Here’s my latest Crucible column for Chemistry World. A techie one, but no harm in that. I also have a feature on nanobubbles in this (September) issue, and will try to stick that up, in extended form, on my website soon.
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Bartosz Grzybowski of Northwestern University in Illinois, who has already established himself as one of the most inventive current practitioners of the chemical art, has unveiled a ‘chemo-informatic’ scheme called Chematica that can stake a reasonable claim to being paradigm-changing. He and his colleagues have spent years assembling the transformations linking chemical species into a vast network that codifies and organizing the known pathways through chemical space. Each node of the network is either a molecule or element, or a chemical reaction. Links connect reactants and products via the nexus of a known reaction. The full network contains around 7 million compound nodes and about the same number of reaction nodes. Grzybowski calls it a “collective chemical brain.”
I predict a mixed reaction from chemists. On the one hand the potential value of such a tool for discovering improved or entirely new synthetic pathways to drugs, materials and other useful products is tremendous, and has already been illustrated by Grzybowski’s team. On the other hand, Chematica seems to imply that chemistry is indeed, as the old jibe puts it, just cookery, and is something now better orchestrated by computer than by chemists.
I’ll come back to that. First let’s look at what Chematica is. Grzybowski first described the network in 2005 [1], when he was mostly concerned with its topological properties rather than with chemical insights. Like the Internet or some social networks, the chemical network has ‘scale-free’ connectivity, meaning that the distribution of nodes with different degrees of connectivity n is a power law: the number of nodes with n links is proportional to n(exp-α), where α is a constant. This means that a few very highly connected nodes are the hubs that bind the network together and provide shortcuts. The same structure is also found in the reaction network of compounds in metabolic pathways.
In a trio of new papers the researchers have now started to put the network to use. In the first, they perform an automated trawl for new one-pot reactions that can replace existing multi-step syntheses [2]. The advantages of single-step processes are obvious: no laborious separation and purification of products after each step, with consequent reductions in yield. Identifying potential one-pot processes linking molecular nodes that hitherto lacked a direct connection here means subjecting the relevant reactions to several filtering steps that check for compatibility – for example, checking that a water-solvated synthesis will not unintentionally hydrolyse functional groups. This filtering is painstaking in principle, but very quick once automated.
It is one thing to demonstrate that such one-pot syntheses are possible in principle, but Grzybowski and colleagues have ensured that at least some of those identified work in practice. Specifically, they looked for syntheses of quinoline-based molecules – common components of drugs and dyes – and thiophenes, which have useful electronic and optical properties. Many of the new pathways worked with high yields, in some cases demonstrably higher than those of alternative multi-step syntheses. Some false positives arise from errors in the literature used to build the network.
Another use of Chematica is to optimize existing syntheses – something previously reliant on manual or inexhaustive semi-automated searches. Looking for improved – basically, cheaper – routes to a given target is a matter of stepping progressively backwards from that molecule to preceding intermediates [3]. An algorithm can calculate the costs of all such steps in the network, working recursively backwards to a specified ‘depth’ (maximum number of synthetic steps) and finding the cheapest option. Applied to syntheses conducted by Grzybowski’s company ProChimia, Chematica offered potential savings of up to 45 percent if instituted for 51 of the company’s targets. The greatest the number of targets, the greater the savings because of the economies of shared ingredients and intermediates.
Finally, and perhaps most controversially, the researchers show how Chematica can be used to identify threats of chemical-weapons manufacture by terrorists [4]. The network can be searched for routes to harmful substances such as nerve agents using unregulated ingredients. Of course, it can also disclose such routes, but as with viral genomic data [5], open access to such data should be the best antidote to the risks they inherently pose.
Does all this, then, mean that synthetic organic chemists are about to be automated? The usual response is to insist that computers will never match human creativity. But that defence is looking increasingly under threat in, say, chess, maths and perhaps even music and visual art. In some ways chemical synthesis is as rule-bound as music if not chess, and thus ripe for an algorithmic approach. Perhaps at least some of the beauty rightly attributed to classic syntheses should be seen as illustrating human ingenuity in the face of tasks for which no better solution then existed. Synthetic schemes designed by humans surely won’t become obsolete any time soon – but there seems no harm in acknowledging that the time may come when the art and creativity of chemistry resides more solidly in our decisions of what to make, and why, than in how we make it.
References
1. M. Fialkowski, K. J. M. Bishop, V. A. Chubukov, C. J. Campbell & B. A. Grzybowski, Angew. Chem. Int. Ed. 44, 7263 (2005).
2. C. M. Gothard et al., Angew. Chem. Int. Ed. online publication 10.1002/anie.201202155 (2012).
3. M. Kowalik et al., Angew. Chem. Int. Ed. online publication 10.1002/anie.201202209 (2012).
4. P. E. Fuller, C. M. Gothard, N. A. Gothard, A. Wieckiewicz & B. A. Grzybowski, Angew. Chem. Int. Ed. online publication 10.1002/anie.201202210 (2012).
5. M. Imai et al., Nature 10.1038/nature10831 (2012).
Friday, August 31, 2012
Friday, August 24, 2012
Computers get emotional
Here’s more on Iamus, the computer programme that writes music you might actually want to listen to. It is published in Nature this week, although there are more references in this version. The CD of Iamus’ music is released in mid-September (not the 1st, as the Nature piece says), and is simply called Iamus (Melomics Records). I’m looking forward to that.
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If a computer can produce something that moves us, would this take artificial intelligence beyond an important threshold? That’s one of the questions seemingly raised by an algorithm called Iamus, developed by Francisco Vico and colleagues at the University of Malaga, which composes music from scratch.
There’s nothing new about computers making music. They have been used by composers since the early days of computer technology in the 1960s, most notably by the experimental Greek composer Iannis Xenakis. Neither is there anything mysterious about an algorithmic approach to composition: most music is highly rule-bound, even formulaic, and so lends itself to that. An early attempt at computer music in the 1980s, the programme called CHORAL devised by computer scientist Kemal Ebcioglu to harmonise chorales in the style of J. S. Bach [1], drew on well formulated principles of harmony and melody that were observed by Bach himself [2].
But it’s one thing to slavishly follow the rules, quite another to come up with original melodies and harmonic progressions that will captivate and even move the listener – especially if the composing is being performed without any human input. Computer scientists have been quite successful in making programmes that can learn from human examples. Early improvisational algorithms such as the jazz-inflected GenJam [3], devised by computer scientist John Biles, and GenBebop, a system created by cognitive scientists Lee Spector and Adam Alpern to produce solos in the style of Charlie Parker [4], gave indifferent results even by their creators’ admission. But Continuator, a programme devised by François Pachet at Sony’s Computer Science Laboratory in Paris, is much more convincing [5]. In a kind of ‘Turing test’ where Continuator alternated with an improvising human pianist and elaborated on his suggestions, expert listeners had trouble telling man and machine apart.
Contrary to common perception, however, improvising is fairly rule-bound too, so it’s not hard to see how human-derived musical material can be plausibly mutated and elaborated in an automated way. Coming up with the raw musical ideas is a much harder task for a computer. Before now, efforts to achieve this have been distinctly underwhelming, sounding like bland pastiche all too evidently shaped by clichéd harmonic progressions and melodic structures.
This is where Iamus’ creators claim to have something new. The algorithm, named after the legendary son of Apollo who could understand the language of birds, is inspired by Darwinian evolution. The computer generates very simple ‘musical genomes’ , rather like little motifs, which are then evolved, mutated and elaborated until they acquire genuine musical content and interest [6]. Genetic and evolutionary algorithms for making music are also not new (see, for example, ref. 7), but Iamus seems capable of dramatic invention: the music is far more than just a succession of transparent variations. Vico and colleagues, in collaboration with composer and pianist Gustavo Díaz-Jerez of the Conservatory of the Basque Country in San Sebastian, recently recorded some of Iamus’s scores with leading musicians, including two orchestral pieces played by the London Symphony Orchestra, for release in September. They broadcast a live performance of two of these pieces from the University of Malaga in July to commemorate the 100th anniversary of Alan Turing’s birth.
The recorded compositions are all in a modernist classical style – full of dissonance, but with hints of harmony and rich textures that might put a listener in mind of composers such as Gyorgy Ligeti and Krzysztof Penderecki. But the same approach can be used for other idioms too, and Vico and colleagues say that it might supply a cheap, convenient way of generating music for commercial purposes.
The willingness of professionals to perform the works marks out Iamus as unique. The LSO’s chairman Lennox Mackenzie was impressed with what it had achieved but felt that the music still fell short of that by good human composers. It struck him as “going nowhere” – a complaint often made of other modernist works – yet ultimately achieving an “epic” quality. Many of the other musicians were pleasantly surprised by the material, and found some of it genuinely expressive.
Which brings us to the initial question. If Iamus can simulate (and thus stimulate) emotionality, is it not just ‘thinking’ in the limited sense of the Turing test but apparently displaying human characteristics?
Here we should heed studies of music cognition which have shown that emotion in music is not some deeply mysterious process but has its own rules and regularities [8]. For example, certain musical structures, such as ‘false trails’ that create and then confound expectations, or judicious injections of dissonance followed by resolution, can elicit emotion quite reliably [9]. This should be no surprise to anyone whose emotions have been helplessly manipulated by formulaic film scores.
What’s more, the involvement of human performers is vital. Music lovers know very well that the same piece can be performed in a dry, unengaged manner or with heart-rending fervour. Good performers achieve expression with a wide range of ‘tricks’, such as subtle distortions of tempo, intonation and timbre [10].
Iamus’ work might therefore be considered to demonstrate the often neglected role of performer and listener in ‘making music’. The nineteenth-century Romantic tradition has fostered a deep-seated belief in the inherent genius of the composer, as though he or she has imbued the very notes with passion. It’s not to deny the undoubted sensitivity and skill of the greatest composers to say that a composition only becomes music in the mind of the listener, through the interaction of the composer’s and the performer’s choices with the wealth of learning and association that even allegedly ‘unskilled’ listeners possess.
This consideration ought also to diminish an inherent prejudice (evident in the critical responses to Iamus so far) against computer-composed music. Neuroscientists Stefan Koelsch and Nikolaus Steinbeis have shown that part of this prejudice is unconscious: the same piece of music may or may not activate parts of the brain associated with ascribing intention to others, depending on whether listeners have been told that the piece was composed by a human or by computer [11]. It’s possible that human performance of computer-made music might at least partly override this obstacle to emotional engagement. But we should also celebrate the way that Iamus, far from threatening our supposedly unique claim to creativity, can put the audience back in the picture as a participant in the creative act.
References
1. Ebcioğlu, K. Proc. 1984 Int. Computer Music Conf., 135-144, held in IRCAM, Paris. Computer Music Association, San Francisco, 1985.
2. Rohrmeier, M. & Cross, I. (2008), in Proc. 10th Int. Conf. on Music Percept. Cognit. (ICMPC 2008), Sapporo, Japan.
3. Biles, J. A., in Proc. 1994 Int. Computer Music Conf., 131-137. International Computer Music Association, San Francisco, 1994.
4. Spector, L. & Alpern, A., in Proc. Twelfth Natl Conf. Artificial Intelligence, AAAI-94, 3-8. AAAI Press/MIT Press, Menlo Park CA and Cambridge MA (1994).
5. Pachet, F., J. New Music Res. 32, 333-341 (2003).
6. Díaz-Jerez, G. Leonardo Music J. 21, 13-14 (2011).
7. MacCallum, R. M., Mauch, M., Burt, A. & Leroi, A. M. Proc. Natl Acad. Sci. USA 10.1073/pnas.1203182109 (2012).
8. Sloboda, J. A. The Musical Mind: The Cognitive Psychology of Music. Clarendon Press, Oxford, 1985.
9. Juslin, P. N. & Sloboda, J. A. (eds). Music and Emotion. Oxford University Press, Oxford, 2001.
10. Meyer, L. B. Emotion and Meaning in Music. University of Chicago Press, Chicago, 1956.
11. Steinbeis, N. & Koelsch, S. Cerebral Cortex 19, 619-623 (2009).
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If a computer can produce something that moves us, would this take artificial intelligence beyond an important threshold? That’s one of the questions seemingly raised by an algorithm called Iamus, developed by Francisco Vico and colleagues at the University of Malaga, which composes music from scratch.
There’s nothing new about computers making music. They have been used by composers since the early days of computer technology in the 1960s, most notably by the experimental Greek composer Iannis Xenakis. Neither is there anything mysterious about an algorithmic approach to composition: most music is highly rule-bound, even formulaic, and so lends itself to that. An early attempt at computer music in the 1980s, the programme called CHORAL devised by computer scientist Kemal Ebcioglu to harmonise chorales in the style of J. S. Bach [1], drew on well formulated principles of harmony and melody that were observed by Bach himself [2].
