction of biology to a set of standardized instrumental procedures repeated ad nauseam an encroachment of corporate strategies into the business of science. It was becoming an industrial process, manufacturing data on the production line: data produced, like consumer goods, because we have the instrumental means to do so, not because anyone knows what to do with it all. Nobel laureate biochemist Otto Loewi saw this happening in the life sciences even in 1954:
“Sometimes one has the impression that in contrast with former times, when one searched for methods in order to solve a problem, frequently nowadays workers look for problems with which they can exploit some special technique.”
High-energy physics now works on a similar industrial scale, with big machines at the centre. It doesn’t suffer the same lack of hypotheses as areas of biology, but arguably it can face the opposite problem: a consensus around a single idea, into which legions of workers burrow single-mindedly. Donald Glaser, the inventor of the bubble chamber, saw this happening in the immediate postwar period, once the Manhattan Project had provided the template:
“I knew that large accelerators were going to be built and they were going to make gobs of strange particles. But I didn’t want to join an army of people working at big machines.”
For Glaser the machines were taking over, and only by getting out of it did he devise his Nobel-prizewinning technique.
The challenge for the scientist, then, particularly in the era of Big Science, is to keep the instrument in its place. The best scientific kit comes from thinking about how to solve a problem. But once they become a part of the standard repertoire, or once they acquire a lumbering momentum of their own, instruments might not assist thinking but start to constrain it. As historians of science Albert van Helden and Thomas Hankins have said, “Because instruments determine what can be done, they also determine to some extent what can be thought.”
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Whenever I visit scientists to discuss their research, there always comes a moment when they say, with pride they can barely conceal, “Do you want a tour of the lab?” It is invariably slightly touching – like Willy Wonka dying to show off his factory. I’m always glad to accept, knowing what lies in store: shelves bright with bottles of coloured liquid and powders, webs of gleaming glass tubing, slabs of perforated steel holding lasers and lenses, cryogenic chambers like ornate bathyspheres whose quartz windows protect slivers of material about to be raked by electron beams.
It’s rarely less than impressive. Even if the kit is off-the-shelf, it will doubtless be wired into a makeshift salmagundi of wires, tubes, cladding, computer-controlled valves and rotors and components with more mysterious functions. Much of the gear, however, is likely to be home-made, custom-built for the research at hand. The typical lab set-up is, among other things, a masterpiece of impromptu engineering – you’d need degrees in electronics and mechanics just to put it all together, never mind how you make sense of the graphs and numbers it produces.
All this usually stays behind the scene in science. Headlines announcing “Scientists have found…” rarely bother to tell you how those discoveries were made. And would you care? The instrumentation of science is so highly specialized that it must often be accepted as a kind of occult machinery for producing knowledge. We figure they must know how it all works.
It makes sense in a way that histories of science tend to focus on the ideas and not the methods – surely what matters most is what was discovered about the workings of the world? But most historians of science today recognize that the relationship of scientists to their instruments is an essential part of the story. It is not simply that the science is dependent on the devices; rather, the devices determine what is known. You explore the things that you have the means to explore, and you plan your questions accordingly. That’s why, when a new instrument comes along – the telescope and the microscope are the most thoroughly investigated examples, but this applies as much today as it did in the seventeenth century – entirely new fields of science can be opened up. Less obviously, such developments demand a fresh negotiation between the scientists and their machines, and it’s not fanciful to see there some of the same characteristics as are found in human relationships. Can you be trusted? What are you trying to tell me? You’ve changed my life! Look, isn’t she beautiful? I’m bored with you, you don’t tell me anything new any more. Sorry, I’m swapping you for a newer model.
That’s why it is possible to speak of interactions between scientists and their instruments that are healthy or dysfunctional. How do we tell one from the other?
The telescope and microscope were celebrated even by their first users as examples of the value of enhancing the powers of human perception. But the most effective, not to mention elegant, scientific instruments serve also as a kind of prosthesis for the mind: they emerge as an extension of the experimenter’s thinking. That is exemplified in the work of the New Zealand physicist Ernest Rutherford, perhaps the finest experimental scientist of the twentieth century. Rutherford famously preferred the sealing-wax-and-string approach to science: it was at a humble benchtop with cheap, improvised and homespun equipment that he discovered the structure of the atom and then split it. This meant that Rutherford would devise his apparatus to tell him precisely what he wanted to know, rather than being limited by someone else’s view of what one needed to know. His experiments thus emerged organically from his ideas: they could almost be seen as theories constructed out of glass and metal foil.
Ernest Rutherford’s working space in the Cavendish Laboratory, Cambridge, in the 1920s.
In one of the finest instances, at Manchester University in 1908 Rutherford and his coworkers figured out that the alpha particles of radioactive decay are the nuclei of helium atoms. If that’s so, then one needs to collect the particles and see if they behave like helium. Rutherford ordered from his glassblower Otto Baumbach a glass capillary tube with extraordinarily thin walls, so that alpha particles emitted from radium could pass right through. Once they had accumulated in an outer chamber, Rutherford connected it up to become a gas-discharge tube, revealing the helium from the fingerprint wavelength of its glow. It was an exceedingly rare example of a piece of apparatus that answers a well defined question – are alpha particles helium? – with a simple yes/no answer, almost literally by whether or not a light switches on.
A more recent example of an instrument embodying the thought behind it is the scanning tunnelling microscope, invented by the late Heinrich Rohrer and Gerd Binnig at IBM’s Zurich research lab in the 1980s. They knew that electrons within the surface of an electrically conducting sample should be able to cross a tiny gap to reach another electrode held just above the surface, thanks to a quantum-mechanical effect called tunnelling. Because tunnelling is acutely sensitive to the width of the gap, a needle-like metal tip moving across the sample, just out of contact, could trace out the sample’s topography. If the movement was fine enough, the map might even show individual atoms and molecules. And so it did.
A ring of iron atoms on the surface of copper, as shown by the scanning tunnelling microscope. The ripples on the surface are electron waves. Image: IBM Almaden Research Center.
Between the basic idea and a working device, however, lay an incredible amount of practical expertise – of sheer craft – allied to rigorous thought. Against all expectation (they were often told the instrument “should not work” on principle), Rohrer and Binnig got it going, invented perhaps the central tool of nanotechnology, and won a Nobel prize in 1986 for their efforts.
So that’s when it goes right. What about when it doesn’t?
Scientific instruments have always been devices of power: those who possess the best can find out more than the others. Galileo recognized this: he conducted a cordial correspondence with Johannes Kepler in Prague, but when Kepler requested the loan of one of Galileo’s telescopes the Italian found excuses, knowing that with one of these instruments Kepler would be an even more serious rival. Instruments, Galileo already knew, confer authority.
But now instruments – newer, bigger, better – have become symbols of prestige as never before. I have several times been invited to admire the most state-of-the-art device in a laboratory purely for its own sake, as though I am being shown a Lamborghini. Historian of medical technology Stuart Blume of the University of Amsterdam has argued that, as science has started to operate according to the rules of a quasi-market, the latest equipment serves as a token of institutional might that enhances one’s competitive position in the marketplace. When I spoke to several chemists recently about their use of second-hand equipment, often acquired from the scientific equivalent of eBay, they all asked to remain anonymous, as though this would mark them out as second-rate scientists.
One of the dysfunctional consequences of this sort of relationship with an instrument is that the machine becomes its own justification, its own measure of worth – a kind of totem rather than a means to and end. A result is then “important” not because of what it tells us but because of how it was obtained. The Hubble Space Telescope is (despite its initial myopia) one of the most glorious instruments ever made, a genuinely new window on the universe. But when it first began to send back images of the cosmos in the mid 1990s, Nature would regularly receive submissions reporting the first “Hubble image” of this or that astrophysical object. The authors would be bemused and affronted when told that what the journal wanted was not the latest pretty picture, but some insight into the process it was observing – a matter that required rather more thought and research.
This kind of instrument-worship is, however, at least relatively harmless in the long run. More problematic is the notion of instrument as “knowledge machine”, an instrument that will churn out new understanding as long as you keep cranking the handle. The European particle-physics centre CERN has flirted with this image for the Large Hadron Collider, which the former director-general Robert Aymar called a “discovery machine.” This idea harks back (usually without knowing it) to a tradition begun by Francis Bacon in his Novum Organum (1620). Here Bacon drew on Atistotle’s notion of an organon, a mechanism for logical deduction. Bacon’s “new organon” was a new method of analysing facts, a systematic procedure (what we would now call an algorithm) for distilling observations of the world into underlying causes and mechanisms. It was a gigantic logic machine, accepting facts at one end and ejecting theorems at the other.
In the event, Bacon’s “organon” was a system so complex and intricate that he never even finished describing it, let alone ever put it into practice. Even if he had, it would have been to not avail, because it is now generally agreed among philosophers and historians of science that this is now how knowledge comes about. The preference of the early experimental scientists, like those who formed the Royal Society, to pile up facts in a Baconian manner while postponing indefinitely the framing of hypotheses to explain them, will get you nowhere. (It’s precisely because they couldn’t in fact restrain their impulse to interpret that men like Isaac Newton and Robert Boyle made any progess.) Unless you begin with some hypothesis, you don’t know which facts you are looking for, and you’re liable to end up with a welter of data, mostly irrelevant and certainly incomprehensible.
This seems obvious, and most scientists would agree. But that doesn’t mean the Baconian “discovery machine” has vanished. As it happens, the LHC doesn’t have this defect after all: the reams of data it has collected are being funnelled towards a very few extremely well defined (even over-refined) hypotheses, in particular the existence of the Higgs particle. But the Baconian impulse is alive and well elsewhere, driven by the allure of “knowledge machines”. The ability to sequence genomes quickly and cheaply will undoubtedly prove valuable for medicine and fundamental genetics, but these experimental techniques have already far outstripped not only our understanding of how genomes operate but our ability to formulate questions about that. As a result, some gene-sequencing projects seem conspicuously to lack a suite of ideas to test. The hope seems to be that, if you have enough data, understanding will somehow fall out of the bottom of the pile. As a result, biologist Robert Weinberg of the Massachusetts Institute of Technology has said, “the dominant position of hypothesis-driven research is under threat.”
And not just in genomics. The United States and Europe have recently announced two immense projects, costing hundreds of millions of dollars, to use the latest imaging technologies to map out the human brain, tracing out every last one of the billions of neural connections. Some neuroscientists are drooling at the thought of all that data. “Think about it,” said one. “The human brain produces in 30 seconds as much data as the Hubble Space Telescope has produced in its lifetime.”
If, however, one wanted to know how cities function, creating a map of every last brick and kerb would be an odd way to go about it. Quite how these brain projects will turn all their data into understanding remains a mystery. One researcher in the European project, simply called the Human Brain Project, inadvertently revealed the paucity of any theoretical framework for navigating this information glut: “It is a chicken and egg situation. Once we know how the brain works, we'll know how to look at the data.” The fact that the Human Brain Project is not quite that clueless hardly mitigates the enormity of this flippant statement. Science has never worked by shooting first and asking questions later, and it never will.
Biology, in which the profusion of evolutionary contingencies makes it particularly hard to formulate broad hypotheses, has long felt the danger of a Baconian retreat to pure data-gathering, substituting instruments for thinking. Austrian biochemist Erwin Chargaff, whose work helped elucidate how DNA stores genetic information, commented on this tendency as early as 1977:
“Now I go through a laboratory… and there they all sit before the same high speed centrifuges or scintillation counters, producing the same superposable graphs. There has been very little room left for the all important play of scientific imagination.”
Thanks to this, Chargaff said, “a pall of monotony has descended on what used to be the liveliest and most attractive of all scientific professions.” Like Chargaff, the pioneer of molecular biology Walter Gilbert saw in this reduction of biology to a set of standardized instrumental procedures repeated ad nauseam an encroachment of corporate strategies into the business of science. It was becoming an industrial process, manufacturing data on the production line: data produced, like consumer goods, because we have the instrumental means to do so, not because anyone knows what to do with it all. Nobel laureate biochemist Otto Loewi saw this happening in the life sciences even in 1954:
“Sometimes one has the impression that in contrast with former times, when one searched for methods in order to solve a problem, frequently nowadays workers look for problems with which they can exploit some special technique.”
High-energy physics now works on a similar industrial scale, with big machines at the centre. It doesn’t suffer the same lack of hypotheses as areas of biology, but arguably it can face the opposite problem: a consensus around a single idea, into which legions of workers burrow single-mindedly. Donald Glaser, the inventor of the bubble chamber, saw this happening in the immediate postwar period, once the Manhattan Project had provided the template:
“I knew that large accelerators were going to be built and they were going to make gobs of strange particles. But I didn’t want to join an army of people working at big machines.”
For Glaser the machines were taking over, and only by getting out of it did he devise his Nobel-prizewinning technique.
The challenge for the scientist, then, particularly in the era of Big Science, is to keep the instrument in its place. The best scientific kit comes from thinking about how to solve a problem. But once they become a part of the standard repertoire, or once they acquire a lumbering momentum of their own, instruments might not assist thinking but start to constrain it. As historians of science Albert van Helden and Thomas Hankins have said, “Because instruments determine what can be done, they also determine to some extent what can be thought.”
Wednesday, January 08, 2014
A splash of colour
More supermarket science for the rather sweet lifestyle magazine The Simple Things. This time it’s a little discourse on colour. Just in case you should happen to pick this up at the checkout and wonder about the first paragraph, this is, for the record, what the piece looked like at the outset.
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Every culture has been entranced by rainbows. The Babylonians kept records of the most spectacular ones, and in Judaeo-Christian tradition the rainbow symbolises the covenant between God and the world. Australian Aborigines honour the Rainbow Serpent; for the Vikings the coloured arch was a bridge to Asgard. All this reflects astonishment at a vision in the sky that seems to be made of pure colour.
It was suspected for a long time that the rainbow holds the key to what colour itself is. Islamic philosophers in the early Middle Ages knew that you could make a kind of artificial rainbow by passing sunlight through glass or water to produce a spectrum, with its sequence of bright colours from red and yellow to blue and violet. The connection was first fully explained by Isaac Newton in the seventeenth century, who showed that “white” sunlight actually contained all the colours of the spectrum and that a glass prism could tease them apart. He said that rainbows are made when water droplets in the atmosphere act like little prisms.