But it’s one thing to slavishly follow the rules, quite another to come up with original melodies and harmonic progressions that will captivate and even move the listener – especially if the composing is being performed without any human input. Computer scientists have been quite successful in making programmes that can learn from human examples. Early improvisational algorithms such as the jazz-inflected GenJam [3], devised by computer scientist John Biles, and GenBebop, a system created by cognitive scientists Lee Spector and Adam Alpern to produce solos in the style of Charlie Parker [4], gave indifferent results even by their creators’ admission. But Continuator, a programme devised by François Pachet at Sony’s Computer Science Laboratory in Paris, is much more convincing [5]. In a kind of ‘Turing test’ where Continuator alternated with an improvising human pianist and elaborated on his suggestions, expert listeners had trouble telling man and machine apart.
Contrary to common perception, however, improvising is fairly rule-bound too, so it’s not hard to see how human-derived musical material can be plausibly mutated and elaborated in an automated way. Coming up with the raw musical ideas is a much harder task for a computer. Before now, efforts to achieve this have been distinctly underwhelming, sounding like bland pastiche all too evidently shaped by clichéd harmonic progressions and melodic structures.
This is where Iamus’ creators claim to have something new. The algorithm, named after the legendary son of Apollo who could understand the language of birds, is inspired by Darwinian evolution. The computer generates very simple ‘musical genomes’ , rather like little motifs, which are then evolved, mutated and elaborated until they acquire genuine musical content and interest [6]. Genetic and evolutionary algorithms for making music are also not new (see, for example, ref. 7), but Iamus seems capable of dramatic invention: the music is far more than just a succession of transparent variations. Vico and colleagues, in collaboration with composer and pianist Gustavo Díaz-Jerez of the Conservatory of the Basque Country in San Sebastian, recently recorded some of Iamus’s scores with leading musicians, including two orchestral pieces played by the London Symphony Orchestra, for release in September. They broadcast a live performance of two of these pieces from the University of Malaga in July to commemorate the 100th anniversary of Alan Turing’s birth.
The recorded compositions are all in a modernist classical style – full of dissonance, but with hints of harmony and rich textures that might put a listener in mind of composers such as Gyorgy Ligeti and Krzysztof Penderecki. But the same approach can be used for other idioms too, and Vico and colleagues say that it might supply a cheap, convenient way of generating music for commercial purposes.
The willingness of professionals to perform the works marks out Iamus as unique. The LSO’s chairman Lennox Mackenzie was impressed with what it had achieved but felt that the music still fell short of that by good human composers. It struck him as “going nowhere” – a complaint often made of other modernist works – yet ultimately achieving an “epic” quality. Many of the other musicians were pleasantly surprised by the material, and found some of it genuinely expressive.
Which brings us to the initial question. If Iamus can simulate (and thus stimulate) emotionality, is it not just ‘thinking’ in the limited sense of the Turing test but apparently displaying human characteristics?
Here we should heed studies of music cognition which have shown that emotion in music is not some deeply mysterious process but has its own rules and regularities [8]. For example, certain musical structures, such as ‘false trails’ that create and then confound expectations, or judicious injections of dissonance followed by resolution, can elicit emotion quite reliably [9]. This should be no surprise to anyone whose emotions have been helplessly manipulated by formulaic film scores.
What’s more, the involvement of human performers is vital. Music lovers know very well that the same piece can be performed in a dry, unengaged manner or with heart-rending fervour. Good performers achieve expression with a wide range of ‘tricks’, such as subtle distortions of tempo, intonation and timbre [10].
Iamus’ work might therefore be considered to demonstrate the often neglected role of performer and listener in ‘making music’. The nineteenth-century Romantic tradition has fostered a deep-seated belief in the inherent genius of the composer, as though he or she has imbued the very notes with passion. It’s not to deny the undoubted sensitivity and skill of the greatest composers to say that a composition only becomes music in the mind of the listener, through the interaction of the composer’s and the performer’s choices with the wealth of learning and association that even allegedly ‘unskilled’ listeners possess.
This consideration ought also to diminish an inherent prejudice (evident in the critical responses to Iamus so far) against computer-composed music. Neuroscientists Stefan Koelsch and Nikolaus Steinbeis have shown that part of this prejudice is unconscious: the same piece of music may or may not activate parts of the brain associated with ascribing intention to others, depending on whether listeners have been told that the piece was composed by a human or by computer [11]. It’s possible that human performance of computer-made music might at least partly override this obstacle to emotional engagement. But we should also celebrate the way that Iamus, far from threatening our supposedly unique claim to creativity, can put the audience back in the picture as a participant in the creative act.
References
1. Ebcioğlu, K. Proc. 1984 Int. Computer Music Conf., 135-144, held in IRCAM, Paris. Computer Music Association, San Francisco, 1985.
2. Rohrmeier, M. & Cross, I. (2008), in Proc. 10th Int. Conf. on Music Percept. Cognit. (ICMPC 2008), Sapporo, Japan.
3. Biles, J. A., in Proc. 1994 Int. Computer Music Conf., 131-137. International Computer Music Association, San Francisco, 1994.
4. Spector, L. & Alpern, A., in Proc. Twelfth Natl Conf. Artificial Intelligence, AAAI-94, 3-8. AAAI Press/MIT Press, Menlo Park CA and Cambridge MA (1994).
5. Pachet, F., J. New Music Res. 32, 333-341 (2003).
6. Díaz-Jerez, G. Leonardo Music J. 21, 13-14 (2011).
7. MacCallum, R. M., Mauch, M., Burt, A. & Leroi, A. M. Proc. Natl Acad. Sci. USA 10.1073/pnas.1203182109 (2012).
8. Sloboda, J. A. The Musical Mind: The Cognitive Psychology of Music. Clarendon Press, Oxford, 1985.
9. Juslin, P. N. & Sloboda, J. A. (eds). Music and Emotion. Oxford University Press, Oxford, 2001.
10. Meyer, L. B. Emotion and Meaning in Music. University of Chicago Press, Chicago, 1956.
11. Steinbeis, N. & Koelsch, S. Cerebral Cortex 19, 619-623 (2009).
Why selfishness still doesn’t pay
Here’s my latest news story for Nature.
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A recent finding that undermines conventional thinking on the evolution of cooperation doesn’t, after all, prevent altruistic behaviour from emerging.
For the past several decades, one of the central results of game theory has seemed to be that self-interest can drive social cooperation, because in the long term selfish behaviour hurts you as much as your competitors. Last May, two leading physicists, William Press of the University of Texas and Freeman Dyson of the Institute of Advanced Study in Princeton, argued otherwise. They showed how, in the classic ‘game’ from which cooperation seems to evolve, called the Prisoner’s Dilemma, it’s possible to be successfully selfish [1].
This apparently revolutionary idea has now been challenged by two evolutionary biologists at Michigan State University in East Lansing. In a preprint [2], Christoph Adami and Arend Hintze say that the strategy proposed by Press and Dyson is “evolutionarily unstable”. In a population of agents all seeking for the best way to play the Prisoner’s Dilemma, those using the new selfish strategy will eventually be bested by more generous players.
The Prisoner’s Dilemma is a simple ‘game’ that captures the fundamental problem faced by a population of organisms competing for limited resources: the temptation to cheat or freeload. You might do better acting together (cooperating) than alone, but the temptation is to let others put in the effort or face the risks while sharing yourself in the rewards.
In the Prisoner’s Dilemma as it was formulated by researchers in the 1950s, two prisoners accused of a crime are questioned. If one helps convict the other by testifying against him, he is offered a lighter sentence. But if both testify against the other, their sentences will be heavier than if both refuse to do so.
In a single ‘round’ of this game, it always makes sense to ‘defect’ – to shop the other guy. That way you’re better off whatever your opponent does. But if the game is played again and again – if you have repeated opportunities to cheat on the other player – you both do better to cooperate. This so-called iterated Prisoner’s Dilemma has been used to show how cooperation could arise in selfish populations: those genetically disposed to cooperate will be more successful than those predisposed to defect.
But what’s the best way to play the iterated game in a population of individuals using many different strategies? In the 1980s, political scientist Robert Axelrod tried to answer that question by staging computerized tournaments, inviting anyone to submit a strategy and then pitching them all against one another in many one-to-one bouts.
The winner was a very simple strategy called Tit-for-Tat (TfT), which merely copies its opponent’s behaviour from the last round. If the opponent defected in the last round, TfT does so in the current one. Against cooperators, TfT always cooperates; against defectors, it always defects. It is, in effect, ‘tough but fair’. The moral message seemed reassuring: it pays to be nice, but nastiness should be punished.
However, in further studies it became clear that TfT might not always dominate in evolutionary games where the most successful strategies are propagated from generation to generation. Slightly more forgiving strategies, which don’t get caught in cycles of mutual recrimination by a single mistaken defection, can do better in the long run. In fact, there is no single best way to play the game – it depends on your opponents. Nonetheless, the iterated Prisoner’s Dilemma seemed to explain how cooperation between unrelated individuals might evolve: why some animals hunt in packs and why we have altruistic instincts.
Press and Dyson seemed to shatter this cosy picture. They showed that there exists a class of strategies, which for technical reasons they call zero-determinant (ZD) strategies, in which one player can force the other to accept a less-than-equal share of the ‘payoff’ in the Prisoner’s Dilemma. In effect, the victim has to either grit his teeth and accept this unfair division, or punish the other player at a greater cost to himself. This turns the game into a different one, known as an Ultimatum game, in which one player is presented with the choice of either accepting an unequal distribution of the payoff or, if he refuses, both players losing out.
It turns out that TfT is just a special case of these ZD strategies in which the payoffs happen to be equal. Like a TfT player, a ZD player bases his next choice of cooperate/defect on what happened in the last round: it is said to be a ‘memory-one’ strategy. But instead of being rigidly deterministic – this previous outcome dictates that choice – it is probabilistic: the choice to cooperate or defect is made with a certain probability for each of the four possible outcomes of the last round. A judicious choice of these probabilities enables one player to control the payoff that the other receives.
According to William Poundstone, author of the 1992 book The Prisoner’s Dilemma, “The Press-Dyson finding directly challenges the two notions at the heart of the Prisoner’s Dilemma – that you can't fool evolution, and that the most successful strategies are fair strategies.” Nonetheless, Press says that “Freeman's and my paper has been warmly received by Prisoner’s Dilemma experts. More than one has expressed regret at not having discovered the ZD strategies previously.”
“The paper did indeed cause quite a stir, because the main result appeared to be completely new, despite intense research in this area for the last 30 years”, says Adami. It wasn’t totally new, however – in 1997 game theorists Karl Sigmund of the University of Vienna and Martin Nowak of Harvard University discovered strategies that similarly allow one player to fix the other’s payoff at a specified level [3]. But they admit that “we didn’t know about the vast and fascinating realm of zero-determinant strategies.” The work of Press and Dyson “opens a new facet in the study of trigger strategies and folk theorems for iterated games, and offers a highly stimulating approach for moral philosophers,” they say.
The ZD strategies are not as dispiriting as perhaps they sound, says Press, because they allow a new balance to be found if both players understand the principles. “Once both players understand ZD, then each has the power to set the other’s score, independent of the other’s actions. This allows them to make an enforceable treaty, not possible using simpler strategies.”
In other words, the ZD strategy forces players to reflect, to think ahead, to consider the opponent’s point of view, and not just try to get the highest possible score. It “allows for a whole range of careful, deliberative negotiations”, Press says. “This is a world in which diplomacy trumps conflict.”
But now Adami and Hintze say that this world might not exist – or not for long. They find that, in an evolutionary iterated Prisoner’s Dilemma game in which the prevalence of particular strategies depends on their success, ZD players are soon out-competed by others using more common strategies, and so they will evolve to become non-ZD players themselves. That’s because ZD players suffer from the same problem as habitual defectors: they do badly against their own kind.
There’s one exception: ZD players can persist if they can figure out whether they are playing another ZD player or not. Then they can exploit the advantages of ZD strategies against non-ZD players, but will switch to a more advantageous non-ZD strategy when faced with their own kind.
That in turn means, however, that non-ZD players could gain the upper hand by using strategies that look like ZD but are not, thus fooling ZD players into abandoning their extortionate strategy. This could lead to the same kind of ‘arms race’ seen in some kinds of biological mimicry, where a harmless species evolves to look like a harmful one, while the harmful one tries to evolve away from its imitator.
Sigmund and Nowak, with colleague Christian Hilbe, have also shown in work not yet published that the ZD strategy is evolutionarily unstable, but can pave the way for the emergence of cooperators from a more selfish community. “ZD strategies do not establish a strong foothold in the population”, says Sigmund.
Game theorist and economist Samuel Bowles at the Santa Fe Institute in New Mexico feels that these results demote the interest of the ZD strategies. “The question of their evolutionary stability is critical, and the paper makes their limitations clear. Because they are not evolutionarily stable, I’d call them merely a curiosity of little interest to evolutionary biology or any of the other biological sciences.”
Adami is not so sure that they won’t be fund in the wild. “We don’t usually have nearly enough information about animal decisions”, he says. “But in my experience, anything that is imaginable has probably evolved somewhere, sometime. To gather conclusive evidence about it is a whole different matter.”
Could they be found in human society too? “It’s not inconceivable”, says Adami, “but we have to keep in mind that humans very rarely make decisions based only on their and their opponent's last move. It is much more likely that this type of strategy is in use in automated trading programs, such as those involved in high-frequency trading of stocks and commodities. However, because these programs are usually secret, we wouldn't know about it.”
References
1. Press, W. H. & Dyson, F. J. Proc. Natl Acad. Sci. USA 10.1073/pnas.1206569109 (2012).
2. Adami, C. & Hintze, A. preprint http://www.arxiv.org/abs/1208.2666 (2012).
3. Nowak, M. A., Boerlijst, M. C. & Sigmund, K., Am. Math. Soc. Monthly 104, 303-307 (1997).
____________________________________________________________________
A recent finding that undermines conventional thinking on the evolution of cooperation doesn’t, after all, prevent altruistic behaviour from emerging.
For the past several decades, one of the central results of game theory has seemed to be that self-interest can drive social cooperation, because in the long term selfish behaviour hurts you as much as your competitors. Last May, two leading physicists, William Press of the University of Texas and Freeman Dyson of the Institute of Advanced Study in Princeton, argued otherwise. They showed how, in the classic ‘game’ from which cooperation seems to evolve, called the Prisoner’s Dilemma, it’s possible to be successfully selfish [1].
This apparently revolutionary idea has now been challenged by two evolutionary biologists at Michigan State University in East Lansing. In a preprint [2], Christoph Adami and Arend Hintze say that the strategy proposed by Press and Dyson is “evolutionarily unstable”. In a population of agents all seeking for the best way to play the Prisoner’s Dilemma, those using the new selfish strategy will eventually be bested by more generous players.
The Prisoner’s Dilemma is a simple ‘game’ that captures the fundamental problem faced by a population of organisms competing for limited resources: the temptation to cheat or freeload. You might do better acting together (cooperating) than alone, but the temptation is to let others put in the effort or face the risks while sharing yourself in the rewards.
In the Prisoner’s Dilemma as it was formulated by researchers in the 1950s, two prisoners accused of a crime are questioned. If one helps convict the other by testifying against him, he is offered a lighter sentence. But if both testify against the other, their sentences will be heavier than if both refuse to do so.
In a single ‘round’ of this game, it always makes sense to ‘defect’ – to shop the other guy. That way you’re better off whatever your opponent does. But if the game is played again and again – if you have repeated opportunities to cheat on the other player – you both do better to cooperate. This so-called iterated Prisoner’s Dilemma has been used to show how cooperation could arise in selfish populations: those genetically disposed to cooperate will be more successful than those predisposed to defect.
But what’s the best way to play the iterated game in a population of individuals using many different strategies? In the 1980s, political scientist Robert Axelrod tried to answer that question by staging computerized tournaments, inviting anyone to submit a strategy and then pitching them all against one another in many one-to-one bouts.
The winner was a very simple strategy called Tit-for-Tat (TfT), which merely copies its opponent’s behaviour from the last round. If the opponent defected in the last round, TfT does so in the current one. Against cooperators, TfT always cooperates; against defectors, it always defects. It is, in effect, ‘tough but fair’. The moral message seemed reassuring: it pays to be nice, but nastiness should be punished.
However, in further studies it became clear that TfT might not always dominate in evolutionary games where the most successful strategies are propagated from generation to generation. Slightly more forgiving strategies, which don’t get caught in cycles of mutual recrimination by a single mistaken defection, can do better in the long run. In fact, there is no single best way to play the game – it depends on your opponents. Nonetheless, the iterated Prisoner’s Dilemma seemed to explain how cooperation between unrelated individuals might evolve: why some animals hunt in packs and why we have altruistic instincts.
Press and Dyson seemed to shatter this cosy picture. They showed that there exists a class of strategies, which for technical reasons they call zero-determinant (ZD) strategies, in which one player can force the other to accept a less-than-equal share of the ‘payoff’ in the Prisoner’s Dilemma. In effect, the victim has to either grit his teeth and accept this unfair division, or punish the other player at a greater cost to himself. This turns the game into a different one, known as an Ultimatum game, in which one player is presented with the choice of either accepting an unequal distribution of the payoff or, if he refuses, both players losing out.
It turns out that TfT is just a special case of these ZD strategies in which the payoffs happen to be equal. Like a TfT player, a ZD player bases his next choice of cooperate/defect on what happened in the last round: it is said to be a ‘memory-one’ strategy. But instead of being rigidly deterministic – this previous outcome dictates that choice – it is probabilistic: the choice to cooperate or defect is made with a certain probability for each of the four possible outcomes of the last round. A judicious choice of these probabilities enables one player to control the payoff that the other receives.
According to William Poundstone, author of the 1992 book The Prisoner’s Dilemma, “The Press-Dyson finding directly challenges the two notions at the heart of the Prisoner’s Dilemma – that you can't fool evolution, and that the most successful strategies are fair strategies.” Nonetheless, Press says that “Freeman's and my paper has been warmly received by Prisoner’s Dilemma experts. More than one has expressed regret at not having discovered the ZD strategies previously.”
“The paper did indeed cause quite a stir, because the main result appeared to be completely new, despite intense research in this area for the last 30 years”, says Adami. It wasn’t totally new, however – in 1997 game theorists Karl Sigmund of the University of Vienna and Martin Nowak of Harvard University discovered strategies that similarly allow one player to fix the other’s payoff at a specified level [3]. But they admit that “we didn’t know about the vast and fascinating realm of zero-determinant strategies.” The work of Press and Dyson “opens a new facet in the study of trigger strategies and folk theorems for iterated games, and offers a highly stimulating approach for moral philosophers,” they say.
The ZD strategies are not as dispiriting as perhaps they sound, says Press, because they allow a new balance to be found if both players understand the principles. “Once both players understand ZD, then each has the power to set the other’s score, independent of the other’s actions. This allows them to make an enforceable treaty, not possible using simpler strategies.”
In other words, the ZD strategy forces players to reflect, to think ahead, to consider the opponent’s point of view, and not just try to get the highest possible score. It “allows for a whole range of careful, deliberative negotiations”, Press says. “This is a world in which diplomacy trumps conflict.”
But now Adami and Hintze say that this world might not exist – or not for long. They find that, in an evolutionary iterated Prisoner’s Dilemma game in which the prevalence of particular strategies depends on their success, ZD players are soon out-competed by others using more common strategies, and so they will evolve to become non-ZD players themselves. That’s because ZD players suffer from the same problem as habitual defectors: they do badly against their own kind.
There’s one exception: ZD players can persist if they can figure out whether they are playing another ZD player or not. Then they can exploit the advantages of ZD strategies against non-ZD players, but will switch to a more advantageous non-ZD strategy when faced with their own kind.
That in turn means, however, that non-ZD players could gain the upper hand by using strategies that look like ZD but are not, thus fooling ZD players into abandoning their extortionate strategy. This could lead to the same kind of ‘arms race’ seen in some kinds of biological mimicry, where a harmless species evolves to look like a harmful one, while the harmful one tries to evolve away from its imitator.
Sigmund and Nowak, with colleague Christian Hilbe, have also shown in work not yet published that the ZD strategy is evolutionarily unstable, but can pave the way for the emergence of cooperators from a more selfish community. “ZD strategies do not establish a strong foothold in the population”, says Sigmund.
Game theorist and economist Samuel Bowles at the Santa Fe Institute in New Mexico feels that these results demote the interest of the ZD strategies. “The question of their evolutionary stability is critical, and the paper makes their limitations clear. Because they are not evolutionarily stable, I’d call them merely a curiosity of little interest to evolutionary biology or any of the other biological sciences.”
Adami is not so sure that they won’t be fund in the wild. “We don’t usually have nearly enough information about animal decisions”, he says. “But in my experience, anything that is imaginable has probably evolved somewhere, sometime. To gather conclusive evidence about it is a whole different matter.”
Could they be found in human society too? “It’s not inconceivable”, says Adami, “but we have to keep in mind that humans very rarely make decisions based only on their and their opponent's last move. It is much more likely that this type of strategy is in use in automated trading programs, such as those involved in high-frequency trading of stocks and commodities. However, because these programs are usually secret, we wouldn't know about it.”
References
1. Press, W. H. & Dyson, F. J. Proc. Natl Acad. Sci. USA 10.1073/pnas.1206569109 (2012).
2. Adami, C. & Hintze, A. preprint http://www.arxiv.org/abs/1208.2666 (2012).
3. Nowak, M. A., Boerlijst, M. C. & Sigmund, K., Am. Math. Soc. Monthly 104, 303-307 (1997).
Thursday, August 23, 2012
Are we not reproducing enough?
Here is my Crucible column from the August issue of Chemistry World. This topic seems to be becoming a big deal, as witnessed for example by the creation of this initiative for replication of results from the PLoS journals – I note that their advisory board includes Brian Nosek, whose work I mention below.
________________________________________________________________
How much of the published literature should you believe? Not much, by some accounts. A 2005 paper by epidemiologist John Ioannidis of the University of Ioannina School of Medicine in Greece had the stark title “Why most published research findings are false” [1]. Ioannidis claimed that “for most study designs and settings, it is more likely for a research claim to be false than true”, and that often published claims simply reflected the prevailing bias of the field. Ioannidis suspected that some “established classics” in the literature wouldn’t stand up to close scrutiny.
His focus was on biomedical research, in particular clinical trials of drugs, where inferences have to be made from complex statistics, perhaps with small sample sizes. Here, not only might the effects being sought be rather marginal but there are also strong biases and prejudices introduced by financial pressures. Reports of drug trials certainly do have a bias towards positive outcomes, prompting valid calls for all drug trials to be registered before the study is undertaken so that negative findings can’t be quietly dropped.
These problems with pharmaceutical research are in themselves troubling for some chemists. But is this mostly an issue for Big Pharma, with its distorting profit motives and its reliance on statistics rather than more reductive, step-by-step experimentation? Probably not, Daniel Sarewitz of the Consortium for Science, Policy and Outcomes at Arizona State University claimed in Nature last May [2]. According to Sarewitz, systematic error due to bias, whether conscious or not, is “is likely to be prevalent in any field that seeks to predict the behaviour of complex systems – economics, ecology, environmental science, epidemiology and so on”. This figures: all these fields tend to depend on statistical inference of often marginal effects operating through mechanisms that may be poorly understood and perhaps nigh impossible to delineate.
But what about the subjects we like to think of as the “hard sciences” – like most of chemistry? Surely you can place more trust in spectra and rate constants and crystal structures than in scatter plots? Perhaps – but ‘trust’ is often what it is. Not many studies are ever repeated verbatim, and it’s generally acknowledged that crystallographic databases are probably full of errors, if only minor. The chance of experiments being replicated is probably proportional to the significance of the results. Maybe the greater good doesn’t suffer much from a literature full of flawed but uninteresting work – but that would offer scant support for science’s supposedly self-correcting nature.
And problems do crop up on close examination. Take, for example, the recent attempt by Darragh Crotty and colleagues at Trinity College Dublin to replicate the claims of Russian biochemist Anatoly Buchachenko and his coworkers, who since 2004 have been documenting (in good journals) the influence of a weak magnetic field on the rate of enzymatic production of ATP [3]. The Russians report that millitesla magnetic fields can more than double the reaction rate when the phosphorylating enzymes contain 25Mg (which is magnetic) rather than the other two stable isotopes 24Mg and 26Mg. Crotty and colleagues set out to test this because it bore on controversial claims of physiological effects from weak electromagnetic fields. They found no difference in reaction rate for all three magnesium isotopes [4]. So far the discrepancy remains puzzling.
If this is indeed a wider problem than is commonly recognized for all sciences, what to do? Sarewitz suggests reducing hype and strengthening ties between fundamental research and real-world testing. Ioannidis implores researchers to be honest with themselves about the ‘pre-study odds’ of their hypothesis being true. This purging of preconception and self-deception is what Francis Bacon called for in the seventeenth century when he argued that natural philosophers seeking truth must free themselves from ‘idols of the mind’. But as Ioannis recognizes, changing mindsets isn’t easy.
Another perspective is offered in a preprint by psychologist Brian Nosek of the University of Virginia and his colleagues [5]. They point out that professional success for scientists relies on publishing, but publication both favours positive results and prefers novelty over replication. What is needed is a way to rescue scientists’ ostensible aim – getting it right – from their short-term, pragmatic aim – getting it published. Among things that won’t work, the authors say, are journals devoted to replications and tougher peer review (which can already display stifling conservatism). Instead we need metrics for evaluating what is worth replicating, journal editorial policies that focus on soundness rather than ‘importance’, less focus on sheer publication productivity for job and tenure applicants, lower barriers to publication (so that it becomes less coveted in itself), and in particular, new ways of releasing results: open access to data, methods, tools and lab books. One can find problems with all of these, but the old ways of science publishing are looking increasingly archaic and flawed. What have we got to hide?