So for Newton, colour was all about light, which he imagined as a stream of tiny particles that strike our eye and cause vibrations of its nerves. Vibrations of different “bigness”, he said, create sensations of different colours. That’s not so different from the modern view, although we now regard light as a wave, not a particle. Little protein molecules in our retina absorb light waves of different wavelength, triggering signals along the optical nerve that our brain interprets as colours. The longer the wavelength, the further towards the red end of the spectrum the colour is.
But is colour really so simple? The odd thing about Newton’s theory is that it implied that, if you mix all the colours of the rainbow, you should get white, whereas painters knew very well that this just makes a murky brown. What’s more, it was well known that a colour can look different in different light (at dusk, say), or depending on what other colours are next to it.
The puzzle about mixing was solved in the nineteenth century, when the Scottish scientist James Clerk Maxwell showed that mixing light is not like mixing paint. Pigments and dyes are coloured because they absorb some parts of the spectrum – the colour we see is what’s left, which is reflected to our eyes. So if you mix them, you absorb more and more colours until there’s virtually none left, and the mixture looks black. But if you mix coloured light, you’re adding rather than taking away. As you can see from looking at television pixels close up, red, blue and green light are enough in combination to look white from far enough away.
Even then, colour – like taste, smell, and music – is ultimately something made in the mind. That’s why colours that are “the same” according to their wavelengths of light can look quite different depending on what’s around them. As the philosopher and writer Johann Wolfgang von Goethe stressed in the early nineteenth century, colour is partly a psychological thing too.
What’s more, there are lots of ways to produce it. Most of the colour we see in nature is made by light-absorbing pigments. Chlorophyll molecules in grass and leaves, for example, absorb red and blue light, reflecting the yellow and green. But the blue of the sky comes from the way light bounces off molecules in the air: the blue light is scattered most strongly, and so seems to come from all over the sky. And some of nature’s most wonderful colour displays are produced in a similar way – not by absorbing light but by scattering it.
Take the blue Morpho butterfly, which seems positively to glow in South American forests as if it is lit up, so that it can be seen from a quarter of a mile away. Its wing scales are covered with microscopic bristles of cuticle-like material, each bearing a stack of shelf-like corrugations. Light waves bouncing off this stack interfere with one another so that some colours disappear and others are enhanced. These interference effects from tiny stacks or layers of material also produce the bright hues of the peacock’s tail and other bird plumage, and the iridescent shells of beetles. The colours are iridescent because the precise wavelength of light picked out by the interference depends on the angle you’re viewing from.
You get a similar bright spectrum of “interference colour” when light is reflected from the tiny dimples in CDs. In fact, technologists are now borrowing such colour-making tricks from nature to control light for fibre-optic telecommunications or to make iridescent paints. We are learning that there are many ways to make colour – and many ways to enjoy it.
__________________________________________________________________
Every culture has been entranced by rainbows. The Babylonians kept records of the most spectacular ones, and in Judaeo-Christian tradition the rainbow symbolises the covenant between God and the world. Australian Aborigines honour the Rainbow Serpent; for the Vikings the coloured arch was a bridge to Asgard. All this reflects astonishment at a vision in the sky that seems to be made of pure colour.
It was suspected for a long time that the rainbow holds the key to what colour itself is. Islamic philosophers in the early Middle Ages knew that you could make a kind of artificial rainbow by passing sunlight through glass or water to produce a spectrum, with its sequence of bright colours from red and yellow to blue and violet. The connection was first fully explained by Isaac Newton in the seventeenth century, who showed that “white” sunlight actually contained all the colours of the spectrum and that a glass prism could tease them apart. He said that rainbows are made when water droplets in the atmosphere act like little prisms.
So for Newton, colour was all about light, which he imagined as a stream of tiny particles that strike our eye and cause vibrations of its nerves. Vibrations of different “bigness”, he said, create sensations of different colours. That’s not so different from the modern view, although we now regard light as a wave, not a particle. Little protein molecules in our retina absorb light waves of different wavelength, triggering signals along the optical nerve that our brain interprets as colours. The longer the wavelength, the further towards the red end of the spectrum the colour is.
But is colour really so simple? The odd thing about Newton’s theory is that it implied that, if you mix all the colours of the rainbow, you should get white, whereas painters knew very well that this just makes a murky brown. What’s more, it was well known that a colour can look different in different light (at dusk, say), or depending on what other colours are next to it.
The puzzle about mixing was solved in the nineteenth century, when the Scottish scientist James Clerk Maxwell showed that mixing light is not like mixing paint. Pigments and dyes are coloured because they absorb some parts of the spectrum – the colour we see is what’s left, which is reflected to our eyes. So if you mix them, you absorb more and more colours until there’s virtually none left, and the mixture looks black. But if you mix coloured light, you’re adding rather than taking away. As you can see from looking at television pixels close up, red, blue and green light are enough in combination to look white from far enough away.
Even then, colour – like taste, smell, and music – is ultimately something made in the mind. That’s why colours that are “the same” according to their wavelengths of light can look quite different depending on what’s around them. As the philosopher and writer Johann Wolfgang von Goethe stressed in the early nineteenth century, colour is partly a psychological thing too.
What’s more, there are lots of ways to produce it. Most of the colour we see in nature is made by light-absorbing pigments. Chlorophyll molecules in grass and leaves, for example, absorb red and blue light, reflecting the yellow and green. But the blue of the sky comes from the way light bounces off molecules in the air: the blue light is scattered most strongly, and so seems to come from all over the sky. And some of nature’s most wonderful colour displays are produced in a similar way – not by absorbing light but by scattering it.
Take the blue Morpho butterfly, which seems positively to glow in South American forests as if it is lit up, so that it can be seen from a quarter of a mile away. Its wing scales are covered with microscopic bristles of cuticle-like material, each bearing a stack of shelf-like corrugations. Light waves bouncing off this stack interfere with one another so that some colours disappear and others are enhanced. These interference effects from tiny stacks or layers of material also produce the bright hues of the peacock’s tail and other bird plumage, and the iridescent shells of beetles. The colours are iridescent because the precise wavelength of light picked out by the interference depends on the angle you’re viewing from.
You get a similar bright spectrum of “interference colour” when light is reflected from the tiny dimples in CDs. In fact, technologists are now borrowing such colour-making tricks from nature to control light for fibre-optic telecommunications or to make iridescent paints. We are learning that there are many ways to make colour – and many ways to enjoy it.
Friday, January 03, 2014
Chemistry with muons
This is my Crucible column for the January issue of Chemistry World.
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The periodic table seems constantly on the verge of expansion. There are of course new superheavy elements being added, literally atom by atom, to its nether reaches by the accelerator-driven synthesis of new nuclei. There’s also talk of systematic organization of new pseudo-atomic building blocks, whether these are polyatomic ‘superatoms’ [1] or nanoparticles assigned a particular ‘valence’ via DNA-based linkers [2]. But one could be forgiven for assuming that the main body of the table that adorns all chemistry lecture theatres will remain largely unchanged, give or take a few arguments over where to put hydrogen.
Yet even that can’t be taken for granted. A preprint [3] by quantum chemists Mohammad Goli and Shant Shahbazian at Shahid Beheshti University in Iran posits two new light elements – although these should formally be considered isotopes. They are muonium (Mu), in which an electron orbits a positively charged muon (μ+), and muonic helium (Heμ), in which an electron orbits a ‘nucleus’ consisting of an alpha particle and a negative muon – the latter in a very tight orbit close to the true nucleus.
Both of these ‘atoms’ can be considered analogues of hydrogen, with a single electron orbiting a nucleus of charge +1. They have, however, quite different masses. Since the muon – a lepton, being a ‘heavy’ cousin of the electron (or of its antiparticle the positron) – has a mass of 0.11 amu, muonium has about a tenth the mass of 1H, while muonic helium has a mass of 4.11 amu.
They have both been made in particle accelerators via high-energy collisions that generate muons, which can then be captured by helium or can themselves capture an electron. Some of these facilities, such as the TRIUMF accelerator in Vancouver, can generate beams of muons which can be thermalized by collisions with a gas, reducing the particle energies sufficiently to make muonic atoms capable of undergoing chemical reactions. True, the muons last for only around 2.2×10**-6 seconds, but that’s a lifetime, so to speak, compared with some superheavy artificial elements. Indeed, their chemistry has been explored already [4]: their reaction rates with molecular hydrogen not only confirm their hydrogen-like behaviour but show isotope effects that are consistent with quantum-chemical theory.
So undoubtedly Mu and Heμ have a chemistry. It seems only reasonable, then, to find a place for them in the periodic table. Indeed, Dick Zare of Stanford University, who probably known more about the classic H+H2 reaction than anyone else, is said to have once commented that if muonium was listed in the table then it would be much better known.
The question, however, is whether these exotic atoms truly behave like other atoms when they form molecules. Do they still look basically hydrogen-like in such a situation, despite the fact that, for example, Mu is so light? After all, conventional quantum-chemical methods rely on the Born-Oppenheimer approximation, predicated on the very different masses of electrons and nuclei, to separate out the electronic and nuclear degrees of freedom. Might the muons perhaps ‘leak’ into other atoms, compromising their own atom-like identity? To explore these questions, Goli and Shahbazian have carried out calculations to look at the electronic configurations of Mu and Heμ compounds using the Quantum Theory of Atoms In Molecules (QTAIM) formalism [5], which classifies chemical bonding according to the topology of the electron density distribution. A recent extension of this theory by the same two authors treats the nuclei as well as the electrons as quantum waves, and so is well placed to relax the Born-Oppenheimer approximation [6].
Goli and Shahbazian have calculated the electronic structures for all the various diatomic permutations of Mu and Heμ with the three conventional isotopes of hydrogen. They find that in all cases the muon-containing species are contained within an ‘atomic basin’ containing only a single positively charged particle – that is, they look like real nuclei, and don’t contaminate the other atoms in the union with any ‘sprinkling of muon’. What’s more, Mu and Heμ fit within the trend observed for heavy hydrogen, whereby the atom’s electronegativity increases as its mass increases. This is particularly the case for Mu-H molecules, which are decidedly polar: Muδ+-Hδ-. That in itself forces the issue of whether Mu is really like light hydrogen or needs its own slot in the periodic table: Goli and Shahbazian raise the latter as an option.
The zoo of fundamental particles might provide yet more opportunities for making unusual atoms. Goli and Shahbazian suggest as candidate constituents the positive and negative pions, which are two-quark mesons rather than leptons. But that will stretch experimentalists to the limit: their mean lifetime is just 26 nanoseconds. Still more exotic would be entire nuclei made of antimatter or containing strange quarks (‘strange matter’)[7]. At any rate, it seems clear that there are more things on heaven and earth than are dreamed of in your periodic table.
1. A. W. Castleman Jr & S. N. Khanna, J. Phys. Chem. C 113, 2662-2675 (2009).
2. R. J. Macfarlane et al., Angew. Chem. Int. Ed. 52, 5688-5698 (2013).
3. M. Goli & Sh. Shahbazian, preprint http://www.arxiv.org/abs/1311.6431 (2013).
4. D. G. Fleming et al., J. Chem. Phys. 135, 184310 (2011).
5. R. F. W. Bader, Atoms in Molecules: A Quantum Theory. Oxford University Press, 1990.
6. M. Goli & Sh. Shahbazian, Theor. Chem. Acc. 129, 235-245 (2011).
7. STAR collaboration, Science 328, 58-62 (2010).
_______________________________________________________________
The periodic table seems constantly on the verge of expansion. There are of course new superheavy elements being added, literally atom by atom, to its nether reaches by the accelerator-driven synthesis of new nuclei. There’s also talk of systematic organization of new pseudo-atomic building blocks, whether these are polyatomic ‘superatoms’ [1] or nanoparticles assigned a particular ‘valence’ via DNA-based linkers [2]. But one could be forgiven for assuming that the main body of the table that adorns all chemistry lecture theatres will remain largely unchanged, give or take a few arguments over where to put hydrogen.
Yet even that can’t be taken for granted. A preprint [3] by quantum chemists Mohammad Goli and Shant Shahbazian at Shahid Beheshti University in Iran posits two new light elements – although these should formally be considered isotopes. They are muonium (Mu), in which an electron orbits a positively charged muon (μ+), and muonic helium (Heμ), in which an electron orbits a ‘nucleus’ consisting of an alpha particle and a negative muon – the latter in a very tight orbit close to the true nucleus.
Both of these ‘atoms’ can be considered analogues of hydrogen, with a single electron orbiting a nucleus of charge +1. They have, however, quite different masses. Since the muon – a lepton, being a ‘heavy’ cousin of the electron (or of its antiparticle the positron) – has a mass of 0.11 amu, muonium has about a tenth the mass of 1H, while muonic helium has a mass of 4.11 amu.
They have both been made in particle accelerators via high-energy collisions that generate muons, which can then be captured by helium or can themselves capture an electron. Some of these facilities, such as the TRIUMF accelerator in Vancouver, can generate beams of muons which can be thermalized by collisions with a gas, reducing the particle energies sufficiently to make muonic atoms capable of undergoing chemical reactions. True, the muons last for only around 2.2×10**-6 seconds, but that’s a lifetime, so to speak, compared with some superheavy artificial elements. Indeed, their chemistry has been explored already [4]: their reaction rates with molecular hydrogen not only confirm their hydrogen-like behaviour but show isotope effects that are consistent with quantum-chemical theory.
So undoubtedly Mu and Heμ have a chemistry. It seems only reasonable, then, to find a place for them in the periodic table. Indeed, Dick Zare of Stanford University, who probably known more about the classic H+H2 reaction than anyone else, is said to have once commented that if muonium was listed in the table then it would be much better known.
The question, however, is whether these exotic atoms truly behave like other atoms when they form molecules. Do they still look basically hydrogen-like in such a situation, despite the fact that, for example, Mu is so light? After all, conventional quantum-chemical methods rely on the Born-Oppenheimer approximation, predicated on the very different masses of electrons and nuclei, to separate out the electronic and nuclear degrees of freedom. Might the muons perhaps ‘leak’ into other atoms, compromising their own atom-like identity? To explore these questions, Goli and Shahbazian have carried out calculations to look at the electronic configurations of Mu and Heμ compounds using the Quantum Theory of Atoms In Molecules (QTAIM) formalism [5], which classifies chemical bonding according to the topology of the electron density distribution. A recent extension of this theory by the same two authors treats the nuclei as well as the electrons as quantum waves, and so is well placed to relax the Born-Oppenheimer approximation [6].