1. J. P. A. Ioannis, PLoS Med. 2, e124 (2005).
2. D. Sarewitz, Nature 485, 149 (2012).
3. A. L. Buchachenko & D. A. Kuznetsov, J. Am. Chem. Soc. 130, 12868-12869 (2008).
4. D. Crotty et al., Proc. Natl Acad. Sci. USA 109, 1437-1442 (2012).
5. B. A. Nosek, J. R. Spies & M. Motyl, preprint http://arxiv.org/abs/1205.4251.
________________________________________________________________
How much of the published literature should you believe? Not much, by some accounts. A 2005 paper by epidemiologist John Ioannidis of the University of Ioannina School of Medicine in Greece had the stark title “Why most published research findings are false” [1]. Ioannidis claimed that “for most study designs and settings, it is more likely for a research claim to be false than true”, and that often published claims simply reflected the prevailing bias of the field. Ioannidis suspected that some “established classics” in the literature wouldn’t stand up to close scrutiny.
His focus was on biomedical research, in particular clinical trials of drugs, where inferences have to be made from complex statistics, perhaps with small sample sizes. Here, not only might the effects being sought be rather marginal but there are also strong biases and prejudices introduced by financial pressures. Reports of drug trials certainly do have a bias towards positive outcomes, prompting valid calls for all drug trials to be registered before the study is undertaken so that negative findings can’t be quietly dropped.
These problems with pharmaceutical research are in themselves troubling for some chemists. But is this mostly an issue for Big Pharma, with its distorting profit motives and its reliance on statistics rather than more reductive, step-by-step experimentation? Probably not, Daniel Sarewitz of the Consortium for Science, Policy and Outcomes at Arizona State University claimed in Nature last May [2]. According to Sarewitz, systematic error due to bias, whether conscious or not, is “is likely to be prevalent in any field that seeks to predict the behaviour of complex systems – economics, ecology, environmental science, epidemiology and so on”. This figures: all these fields tend to depend on statistical inference of often marginal effects operating through mechanisms that may be poorly understood and perhaps nigh impossible to delineate.
But what about the subjects we like to think of as the “hard sciences” – like most of chemistry? Surely you can place more trust in spectra and rate constants and crystal structures than in scatter plots? Perhaps – but ‘trust’ is often what it is. Not many studies are ever repeated verbatim, and it’s generally acknowledged that crystallographic databases are probably full of errors, if only minor. The chance of experiments being replicated is probably proportional to the significance of the results. Maybe the greater good doesn’t suffer much from a literature full of flawed but uninteresting work – but that would offer scant support for science’s supposedly self-correcting nature.
And problems do crop up on close examination. Take, for example, the recent attempt by Darragh Crotty and colleagues at Trinity College Dublin to replicate the claims of Russian biochemist Anatoly Buchachenko and his coworkers, who since 2004 have been documenting (in good journals) the influence of a weak magnetic field on the rate of enzymatic production of ATP [3]. The Russians report that millitesla magnetic fields can more than double the reaction rate when the phosphorylating enzymes contain 25Mg (which is magnetic) rather than the other two stable isotopes 24Mg and 26Mg. Crotty and colleagues set out to test this because it bore on controversial claims of physiological effects from weak electromagnetic fields. They found no difference in reaction rate for all three magnesium isotopes [4]. So far the discrepancy remains puzzling.
If this is indeed a wider problem than is commonly recognized for all sciences, what to do? Sarewitz suggests reducing hype and strengthening ties between fundamental research and real-world testing. Ioannidis implores researchers to be honest with themselves about the ‘pre-study odds’ of their hypothesis being true. This purging of preconception and self-deception is what Francis Bacon called for in the seventeenth century when he argued that natural philosophers seeking truth must free themselves from ‘idols of the mind’. But as Ioannis recognizes, changing mindsets isn’t easy.
Another perspective is offered in a preprint by psychologist Brian Nosek of the University of Virginia and his colleagues [5]. They point out that professional success for scientists relies on publishing, but publication both favours positive results and prefers novelty over replication. What is needed is a way to rescue scientists’ ostensible aim – getting it right – from their short-term, pragmatic aim – getting it published. Among things that won’t work, the authors say, are journals devoted to replications and tougher peer review (which can already display stifling conservatism). Instead we need metrics for evaluating what is worth replicating, journal editorial policies that focus on soundness rather than ‘importance’, less focus on sheer publication productivity for job and tenure applicants, lower barriers to publication (so that it becomes less coveted in itself), and in particular, new ways of releasing results: open access to data, methods, tools and lab books. One can find problems with all of these, but the old ways of science publishing are looking increasingly archaic and flawed. What have we got to hide?
1. J. P. A. Ioannis, PLoS Med. 2, e124 (2005).
2. D. Sarewitz, Nature 485, 149 (2012).
3. A. L. Buchachenko & D. A. Kuznetsov, J. Am. Chem. Soc. 130, 12868-12869 (2008).
4. D. Crotty et al., Proc. Natl Acad. Sci. USA 109, 1437-1442 (2012).
5. B. A. Nosek, J. R. Spies & M. Motyl, preprint http://arxiv.org/abs/1205.4251.
Still talking about colour
Here’s another take on the recent paper on modelling of the evolution of colour terms – this time, published in Prospect.
___________________________________________
Languages are extremely diverse, but not arbitrary. Behind the bewildering diversity and the apparently contradictory ways in which different tongues elect to conceptualise the world, we can sometimes discern order and regularity. Many linguists have assumed that this reflects a hard-wired linguistic aptitude of the human brain. Some recent studies propose, however, that language ‘universals’ aren’t simply prescribed by genes but arise from the interaction between the biology of human perception and the bustle, exchange and negotiation of human culture.
Language has a perfectly logical job to do—to convey information— and yet is seemingly riddled with irrationality. Why all those irregular verbs, those random genders, those silent vowels and ambiguous homophones? You’d think languages would evolve towards some optimal model of clarity and concision, but instead they accumulate quirks that hinder learning, not only for foreigners but also in native speakers.
Traditionally, linguists have tended to explain the peculiarities of language through the history of the people who speak it. That’s often fascinating, but does not yield general principles about how languages have developed in the past—or how they will develop in future. As languages evolve and diverge, what guides their form?
Linguists have long suspected that language is like a game, in which individuals in a group or culture vie to impose their way of speaking. We adopt words and phrases we hear from others, and by using them, help them to propagate. Through face-to-face encounters, language evolves to reconcile our conflicting impulses as speakers or listeners. When speaking, we want to say our bit with minimal effort: we want language to be simple. As listeners, we want the speaker to make the meaning clear: we want language to be informative. In other words, speakers try to shift the effort onto listeners, and vice versa.
All this makes language what scientists call a complex system, meaning that it involves many agents interacting with each other via fairly well-defined rules. From these interactions there typically emerges an organised, global mode of behaviour that could not be deduced from local rules alone. Complex social systems have in recent years become widely studied by computer modelling: you define a population of agents, set the rules of engagement, and let the system run. Here the methods and concepts of the hard sciences—not so different to those used to model the behaviour of fundamental particles or molecules—are being imported into the traditionally empirical or narrative-dominated subjects of the social sciences. This approach has notched up successes in areas ranging from traffic flow to analysis of economic markets. No one pretends that a cultural artefact like language will ever be as tightly rule-bound or predictive as physics or chemistry, yet a complex-systems view might prove key to understanding how it evolves.
A significant success was recently claimed by and Italian group led by physicist Vittorio Loreto of the University of Rome La Sapienza. They looked at the paradigmatic example among linguists of how language segments and labels the objective world: the naming of colours.
As early anthropologists began to study non-Western languages in the nineteenth century, particularly those of pre-literate “savages”—they discovered that the familiar European colour terms of red, yellow, blue, green and so on are not as obvious and natural as they seem. Some indigenous people have far fewer colour terms. Many get by with perhaps three or four, so that for example “red” could refer to anything from green to orange, while blue, purple and black are all lumped together as types of black.
Inevitably, this was at first considered sheer backwardness. Researchers even concluded that such people were at an earlier stage of evolution, with a defective sense of colour vision that left them unable to tell the difference between, say, black and blue. Once they started testing natives using colour charts, however, they found them perfectly capable of distinguishing blue from black—they just saw no need to assign them different colour words. Uncomfortably for Western supremacists, we are in the same boat when it comes to blue, for Russians find it odd that an Englishman uses the same basic term for light blue (Russian goluboy) and dark blue (siniy).
Then in the 1860s the German philologist Lazarus Geiger proposed that the subdivision of colour always follows the same hierarchy. The simplest colour lexicons (such as the Dugerm Dani language of New Guinea) distinguish only black/dark and white/light. The next colour to be given a separate word is always centred on the red part of the visible spectrum. Then, according to Geiger, comes yellow, then green, then blue. Lazarus’s colour hierarchy was forgotten until restated in almost the same form in 1969 by US anthropologists Brent Berlin and Paul Kay, when it was hailed as one of the most significant discoveries in modern linguistics. Here was an apparently universal regularity underlying the way language is used to describe the world.
Berlin and Kay’s hypothesis has since fallen in and out of favour, and certainly there are exceptions to the scheme they proposed. But the fundamental colour hierarchy, at least in terms of the ordering black/white, red, yellow/green (either may come first) and blue, remains generally accepted. The problem is that no one could explain it.
Why, for example, do the blue of sky and sea, or the green of foliage, not register as distinct before the far less common red? It’s true that our visual system has evolved to be particularly sensitive to yellow (that’s why it appears so bright), probably because this enabled our pre-human ancestors to spot ripe fruit among foliage. But we have no trouble distinguishing purple, blue and green in the spectrum.
There are several schools of thought about how colours get named. “Nativists”, who include Berlin and Kay and Steven Pinker, argue that the concepts to which we attach words are innately determined by how we perceive the world. As Pinker has put it, “the way we see colours determines how we learn words for them, not vice versa”. In this view, often associated with Noam Chomsky, our perceptual apparatus has evolved to ensure that we make “sensible”—that is useful—choices of what to label with distinct words: we are hard-wired for particular forms of language. “Empiricists”, in contrast, argue that we don’t need this innate programming, but just the capacity to learn the conventional (but arbitrary) labels for things we can percieve.
In both cases, the categories themselves are deemed “obvious”: language just labels them. But the conclusions of Loreto and colleagues fit with a third possibility: the “culturist” view, which says that shared communication is needed to help organise category formation, so that categories and language co-evolve in an interaction between biological predisposition and culture. In other words, the starting point for colour terms is not some inevitably distinct block of the spectrum that we might decide to call ‘red’, ‘rouge’ and so on – but neither do we just divide up the spectrum any old how, because the human eye has different sensitivity to different parts of it. Given this, we have to arrive at some consensus not just on which label to use, but on what it labels.
The Italian team devised a computer model of language evolution in which new words arise through the game played by pairs of ’agents’, a speaker and a listener. The speaker uses words to refer to objects in a scene, and if she uses a word that is new to the listener (for a new colour, say), there’s a chance that the listener will figure out what the word refers to and adopt it. Alternatively, the listener might already have a word for that colour, but choose to replace it with the speaker’s word anyway. The language of this population of agents emerges and evolves from many such exchanges.
For colour, our visual physiology biases this process, picking out some parts of the spectrum as more worthy of a distinct colour term than others. The crucial factor is how well we can discriminate between very similar colours – we do that most poorly in the red, yellowish green and purple-violet. So we can’t distinguish two closely related reds as we can blues, say.
When the researchers included this bias in the colour-naming game, they found that colour terms emerged over time in their population of agents in much the same order proposed by Berlin and Key: first red, then violet, yellow, green, blue and orange. Violet doesn’t quite fit, but Loreto and colleagues think this is just an artefact of the way reddish hues crop up at both ends of the spectrum. Importantly, they don’t get the correct sequence unless they incorporate the colour sensitivity of actual human vision, but neither could the sequence be predicted from that alone, without the inter-agent negotiations that generate a consensus on colour words. You need both biology and culture to get it right.
The use of agent-based models to explore language evolution has been pioneered by Luc Steels of the Free University of Brussels, who is motivated by artificial intelligence: he wants to know how best to design robots so that they might develop a shared language. Steels and his coworkers have also favoured the acquisition of colour terms as their test case, and have previously argued in favour of the “cultural” picture that Loreto’s team now supports. The computer modelling of Steels’ group deserves much of the credit for starting to change the prevailing view of language acquisition, and the existence of near-universal patterns like Berlin and Kay’s colour hierarchy, from the influence of inherent, genetic factors to that of culture and environment.
Steels and his colleagues Joris Bleys and Joachim de Beule, for example, have presented an agent-based model of language negotiation, similar to that used by Loreto’s team, which purports to explain how a colour-language system can change from one based mostly on differences in brightness, using words like ‘dark’, ‘light’ and ‘shiny’, to one that makes distinctions of hue. (There are more ways to think about colour than Berlin and Kay’s rainbow-slicing.) The brightness system was used in Old English between around 600 and 1150, while Middle English (1150-1500) used hue-related words. A coeval switch was seen in other European languages, coinciding with the development of textile dyeing. This technology altered the constraints on what needed to be communicated: people now had to talk about a wider range of colours of similar brightness but different hue. Steels and colleagues showed that this sort of environmental pressure could tip the balance from a brightness-based colour terminology to a hue-based one. Again, it is one thing to tell that story, another to show that it really works in (a model of) the complex give and take of daily discourse. It increasingly seems, then, that language is determined not simply by ‘how we are’, but how it is used: by what we need to say.
___________________________________________
Languages are extremely diverse, but not arbitrary. Behind the bewildering diversity and the apparently contradictory ways in which different tongues elect to conceptualise the world, we can sometimes discern order and regularity. Many linguists have assumed that this reflects a hard-wired linguistic aptitude of the human brain. Some recent studies propose, however, that language ‘universals’ aren’t simply prescribed by genes but arise from the interaction between the biology of human perception and the bustle, exchange and negotiation of human culture.
Language has a perfectly logical job to do—to convey information— and yet is seemingly riddled with irrationality. Why all those irregular verbs, those random genders, those silent vowels and ambiguous homophones? You’d think languages would evolve towards some optimal model of clarity and concision, but instead they accumulate quirks that hinder learning, not only for foreigners but also in native speakers.
Traditionally, linguists have tended to explain the peculiarities of language through the history of the people who speak it. That’s often fascinating, but does not yield general principles about how languages have developed in the past—or how they will develop in future. As languages evolve and diverge, what guides their form?
Linguists have long suspected that language is like a game, in which individuals in a group or culture vie to impose their way of speaking. We adopt words and phrases we hear from others, and by using them, help them to propagate. Through face-to-face encounters, language evolves to reconcile our conflicting impulses as speakers or listeners. When speaking, we want to say our bit with minimal effort: we want language to be simple. As listeners, we want the speaker to make the meaning clear: we want language to be informative. In other words, speakers try to shift the effort onto listeners, and vice versa.
All this makes language what scientists call a complex system, meaning that it involves many agents interacting with each other via fairly well-defined rules. From these interactions there typically emerges an organised, global mode of behaviour that could not be deduced from local rules alone. Complex social systems have in recent years become widely studied by computer modelling: you define a population of agents, set the rules of engagement, and let the system run. Here the methods and concepts of the hard sciences—not so different to those used to model the behaviour of fundamental particles or molecules—are being imported into the traditionally empirical or narrative-dominated subjects of the social sciences. This approach has notched up successes in areas ranging from traffic flow to analysis of economic markets. No one pretends that a cultural artefact like language will ever be as tightly rule-bound or predictive as physics or chemistry, yet a complex-systems view might prove key to understanding how it evolves.
A significant success was recently claimed by and Italian group led by physicist Vittorio Loreto of the University of Rome La Sapienza. They looked at the paradigmatic example among linguists of how language segments and labels the objective world: the naming of colours.
As early anthropologists began to study non-Western languages in the nineteenth century, particularly those of pre-literate “savages”—they discovered that the familiar European colour terms of red, yellow, blue, green and so on are not as obvious and natural as they seem. Some indigenous people have far fewer colour terms. Many get by with perhaps three or four, so that for example “red” could refer to anything from green to orange, while blue, purple and black are all lumped together as types of black.
Inevitably, this was at first considered sheer backwardness. Researchers even concluded that such people were at an earlier stage of evolution, with a defective sense of colour vision that left them unable to tell the difference between, say, black and blue. Once they started testing natives using colour charts, however, they found them perfectly capable of distinguishing blue from black—they just saw no need to assign them different colour words. Uncomfortably for Western supremacists, we are in the same boat when it comes to blue, for Russians find it odd that an Englishman uses the same basic term for light blue (Russian goluboy) and dark blue (siniy).
Then in the 1860s the German philologist Lazarus Geiger proposed that the subdivision of colour always follows the same hierarchy. The simplest colour lexicons (such as the Dugerm Dani language of New Guinea) distinguish only black/dark and white/light. The next colour to be given a separate word is always centred on the red part of the visible spectrum. Then, according to Geiger, comes yellow, then green, then blue. Lazarus’s colour hierarchy was forgotten until restated in almost the same form in 1969 by US anthropologists Brent Berlin and Paul Kay, when it was hailed as one of the most significant discoveries in modern linguistics. Here was an apparently universal regularity underlying the way language is used to describe the world.
Berlin and Kay’s hypothesis has since fallen in and out of favour, and certainly there are exceptions to the scheme they proposed. But the fundamental colour hierarchy, at least in terms of the ordering black/white, red, yellow/green (either may come first) and blue, remains generally accepted. The problem is that no one could explain it.
Why, for example, do the blue of sky and sea, or the green of foliage, not register as distinct before the far less common red? It’s true that our visual system has evolved to be particularly sensitive to yellow (that’s why it appears so bright), probably because this enabled our pre-human ancestors to spot ripe fruit among foliage. But we have no trouble distinguishing purple, blue and green in the spectrum.
There are several schools of thought about how colours get named. “Nativists”, who include Berlin and Kay and Steven Pinker, argue that the concepts to which we attach words are innately determined by how we perceive the world. As Pinker has put it, “the way we see colours determines how we learn words for them, not vice versa”. In this view, often associated with Noam Chomsky, our perceptual apparatus has evolved to ensure that we make “sensible”—that is useful—choices of what to label with distinct words: we are hard-wired for particular forms of language. “Empiricists”, in contrast, argue that we don’t need this innate programming, but just the capacity to learn the conventional (but arbitrary) labels for things we can percieve.
In both cases, the categories themselves are deemed “obvious”: language just labels them. But the conclusions of Loreto and colleagues fit with a third possibility: the “culturist” view, which says that shared communication is needed to help organise category formation, so that categories and language co-evolve in an interaction between biological predisposition and culture. In other words, the starting point for colour terms is not some inevitably distinct block of the spectrum that we might decide to call ‘red’, ‘rouge’ and so on – but neither do we just divide up the spectrum any old how, because the human eye has different sensitivity to different parts of it. Given this, we have to arrive at some consensus not just on which label to use, but on what it labels.
The Italian team devised a computer model of language evolution in which new words arise through the game played by pairs of ’agents’, a speaker and a listener. The speaker uses words to refer to objects in a scene, and if she uses a word that is new to the listener (for a new colour, say), there’s a chance that the listener will figure out what the word refers to and adopt it. Alternatively, the listener might already have a word for that colour, but choose to replace it with the speaker’s word anyway. The language of this population of agents emerges and evolves from many such exchanges.
For colour, our visual physiology biases this process, picking out some parts of the spectrum as more worthy of a distinct colour term than others. The crucial factor is how well we can discriminate between very similar colours – we do that most poorly in the red, yellowish green and purple-violet. So we can’t distinguish two closely related reds as we can blues, say.
When the researchers included this bias in the colour-naming game, they found that colour terms emerged over time in their population of agents in much the same order proposed by Berlin and Key: first red, then violet, yellow, green, blue and orange. Violet doesn’t quite fit, but Loreto and colleagues think this is just an artefact of the way reddish hues crop up at both ends of the spectrum. Importantly, they don’t get the correct sequence unless they incorporate the colour sensitivity of actual human vision, but neither could the sequence be predicted from that alone, without the inter-agent negotiations that generate a consensus on colour words. You need both biology and culture to get it right.
The use of agent-based models to explore language evolution has been pioneered by Luc Steels of the Free University of Brussels, who is motivated by artificial intelligence: he wants to know how best to design robots so that they might develop a shared language. Steels and his coworkers have also favoured the acquisition of colour terms as their test case, and have previously argued in favour of the “cultural” picture that Loreto’s team now supports. The computer modelling of Steels’ group deserves much of the credit for starting to change the prevailing view of language acquisition, and the existence of near-universal patterns like Berlin and Kay’s colour hierarchy, from the influence of inherent, genetic factors to that of culture and environment.
Steels and his colleagues Joris Bleys and Joachim de Beule, for example, have presented an agent-based model of language negotiation, similar to that used by Loreto’s team, which purports to explain how a colour-language system can change from one based mostly on differences in brightness, using words like ‘dark’, ‘light’ and ‘shiny’, to one that makes distinctions of hue. (There are more ways to think about colour than Berlin and Kay’s rainbow-slicing.) The brightness system was used in Old English between around 600 and 1150, while Middle English (1150-1500) used hue-related words. A coeval switch was seen in other European languages, coinciding with the development of textile dyeing. This technology altered the constraints on what needed to be communicated: people now had to talk about a wider range of colours of similar brightness but different hue. Steels and colleagues showed that this sort of environmental pressure could tip the balance from a brightness-based colour terminology to a hue-based one. Again, it is one thing to tell that story, another to show that it really works in (a model of) the complex give and take of daily discourse. It increasingly seems, then, that language is determined not simply by ‘how we are’, but how it is used: by what we need to say.
Tuesday, August 14, 2012
Speaking from the attic
I discuss my new book Curiosity on the Guardian podcast here. No, I’m not at my most eloquent, but I hope you get the idea. Incidentally, water nerds (I know you’re out there) can see my talk on the chemistry of water at the UCLA “Fourth State of Water” symposium in March. This was an experiment in teleconferencing: I prepared a short intro movie using my cheap and cheerful Photo Booth, then recorded my Powerpoint talk with narration, and tuned in afterwards for a Q&A via Skype. All from my home study. And it seemed to kind of work – great to know that it’s possible to do this sort of thing now without a transatlantic flight.
Sunday, August 05, 2012
Falling down
I have mixed feelings about the Jonah Lehrer affair. No matter how I dislike the rivalries and jealousies of the writing world, it’s impossible for an old grafter, in these tough times of shrinking advances and dwindling opportunity for writers, not to feel a pang or two at the sight of a Wunderkind commanding whopping great speaker’s fees and being showered with adulatory reviews proclaiming him to be the future of science writing. But I’d like to think I was able not to be too begrudging, to feel that at least one of ‘us’ was making it big, and to recognize that this is just how things work, especially in the US. I’m sure that a fair bit of the venom that has come Jonah’s way in the light of the revelations of fabrication and self-plagiarism is fuelled by resentment at his youth and fame.
It is in any case all very sad. He may now be set up for life anyway, but I shudder to think how excruciating and embarrassing it must be to fall from such heights in such an ignominious way. And though it’s not what he meant by it, Lehrer’s remark in Imagine that “the young know less, which is why they often invent more” is not one that he’s going to be allowed to forget in a hurry.
He’ll recover, I expect, but it’s hard to see how he’ll ever quite shake off the stigma. And all for a few moments of there-but-for-the-grace-of-God hubris and deception. I don’t find it at all hard to understand the panic which compelled him to dig his hole deeper with outright lies about his fictitious sources on Dylan. It was foolish, of course, and unethical, but hardly a terrible sin.
Jonah was clearly far on the wrong side of grey territory in making up those quotes and pretending they were genuine. But there certainly is grey territory here, as there was in the case of Johann Hari using quotes from old interviews with his subjects as though they had been told directly to him. It’s not clear just how finickety must one be in making one’s sources plain – is it enough, for example, to say that your interviewee “has said…”, or do you need to mention to whom it was said? And there are no rules for how to ‘tidy up’ quotes. Do you just leave out repetitions and digressions? Or correct obviously unintended errors and grammatical slips? Or make a statement a bit more concise while preserving the meaning? I have certainly seen my own words recast, generally to good effect by making me sound much more articulate than I really am – I’ve no objection to that. I’ve also seen my meaning occasionally distorted once or twice, but evidently without that intention, and I’ve not been affronted by it. What Lehrer did obviously goes beyond such things, but I’ve a sense that, if you make a big blunder like this, the little slips and elisions are then hauled up as evidence against you too.
The charges of self-plagiarism are particularly ambiguous. I simply don’t know what the rules are here. It seems clear that one should never recycle articles for different publications unless there’s an explicit reason for that, and open acknowledgement of it. To accept a commission without admitting that you’ve written something similar or related before would be bad form. But what if you need to explain some particular concept or theory and feel that you couldn’t better the way you put it elsewhere? Is it okay to reuse a few phrases? A paragraph? I would think so, if they’re your own words anyway. There’s evidently a question of degree here – how much, how similar? I’ve not looked into exactly what Jonah has done in this regard, but self-plagiarism is a slippery concept. The comment by his publisher Houghton Mifflin Harcourt that “Jonah Lehrer fully acknowledges that Imagine draws upon work he has published in shorter form during the past several years and is sorry that was not made clear” sends a few shivers down the spine – is one really not meant to draw on one’s earlier, related journalistic work when preparing a book, or at least does one really have to specify in detail what's new and what you've said before?
And yet my response to this affair is coloured by another consideration. Some years ago, Jonah spoke to me while he was an editor for Seed magazine. He was preparing an article on people who were transforming science and how we think about it, or something, and for some reason I’d been selected as one of them. And you know, I have always remembered (at least, I think so, but Charles Fernyhough might argue about that) getting off the phone and thinking “Is it just me, or was there something sour in that bloke’s tone?” I’d sensed he was not persuaded that I quite deserved the accolade I was being given, that I had somehow disappointed him. Then he became famous, and I thought, ah OK, evidently here’s a very ambitious and competitive young man. So it goes. But now it seems there might have been just a tad too much of that.
It is in any case all very sad. He may now be set up for life anyway, but I shudder to think how excruciating and embarrassing it must be to fall from such heights in such an ignominious way. And though it’s not what he meant by it, Lehrer’s remark in Imagine that “the young know less, which is why they often invent more” is not one that he’s going to be allowed to forget in a hurry.