Goli and Shahbazian have calculated the electronic structures for all the various diatomic permutations of Mu and Heμ with the three conventional isotopes of hydrogen. They find that in all cases the muon-containing species are contained within an ‘atomic basin’ containing only a single positively charged particle – that is, they look like real nuclei, and don’t contaminate the other atoms in the union with any ‘sprinkling of muon’. What’s more, Mu and Heμ fit within the trend observed for heavy hydrogen, whereby the atom’s electronegativity increases as its mass increases. This is particularly the case for Mu-H molecules, which are decidedly polar: Muδ+-Hδ-. That in itself forces the issue of whether Mu is really like light hydrogen or needs its own slot in the periodic table: Goli and Shahbazian raise the latter as an option.
The zoo of fundamental particles might provide yet more opportunities for making unusual atoms. Goli and Shahbazian suggest as candidate constituents the positive and negative pions, which are two-quark mesons rather than leptons. But that will stretch experimentalists to the limit: their mean lifetime is just 26 nanoseconds. Still more exotic would be entire nuclei made of antimatter or containing strange quarks (‘strange matter’)[7]. At any rate, it seems clear that there are more things on heaven and earth than are dreamed of in your periodic table.
1. A. W. Castleman Jr & S. N. Khanna, J. Phys. Chem. C 113, 2662-2675 (2009).
2. R. J. Macfarlane et al., Angew. Chem. Int. Ed. 52, 5688-5698 (2013).
3. M. Goli & Sh. Shahbazian, preprint http://www.arxiv.org/abs/1311.6431 (2013).
4. D. G. Fleming et al., J. Chem. Phys. 135, 184310 (2011).
5. R. F. W. Bader, Atoms in Molecules: A Quantum Theory. Oxford University Press, 1990.
6. M. Goli & Sh. Shahbazian, Theor. Chem. Acc. 129, 235-245 (2011).
7. STAR collaboration, Science 328, 58-62 (2010).
Chips in space
Here’s the initial version of my latest piece for the Under the Radar column of BBC Future.
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If humans ever voyage to Jupiter, the journey is sure to be arduous and full of danger. But there’s a consolation: chips cooked at the planet’s surface will be crispier.
Perhaps that’s too glib a conclusion to draw from recent work investigating the effect of high gravity on chip frying (that’s French fries or frites outside the UK), not least because gaseous Jupiter of course doesn’t really have a surface and no one plans to go there. But the gastronomic preferences of future astronauts are the genuine motivation for experiments conducted by chemists John Lioumbas and Thodoris Karapantsios of the Aristotle University of Thessaloniki in Greece, and reported in the journal Food Science International. That’s why their work is supported by the European Space Agency.
You see, astronauts sometimes lament the drabness of their pre-prepared space meals, and have even expressed cravings for chips. Some thought has already gone into methods of food preparation in space (if you don’t want potato peelings floating around, it has to be done in a hands-free self-contained system), as well as developing novel sources of fresh food, such as the culturing of artificial meat. But aside from these logistics, there’s also the problem that in zero gravity some of the basic physics of cooking is different.
The wish for decent grub in space is understandable, but also highlights one of the conundrums of human spaceflight. The quest to send humans into space is generally presented in heroic terms as a bold adventure that might bring benefits for all humanity. But once you consider what it really entails, you’re confronted with some pretty prosaic, even bathetic, questions of detail. How will they cope with the boredom and confinement? Will the toilet facilities work? (To judge from the International Space Station, not necessarily.) And will a good fry-up raise their morale? Such questions sit uneasily with the “Columbus” narrative, and arguably might force us to ask whether space is such a good place to put humans anyway.
But back to the deep-fryer. You might wonder why, if we’re talking about chips in space, Lioumbas and Karapantsios are cooking in increased gravity rather than zero gravity. The answer is that they want to map out the whole landscape of how gravity influences the cooking process, to get some idea of the overall trends and patterns as the tug of gravity changes. They are now working on the same questions in microgravity experiments – gravity much weaker than that of the Earth.
For frying and boiling, the key issue is convection. The rate at which foods heat up in water or oil is affected by the way heat circulates in the liquid. This depends on the convection currents created by buoyancy, as hot and therefore less dense liquid rises from the bottom of the pan. This convection won’t happen in zero gravity, because a difference in density doesn’t produce a difference in weight if everything is weightless anyway: there’s no buoyancy. Conversely, in increased gravity convective effects should be more pronounced.
The researchers wanted to know how these differences affect the way chips fry. While achieving low gravity is difficult unless you go into space (or want to brave the free-falling ‘Vomit Comet’ aircraft used by space agencies, which is enough to put anyone off their chips), artificially increasing the force of gravity is relatively easy. You simply attach the apparatus to the arm of a rapidly spinning centrifuge, the rotation of which produces a centrifugal force that mimics gravity.
So that’s what Lioumbas and Karapantsios did. They fixed a deep-fat fryer containing potato sticks in half a litre of hot oil onto the end of the 8m-long arms of the Large Diameter Centrifuge at the European Space Research and Technology Centre in Noordwijk, the Netherlands. This device could generate the equivalent of a gravitational force up to nine times that at the Earth’s surface (that is, 9g).
The researchers monitored the temperature just below the surface of the potatoes, where the crust of the chip forms, and also examined the thickness and profile of the crust under the microscope. Convection currents are created both within the pan as a whole and from the rising of bubbles that grow on the potato surface as the oil begins to boil. As the g-force rises, these bubbles become smaller and more numerous and they rise faster. However, when it reaches 3g, the bubbles are so small that they get stuck to the potato by capillary forces, and so further increases in gravity make little difference.
What’s more, while the crust steadily thickens up to 3g, still stronger gravity has less of an effect on the thickness. Instead, Lioumbas and Karapantsios find that the crust then starts to separate from the softer core of the potato, as superheated steam from the moist potato flesh blows a bubble between the two. But who wishes to eat chips with bubbles in?
So the researchers conclude that if you want chips (or anything else) to deep-fry faster, making them crispy in a shorter time, there’s nothing to be gained, and in fact some disadvantages, from centrifuging to a force greater than 3g. That much is not really a lesson for space cooking, where in general gravity will be much lower than Earth’s (and anyway, on the International Space Station can’t you simply order a takeaway from Pizza Hut?). But it could be worth knowing for the food industry, where centrifugal ‘flash-frying’ might be considered worth a try.
Reference: J. S. Lioumbas & T. D. Karapantsios, Food Research International 55, 110-118 (2014).
____________________________________________________________________
If humans ever voyage to Jupiter, the journey is sure to be arduous and full of danger. But there’s a consolation: chips cooked at the planet’s surface will be crispier.
Perhaps that’s too glib a conclusion to draw from recent work investigating the effect of high gravity on chip frying (that’s French fries or frites outside the UK), not least because gaseous Jupiter of course doesn’t really have a surface and no one plans to go there. But the gastronomic preferences of future astronauts are the genuine motivation for experiments conducted by chemists John Lioumbas and Thodoris Karapantsios of the Aristotle University of Thessaloniki in Greece, and reported in the journal Food Science International. That’s why their work is supported by the European Space Agency.
You see, astronauts sometimes lament the drabness of their pre-prepared space meals, and have even expressed cravings for chips. Some thought has already gone into methods of food preparation in space (if you don’t want potato peelings floating around, it has to be done in a hands-free self-contained system), as well as developing novel sources of fresh food, such as the culturing of artificial meat. But aside from these logistics, there’s also the problem that in zero gravity some of the basic physics of cooking is different.
The wish for decent grub in space is understandable, but also highlights one of the conundrums of human spaceflight. The quest to send humans into space is generally presented in heroic terms as a bold adventure that might bring benefits for all humanity. But once you consider what it really entails, you’re confronted with some pretty prosaic, even bathetic, questions of detail. How will they cope with the boredom and confinement? Will the toilet facilities work? (To judge from the International Space Station, not necessarily.) And will a good fry-up raise their morale? Such questions sit uneasily with the “Columbus” narrative, and arguably might force us to ask whether space is such a good place to put humans anyway.
But back to the deep-fryer. You might wonder why, if we’re talking about chips in space, Lioumbas and Karapantsios are cooking in increased gravity rather than zero gravity. The answer is that they want to map out the whole landscape of how gravity influences the cooking process, to get some idea of the overall trends and patterns as the tug of gravity changes. They are now working on the same questions in microgravity experiments – gravity much weaker than that of the Earth.
For frying and boiling, the key issue is convection. The rate at which foods heat up in water or oil is affected by the way heat circulates in the liquid. This depends on the convection currents created by buoyancy, as hot and therefore less dense liquid rises from the bottom of the pan. This convection won’t happen in zero gravity, because a difference in density doesn’t produce a difference in weight if everything is weightless anyway: there’s no buoyancy. Conversely, in increased gravity convective effects should be more pronounced.
The researchers wanted to know how these differences affect the way chips fry. While achieving low gravity is difficult unless you go into space (or want to brave the free-falling ‘Vomit Comet’ aircraft used by space agencies, which is enough to put anyone off their chips), artificially increasing the force of gravity is relatively easy. You simply attach the apparatus to the arm of a rapidly spinning centrifuge, the rotation of which produces a centrifugal force that mimics gravity.
So that’s what Lioumbas and Karapantsios did. They fixed a deep-fat fryer containing potato sticks in half a litre of hot oil onto the end of the 8m-long arms of the Large Diameter Centrifuge at the European Space Research and Technology Centre in Noordwijk, the Netherlands. This device could generate the equivalent of a gravitational force up to nine times that at the Earth’s surface (that is, 9g).
The researchers monitored the temperature just below the surface of the potatoes, where the crust of the chip forms, and also examined the thickness and profile of the crust under the microscope. Convection currents are created both within the pan as a whole and from the rising of bubbles that grow on the potato surface as the oil begins to boil. As the g-force rises, these bubbles become smaller and more numerous and they rise faster. However, when it reaches 3g, the bubbles are so small that they get stuck to the potato by capillary forces, and so further increases in gravity make little difference.
What’s more, while the crust steadily thickens up to 3g, still stronger gravity has less of an effect on the thickness. Instead, Lioumbas and Karapantsios find that the crust then starts to separate from the softer core of the potato, as superheated steam from the moist potato flesh blows a bubble between the two. But who wishes to eat chips with bubbles in?
So the researchers conclude that if you want chips (or anything else) to deep-fry faster, making them crispy in a shorter time, there’s nothing to be gained, and in fact some disadvantages, from centrifuging to a force greater than 3g. That much is not really a lesson for space cooking, where in general gravity will be much lower than Earth’s (and anyway, on the International Space Station can’t you simply order a takeaway from Pizza Hut?). But it could be worth knowing for the food industry, where centrifugal ‘flash-frying’ might be considered worth a try.
Reference: J. S. Lioumbas & T. D. Karapantsios, Food Research International 55, 110-118 (2014).
Thursday, December 19, 2013
Binary used in Polynesia 600 years ago
Here’s my latest news story for Nature.
______________________________________________________________
A tiny island in the Pacific was already using a kind of binary arithmetic in the Middle Ages
Binary arithmetic, the basis of all digital computation today, is usually said to have been invented at the start of the eighteenth century by the German mathematician Gottfried Leibniz. But a new study shows that a kind of binary was already in use three hundred years earlier among the people of the tiny Pacific island of Mangareva in French Polynesia.
The discovery, made by consulting historical records of the now almost wholly assimilated Mangarevan culture and language and reported in the Proceedings of the National Academy of Sciences [1], suggests that some of the advantages of the binary system adduced by Leibniz might create a cognitive motivation for this system to arise spontaneously even in a society without advanced science and technology.
Pure binary arithmetic works in base 2 rather than the conventional base 10 (the latter quite possibly a consequence of counting on ten fingers). This means that numbers are enumerated as powers of 2: instead of units, tens, hundreds (10**2) and thousands (10**3), the digits of a binary number refer to 1 (2**0), 2 (2**1), 4 (2**2), 8 (2**3) and so on.
Every whole number can be represented in this way using just 1s and 0s, which is why they can be encoded in computers in a system of on-off electrical pulses or switches. The number 13 in binary is 1101 (2**3+2**2+(0x2)+1), for example.
Leibniz pointed out in 1703 that to do simple arithmetic in binary, such as addition and multiplication, you don’t need to remember a whole lot of ‘facts’ about numbers, such as 5+4=9 or 6x7=42. Instead, you need only apply a few simple rules. For addition, say, you just add the 1s and 0s, remembering that 1+1=1 in the next position: 100+101=1001.
The downside to binary is that large numbers require lots of digits. But according to psychologists Andrea Bender and Sieghard Beller of the University of Bergen in Norway, the Mangarevan people found an ingenious answer to that, which they were apparently using even before 1450 AD.
Mangareva is a volcanic island first settled around 500-800 AD, which probably had a population of several thousand before substantial interactions with Europeans began in the eighteenth century. Its highly stratified society survived mostly on seafood and root crops, and needed a number system to quantify large transactions in trade and tributes to chieftains.
Only about 600 Mangarevan speakers now remain on the island, and in any case its indigenous number system has long been superseded by Arabic digits owing to French colonialism. But Bender and Beller have reconstructed it from descriptions written by (mostly European) authors in the nineteenth and early twentieth centuries [2].
They find that the former Mangarevans combined a base 10 with a binary system. They had number words for 1 to10, and then for 10 multiplied by several powers of 2: 10 (takau, denoted K in the new work), 20 (denoted P), 40 (T) and 80 (V). In this notation, for example, 70 is TPK and 57 is TK7.
Bender and Beller show that this system retains the key arithmetical simplifications of true binary, in that you don’t need to memorize lots of number facts but just to enact a few simple rules, such as 2xK=P and 2xP=T.
There are complications with the system too, but the authors argue that “the advantages outweigh the disadvantages.”
Cognitive scientist Rafael Nuñez of the University of California at San Diego points out that some notion of binary systems is actually older than Mangarevan culture. “It can be traced back to at least ancient China, around the 9th century BC”, he says – it can be found in the I Ching, which inspired Leibniz. Nuñez adds that “other ancient groups, such as the Maya, used sophisticated combinations of binary and decimal systems to keep track of time and astronomical phenomena. Thus, the cognitive advantages underlying the Mangarevan counting system may not be unique.”