He’ll recover, I expect, but it’s hard to see how he’ll ever quite shake off the stigma. And all for a few moments of there-but-for-the-grace-of-God hubris and deception. I don’t find it at all hard to understand the panic which compelled him to dig his hole deeper with outright lies about his fictitious sources on Dylan. It was foolish, of course, and unethical, but hardly a terrible sin.
Jonah was clearly far on the wrong side of grey territory in making up those quotes and pretending they were genuine. But there certainly is grey territory here, as there was in the case of Johann Hari using quotes from old interviews with his subjects as though they had been told directly to him. It’s not clear just how finickety must one be in making one’s sources plain – is it enough, for example, to say that your interviewee “has said…”, or do you need to mention to whom it was said? And there are no rules for how to ‘tidy up’ quotes. Do you just leave out repetitions and digressions? Or correct obviously unintended errors and grammatical slips? Or make a statement a bit more concise while preserving the meaning? I have certainly seen my own words recast, generally to good effect by making me sound much more articulate than I really am – I’ve no objection to that. I’ve also seen my meaning occasionally distorted once or twice, but evidently without that intention, and I’ve not been affronted by it. What Lehrer did obviously goes beyond such things, but I’ve a sense that, if you make a big blunder like this, the little slips and elisions are then hauled up as evidence against you too.
The charges of self-plagiarism are particularly ambiguous. I simply don’t know what the rules are here. It seems clear that one should never recycle articles for different publications unless there’s an explicit reason for that, and open acknowledgement of it. To accept a commission without admitting that you’ve written something similar or related before would be bad form. But what if you need to explain some particular concept or theory and feel that you couldn’t better the way you put it elsewhere? Is it okay to reuse a few phrases? A paragraph? I would think so, if they’re your own words anyway. There’s evidently a question of degree here – how much, how similar? I’ve not looked into exactly what Jonah has done in this regard, but self-plagiarism is a slippery concept. The comment by his publisher Houghton Mifflin Harcourt that “Jonah Lehrer fully acknowledges that Imagine draws upon work he has published in shorter form during the past several years and is sorry that was not made clear” sends a few shivers down the spine – is one really not meant to draw on one’s earlier, related journalistic work when preparing a book, or at least does one really have to specify in detail what's new and what you've said before?
And yet my response to this affair is coloured by another consideration. Some years ago, Jonah spoke to me while he was an editor for Seed magazine. He was preparing an article on people who were transforming science and how we think about it, or something, and for some reason I’d been selected as one of them. And you know, I have always remembered (at least, I think so, but Charles Fernyhough might argue about that) getting off the phone and thinking “Is it just me, or was there something sour in that bloke’s tone?” I’d sensed he was not persuaded that I quite deserved the accolade I was being given, that I had somehow disappointed him. Then he became famous, and I thought, ah OK, evidently here’s a very ambitious and competitive young man. So it goes. But now it seems there might have been just a tad too much of that.
Saturday, August 04, 2012
Stop me if you've heard this before
My latest story for Chemistry World is strictly for chemistry nerds. But this one for Physics World – extended version below – has hopefully a little more general interest in its all-round boggleworthiness. Charles Bennett’s comments seem to imply that the key question is whether it is possible to find a way of experimentally distinguishing between this remarkable interpretation and the more prosaic one he suggests.
___________________________________________________________________
What you do today could affect what happened yesterday. This is the bizarre conclusion of a thought experiment in quantum physics described in a preprint by Yakir Aharonov of Tel-Aviv University in Israel and his colleagues.
It sounds impossible, indeed as though it is violating one of science’s most cherished principles: causality. But the researchers say that the rules of the quantum world conspire to decorously preserve causality by ‘hiding’ the influence of future choices until those choices have actually been made.
At the centre of the idea is the quantum phenomenon of nonlocality, in which two or more particles exist in inter-related (‘entangled’) states that remain undetermined until a measurement is made on one of them – whereupon the state of the other particle is instantly fixed too, no matter how far away it is. Albert Einstein first pointed out this instantaneous ‘action at a distance’ in 1935, when he argued that it meant quantum theory must be incomplete. But modern experiments have confirmed that this instantaneous action is real, and it now holds the key to practical quantum technologies such as quantum computing and cryptography.
Aharonov and his coworkers describe an experiment much like that proposed by Einstein, but on a large group of entangled particles rather than just two. They argue that under certain conditions, the experimenter’s choice of a measurement of the states of the particles can be demonstrated to affect the states they were in at an earlier time, when a very loose measurement was made. In effect, the earlier ‘weak’ measurements anticipate the choice made in the later ‘strong’ measurement.
The work builds on a way of thinking about entanglement proposed by Aharanov three decades ago. This entails looking at the correlations between particles in the four dimensions of spacetime rather than the three of space. “In three dimensions it looks like some miraculous influence between two distant particles”, says Aharonov’s coworker Avshalom Elitzur. “In spacetime as a whole, it is a continuous interaction extending between past and future events.” Quantum systems are generally described by a ‘state vector’: a set of quantum states propagating forward in time. But Aharanov’s view considers also a second state vector propagating from future to past – which is why it is called the ‘two state vector formalism’ (TSVF).
Aharonov and coworkers have now discovered a remarkable implication of the TSVF. It bears on the question posed by Einstein once the early quantum theorists began to appreciate how measurement not just reveals but may determine the state of quantum systems. If observation has this effect of fixing how the world is, said Einstein, then can we be so sure that the Moon is there when no one is looking?
“The ordinary physicist replies, ‘Go away, this is a philosophy not physics’”, says Elitzur. It is equivalent to asking what is the state of a particle between two measurements. “Of course you're not going to measure the particle, because then you will have the particle's state upon measurement rather than between measurements.” But Aharanov’s perspective shows that it is possible to get at the intermediate information – by making sufficiently ‘weak’ measurements on a whole bunch of entangled particles prepared in the same way, and then averaging the statistics. Elitzur explains that this amounts to saying “Give me sufficiently many particles during of this time interval and I'll tell you precisely what you want to know.”
The weak measurements tell you something about the probabilities of different states (spin value up or down, say) – albeit with a lot of error – without actually collapsing them into definite states, as a strong measurement does. The weak measurement does perturb the system, but not enough to fix an outcome for sure. “A weak measurement both changes the measured state and informs you about the resulting localized state”, says Elitzur. “But it does both jobs very loosely. Moreover, the change it inflicts on the system must be weaker than the information it gives you.”
As a result, Elitzur explains, “every single weak measurement in itself tells you nearly nothing. The measurements provide reliable outcomes only after you sum them all up. Then the errors cancel out and you can extract some information about the ensemble as a whole.”
In the researchers’ thought experiment, the conclusions of these weak measurements will agree with those of later strong measurements, in which the experimenter chooses freely which spin orientation to measure – even though, after the weak measurements, the particles’ states are still undetermined.
What this means within the TSVF, says Elitzur, is that “a particle between two measurements possesses the two states indicated by both of them, past and future!” This even seems to evade Heisenberg’s uncertainty principle, which forbids simultaneous precise knowledge of a particles position and momentum. “If you measured position first and momentum later, then the particle possesses both precise values, never mind Heisenberg”, says Elitzur. Heisenberg himself felt that his uncertainty principle undermined causality – he’d have been shocked to find this kind of backward causality actually seeming to undermine his own law.
But causality does emerge intact, after a fashion. For the catch is that the weak measurements in themselves appear to leave many options for what the particles states are. Only by adding subsequent information from the strong measurements can one reveal what the weak measurements were ‘really’ saying. This means that the weak measurements by themselves can’t show you what the later strong measurements will reveal. The information is there, but encrypted and only exposed in retrospect. So causality is preserved, even if it is not exactly causality as we normally know it.
Why there is this censorship is not clear, except from an almost metaphysical perspective. “Nature is known to be fussy about never appearing inconsistent”, says Elitzur. “So she's not going to appreciate overt backwards causality – people killing their grandfathers and so on.”
He says that some specialists in quantum optics have expressed interest in conducting the experiment, which he thinks should be no more difficult than previous studies of entanglement.
Charles Bennett of IBM’s research laboratories in Yorktown Heights, New York, a specialist on quantum information theory, is not convinced. For a start, he sees the TSVF as only one way of looking at the results. “People in quantum foundations are often so wedded to their own interpretation or formalism that they say it is the only reasonable one, when in fact quantum mechanics admits multiple interpretations, which except for a few outliers are entirely equivalent to one another. The differences are aesthetic and philosophical, not scientific.”
Bennett believes that the findings can be interpreted without any apparent ‘backwards causation’, so that the authors are erecting a straw man. “To make their straw man seem stronger, they use language that in my opinion obscures the crucial difference between communication and correlation. They say that the initial weak measurement outcomes anticipate the experimenter's future choice but that doing so causes no violation of causality because the anticipation is encrypted.” But he thinks this is a bit like an experiment in quantum cryptography in which the sender sends the receiver the decryption key before sending (or even deciding on) the message, and then claims that the key is somehow an ‘anticipation’ of the message. With this in mind, it is not clear whether even an experiment will resolve the issue, since it would come down to a matter of how to interpret the results.
Aharonov and colleagues suspect that their findings might even have implications for free will. “Our group remains somewhat divided on these philosophical questions”, says Elitzur. “I keep teasing Yakir that he will go down in history as the person who has abolished free choice. He on the other hand is confident that TSVF secures free will a place within physical formalism. His conclusion is somewhat Talmudic: Everything you're going to do is already known to God, but you still have the choice. On the other hand Yakir's God sharply differs from Einstein's in that she loves to play dice from morning to night.”
___________________________________________________________________
What you do today could affect what happened yesterday. This is the bizarre conclusion of a thought experiment in quantum physics described in a preprint by Yakir Aharonov of Tel-Aviv University in Israel and his colleagues.
It sounds impossible, indeed as though it is violating one of science’s most cherished principles: causality. But the researchers say that the rules of the quantum world conspire to decorously preserve causality by ‘hiding’ the influence of future choices until those choices have actually been made.
At the centre of the idea is the quantum phenomenon of nonlocality, in which two or more particles exist in inter-related (‘entangled’) states that remain undetermined until a measurement is made on one of them – whereupon the state of the other particle is instantly fixed too, no matter how far away it is. Albert Einstein first pointed out this instantaneous ‘action at a distance’ in 1935, when he argued that it meant quantum theory must be incomplete. But modern experiments have confirmed that this instantaneous action is real, and it now holds the key to practical quantum technologies such as quantum computing and cryptography.
Aharonov and his coworkers describe an experiment much like that proposed by Einstein, but on a large group of entangled particles rather than just two. They argue that under certain conditions, the experimenter’s choice of a measurement of the states of the particles can be demonstrated to affect the states they were in at an earlier time, when a very loose measurement was made. In effect, the earlier ‘weak’ measurements anticipate the choice made in the later ‘strong’ measurement.
The work builds on a way of thinking about entanglement proposed by Aharanov three decades ago. This entails looking at the correlations between particles in the four dimensions of spacetime rather than the three of space. “In three dimensions it looks like some miraculous influence between two distant particles”, says Aharonov’s coworker Avshalom Elitzur. “In spacetime as a whole, it is a continuous interaction extending between past and future events.” Quantum systems are generally described by a ‘state vector’: a set of quantum states propagating forward in time. But Aharanov’s view considers also a second state vector propagating from future to past – which is why it is called the ‘two state vector formalism’ (TSVF).
Aharonov and coworkers have now discovered a remarkable implication of the TSVF. It bears on the question posed by Einstein once the early quantum theorists began to appreciate how measurement not just reveals but may determine the state of quantum systems. If observation has this effect of fixing how the world is, said Einstein, then can we be so sure that the Moon is there when no one is looking?
“The ordinary physicist replies, ‘Go away, this is a philosophy not physics’”, says Elitzur. It is equivalent to asking what is the state of a particle between two measurements. “Of course you're not going to measure the particle, because then you will have the particle's state upon measurement rather than between measurements.” But Aharanov’s perspective shows that it is possible to get at the intermediate information – by making sufficiently ‘weak’ measurements on a whole bunch of entangled particles prepared in the same way, and then averaging the statistics. Elitzur explains that this amounts to saying “Give me sufficiently many particles during of this time interval and I'll tell you precisely what you want to know.”
The weak measurements tell you something about the probabilities of different states (spin value up or down, say) – albeit with a lot of error – without actually collapsing them into definite states, as a strong measurement does. The weak measurement does perturb the system, but not enough to fix an outcome for sure. “A weak measurement both changes the measured state and informs you about the resulting localized state”, says Elitzur. “But it does both jobs very loosely. Moreover, the change it inflicts on the system must be weaker than the information it gives you.”
As a result, Elitzur explains, “every single weak measurement in itself tells you nearly nothing. The measurements provide reliable outcomes only after you sum them all up. Then the errors cancel out and you can extract some information about the ensemble as a whole.”
In the researchers’ thought experiment, the conclusions of these weak measurements will agree with those of later strong measurements, in which the experimenter chooses freely which spin orientation to measure – even though, after the weak measurements, the particles’ states are still undetermined.
What this means within the TSVF, says Elitzur, is that “a particle between two measurements possesses the two states indicated by both of them, past and future!” This even seems to evade Heisenberg’s uncertainty principle, which forbids simultaneous precise knowledge of a particles position and momentum. “If you measured position first and momentum later, then the particle possesses both precise values, never mind Heisenberg”, says Elitzur. Heisenberg himself felt that his uncertainty principle undermined causality – he’d have been shocked to find this kind of backward causality actually seeming to undermine his own law.
But causality does emerge intact, after a fashion. For the catch is that the weak measurements in themselves appear to leave many options for what the particles states are. Only by adding subsequent information from the strong measurements can one reveal what the weak measurements were ‘really’ saying. This means that the weak measurements by themselves can’t show you what the later strong measurements will reveal. The information is there, but encrypted and only exposed in retrospect. So causality is preserved, even if it is not exactly causality as we normally know it.
Why there is this censorship is not clear, except from an almost metaphysical perspective. “Nature is known to be fussy about never appearing inconsistent”, says Elitzur. “So she's not going to appreciate overt backwards causality – people killing their grandfathers and so on.”
He says that some specialists in quantum optics have expressed interest in conducting the experiment, which he thinks should be no more difficult than previous studies of entanglement.
Charles Bennett of IBM’s research laboratories in Yorktown Heights, New York, a specialist on quantum information theory, is not convinced. For a start, he sees the TSVF as only one way of looking at the results. “People in quantum foundations are often so wedded to their own interpretation or formalism that they say it is the only reasonable one, when in fact quantum mechanics admits multiple interpretations, which except for a few outliers are entirely equivalent to one another. The differences are aesthetic and philosophical, not scientific.”
Bennett believes that the findings can be interpreted without any apparent ‘backwards causation’, so that the authors are erecting a straw man. “To make their straw man seem stronger, they use language that in my opinion obscures the crucial difference between communication and correlation. They say that the initial weak measurement outcomes anticipate the experimenter's future choice but that doing so causes no violation of causality because the anticipation is encrypted.” But he thinks this is a bit like an experiment in quantum cryptography in which the sender sends the receiver the decryption key before sending (or even deciding on) the message, and then claims that the key is somehow an ‘anticipation’ of the message. With this in mind, it is not clear whether even an experiment will resolve the issue, since it would come down to a matter of how to interpret the results.
Aharonov and colleagues suspect that their findings might even have implications for free will. “Our group remains somewhat divided on these philosophical questions”, says Elitzur. “I keep teasing Yakir that he will go down in history as the person who has abolished free choice. He on the other hand is confident that TSVF secures free will a place within physical formalism. His conclusion is somewhat Talmudic: Everything you're going to do is already known to God, but you still have the choice. On the other hand Yakir's God sharply differs from Einstein's in that she loves to play dice from morning to night.”
Tuesday, July 31, 2012
Climate conversion
I have a piece in the Guardian online about this paper from Richard Muller that is causing so much fuss, though it says nothing new and hasn’t even passed peer review yet (and might not). Actually my piece is not really about the paper itself, which is discussed elsewhere, but the question of scientists revising their views (or not).
I suspect one could publish a piece on the Guardian’s Comment is Free that read simply “climate change”, and then let them get on with it. There are a few comments below my piece that relate to the article, but they quickly settle down into yet another debate among themselves about whether climate change is real. Hadn’t they all exhausted themselves in the 948 comments following Leo Hickman’s other piece on this issue? But there’s some value in it, not least in sampling the range of views that non-scientist climate sceptics hold. I don’t mean that sarcastically – it seems important to know how all the scepticism justifies itself. Disheartening, sure, but useful.
________________________________________________________________
It’s tempting to infer from the reports of University of California physicist Richard Muller’s conversion that climate sceptics really can change their spots. Analyses by Muller’s Berkeley Earth Surface Temperature project, which have been made publicly available, reveal that the Earth’s land surface is on average 1.5 C warmer than it was when Mozart was born, and that, as Muller puts it “humans are almost entirely the cause”. He says that his findings are even stronger than those of the Intergovernmental Panel on Climate Change, which presents the consensus of the climate-science community that most of the warming in the past half century is almost certainly due to human activities. “Call me a converted skeptic”, says Muller in the New York Times.
Full marks for the professor’s scientific integrity, then. But those of us who agree with the conclusions of nearly every serious climate scientist on the planet shouldn’t be too triumphant. Muller was never your usual sceptic, picking and choosing his data to shore up an ideological position. He was sceptical only in the proper scientific sense of withholding judgement until he felt persuaded by the evidence.
Besides, Muller already stated four years ago that he accepted the consensus view – not because everyone else said so, but because he’d conducted his own research. That didn’t stop him from pointing out the (real) flaws with the infamous ‘hockey stick’ graph of temperature change over the past millennium, nor from accusing Al Gore of cherry-picking facts in An Inconvenient Truth.
In one sense, Muller is here acting as a model scientist: demanding strong evidence, damning distortions in any direction, and most of all, exemplifying the Royal Society’s motto Nullius in verba, ‘take no one’s word for it.’ But that’s not necessarily as virtuous as it seems. For one thing, as the Royal Society’s founders discovered, you have to take someone’s word for some things, since you lack the time and knowledge to verify everything yourself. And as one climatologist said, Muller’s findings only “demonstrate once again what scientists have known with some degree of certainty for nearly two decades”. Wasn’t it verging on arrogant to have so doubted his peers’ abilities? There’s a fine line between trusting your own judgement and assuming everyone else is a blinkered incompetent.
All the same, Muller’s self-confessed volte-face is commendably frank. It’s also unusual. In another rare instance, James Lovelock was refreshingly insouciant when he recently admitted that climate change, while serious, might not be quite as apocalyptic as he had previously forecast – precisely the kind of doom-mongering view that fuelled Muller’s scepticism. There’s surely something in Lovelock’s suggestion that being an independent scientist makes it easier to change your mind – the academic system still struggles to accept that getting things wrong occasionally is part of being a scientist.
But the problem is as much constitutional as institutional. Despite their claim that evidence is the arbiter, scientists rarely alter their views in major ways. Sure, they are often surprised by their discoveries, but on fundamental questions they are typically trenchant. The great astronomer Tycho Brahe never accepted the Copernican cosmos, Joseph Priestley never renounced phlogiston, Einstein never fully accepted quantum theory. Most great scientists have carried some obsolete convictions to the grave, which is why Max Planck claimed that science advances one funeral at a time.
This sounds scandalous, but actually it’s useful. Big questions in science are rarely resolved at a stroke by transparent experimental results. So they require vigorous debate, and the opposing views need resolute champions. Richard Dawkins and E. O. Wilson are currently locking horns about the existence of group selection in Darwinian evolution precisely because the answer is so far from obvious. I’d place money on neither ever rescinding.
The fact is that most scientists seek not to convert themselves but to convert others. That’s fair enough, for it’s those others who can most objectively judge who has the best case.
Could this mean we actually need climate sceptics? Better to say that we need to subject both sides of the debate to rigorous scientific testing. Just as Muller has done.
I suspect one could publish a piece on the Guardian’s Comment is Free that read simply “climate change”, and then let them get on with it. There are a few comments below my piece that relate to the article, but they quickly settle down into yet another debate among themselves about whether climate change is real. Hadn’t they all exhausted themselves in the 948 comments following Leo Hickman’s other piece on this issue? But there’s some value in it, not least in sampling the range of views that non-scientist climate sceptics hold. I don’t mean that sarcastically – it seems important to know how all the scepticism justifies itself. Disheartening, sure, but useful.
________________________________________________________________
It’s tempting to infer from the reports of University of California physicist Richard Muller’s conversion that climate sceptics really can change their spots. Analyses by Muller’s Berkeley Earth Surface Temperature project, which have been made publicly available, reveal that the Earth’s land surface is on average 1.5 C warmer than it was when Mozart was born, and that, as Muller puts it “humans are almost entirely the cause”. He says that his findings are even stronger than those of the Intergovernmental Panel on Climate Change, which presents the consensus of the climate-science community that most of the warming in the past half century is almost certainly due to human activities. “Call me a converted skeptic”, says Muller in the New York Times.
Full marks for the professor’s scientific integrity, then. But those of us who agree with the conclusions of nearly every serious climate scientist on the planet shouldn’t be too triumphant. Muller was never your usual sceptic, picking and choosing his data to shore up an ideological position. He was sceptical only in the proper scientific sense of withholding judgement until he felt persuaded by the evidence.
Besides, Muller already stated four years ago that he accepted the consensus view – not because everyone else said so, but because he’d conducted his own research. That didn’t stop him from pointing out the (real) flaws with the infamous ‘hockey stick’ graph of temperature change over the past millennium, nor from accusing Al Gore of cherry-picking facts in An Inconvenient Truth.
In one sense, Muller is here acting as a model scientist: demanding strong evidence, damning distortions in any direction, and most of all, exemplifying the Royal Society’s motto Nullius in verba, ‘take no one’s word for it.’ But that’s not necessarily as virtuous as it seems. For one thing, as the Royal Society’s founders discovered, you have to take someone’s word for some things, since you lack the time and knowledge to verify everything yourself. And as one climatologist said, Muller’s findings only “demonstrate once again what scientists have known with some degree of certainty for nearly two decades”. Wasn’t it verging on arrogant to have so doubted his peers’ abilities? There’s a fine line between trusting your own judgement and assuming everyone else is a blinkered incompetent.
All the same, Muller’s self-confessed volte-face is commendably frank. It’s also unusual. In another rare instance, James Lovelock was refreshingly insouciant when he recently admitted that climate change, while serious, might not be quite as apocalyptic as he had previously forecast – precisely the kind of doom-mongering view that fuelled Muller’s scepticism. There’s surely something in Lovelock’s suggestion that being an independent scientist makes it easier to change your mind – the academic system still struggles to accept that getting things wrong occasionally is part of being a scientist.
But the problem is as much constitutional as institutional. Despite their claim that evidence is the arbiter, scientists rarely alter their views in major ways. Sure, they are often surprised by their discoveries, but on fundamental questions they are typically trenchant. The great astronomer Tycho Brahe never accepted the Copernican cosmos, Joseph Priestley never renounced phlogiston, Einstein never fully accepted quantum theory. Most great scientists have carried some obsolete convictions to the grave, which is why Max Planck claimed that science advances one funeral at a time.
This sounds scandalous, but actually it’s useful. Big questions in science are rarely resolved at a stroke by transparent experimental results. So they require vigorous debate, and the opposing views need resolute champions. Richard Dawkins and E. O. Wilson are currently locking horns about the existence of group selection in Darwinian evolution precisely because the answer is so far from obvious. I’d place money on neither ever rescinding.
The fact is that most scientists seek not to convert themselves but to convert others. That’s fair enough, for it’s those others who can most objectively judge who has the best case.
Could this mean we actually need climate sceptics? Better to say that we need to subject both sides of the debate to rigorous scientific testing. Just as Muller has done.
Friday, July 27, 2012
Political interference
I’ve a mountain of stuff to put up here after a holiday. For starters, here’s the pre-edited version of an editorial for last week’s issue of Nature. I mention here in passing an opinion piece by Charles Lane of the Washington Post, but couldn’t sink my teeth into it as much as I’d have liked. It is breathtaking what passes as political commentary in the right-wing US media. Lane is worried that US social scientists have an unduly high proportion of Democrats. As I say below, that’s true for US academia generally. To Lane, this means there is a risk of political bias (so that social science is dangerous). Needless to say, there is quite a different interpretation that one might place on the fact that a majority of intelligent, educated Americans are liberals.
But the truly stupid part of his argument is that “Politicization was a risk political scientists accepted when they took government funding in the first place.” No one, Lane trumpets, has offered any counter-argument to that, “so I’ll consider that point conceded.” He’d do better to interpret the lack of response as an indication of the asinine nature of the assertion. Basically he is saying that all governments may reserve the right to employ the methods of dictatorship, imposing censorship and restricting academic freedoms. So if Congress acts like the Turkish government, what are those damned academics whining about? This is the thing about US right-wingers that just leaves we Europeans scratching our heads: they seem to believe that government is a necessary evil that should interfere with the state as little as possible, unless that interference is based on right-wing ideology (for example, by tampering with climate research). Perhaps there’s nothing surprising about that view in itself, though; what’s weird is how blind those who hold it are to its inconsistency.
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A fundamental question for democracy is what to submit to the democratic process. The laws of physics should presumably be immune. But should public opinion decide which science gets studied, or at least funded? That’s the implication of an amendment to the US National Science Foundation’s 2013 spending bill approved by the House of Representatives in May. Proposed by Republican Jeff Flake, it would prevent the NSF from funding political science, for which it awarded about $11m in grants this year. The Senate may well squash the amendment, but it’s deeply concerning that it got so far. Flake was hoping for bigger cuts to the NSF’s overall budget, but had to settle for an easier target. He indulged in the familiar trick in the US Congress of finding research with apparently obscure or trivial titles and parading it as a waste of taxpayers’ money.