All the same, say Bender and Beller, a ‘mixed’ system like this isn’t easy or obvious to create. “It’s puzzling that anybody would come up with such a solution, especially on a tiny island with a small population”, Bender and Beller say. “But this very fact also demonstrates just how important culture is for the development of numerical cognition”, they add – for example, how in this case dealing with big numbers can motivate inventive solutions.
Nuñez agrees that the study shows “the primacy of cultural factors underlying the invention of number systems, and the diversity in human numerical cognition.”
References
1. Bender, A. & Beller, S. Proc. Natl. Acad. Sci. USA doi:10.1073/pnas.1309160110 (2013).
2. Bender, A., J. Polynesian Soc. 122, in press (2013).
______________________________________________________________
A tiny island in the Pacific was already using a kind of binary arithmetic in the Middle Ages
Binary arithmetic, the basis of all digital computation today, is usually said to have been invented at the start of the eighteenth century by the German mathematician Gottfried Leibniz. But a new study shows that a kind of binary was already in use three hundred years earlier among the people of the tiny Pacific island of Mangareva in French Polynesia.
The discovery, made by consulting historical records of the now almost wholly assimilated Mangarevan culture and language and reported in the Proceedings of the National Academy of Sciences [1], suggests that some of the advantages of the binary system adduced by Leibniz might create a cognitive motivation for this system to arise spontaneously even in a society without advanced science and technology.
Pure binary arithmetic works in base 2 rather than the conventional base 10 (the latter quite possibly a consequence of counting on ten fingers). This means that numbers are enumerated as powers of 2: instead of units, tens, hundreds (10**2) and thousands (10**3), the digits of a binary number refer to 1 (2**0), 2 (2**1), 4 (2**2), 8 (2**3) and so on.
Every whole number can be represented in this way using just 1s and 0s, which is why they can be encoded in computers in a system of on-off electrical pulses or switches. The number 13 in binary is 1101 (2**3+2**2+(0x2)+1), for example.
Leibniz pointed out in 1703 that to do simple arithmetic in binary, such as addition and multiplication, you don’t need to remember a whole lot of ‘facts’ about numbers, such as 5+4=9 or 6x7=42. Instead, you need only apply a few simple rules. For addition, say, you just add the 1s and 0s, remembering that 1+1=1 in the next position: 100+101=1001.
The downside to binary is that large numbers require lots of digits. But according to psychologists Andrea Bender and Sieghard Beller of the University of Bergen in Norway, the Mangarevan people found an ingenious answer to that, which they were apparently using even before 1450 AD.
Mangareva is a volcanic island first settled around 500-800 AD, which probably had a population of several thousand before substantial interactions with Europeans began in the eighteenth century. Its highly stratified society survived mostly on seafood and root crops, and needed a number system to quantify large transactions in trade and tributes to chieftains.
Only about 600 Mangarevan speakers now remain on the island, and in any case its indigenous number system has long been superseded by Arabic digits owing to French colonialism. But Bender and Beller have reconstructed it from descriptions written by (mostly European) authors in the nineteenth and early twentieth centuries [2].
They find that the former Mangarevans combined a base 10 with a binary system. They had number words for 1 to10, and then for 10 multiplied by several powers of 2: 10 (takau, denoted K in the new work), 20 (denoted P), 40 (T) and 80 (V). In this notation, for example, 70 is TPK and 57 is TK7.
Bender and Beller show that this system retains the key arithmetical simplifications of true binary, in that you don’t need to memorize lots of number facts but just to enact a few simple rules, such as 2xK=P and 2xP=T.
There are complications with the system too, but the authors argue that “the advantages outweigh the disadvantages.”
Cognitive scientist Rafael Nuñez of the University of California at San Diego points out that some notion of binary systems is actually older than Mangarevan culture. “It can be traced back to at least ancient China, around the 9th century BC”, he says – it can be found in the I Ching, which inspired Leibniz. Nuñez adds that “other ancient groups, such as the Maya, used sophisticated combinations of binary and decimal systems to keep track of time and astronomical phenomena. Thus, the cognitive advantages underlying the Mangarevan counting system may not be unique.”
All the same, say Bender and Beller, a ‘mixed’ system like this isn’t easy or obvious to create. “It’s puzzling that anybody would come up with such a solution, especially on a tiny island with a small population”, Bender and Beller say. “But this very fact also demonstrates just how important culture is for the development of numerical cognition”, they add – for example, how in this case dealing with big numbers can motivate inventive solutions.
Nuñez agrees that the study shows “the primacy of cultural factors underlying the invention of number systems, and the diversity in human numerical cognition.”
References
1. Bender, A. & Beller, S. Proc. Natl. Acad. Sci. USA doi:10.1073/pnas.1309160110 (2013).
2. Bender, A., J. Polynesian Soc. 122, in press (2013).
Wednesday, December 18, 2013
Mining black holes
Here’s something that boggled my mind, and which I wrote up for BBC Future.
__________________________________________________________
It’s a staple of science fiction: highly advanced civilizations getting their energy by mining black holes, extracting it from collapsed stars or making artificial mini-holes that power spaceships. These aren’t idle or quasi-magical speculations, for physicists have believed for at least 30 years that it might be possible. However, sci-fi writers wishing to draw on this technological miracle are going to have to get more inventive, for a paper published in the premier physics journal Physical Review Letters now argues that mining black holes would not be as productive as was thought.
The classical view of black holes as stars that have burnt out and collapsed under their own gravity to an infinitesimally small point in space – a singularity – offered little prospect that they were anything other than dead, barren light traps. Inside the so-called event horizon around the hole’s absurdly dense centre, nothing can escape from the hole’s gravity, and it just sits there forever like a blot on spacetime.
But that changed once Stephen Hawking and others brought quantum physics to bear on this picture. Hawking showed in the 1970s that black holes don’t last forever, and that to the world outside the event horizon they are not black at all. He argued that black holes emit energy from their boundaries in the form of radiation produced by quantum fluctuations of empty space itself. Eventually this Hawking radiation leads to evaporation of the black hole itself.
That happens so slowly that black holes with the mass of a star are still hardly less than eternal. But might it be possible to induce a black hole to release all its Hawking radiation sooner, so that in effect it becomes like a ball of fuel? In 1983 physicists George Unruh and Robert Wald suggested how to do that. One could lower a box down close to the hole’s event horizon, let it fill up with Hawking radiation, and then bring it back up again, just like filling a bucket with water from a well. Performed repeatedly, this manoeuvre would gradually strip the black hole of its ‘hot atmosphere’ of radiation. True, you’d need a mighty rope and winding mechanism to prevent the box from being tugged beyond the event horizon and swallowed, but in principle it could be done.
Or can it? Adam Brown of the Princeton Center for Theoretical Science says that it would take far longer than Unruh and Wald anticipated. He shows that the attempt would cause the black hole to swell and engulf the box. “Rather than using the box to rob the black hole of its radiation”, he writes, “the black hole instead robs us of our box.”
The problem, says Brown, lies with the plain old mechanics of the rope holding the box. Because it would be in a gravitational field, the rope would be subject to the inevitable constraint that it can’t be heavier than its own strength can support. This is true even for exotic ‘ropes’ that aren’t material at all, such as electric or magnetic fields: they too have an energy density and thus (via E=mc**2) an effective mass.
For an ordinary rope hanging down in the Earth’s gravity, the tension in the rope increases with height, because it is carrying more of its own weight. But weirdly, in a very strong gravitational field, where spacetime itself is highly curved, the tension remains the same all along the length. However, for the rope to be stable, it turns out that this tension must exactly equal the mass per unit length of the rope: the rope has to be in effect at breaking point purely to support its own weight, so that there is no strength left over to support the box that will collect Hawking radiation.
Another constraint on the rope is that it mustn’t disintegrate. Close to a black hole, the intense Hawking radiation creates a hot environment. If the rope is lowered too close to the event horizon, where the radiation is most plentiful, there’s a danger that the temperature will exceed that at which all ordinary matter – in other words, atoms themselves – melt into a gloop of their constituent quarks. If you make the rope too light, it’s more likely to melt. But if you make it too heavy, the rope itself is in danger of collapsing under its own gravity.
There’s another complication too. Brown shows that the box itself can’t be wider than a single wavelength of the Hawking radiation it is collecting, since otherwise the effects of relativity will pull it awry and cause the rope to break. That would make the collection process very cumbersome in any case – it would have to happen one photon (‘light particle’) at a time. To collect Hawking radiation of the wavelength of light, the boxes could be no bigger than typical bacteria, and to collect X-rays you’d need atom-sized boxes.
So here’s the deal. If you get too close to the black hole, the rope might melt or snap – or, if it’s made too massive to avoid that, it might collapse into itself. But if you try mining at a more cautious distance, there isn’t so much Hawking radiation there to collect. And Brown shows that even the best compromise makes energy extraction much slower than Unruh and Wald suggested.
Yet there is a better way, he says: do away with boxes altogether. In 1994, Albion Lawrence and Emil Martinec of the University of Chicago proposed that one could simply dip strings into a black hole and let Hawking radiation run up them like oil up the wick of an oil lamp. This was thought to be a slower process than hauling up boxes full of Hawking radiation, because each string carries up only one photon at a time. But Brown’s analysis shows that they would in fact both mine the hole at the same (slow) rate. Since dangling boxes introduce more potential for malfunction, Brown therefore argues that the preferable way to draw the energy from black holes is to puncture the event horizon with lots of photon-wicking strings, and let them drain it out of existence.
Reference: A. R. Brown, Physical Review Letters 111, 211301 (2013)
__________________________________________________________
It’s a staple of science fiction: highly advanced civilizations getting their energy by mining black holes, extracting it from collapsed stars or making artificial mini-holes that power spaceships. These aren’t idle or quasi-magical speculations, for physicists have believed for at least 30 years that it might be possible. However, sci-fi writers wishing to draw on this technological miracle are going to have to get more inventive, for a paper published in the premier physics journal Physical Review Letters now argues that mining black holes would not be as productive as was thought.
The classical view of black holes as stars that have burnt out and collapsed under their own gravity to an infinitesimally small point in space – a singularity – offered little prospect that they were anything other than dead, barren light traps. Inside the so-called event horizon around the hole’s absurdly dense centre, nothing can escape from the hole’s gravity, and it just sits there forever like a blot on spacetime.
But that changed once Stephen Hawking and others brought quantum physics to bear on this picture. Hawking showed in the 1970s that black holes don’t last forever, and that to the world outside the event horizon they are not black at all. He argued that black holes emit energy from their boundaries in the form of radiation produced by quantum fluctuations of empty space itself. Eventually this Hawking radiation leads to evaporation of the black hole itself.
That happens so slowly that black holes with the mass of a star are still hardly less than eternal. But might it be possible to induce a black hole to release all its Hawking radiation sooner, so that in effect it becomes like a ball of fuel? In 1983 physicists George Unruh and Robert Wald suggested how to do that. One could lower a box down close to the hole’s event horizon, let it fill up with Hawking radiation, and then bring it back up again, just like filling a bucket with water from a well. Performed repeatedly, this manoeuvre would gradually strip the black hole of its ‘hot atmosphere’ of radiation. True, you’d need a mighty rope and winding mechanism to prevent the box from being tugged beyond the event horizon and swallowed, but in principle it could be done.
Or can it? Adam Brown of the Princeton Center for Theoretical Science says that it would take far longer than Unruh and Wald anticipated. He shows that the attempt would cause the black hole to swell and engulf the box. “Rather than using the box to rob the black hole of its radiation”, he writes, “the black hole instead robs us of our box.”
The problem, says Brown, lies with the plain old mechanics of the rope holding the box. Because it would be in a gravitational field, the rope would be subject to the inevitable constraint that it can’t be heavier than its own strength can support. This is true even for exotic ‘ropes’ that aren’t material at all, such as electric or magnetic fields: they too have an energy density and thus (via E=mc**2) an effective mass.
For an ordinary rope hanging down in the Earth’s gravity, the tension in the rope increases with height, because it is carrying more of its own weight. But weirdly, in a very strong gravitational field, where spacetime itself is highly curved, the tension remains the same all along the length. However, for the rope to be stable, it turns out that this tension must exactly equal the mass per unit length of the rope: the rope has to be in effect at breaking point purely to support its own weight, so that there is no strength left over to support the box that will collect Hawking radiation.
Another constraint on the rope is that it mustn’t disintegrate. Close to a black hole, the intense Hawking radiation creates a hot environment. If the rope is lowered too close to the event horizon, where the radiation is most plentiful, there’s a danger that the temperature will exceed that at which all ordinary matter – in other words, atoms themselves – melt into a gloop of their constituent quarks. If you make the rope too light, it’s more likely to melt. But if you make it too heavy, the rope itself is in danger of collapsing under its own gravity.
There’s another complication too. Brown shows that the box itself can’t be wider than a single wavelength of the Hawking radiation it is collecting, since otherwise the effects of relativity will pull it awry and cause the rope to break. That would make the collection process very cumbersome in any case – it would have to happen one photon (‘light particle’) at a time. To collect Hawking radiation of the wavelength of light, the boxes could be no bigger than typical bacteria, and to collect X-rays you’d need atom-sized boxes.
So here’s the deal. If you get too close to the black hole, the rope might melt or snap – or, if it’s made too massive to avoid that, it might collapse into itself. But if you try mining at a more cautious distance, there isn’t so much Hawking radiation there to collect. And Brown shows that even the best compromise makes energy extraction much slower than Unruh and Wald suggested.
Yet there is a better way, he says: do away with boxes altogether. In 1994, Albion Lawrence and Emil Martinec of the University of Chicago proposed that one could simply dip strings into a black hole and let Hawking radiation run up them like oil up the wick of an oil lamp. This was thought to be a slower process than hauling up boxes full of Hawking radiation, because each string carries up only one photon at a time. But Brown’s analysis shows that they would in fact both mine the hole at the same (slow) rate. Since dangling boxes introduce more potential for malfunction, Brown therefore argues that the preferable way to draw the energy from black holes is to puncture the event horizon with lots of photon-wicking strings, and let them drain it out of existence.
Reference: A. R. Brown, Physical Review Letters 111, 211301 (2013)
Tuesday, December 10, 2013
Who are you calling selfish?
There’s a bit of a fracas going on about David Dobbs’ article in Aeon on the obsolescence of the ‘selfish gene’: see here and here. In the first of these, Jerry Coyne has criticised the article as woefully misinformed; the second is Richard Dawkins’ response to it. There’s a crucial distinction here (though I’m not sure Coyne really wants to acknowledge it) between the accuracy of Dobbs’ scientific claims and the appropriateness of his objections to the selfish-gene metaphor. Steven Pinker apparently considers the problem here to be the fact that it “seems to be a congenital problem with science journalists [that] they think that it's a profound and revolutionary discovery that genes are regulated”. It might be nice if it were really that simple. But it isn’t. The real issue is whether the fact that genes are regulated (and perhaps more crucially, networked) means that “selfishness” is still an illuminating way to describe how they operate.
Take, for example, Coyne’s point that the polyphenism that Dobbs talks about – the fact that the same genes can create radically different phenotypes in a single organism – is triggered by a regulatory gene. (Coyne doesn’t say whether such a gene has yet been identified for grasshoppers or caterpillar/butterflies, but I’m happy to believe that this is indeed the probable origin of the morphological switch, regardless of whether we know the details.) In this sense, then, the transformation is certainly still under ‘genetic control’ and therefore adaptive in the same sense as any other genetic trait.
But does it mean that the regulatory gene in question – let’s call it gene A – is ‘selfish’? It’s hard to see any meaningful way in which this can be true. Coyne offers his own view of what it means: “during the process of natural selection, genes ‘act’ as if they were selfish.” In other words, it’s a metaphor. You know what, I think we got that already. We didn’t imagine it meant that genes habitually push to the front of queues and steal other genes’ wallets. What we need, though, is some notion of what he thinks “selfish” itself means in this context. That the gene plays a part in its own replication? I guess that’s what Coyne means, because later he says a selfish gene “promotes the reproduction of itself or its carrier.” But hang on – so it’s selfish if it promotes the reproduction of its carrier, meaning all those other genes too? So it’s not behaving in a way that is actually at the expense of other genes, but in fact benefits them? Like, say, the way we might play an active role in society so that it doesn’t collapse and we get shot up by looters? Sorry, so where exactly does the selfishness come in – or do you mean that the gene acts “as if with enlightened self-interest” – which, behaviourists and indeed linguists will tell you, is not the same as selfishness?
Let’s see if we can figure out which of these versions of the pathetic fallacy we’re talking about here. (I fear I might be patronising if I point out that ‘pathetic fallacy’ is not a term of abuse, as though to say that the idea of the selfish gene is pathetically fallacious, but on past experience I’ve found it’s best not to underestimate some folks’ unfamiliarity with figures of speech, particularly if they can otherwise extract offence from them.) This adaptation of gene A relies on the other genes whose expression is modified by the switch doing what they need to do in response to the signal from A. If they don’t ‘comply’, A gets no advantage. Likewise, the adaptation that enables the other genes to realise these alternative phenotypes in response to A’s signal relies on A actually giving that signal at the appropriate time. In other words, there is an intimate cooperativity required here between the way the genes operate, if any of them is to enjoy the mutual benefit.
Now, selfish geneticists might say “But A doesn’t care about those other genes, it is only working for its own benefit!” Well, they might say that, but I hope they won’t, for then they’d be showing that they have fallen for their own metaphor. Gene A doesn’t of course care about its own survival either. A gene doesn’t care about anything; it’s just a bit of a molecule. To use its ‘indifference’ to its fellow genes as an argument for why it is ‘selfish’ is absurd. You could of course say “isn’t it equally spurious to call this behaviour cooperative?” But cooperative behaviour in inanimate particles has a clear meaning in chemical physics: it means that the result depends on the collective interactions between the particles: it can’t result from the behaviour of any one of them acting alone. It doesn’t mean the particles are ‘nice’, or even that they act as if they were ‘nice’.
This sort of argument for why genes can be better regarded as cooperative than selfish is well rehearsed. It is a key aspect of the objections to the selfish-gene metaphor raised by people like Gabriel Dover, Denis Noble and Steven Rose. Noble’s argument in The Music of Life is particularly compelling, and the fact that it is seldom addressed by selfish geneticists, who prefer to imply that it’s just ignorant journalists who get this stuff wrong, is I think something of a backhanded compliment to Denis. (Let me, for the record, point out that Jerry Coyne has certainly laid into Noble in no uncertain terms – but I haven’t seen a good refutation of his specific criticisms of the selfish-gene metaphor.)
To his credit, Richard Dawkins himself does acknowledge some of this. In the 30th anniversary edition of The Selfish Gene he says, for example, “Another good alternative to The Selfish Gene would have been The Cooperative Gene.” That’s because, he says, genes sometimes act in mutually supportive gangs. “Natural selection therefore sees to it that gangs of mutually compatible—which is almost to say cooperating— genes are favoured in the presence of each other.” The genes are, however, individually still “selfish”, Dawkins says, because they are not cooperating for the benefit of the others. But that assertion only makes sense if you ascribe intentions to the genes – in other words, if you fall for the metaphor (I guess it is for reasons like this that Steven Rose thinks Dawkins doesn’t really understand what a metaphor is). All you can say is that a mutual operation of genes works to their collective benefit. It is simply meaningless to say that in such a circumstance they are acting “as if” they are selfish, just as it is meaningless to say that they are acting “as if” they are altruistic. It is, in effect, implanting a value judgement where none is warranted. Why do that? Well, I’ll come to that shortly.
There’s a deeper level to this debate, however, which I don’t see Coyne taking on board at all. It is about causality. The argument for selfish geneism seems to be that if a gene’s activity results in a change in phenotype, the gene is responsible for it – that it is the ‘cause’. This is equivalent to the old argument that the assassination of Archduke Ferdinand caused World War I. I like to think of it another way. Suppose Howard Webb referees Chelsea vs Manchester United, and Chelsea win 1-0 (I’m going to trust US readers to make the necessary changes mutatis mutandis). Webb has obviously ‘caused’ that result, as well as all the moves that led to it, because he blew the whistle that began the game (and indeed, intervened several times during the match too). The next season Webb referees the same game, but this time Man U triumph 2-0. That’s weird, because the teams have identical players, the pitch is the same, and so on. But Webb did a few things differently this time – he awarded Man U a penalty, say – so he’s obviously the cause of the difference.
The fact is that, of course, the course and outcome of both matches relies on all the players knowing what is required of them, and doing it. There’s already other crucial information in the system. Howard Webb wasn’t the cause of any of it, except in the important sense that without him either chaos would have ensued or the matches would never have started.
If this seems like a fatuous example, or a thin analogy (and sure, best not to push it too far), take a look at Hoel et al., PNAS 110, 19790; 2013 (here). This makes it clear that there are some complex systems in which causality must be seen as a property of higher-level modes of organization, and can’t be meaningfully ascribed to a microscopic event. If that is true in genetics, then neither evo nor devo can necessarily be considered to be under the causal control of specific genes. I don’t mean that the genes don’t underlie the processes, but just that causality does not reside therein. Or to be clear (because there’s a pathological inclination for words to be twisted in some of these disputes), there are of course plenty of cases where specific adaptive phenotypes can be attributed to specific genes (and so can be considered the result of selection at the genetic level), but there’s no reason to think that this is the generic or universal picture, and plenty of reason not to. That doesn’t deny the crucial importance of genes in evo/devo, any more than one would deny the importance of individual actions and decisions in the outbreak of World War I.
One might want to say that if the selfish gene’ metaphor works for Coyne, why not let him have it – it’s only a metaphor, after all. And I’m not unsympathetic to that. But it is of course not just Coyne – this metaphor has powerfully affected the way genetics and evolution have been presented to the public. And I don’t think it is at all unlikely (nor does Gabby Dover) that it has contributed in a major way to the prevailing notion of the “one-gene one-trait” picture that now even geneticists are finding an albatross: how can genes be operating in cooperative networks if each is only looking out for itself? I’m not saying that the selfish geneticists deny that they do, only that one of the many problems with the selfish gene picture is that it implies relentless individualism.
We should probably be honest about this too: it is surely no coincidence that the most vocal adherents of the selfish gene are the same folks who are most vocally anti-religious. It’s hard not to suspect that one of the attractions of this picture is its very harshness: not only does the universe not care in the slightest about your welfare (and I agree with that) but the most fundamental principles of life are positively ‘unkind’ and antagonistic – nasty if you like – and thus as far as it’s possible to get from your fluffy divine benevolence. Can’t you sense a gleeful “take that!” in the way Richard Dawkins serves up this stuff?
I might be unfair here, but I guess I’m searching for a reason why these smart folks are so reluctant to relinquish what is demonstrably a bad metaphor. After all, as Larry Moran (who is no slouch when it comes to beating on religion, although he picks his targets – creationists and ID-ers – rather more selectively) has pointed out, the selfish gene has been largely dead for decades in evolutionary biology.
One final point, since it seems to be a common trope in cases like this for scientists to decry journalists’ ignorance of their subject’s history. Forgive me if I’m wrong, but I have never seen selfish geneticists acknowledge that or explain why their definition of ‘selfish gene’ is different from that typically used in the 1980s by leading thinkers such as Francis Crick, Leslie Orgel, Gabriel Dover, and Ford Doolittle (Nature 284, 601 & 604; 1980). Those guys used selfishness specifically to refer to that subset of genes or genetic elements that have a propensity to proliferate in multiple copies throughout the genome – it was not a property of all genes that enabled them to benefit from natural selection. Indeed, this kind of selfish DNA, said Orgel and Crick, makes no specific contribution to the phenotype. Dawkins mentioned such genetic elements in The Selfish Gene, but selfish geneticists have subsequently been quite happy to see this ‘selfishness’ become a universal attribute of genes. That is evidently not how Crick saw it: he and Orgel make the distinction with ‘business as usual’ genetic selection very explicit.
Theirs seems to be a much more viable idea of selfishness, for the multiple copies of genes don’t benefit the organism. At best this accumulation of ‘junk’ is neutral to the organism, but it is potentially detrimental in the long term, providing a good illustration of the short-termism of natural selection. In this sense, then, selfishness is not a property that enables evolution to happen, but an inevitable by-product caused by its difficulty in dealing with parasitic freeloaders (for a modern view, see J. H. Werren, PNAS 108 (supplement 2), 10863; 2011). I’d much rather see selfishness reserved for this kind of situation. And so would many others. It seems to me that Coyne does a disservice by not acknowledging that the ‘selfish’ metaphor has a long and distinguished history of being applied only in this very restrictive and particular context.
I don’t want to be unnecessarily confrontational. Coyne has done a fine and important job in the past of defending evolution against idiotic attacks, and arguably this is just a debate about the packaging of a process whose basic details are not in doubt. But it’s because I do what I do that I think that packaging is important.
Take, for example, Coyne’s point that the polyphenism that Dobbs talks about – the fact that the same genes can create radically different phenotypes in a single organism – is triggered by a regulatory gene. (Coyne doesn’t say whether such a gene has yet been identified for grasshoppers or caterpillar/butterflies, but I’m happy to believe that this is indeed the probable origin of the morphological switch, regardless of whether we know the details.) In this sense, then, the transformation is certainly still under ‘genetic control’ and therefore adaptive in the same sense as any other genetic trait.
But does it mean that the regulatory gene in question – let’s call it gene A – is ‘selfish’? It’s hard to see any meaningful way in which this can be true. Coyne offers his own view of what it means: “during the process of natural selection, genes ‘act’ as if they were selfish.” In other words, it’s a metaphor. You know what, I think we got that already. We didn’t imagine it meant that genes habitually push to the front of queues and steal other genes’ wallets. What we need, though, is some notion of what he thinks “selfish” itself means in this context. That the gene plays a part in its own replication? I guess that’s what Coyne means, because later he says a selfish gene “promotes the reproduction of itself or its carrier.” But hang on – so it’s selfish if it promotes the reproduction of its carrier, meaning all those other genes too? So it’s not behaving in a way that is actually at the expense of other genes, but in fact benefits them? Like, say, the way we might play an active role in society so that it doesn’t collapse and we get shot up by looters? Sorry, so where exactly does the selfishness come in – or do you mean that the gene acts “as if with enlightened self-interest” – which, behaviourists and indeed linguists will tell you, is not the same as selfishness?
Let’s see if we can figure out which of these versions of the pathetic fallacy we’re talking about here. (I fear I might be patronising if I point out that ‘pathetic fallacy’ is not a term of abuse, as though to say that the idea of the selfish gene is pathetically fallacious, but on past experience I’ve found it’s best not to underestimate some folks’ unfamiliarity with figures of speech, particularly if they can otherwise extract offence from them.) This adaptation of gene A relies on the other genes whose expression is modified by the switch doing what they need to do in response to the signal from A. If they don’t ‘comply’, A gets no advantage. Likewise, the adaptation that enables the other genes to realise these alternative phenotypes in response to A’s signal relies on A actually giving that signal at the appropriate time. In other words, there is an intimate cooperativity required here between the way the genes operate, if any of them is to enjoy the mutual benefit.
Now, selfish geneticists might say “But A doesn’t care about those other genes, it is only working for its own benefit!” Well, they might say that, but I hope they won’t, for then they’d be showing that they have fallen for their own metaphor. Gene A doesn’t of course care about its own survival either. A gene doesn’t care about anything; it’s just a bit of a molecule. To use its ‘indifference’ to its fellow genes as an argument for why it is ‘selfish’ is absurd. You could of course say “isn’t it equally spurious to call this behaviour cooperative?” But cooperative behaviour in inanimate particles has a clear meaning in chemical physics: it means that the result depends on the collective interactions between the particles: it can’t result from the behaviour of any one of them acting alone. It doesn’t mean the particles are ‘nice’, or even that they act as if they were ‘nice’.
This sort of argument for why genes can be better regarded as cooperative than selfish is well rehearsed. It is a key aspect of the objections to the selfish-gene metaphor raised by people like Gabriel Dover, Denis Noble and Steven Rose. Noble’s argument in The Music of Life is particularly compelling, and the fact that it is seldom addressed by selfish geneticists, who prefer to imply that it’s just ignorant journalists who get this stuff wrong, is I think something of a backhanded compliment to Denis. (Let me, for the record, point out that Jerry Coyne has certainly laid into Noble in no uncertain terms – but I haven’t seen a good refutation of his specific criticisms of the selfish-gene metaphor.)
To his credit, Richard Dawkins himself does acknowledge some of this. In the 30th anniversary edition of The Selfish Gene he says, for example, “Another good alternative to The Selfish Gene would have been The Cooperative Gene.” That’s because, he says, genes sometimes act in mutually supportive gangs. “Natural selection therefore sees to it that gangs of mutually compatible—which is almost to say cooperating— genes are favoured in the presence of each other.” The genes are, however, individually still “selfish”, Dawkins says, because they are not cooperating for the benefit of the others. But that assertion only makes sense if you ascribe intentions to the genes – in other words, if you fall for the metaphor (I guess it is for reasons like this that Steven Rose thinks Dawkins doesn’t really understand what a metaphor is). All you can say is that a mutual operation of genes works to their collective benefit. It is simply meaningless to say that in such a circumstance they are acting “as if” they are selfish, just as it is meaningless to say that they are acting “as if” they are altruistic. It is, in effect, implanting a value judgement where none is warranted. Why do that? Well, I’ll come to that shortly.
There’s a deeper level to this debate, however, which I don’t see Coyne taking on board at all. It is about causality. The argument for selfish geneism seems to be that if a gene’s activity results in a change in phenotype, the gene is responsible for it – that it is the ‘cause’. This is equivalent to the old argument that the assassination of Archduke Ferdinand caused World War I. I like to think of it another way. Suppose Howard Webb referees Chelsea vs Manchester United, and Chelsea win 1-0 (I’m going to trust US readers to make the necessary changes mutatis mutandis). Webb has obviously ‘caused’ that result, as well as all the moves that led to it, because he blew the whistle that began the game (and indeed, intervened several times during the match too). The next season Webb referees the same game, but this time Man U triumph 2-0. That’s weird, because the teams have identical players, the pitch is the same, and so on. But Webb did a few things differently this time – he awarded Man U a penalty, say – so he’s obviously the cause of the difference.
The fact is that, of course, the course and outcome of both matches relies on all the players knowing what is required of them, and doing it. There’s already other crucial information in the system. Howard Webb wasn’t the cause of any of it, except in the important sense that without him either chaos would have ensued or the matches would never have started.
If this seems like a fatuous example, or a thin analogy (and sure, best not to push it too far), take a look at Hoel et al., PNAS 110, 19790; 2013 (here). This makes it clear that there are some complex systems in which causality must be seen as a property of higher-level modes of organization, and can’t be meaningfully ascribed to a microscopic event. If that is true in genetics, then neither evo nor devo can necessarily be considered to be under the causal control of specific genes. I don’t mean that the genes don’t underlie the processes, but just that causality does not reside therein. Or to be clear (because there’s a pathological inclination for words to be twisted in some of these disputes), there are of course plenty of cases where specific adaptive phenotypes can be attributed to specific genes (and so can be considered the result of selection at the genetic level), but there’s no reason to think that this is the generic or universal picture, and plenty of reason not to. That doesn’t deny the crucial importance of genes in evo/devo, any more than one would deny the importance of individual actions and decisions in the outbreak of World War I.
One might want to say that if the selfish gene’ metaphor works for Coyne, why not let him have it – it’s only a metaphor, after all. And I’m not unsympathetic to that. But it is of course not just Coyne – this metaphor has powerfully affected the way genetics and evolution have been presented to the public. And I don’t think it is at all unlikely (nor does Gabby Dover) that it has contributed in a major way to the prevailing notion of the “one-gene one-trait” picture that now even geneticists are finding an albatross: how can genes be operating in cooperative networks if each is only looking out for itself? I’m not saying that the selfish geneticists deny that they do, only that one of the many problems with the selfish gene picture is that it implies relentless individualism.
We should probably be honest about this too: it is surely no coincidence that the most vocal adherents of the selfish gene are the same folks who are most vocally anti-religious. It’s hard not to suspect that one of the attractions of this picture is its very harshness: not only does the universe not care in the slightest about your welfare (and I agree with that) but the most fundamental principles of life are positively ‘unkind’ and antagonistic – nasty if you like – and thus as far as it’s possible to get from your fluffy divine benevolence. Can’t you sense a gleeful “take that!” in the way Richard Dawkins serves up this stuff?
I might be unfair here, but I guess I’m searching for a reason why these smart folks are so reluctant to relinquish what is demonstrably a bad metaphor. After all, as Larry Moran (who is no slouch when it comes to beating on religion, although he picks his targets – creationists and ID-ers – rather more selectively) has pointed out, the selfish gene has been largely dead for decades in evolutionary biology.
One final point, since it seems to be a common trope in cases like this for scientists to decry journalists’ ignorance of their subject’s history. Forgive me if I’m wrong, but I have never seen selfish geneticists acknowledge that or explain why their definition of ‘selfish gene’ is different from that typically used in the 1980s by leading thinkers such as Francis Crick, Leslie Orgel, Gabriel Dover, and Ford Doolittle (Nature 284, 601 & 604; 1980). Those guys used selfishness specifically to refer to that subset of genes or genetic elements that have a propensity to proliferate in multiple copies throughout the genome – it was not a property of all genes that enabled them to benefit from natural selection. Indeed, this kind of selfish DNA, said Orgel and Crick, makes no specific contribution to the phenotype. Dawkins mentioned such genetic elements in The Selfish Gene, but selfish geneticists have subsequently been quite happy to see this ‘selfishness’ become a universal attribute of genes. That is evidently not how Crick saw it: he and Orgel make the distinction with ‘business as usual’ genetic selection very explicit.
Theirs seems to be a much more viable idea of selfishness, for the multiple copies of genes don’t benefit the organism. At best this accumulation of ‘junk’ is neutral to the organism, but it is potentially detrimental in the long term, providing a good illustration of the short-termism of natural selection. In this sense, then, selfishness is not a property that enables evolution to happen, but an inevitable by-product caused by its difficulty in dealing with parasitic freeloaders (for a modern view, see J. H. Werren, PNAS 108 (supplement 2), 10863; 2011). I’d much rather see selfishness reserved for this kind of situation. And so would many others. It seems to me that Coyne does a disservice by not acknowledging that the ‘selfish’ metaphor has a long and distinguished history of being applied only in this very restrictive and particular context.
I don’t want to be unnecessarily confrontational. Coyne has done a fine and important job in the past of defending evolution against idiotic attacks, and arguably this is just a debate about the packaging of a process whose basic details are not in doubt. But it’s because I do what I do that I think that packaging is important.
Monday, December 09, 2013
Birds reveal a new facet of their personality
Here’s the original of my latest news story for Nature.
_____________________________________________________________
Some birds are predisposed to signal their intentions more clearly than others.
Some animals, like some people, are more aggressive than others - it's just the way they are. But new research suggests that, for birds at least, this personality is more subtle. Some are inclined to give out exaggerated signs of their aggressiveness, others to underplay it.
It's rather like the menacing biker who turns out to be a pussy-cat, or the wimpy geek who will break a bottle over your head. But the analogy with humans goes only so far, because many birds announce their aggression about mating and territory not by appearance but by song and gesture.
For example, the song sparrow indicates its intention to attack a dummy bird or a loudspeaker playing back its songs by either vocalizing distinctive ‘soft songs’ or by fluttering its wings (so-called wing waves), both of which are perceived as threatening [2].
Both aggressive signaling and the ensuing aggressive behaviour varies from one bird to another, in a way that correlates with other personality traits such as boldness [1]. But these attributes also vary for a single individual at different times – they can have particularly grouchy or placid days. The degree of aggression implied by the precursory signals generally reflects the actual behaviour – it is what evolutionary biologists call an “honest signal”.
But not entirely honest. Earlier this year Michael Beecher and colleagues at the University of Washington in Seattle showed that there’s some variability in aggressive signaling that doesn’t match the behaviour: a bird might act stroppy but not follow through with an attack [2].
This variability could be just random, an imponderable quirk of bird-brains. But now Beecher and colleagues say it isn’t [3].
The researchers studied 69 labelled male song sparrows in their natural habitat during autumn and spring. They played the birds their own songs (which elicit aggression just as ‘stranger songs’ do) and watched how they responded – whether they displayed the aggression signals of soft songs and wing waves, and whether they followed through by attacking the loudspeakers or a dummy bird.
They found that, after allowing for variations that provide an honest signal of a bird’s fluctuations in aggressive mood, the remaining variability – if you like, the dishonest part of it – seems to be consistently displayed by particular birds.
Some have a predisposition – consistent from one season to the next – to give out false signals of how aggressive they intend to be, suggesting either too much or too little. Others are more consistently ‘honest’. Beecher and colleagues say that this behaviour too seems to be a robust characteristic of an individual bird’s personality, which the researchers call “communicativeness”.
“This is an important and novel paper”, says William Searcy, a behavioural ecologist at the University of Miami. “I think it’s highly likely that behaviours one can define in song sparrows can be identified in other birds, and other animals as well”, adds Jeremy Hyman of Western Carolina University, a specialist in bird behaviour.
Habitual ‘over-signallers’ may be good bluffers, who gamble on scaring away rivals that they won’t actually dare fight. ‘Under-signallers’, who attack without much warning, are harder to explain. “This behaviour is intriguing, and hasn't really been discussed in theory”, says Beecher. “There are benefits to signaling – a fight is avoided, potentially beneficial to both parties – so why not do it?”
One possibility is that under-signallers are genuine tough guys, so likely to win a bout that it’s not worth their while bothering with scare tactics. In this case the behaviour could be a beneficial adaptation. But another possibility is that some individuals just aren’t very good at getting the signaling codes right – it’s not an adaptation but a mistake.
“I don’t think there is enough evidence yet to know whether individual adaptive or error-based theories are more correct”, says Hyman. He adds that why personality traits exist at all is still a big question, but says “I think there’s enough evidence of links between personality and fitness to conclude that behavioural variation isn’t [adaptively] neutral.”
References
1. Bell, A., Hankison, S. J. & Laslowski, K. L. Anim. Behav. 77, 771-783 (2009).
2. Akçay, Ç., Campbell, S. E., Tom, M. E. & Beecher, M. D., Proc. R. Soc. B 280, 20122517 (2013).
3. Akçay, Ç., Campbell, S. E. & Beecher, M. D., Proc. R. Soc. B 281, 20132496 (2014).
_____________________________________________________________
Some birds are predisposed to signal their intentions more clearly than others.
Some animals, like some people, are more aggressive than others - it's just the way they are. But new research suggests that, for birds at least, this personality is more subtle. Some are inclined to give out exaggerated signs of their aggressiveness, others to underplay it.
It's rather like the menacing biker who turns out to be a pussy-cat, or the wimpy geek who will break a bottle over your head. But the analogy with humans goes only so far, because many birds announce their aggression about mating and territory not by appearance but by song and gesture.
For example, the song sparrow indicates its intention to attack a dummy bird or a loudspeaker playing back its songs by either vocalizing distinctive ‘soft songs’ or by fluttering its wings (so-called wing waves), both of which are perceived as threatening [2].
Both aggressive signaling and the ensuing aggressive behaviour varies from one bird to another, in a way that correlates with other personality traits such as boldness [1]. But these attributes also vary for a single individual at different times – they can have particularly grouchy or placid days. The degree of aggression implied by the precursory signals generally reflects the actual behaviour – it is what evolutionary biologists call an “honest signal”.
But not entirely honest. Earlier this year Michael Beecher and colleagues at the University of Washington in Seattle showed that there’s some variability in aggressive signaling that doesn’t match the behaviour: a bird might act stroppy but not follow through with an attack [2].
This variability could be just random, an imponderable quirk of bird-brains. But now Beecher and colleagues say it isn’t [3].
The researchers studied 69 labelled male song sparrows in their natural habitat during autumn and spring. They played the birds their own songs (which elicit aggression just as ‘stranger songs’ do) and watched how they responded – whether they displayed the aggression signals of soft songs and wing waves, and whether they followed through by attacking the loudspeakers or a dummy bird.
They found that, after allowing for variations that provide an honest signal of a bird’s fluctuations in aggressive mood, the remaining variability – if you like, the dishonest part of it – seems to be consistently displayed by particular birds.
Some have a predisposition – consistent from one season to the next – to give out false signals of how aggressive they intend to be, suggesting either too much or too little. Others are more consistently ‘honest’. Beecher and colleagues say that this behaviour too seems to be a robust characteristic of an individual bird’s personality, which the researchers call “communicativeness”.
“This is an important and novel paper”, says William Searcy, a behavioural ecologist at the University of Miami. “I think it’s highly likely that behaviours one can define in song sparrows can be identified in other birds, and other animals as well”, adds Jeremy Hyman of Western Carolina University, a specialist in bird behaviour.
Habitual ‘over-signallers’ may be good bluffers, who gamble on scaring away rivals that they won’t actually dare fight. ‘Under-signallers’, who attack without much warning, are harder to explain. “This behaviour is intriguing, and hasn't really been discussed in theory”, says Beecher. “There are benefits to signaling – a fight is avoided, potentially beneficial to both parties – so why not do it?”
One possibility is that under-signallers are genuine tough guys, so likely to win a bout that it’s not worth their while bothering with scare tactics. In this case the behaviour could be a beneficial adaptation. But another possibility is that some individuals just aren’t very good at getting the signaling codes right – it’s not an adaptation but a mistake.
“I don’t think there is enough evidence yet to know whether individual adaptive or error-based theories are more correct”, says Hyman. He adds that why personality traits exist at all is still a big question, but says “I think there’s enough evidence of links between personality and fitness to conclude that behavioural variation isn’t [adaptively] neutral.”
References
1. Bell, A., Hankison, S. J. & Laslowski, K. L. Anim. Behav. 77, 771-783 (2009).
2. Akçay, Ç., Campbell, S. E., Tom, M. E. & Beecher, M. D., Proc. R. Soc. B 280, 20122517 (2013).
3. Akçay, Ç., Campbell, S. E. & Beecher, M. D., Proc. R. Soc. B 281, 20132496 (2014).
Sunday, December 08, 2013
Quantum computers: when, what, who and why
I have a piece in December’s Prospect on quantum computing – here’s the original draft.
__________________________________________________
When people first hear about quantum computers, a common response is “where and when can I get one?” But that’s the wrong question, and not just because you’ll be disappointed with the answer. Quantum computers are often said to promise faster, bigger, more multi-layered computation – but they are not, and might never be, an upgrade of your laptop. They’re just not that sort of machine. So what are they, and why do we want them?
You could argue that your laptop is already a quantum computer, because the laws of quantum physics govern the ways electrical currents pass through its ultra-small transistors and wires. Partly that’s just saying that ultimately quantum physics governs all the properties of materials. Increasingly, however, strange quantum effects that don’t usually manifest in the everyday world, such as the ability of electrons to leap through walls, are becoming important as the scale of microelectronics shrinks. This ‘quantum tunnelling’, for example, is the basis of flash memory, and also threatens to make transistors ‘leaky’ as they get ever smaller.
Real quantum computers go far beyond any of that, however. In the end, all of today’s computers work using old-fashioned binary logic: by encoding information in strings of 1’s and 0’s, represented for example by electrical pulses in circuits or by flashes of light in optical fibres. These so-called bits are manipulated in logic gates, built from electronic components such as transistors. Here a particular set of input bits prompt the gate to produce another set of output bits. That’s what computation is; the rest is a question of building software and interfaces that turn these bits into a letter to Mum glowing on the screen.
Quantum computers will also use 1’s and 0’s, but with a crucial difference. As well as having one or the other of these values, a quantum bit (qubit) could have any mixture of them. Counter-intuitively, it can be simultaneously a 1 and a 0, or 1 with a tiny bit of 0, and so on. These mixtures are called superpositions, and they are a fundamental feature of objects that obey quantum rules. A photon of light, for example, can be polarized either vertically or horizontally, or can be in a superposition of both polarizations.
That gives qubits access to a vast range of states, so you can encode much more information in them. [OK, I’m keeping this in for now in the interests of honesty to the moment – but watch this space for an explanation of why this is far too simplistic, and perhaps even too erroneous, a way to describe quantum computing…] In short, it enables quantum computers to perform very many calculations simultaneously where a classical computer can do only one at a time with any given set of bits. It is this that provides the quantum computer with its tremendous speed-up. To factorize a big number classically (to find all its divisors), a computer plods through all the possible answers, while a quantum computer can assess them all, encoded in superpositions of qubits, at basically the same time.
So where’s the catch? It is that quantum phenomena such as superpositions are generally very delicate. They get easily disrupted or destroyed by disturbances from the surrounding environment, particularly the randomizing effects of heat. So to make such states usually requires very low temperatures. This fragility of quantum effects means that, while the question of what you could do with a quantum computer has been explored extensively already by physicists and mathematicians, actually building a device that can do any of it is taxing electrical engineers and applied physicists to the limit.
Now there are signs of real progress. The community was set buzzing two years ago when a Canadian company called D-Wave (“the world’s first commercial quantum computing company”) announced that it had created the first practical quantum computer: a black box, if you will, that could actually solve stuff. But several researchers questioned whether D-Wave’s device was really a true quantum computer at all, or just a fancy box of tricks that made token nods towards quantum effects. It employs an approach called ‘quantum annealing’, which is different from most theories of quantum computing and for which any real advantages over classical computing have yet to be shown.
At Raytheon BBN Technologies, based in Cambridge, Massachusetts, researchers are convinced that they are closing in on the real thing. Conveniently close to Harvard and the Massachusetts Institute of Technology, BBN was founded in 1948 and was intimately involved in the development of the earliest military networks that became the Internet. In 2009 the company became a subsidiary of the US defence contractor Raytheon. It has been seeking to develop so-called quantum information technologies since 2001, when the company’s researchers devised an optical telecommunications network that could exchange light signals between their headquarters and nearby Harvard and Boston Universities that encoded information in superpositions of photons. Such networks, which could be immune to eavesdropping, have now been developed in many places in the world.
But the quantum computer, which actually does number-crunching, is a bigger challenge. To make qubits, Raytheon BBN uses the same fundamental circuit components as D-Wave does. Called superconducting Josephson junctions, these are metal contacts cooled so deeply that they have become superconductors (that is, they have no electrical resistance), electrically connected to each other via a thin barrier of insulating material. Superconductivity is itself a quantum-mechanical effect, which is why it requires low temperatures, and the superconducting current can flow in distinct quantum states. A Josephson junction helps to filter out all but two states, which correspond to the binary 1’s and 0’s. It is possible to manipulate these states, for example creating specific superpositions, using pulses of microwave radiation. That’s the physical basis of BBN’s qubit circuits, which have to be cooled to within a daunting 50 thousandths of a degree of absolute zero.
Even then, the superpositions don’t last long. Yet to do practical quantum computing, they only need to survive for at least as long as the time needed to juggle with them in quantum logic gates. In recent years, says Zachary Dutton, lead scientist of Raytheon BBN’s Quantum Information Processing group, these so-called coherence times have increased dramatically, and are now at a level – tens to hundreds of microseconds – where the devices can actually perform logic processing.
Another critical issue for these quantum gates is the so-called error rate: how accurately they can be switched between states by the microwave signal. If you get this a little wrong – say, by making too much of one state in the superposition – the errors accumulate until, even if one stores the same information several times for cross-checking, too many mistakes derail the whole computation. Getting the error rate small enough to avoid this remains one of the key tasks.
At present the Raytheon BBN team, who are collaborating with computer giants IBM, doesn’t have anything even vaguely like a quantum computer. Rather, they are focusing on getting very small systems – currently three qubits, but soon to be eight – to work well enough that they can be assembled into large-scale circuits. “If you looked at a circuit diagram of a quantum computer”, says Dutton, “this would be a little piece of it.” The extreme cooling “needn’t be a showstopper”, he adds, because refrigeration technologies have advanced so much in recent years, for example so that they don’t need constant refilling with a coolant such as liquid helium.
Exotic quantum states in ultracold superconducting wires might sound like a complicated basis for making qubits. But the same approach is being taken by several of the leading academic centres of quantum computing, including MIT, Yale and the University of California at Santa Barbara. It’s by no means the only option. Another popular approach, for example, is to encode information in the quantum-mechanical energy states of individual atoms or ions suspended in free space using electromagnetic fields to trap them there. The information can be programmed, manipulated and read out using lasers to probe and alter the states of the trapped ions. Christopher Monroe, who is using this approach at the University of Maryland, feels that “there will be some interesting results in the next several years in both Josephson junction and [ion-trap] atomic machines”. He concurs that, unlike the 512-qubit D-Wave devices, those under development at Raytheon BBN are “legitimately quantum”.
What would you use a quantum computer for? Monroe says that the first demonstrations of quantum computing will probably be solving “some esoteric physics problem”, not providing a general-purpose computer. There are, however, some important possible uses that anyone can appreciate. Fast factorizing of huge numbers is one such, since all current data encryption methods rely on the difficulty of doing this with classical computers. Quantum computers would change the whole game in data security.
For basic science, one of the most appealing applications would be to perform computer simulations of molecules and materials. These are governed by quantum rules, and classical computers are forced to solve the equations by laborious and merely approximate mathematical methods. Quantum computers, in contrast, could map such quantum behaviour directly and exactly into its algorithms, so that simulations that take days currently might be possible in seconds, helping to make better predictions of the properties of new drugs and materials.
Currently, the most taxing computational problems are tackled by massive, expensive supercomputers housed in a few specialized centres and leased to users. That’s what the initial market for quantum computers will look like too, says Dutton – not really a market at all, but a highly centralized oligopoly. But of course all computers used to be like this: huge mainframes dedicated to recondite problems. Mindful of IBM founder Thomas Watson’s (possibly apocryphal) prediction in 1943 that this is what computers would always be – Watson is said to have forecast a world market for perhaps five of them in total – it would be an unwise prophet who forecasts where quantum computers might be decades down the line.
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When people first hear about quantum computers, a common response is “where and when can I get one?” But that’s the wrong question, and not just because you’ll be disappointed with the answer. Quantum computers are often said to promise faster, bigger, more multi-layered computation – but they are not, and might never be, an upgrade of your laptop. They’re just not that sort of machine. So what are they, and why do we want them?
You could argue that your laptop is already a quantum computer, because the laws of quantum physics govern the ways electrical currents pass through its ultra-small transistors and wires. Partly that’s just saying that ultimately quantum physics governs all the properties of materials. Increasingly, however, strange quantum effects that don’t usually manifest in the everyday world, such as the ability of electrons to leap through walls, are becoming important as the scale of microelectronics shrinks. This ‘quantum tunnelling’, for example, is the basis of flash memory, and also threatens to make transistors ‘leaky’ as they get ever smaller.
Real quantum computers go far beyond any of that, however. In the end, all of today’s computers work using old-fashioned binary logic: by encoding information in strings of 1’s and 0’s, represented for example by electrical pulses in circuits or by flashes of light in optical fibres. These so-called bits are manipulated in logic gates, built from electronic components such as transistors. Here a particular set of input bits prompt the gate to produce another set of output bits. That’s what computation is; the rest is a question of building software and interfaces that turn these bits into a letter to Mum glowing on the screen.
Quantum computers will also use 1’s and 0’s, but with a crucial difference. As well as having one or the other of these values, a quantum bit (qubit) could have any mixture of them. Counter-intuitively, it can be simultaneously a 1 and a 0, or 1 with a tiny bit of 0, and so on. These mixtures are called superpositions, and they are a fundamental feature of objects that obey quantum rules. A photon of light, for example, can be polarized either vertically or horizontally, or can be in a superposition of both polarizations.
That gives qubits access to a vast range of states, so you can encode much more information in them. [OK, I’m keeping this in for now in the interests of honesty to the moment – but watch this space for an explanation of why this is far too simplistic, and perhaps even too erroneous, a way to describe quantum computing…] In short, it enables quantum computers to perform very many calculations simultaneously where a classical computer can do only one at a time with any given set of bits. It is this that provides the quantum computer with its tremendous speed-up. To factorize a big number classically (to find all its divisors), a computer plods through all the possible answers, while a quantum computer can assess them all, encoded in superpositions of qubits, at basically the same time.
So where’s the catch? It is that quantum phenomena such as superpositions are generally very delicate. They get easily disrupted or destroyed by disturbances from the surrounding environment, particularly the randomizing effects of heat. So to make such states usually requires very low temperatures. This fragility of quantum effects means that, while the question of what you could do with a quantum computer has been explored extensively already by physicists and mathematicians, actually building a device that can do any of it is taxing electrical engineers and applied physicists to the limit.
Now there are signs of real progress. The community was set buzzing two years ago when a Canadian company called D-Wave (“the world’s first commercial quantum computing company”) announced that it had created the first practical quantum computer: a black box, if you will, that could actually solve stuff. But several researchers questioned whether D-Wave’s device was really a true quantum computer at all, or just a fancy box of tricks that made token nods towards quantum effects. It employs an approach called ‘quantum annealing’, which is different from most theories of quantum computing and for which any real advantages over classical computing have yet to be shown.
At Raytheon BBN Technologies, based in Cambridge, Massachusetts, researchers are convinced that they are closing in on the real thing. Conveniently close to Harvard and the Massachusetts Institute of Technology, BBN was founded in 1948 and was intimately involved in the development of the earliest military networks that became the Internet. In 2009 the company became a subsidiary of the US defence contractor Raytheon. It has been seeking to develop so-called quantum information technologies since 2001, when the company’s researchers devised an optical telecommunications network that could exchange light signals between their headquarters and nearby Harvard and Boston Universities that encoded information in superpositions of photons. Such networks, which could be immune to eavesdropping, have now been developed in many places in the world.
But the quantum computer, which actually does number-crunching, is a bigger challenge. To make qubits, Raytheon BBN uses the same fundamental circuit components as D-Wave does. Called superconducting Josephson junctions, these are metal contacts cooled so deeply that they have become superconductors (that is, they have no electrical resistance), electrically connected to each other via a thin barrier of insulating material. Superconductivity is itself a quantum-mechanical effect, which is why it requires low temperatures, and the superconducting current can flow in distinct quantum states. A Josephson junction helps to filter out all but two states, which correspond to the binary 1’s and 0’s. It is possible to manipulate these states, for example creating specific superpositions, using pulses of microwave radiation. That’s the physical basis of BBN’s qubit circuits, which have to be cooled to within a daunting 50 thousandths of a degree of absolute zero.
Even then, the superpositions don’t last long. Yet to do practical quantum computing, they only need to survive for at least as long as the time needed to juggle with them in quantum logic gates. In recent years, says Zachary Dutton, lead scientist of Raytheon BBN’s Quantum Information Processing group, these so-called coherence times have increased dramatically, and are now at a level – tens to hundreds of microseconds – where the devices can actually perform logic processing.
Another critical issue for these quantum gates is the so-called error rate: how accurately they can be switched between states by the microwave signal. If you get this a little wrong – say, by making too much of one state in the superposition – the errors accumulate until, even if one stores the same information several times for cross-checking, too many mistakes derail the whole computation. Getting the error rate small enough to avoid this remains one of the key tasks.
At present the Raytheon BBN team, who are collaborating with computer giants IBM, doesn’t have anything even vaguely like a quantum computer. Rather, they are focusing on getting very small systems – currently three qubits, but soon to be eight – to work well enough that they can be assembled into large-scale circuits. “If you looked at a circuit diagram of a quantum computer”, says Dutton, “this would be a little piece of it.” The extreme cooling “needn’t be a showstopper”, he adds, because refrigeration technologies have advanced so much in recent years, for example so that they don’t need constant refilling with a coolant such as liquid helium.
Exotic quantum states in ultracold superconducting wires might sound like a complicated basis for making qubits. But the same approach is being taken by several of the leading academic centres of quantum computing, including MIT, Yale and the University of California at Santa Barbara. It’s by no means the only option. Another popular approach, for example, is to encode information in the quantum-mechanical energy states of individual atoms or ions suspended in free space using electromagnetic fields to trap them there. The information can be programmed, manipulated and read out using lasers to probe and alter the states of the trapped ions. Christopher Monroe, who is using this approach at the University of Maryland, feels that “there will be some interesting results in the next several years in both Josephson junction and [ion-trap] atomic machines”. He concurs that, unlike the 512-qubit D-Wave devices, those under development at Raytheon BBN are “legitimately quantum”.
What would you use a quantum computer for? Monroe says that the first demonstrations of quantum computing will probably be solving “some esoteric physics problem”, not providing a general-purpose computer. There are, however, some important possible uses that anyone can appreciate. Fast factorizing of huge numbers is one such, since all current data encryption methods rely on the difficulty of doing this with classical computers. Quantum computers would change the whole game in data security.
For basic science, one of the most appealing applications would be to perform computer simulations of molecules and materials. These are governed by quantum rules, and classical computers are forced to solve the equations by laborious and merely approximate mathematical methods. Quantum computers, in contrast, could map such quantum behaviour directly and exactly into its algorithms, so that simulations that take days currently might be possible in seconds, helping to make better predictions of the properties of new drugs and materials.
Currently, the most taxing computational problems are tackled by massive, expensive supercomputers housed in a few specialized centres and leased to users. That’s what the initial market for quantum computers will look like too, says Dutton – not really a market at all, but a highly centralized oligopoly. But of course all computers used to be like this: huge mainframes dedicated to recondite problems. Mindful of IBM founder Thomas Watson’s (possibly apocryphal) prediction in 1943 that this is what computers would always be – Watson is said to have forecast a world market for perhaps five of them in total – it would be an unwise prophet who forecasts where quantum computers might be decades down the line.
Tuesday, December 03, 2013
What innovation really is
Here is my current Crucible column for Chemistry World. The plot above shows how chemistry’s ‘connectedness’ to other disciplines falls out in this analysis of citations – the size of the circles reflects the number of papers included in the analysis, the colours show the interdisciplinarity: the bluer, the more so.
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How do you write a hit paper? The rise of bibliometrics and citation data-mining threatens to turn the answer into a reductive prescription: have many coauthors, make the paper longer, choose an assertive, catchy title. Yet the truth is that we have always known what generates the best chance of success: have a really interesting and productive idea, report it clearly and carefully, and publish it in a good journal.
That’s why a new paper analysing the ingredients of high-impact scientific papers (as defined by their citation counts) is best not viewed as another ‘how to’ formula. Rather, what Brian Uzzi and Ben Jones of Northwestern University in Illinois and their colleagues have supplied [B. Uzzi et al., Science 342, 468 (2013)] is a retrospective account of why some papers made their mark. It’s a bit like examining why the Beatles’ songs are so popular – it’s one thing to explain it, quite another to use that knowledge to write another “Eleanor Rigby”.
The real value of this work is in underlining the importance of innovative thinking – as well as clarifying what genuine novelty consists of. The idea is ingenious in itself (my guess is that if the researchers trained their lens on their own paper, it would predict considerable impact). While it is hard to quantify the intrinsic novelty of the ideas expressed in a paper, the reference list generally gives a fair indication of the intellectual heritage on which they draw.
So if the references are all taken from a narrow body of highly specialized and specific work, the chances are that the paper itself represents just another incremental advance in that area, and is going to have limited appeal outside a tiny circle. But a paper with a bizarrely diverse selection of references – here the Journal of Natural Products, there Kierkegaard’s Fear and Trembling – all too probably indicates a comparable incoherence in the authors’ minds.
What Uzzi and colleagues consider, then, is the balance between ‘typical’ and ‘atypical’ in the reference list. Using a database of 17.9 million papers in the Web of Science spanning all scientific fields (in fact they have ventured into the humanities too), the researchers looked at how often all possible pairs of papers (or journals) in a given year were cited together. A comparison against purely random pairings then reveals how ‘conventional’ such a pairing is, enabling an enumeration of the conventionality of any paper’s entire reference list.
It will surprise no one to hear that scientific papers are on the whole highly conservative by this measure. But Uzzi and colleagues figured that the relatively unusual combinations of citations – those in the tail of the distributions – might be particularly revealing. They found that even these tended to be ‘typical’: the less-common pairings of journal A, say, tend to be with journals G and K rather than more random.
The real story emerges when these citation patterns are compared between high-impact and low-impact papers. The former are no less firmly embedded in convention – except, crucially, for the unusual reference combinations in the tail of their distributions, which show a strong degree of novelty. In other words, these papers anchor themselves to a substantial body of related, specialized work, but inject into it ideas and results from farther afield than lower-impact papers tend to reach. “Thus, novelty and conventionality are not opposing factors in the production of science”, Uzzi and colleagues say. As one might imagine, novelty in this sense seems to appear more often in papers written by collaborating teams, which can mine insights from different disciplines.
What does all this mean in chemistry? The paper itself gives no breakdown by discipline, but Brian Uzzi has kindly supplied me with a few indicators. On the basis of how often papers within the discipline cite ones from outside, chemistry scores highly as an interdisciplinary subject – second only to biology, comparable to medical research, and better than, say, physics or earth sciences. Moreover, its cross-disciplinary handshakes are very diverse, although the affinities with medicine and biology are evident. On this basis, the common claim that chemistry is the “central science” seems well justified.
No doubt individual case histories of high-impact chemical papers would tell instructive stories. Two papers from Chemical Communications in 1994 offer a representative snapshot. One, on polymer synthesis, reaches out only to other journals of polymer and organic chemistry but without even the benefit of conventional pairings therein. It had 12 citations. Another, on the synthesis of derivatized gold nanoparticles, combines popular pairings such as JACS-Angewandte Chemie with novel links to the literature on clusters; it had nearly 4,000 citations.
If you want a moral, it is surely to talk to people outside your group, and ideally outside your department, and if possible work with them. But at the same time don’t neglect the core of your own subject. Easily said, I know – but the best advice usually is.
Monday, December 02, 2013
Ome sweet ome?
Here’s my latest piece for the Prospect blog.
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Chances are that every biologist now has an ome to go to. This suffix, first introduced in the genome (the sum total of all an organism’s genes), can now be found attached to just about every aspect of life’s molecular basis. There is the proteome (the full complement of protein molecules in an organism), the glycome (all the sugars), the epigenome (all the non-genetically encoded regulation of gene activity), the lipidome (all the fatty-acid lipids of cell membranes). Omes embrace wider concepts too. The metabolome comprises all the molecules involved in metabolism; the interactome is the network of interactions between genes and other molecules; the phenome is the total of all distinct observable traits (phenotypes), and so on. The integrome is the ome of all the omes: an ome from ome, you might say.
The proliferation of these neologisms has understandably attracted criticisms and ridicule, and even the founding editor of a new journal called Omics told Nature that “most of them will not make sense.” Some researchers suggest that they are just a way of investing an established field – such as the study of metabolic biochemical processes – with the kudos that has become attached to genomics. They are also a marketing ploy: if you have an ome, you surely need your own distinct funding stream.
Geneticist Jonathan Eisen of the University of California at Davis talks about “badomics”, and sees the spread of omes as a pernicious meme that adds clutter and confusion, as well as implying a sometimes misleading analogy to the aims and concepts of genomics. He compares it with the indiscriminate appending of -gate to every political blunder post-Watergate. “Some of the omes I have the most trouble with are not even remotely comprehensive, but are simply collections of a small set of some facts about one minor entity”, says Eisen, citing for example the nascentosome (incompletely assembled protein molecules) and the predatosome (genes involved in bacterial predation).
This scepticism is valid, but it doesn’t necessarily get to the core of what is both bad and potentially constructive in the omics fad. An ome is basically a list of parts, whether those are physical entities such as molecules or more abstract such as connections or properties. There is great potential value in such a list, provided that it is comprehensive. If one can consult the proteome to look up the chemical structure of a protein associated with a disease-linked gene, say, then one might be able to design a drug molecule that intervenes in the protein’s behaviour. But a list of parts is not an explanation for their collective function, as any electrical engineer or car mechanic will tell you. Omes are in fact the modern equivalent of what Francis Bacon in the seventeenth century called ‘histories’ – exhaustive collections of all possible facts about a given phenomenon, such as cold or comets. Bacon was convinced that preparing histories was the essential first step in natural philosophy, and he set about devising a scheme for distilling these heaps of facts into real knowledge and insight. But that scheme was absurdly elaborate and never even completed, let alone put into practice. The early scientists found, in spite of their Baconian convictions, that this could never be the way to do science – they were compelled to draw up hypotheses and theories, even before all the ‘facts’ were in, for otherwise there is no way to prioritize or organise what you are looking for.
This is another way of saying that omics will not be science until it works within a framework that allows for hypothesis-testing. Merely searching vast databases for correlations is worse than futile, because it will inevitably produce false positives – spurious relationships between events or entities – while remaining silent about the root mechanisms. There’s a difference between knowing which parts work together and knowing how they do so.
It seems that the converse is also true: causative principles might not announce themselves at the level of the basic components. This has become embarrassingly clear in genomics: for many traits or diseases that are evidently inheritable, it has proved possible to identify only a small fraction of the genes responsible, even with the whole human genome at our fingertips. Causation might stem instead from higher levels of organization.
But that leads to one of the positive aspects of the omics craze. It was largely stimulated in the first place by the anticlimactic realization of how much was left unsaid by the human genome projects. We need to know not just what genes we have, but what protein molecules they encode (for these are ultimately the cell’s primary machinery), and how much the gene is actually used, or ‘transcribed’. Enter the proteome and transcriptome. Then we need to know how genes and proteins act together – the interactome, metabolome and so forth – and what other molecules are crucially involved – the glycome, lipidome and so on. What’s more, because some of these sets of molecules are closer to the physiological end of an organism’s functioning, it seems likely that we might find clearer, less ambiguous and more immediate markers of disease and pathology in these other omes than in the genome. Profiling of lipids, for example, might point to incipient diet-related disease.
In other words, the proliferation of omes marks a recognition – never doubted, but long sidelined by the glamour of genomics – that there is much more to life than genes, many of which are better regarded not as ruthless dictators of the cell but as referees that keep the game on track. Omics could thus represent the start – even if clumsy and too overtly list-obsessed – of a return to a more integrated view of what life is.
________________________________________________________________
Chances are that every biologist now has an ome to go to. This suffix, first introduced in the genome (the sum total of all an organism’s genes), can now be found attached to just about every aspect of life’s molecular basis. There is the proteome (the full complement of protein molecules in an organism), the glycome (all the sugars), the epigenome (all the non-genetically encoded regulation of gene activity), the lipidome (all the fatty-acid lipids of cell membranes). Omes embrace wider concepts too. The metabolome comprises all the molecules involved in metabolism; the interactome is the network of interactions between genes and other molecules; the phenome is the total of all distinct observable traits (phenotypes), and so on. The integrome is the ome of all the omes: an ome from ome, you might say.
The proliferation of these neologisms has understandably attracted criticisms and ridicule, and even the founding editor of a new journal called Omics told Nature that “most of them will not make sense.” Some researchers suggest that they are just a way of investing an established field – such as the study of metabolic biochemical processes – with the kudos that has become attached to genomics. They are also a marketing ploy: if you have an ome, you surely need your own distinct funding stream.
Geneticist Jonathan Eisen of the University of California at Davis talks about “badomics”, and sees the spread of omes as a pernicious meme that adds clutter and confusion, as well as implying a sometimes misleading analogy to the aims and concepts of genomics. He compares it with the indiscriminate appending of -gate to every political blunder post-Watergate. “Some of the omes I have the most trouble with are not even remotely comprehensive, but are simply collections of a small set of some facts about one minor entity”, says Eisen, citing for example the nascentosome (incompletely assembled protein molecules) and the predatosome (genes involved in bacterial predation).
This scepticism is valid, but it doesn’t necessarily get to the core of what is both bad and potentially constructive in the omics fad. An ome is basically a list of parts, whether those are physical entities such as molecules or more abstract such as connections or properties. There is great potential value in such a list, provided that it is comprehensive. If one can consult the proteome to look up the chemical structure of a protein associated with a disease-linked gene, say, then one might be able to design a drug molecule that intervenes in the protein’s behaviour. But a list of parts is not an explanation for their collective function, as any electrical engineer or car mechanic will tell you. Omes are in fact the modern equivalent of what Francis Bacon in the seventeenth century called ‘histories’ – exhaustive collections of all possible facts about a given phenomenon, such as cold or comets. Bacon was convinced that preparing histories was the essential first step in natural philosophy, and he set about devising a scheme for distilling these heaps of facts into real knowledge and insight. But that scheme was absurdly elaborate and never even completed, let alone put into practice. The early scientists found, in spite of their Baconian convictions, that this could never be the way to do science – they were compelled to draw up hypotheses and theories, even before all the ‘facts’ were in, for otherwise there is no way to prioritize or organise what you are looking for.
This is another way of saying that omics will not be science until it works within a framework that allows for hypothesis-testing. Merely searching vast databases for correlations is worse than futile, because it will inevitably produce false positives – spurious relationships between events or entities – while remaining silent about the root mechanisms. There’s a difference between knowing which parts work together and knowing how they do so.
It seems that the converse is also true: causative principles might not announce themselves at the level of the basic components. This has become embarrassingly clear in genomics: for many traits or diseases that are evidently inheritable, it has proved possible to identify only a small fraction of the genes responsible, even with the whole human genome at our fingertips. Causation might stem instead from higher levels of organization.
But that leads to one of the positive aspects of the omics craze. It was largely stimulated in the first place by the anticlimactic realization of how much was left unsaid by the human genome projects. We need to know not just what genes we have, but what protein molecules they encode (for these are ultimately the cell’s primary machinery), and how much the gene is actually used, or ‘transcribed’. Enter the proteome and transcriptome. Then we need to know how genes and proteins act together – the interactome, metabolome and so forth – and what other molecules are crucially involved – the glycome, lipidome and so on. What’s more, because some of these sets of molecules are closer to the physiological end of an organism’s functioning, it seems likely that we might find clearer, less ambiguous and more immediate markers of disease and pathology in these other omes than in the genome. Profiling of lipids, for example, might point to incipient diet-related disease.
In other words, the proliferation of omes marks a recognition – never doubted, but long sidelined by the glamour of genomics – that there is much more to life than genes, many of which are better regarded not as ruthless dictators of the cell but as referees that keep the game on track. Omics could thus represent the start – even if clumsy and too overtly list-obsessed – of a return to a more integrated view of what life is.
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