One can do this in any area of science. The particular vulnerability of the social sciences is that, being less cluttered with technical terminology, it seems superficially easier for the lay person to assess. As social scientist Duncan Watts of Microsoft Research in New York has pointed out, “everyone has experience being human, and so the vast majority of findings in social science coincide with something that we have either experienced or can imagine experiencing”. This means the Flakes of this world have little trouble proclaiming such findings obvious or insignificant.
Part of the blame must lie with the practice of labelling the social sciences ‘soft’, which too readily translates as woolly or soft-headed. Because they deal with systems that are highly complex, adaptive and not rigorously rule-bound, the social sciences are among the hardest of disciplines, both methodologically and intellectually. What is more, they suffer because their findings do sometimes seem obvious. Yet equally, the “obvious”, common-sense answer may prove quite false when subjected to scrutiny. There are countless examples, from economics to traffic planning, which is one reason why the social sciences probably unnerve some politicians used to making decisions based not on evidence but on intuition, wishful thinking and an eye on the polls.
What of the critics’ other arguments against publicly funded political science? They say it is more susceptible to political bias; in particular, more social scientists have Democratic leanings than Republican. The latter is true, but equally so for US academics generally. We can argue about why, but why single out political science? The charge of bias, meanwhile, is asserted rather than demonstrated.
And what has political science ever done for us? We don’t know why crime rates rise and fall or the effect of deterrents, we can’t solve the financial crisis or stop civil wars, we can’t agree on the state’s role in systems of justice or taxation. As Washington Post columnist Charles Lane argues, “the larger the social or political issue, the more difficult it is to illuminate definitively through the methods of ‘hard science’.” In part this just restates the fact that political science is among the most difficult of the sciences. To conclude that hard problems are better solved by not studying them is ludicrous. Should we slash the physics budget unless the dark-matter and dark-energy problems are solved? Lane’s statement falls for the very myth it wants to attack: that political science is ruled, like physics, by precise, unique, universal rules. In any case, we have little idea how successful political science has been, for politicians rarely pay much heed to evidence-based advice from the social sciences, unless of course the evidence suits them. And to constrain political scientists with utilitarian bean-counting is to miss what is mostly its point anyway. As the likes of John Rawls, Herbert Simon, Robert Axelrod, Kenneth Waltz and Karl Popper have shown, they have enriched political debate beyond measure.
The general notion that politicians should decide what is or is not worthy of research is perilous. Here, the proper function of democracy is to establish impartial bodies of experts and leave it to them. But Flake’s amendment does more than just traduce a culture of expertise. Among the research he selected for ridicule were studies of gender disparity in politics and models for international analysis of climate change: issues unpopular with right-wingers. In other words, his interference is not just about cost-cutting but has a political agenda. That he and his political allies feel threatened by evidence-based study of politics and society does not speak highly of their confidence in the objective case for their policies. Flake’s amendment is no different in principle to the ideological infringements of academic freedom in Turkey or Iran. It has nothing to do with democracy.
But the truly stupid part of his argument is that “Politicization was a risk political scientists accepted when they took government funding in the first place.” No one, Lane trumpets, has offered any counter-argument to that, “so I’ll consider that point conceded.” He’d do better to interpret the lack of response as an indication of the asinine nature of the assertion. Basically he is saying that all governments may reserve the right to employ the methods of dictatorship, imposing censorship and restricting academic freedoms. So if Congress acts like the Turkish government, what are those damned academics whining about? This is the thing about US right-wingers that just leaves we Europeans scratching our heads: they seem to believe that government is a necessary evil that should interfere with the state as little as possible, unless that interference is based on right-wing ideology (for example, by tampering with climate research). Perhaps there’s nothing surprising about that view in itself, though; what’s weird is how blind those who hold it are to its inconsistency.
______________________________________________________________
A fundamental question for democracy is what to submit to the democratic process. The laws of physics should presumably be immune. But should public opinion decide which science gets studied, or at least funded? That’s the implication of an amendment to the US National Science Foundation’s 2013 spending bill approved by the House of Representatives in May. Proposed by Republican Jeff Flake, it would prevent the NSF from funding political science, for which it awarded about $11m in grants this year. The Senate may well squash the amendment, but it’s deeply concerning that it got so far. Flake was hoping for bigger cuts to the NSF’s overall budget, but had to settle for an easier target. He indulged in the familiar trick in the US Congress of finding research with apparently obscure or trivial titles and parading it as a waste of taxpayers’ money.
One can do this in any area of science. The particular vulnerability of the social sciences is that, being less cluttered with technical terminology, it seems superficially easier for the lay person to assess. As social scientist Duncan Watts of Microsoft Research in New York has pointed out, “everyone has experience being human, and so the vast majority of findings in social science coincide with something that we have either experienced or can imagine experiencing”. This means the Flakes of this world have little trouble proclaiming such findings obvious or insignificant.
Part of the blame must lie with the practice of labelling the social sciences ‘soft’, which too readily translates as woolly or soft-headed. Because they deal with systems that are highly complex, adaptive and not rigorously rule-bound, the social sciences are among the hardest of disciplines, both methodologically and intellectually. What is more, they suffer because their findings do sometimes seem obvious. Yet equally, the “obvious”, common-sense answer may prove quite false when subjected to scrutiny. There are countless examples, from economics to traffic planning, which is one reason why the social sciences probably unnerve some politicians used to making decisions based not on evidence but on intuition, wishful thinking and an eye on the polls.
What of the critics’ other arguments against publicly funded political science? They say it is more susceptible to political bias; in particular, more social scientists have Democratic leanings than Republican. The latter is true, but equally so for US academics generally. We can argue about why, but why single out political science? The charge of bias, meanwhile, is asserted rather than demonstrated.
And what has political science ever done for us? We don’t know why crime rates rise and fall or the effect of deterrents, we can’t solve the financial crisis or stop civil wars, we can’t agree on the state’s role in systems of justice or taxation. As Washington Post columnist Charles Lane argues, “the larger the social or political issue, the more difficult it is to illuminate definitively through the methods of ‘hard science’.” In part this just restates the fact that political science is among the most difficult of the sciences. To conclude that hard problems are better solved by not studying them is ludicrous. Should we slash the physics budget unless the dark-matter and dark-energy problems are solved? Lane’s statement falls for the very myth it wants to attack: that political science is ruled, like physics, by precise, unique, universal rules. In any case, we have little idea how successful political science has been, for politicians rarely pay much heed to evidence-based advice from the social sciences, unless of course the evidence suits them. And to constrain political scientists with utilitarian bean-counting is to miss what is mostly its point anyway. As the likes of John Rawls, Herbert Simon, Robert Axelrod, Kenneth Waltz and Karl Popper have shown, they have enriched political debate beyond measure.
The general notion that politicians should decide what is or is not worthy of research is perilous. Here, the proper function of democracy is to establish impartial bodies of experts and leave it to them. But Flake’s amendment does more than just traduce a culture of expertise. Among the research he selected for ridicule were studies of gender disparity in politics and models for international analysis of climate change: issues unpopular with right-wingers. In other words, his interference is not just about cost-cutting but has a political agenda. That he and his political allies feel threatened by evidence-based study of politics and society does not speak highly of their confidence in the objective case for their policies. Flake’s amendment is no different in principle to the ideological infringements of academic freedom in Turkey or Iran. It has nothing to do with democracy.
Thursday, July 12, 2012
Name that colour
I don’t read much popular science. That’s not a boast, as if to say that I’m above such things, but a guilty confession – I ought to read more, but am too slow a reader. That I’m missing out is being confirmed for me now as I finally get round to reading Guy Deutscher’s Through the Language Glass, which was shortlisted for the Royal Society Winton Prize last year. I knew this was a book I wanted to read, because it deals in some detail with the linguistics of colour terminology, which I looked into while writing Bright Earth. I was finally moved to get it after writing the piece below for the BBC Future site a month or so ago, and wanting to do more with this very interesting work. Whether I will be able to do that or not remains to be seen, but I’m glad it motivated me to get Deutscher’s book, because it is absolutely splendid. I remember Richard Holmes, chairing the book prize panel, questioning how helpful it really was for a book to advertise itself with Stephen Fry’s quote “Jaw-droppingly wonderful”, but His Fryness is quite correct. There’s another chapter – well, perhaps another section – that I would have added to Bright Earth, had I known some of this stuff: I wasn’t aware that Gladstone (that Gladstone) had postulated that the invention of new dyes and pigments actually stimulated the development of colour terminology itself, since it was only (he said) when people could abstract colours from their manifestations in natural objects that they figured they needed words for them. It’s not at all clear if this is true, but it is an intriguing idea, and not obviously nonsense.
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The artist Derek Jarman once met a friend on London’s Oxford Street and complimented him on his beautiful yellow coat. His friend replied that he’d bought it in Tokyo, where it wasn’t considered yellow at all, but green.
We don’t always agree about colour. Your red might be my pink or orange. Vietnamese and Korean don’t differentiate blue from green – leaves and sky are both coloured xanh in Vietnam. These overlaps and omissions can seem bizarre if they’re not part of your culture, but aren’t even visible if they are.
But we shouldn’t be too surprised by them. The visible spectrum isn’t like a paint colour chart, neatly separated into blocks of distinct hue, but is a continuum in which each colour blends into the next. Why should we expect to agree on where to set the boundaries, or on which colours are the most fundamental? The yellow band, say, is as wide as the cyan band, so why is yellow considered any more ‘basic’ than cyan?
A new study by physicist Vittorio Loreto at the University of Rome ‘La Sapienza’ and his colleagues argues that this naming and hierarchical ranking of colours isn’t, after all, arbitrary. The researchers say that there is a natural hierarchy of colour terms that arises from the interplay between our innate ability to distinguish one hue from another and the complex cultural negotiation out of which language itself appears.
In essence, their argument pertains to the entire edifice of language: how it is that we come to divide the world into specific categories of object or concept that we can all, within a given culture, agree on. Somehow we arrive at a language that distinguishes ‘cup’, ‘mug’, ‘glass’, ‘bowl’ and so on, without there being well-defined and mutually exclusive ‘natural’ criteria for these terms.
But the researchers have not chosen arbitrarily to focus on colour words. These have long been a focus for linguists, since they offer an ideal multicultural example of how we construct discrete categories from a world that lacks such inherent distinctions. Why don’t we have a hundred basic colour terms like ‘red’, ‘blue’ and so on, given that we can in principle tell apart at least this many hues (think back to those paint charts)? Or why not get by with just four or five colours?
In fact, some cultures do. The Dugerm Dani people of New Guinea, for example, have only two colour words, which can best be translated as ‘black’ and ‘white’, or light and dark. A few other pre-literate cultures recognize only three colours: black, white and red. Others have only a handful more.
The curious thing is that these simplified colour schemes are not capricious. For one thing, the named colours tend to match the ‘basic’ colours of more complex chromatic lexicons: red, yellow, blue and so on. What’s more, the colours seem to ‘arrive’ in a culture’s evolving vocabulary in a universal order: first black and white, then red, then green or yellow (followed by the other of this pair), then blue... So there is no known culture that recognizes, say, just red and blue: you don’t tend to ‘get’ blue unless you already have black, white, red, yellow and (perhaps) green.
This universal hierarchy of colour names was first observed [actually Deutscher shows that this wasn’t the first observation, but a rediscovery of an idea proposed in the nineteenth century by the German philologist Lazarus Geiger] by anthropologists Brent Berlin and Paul Kay in 1969, but there has been no explanation for it. This is what Loreto and colleagues now purport to offer. They use a computer model of language evolution in which new words arise as if through a kind of ‘game’ played repeatedly between pairs of individuals in a population: one the speaker, the other the hearer. The speaker might talk about a particular object – a colour say – using a word that the hearer doesn’t already possess. Will the hearer figure out what the speaker is referring to, and if so, will she then adopt the same word herself, jettisoning her own word for that object or recognizing a new sub-category of such objects? It is out of many interactions of this sort, which may or may not act to spread a word, that the population’s shared language arises.
For colour words, this negotiation is biased by our visual perception. We don’t see all parts of the visible spectrum equally: it is easier for us to see small changes in hue (that is, in the wavelength of the light entering our eyes) in some parts than in others. Loreto and colleagues impose this so-called “just noticeable difference function” of colour perception on the inter-agent interactions in their model. That’s what makes it more likely that some bands of the spectrum will begin to emerge as more ‘deserving’ than others of their own colour word. In other words, the population of agents will agree faster on a word associated with some hues than others.
This speed at which a consensus arises about a colour word with an agreed meaning specifies the resulting hierarchy of such words. And the order in which this happens in the computer experiments – red first, then violet, green/yellow, blue, orange and then cyan – is very close to that identified by Berlin and Kay. (Black and white, which aren’t themselves spectral colours, must be assumed at the outset as the crude distinction between dark and light.) Crucially, this sequence can’t be predicted purely from the “just noticeable difference function” – that is, from the physiology of colour vision – but arises only when it is fed into the ‘naming game’.
The match isn’t perfect, however. For one thing, violet doesn’t appear in Berlin and Kay’s hierarchy. Loreto and colleagues explain its emergence in their sequence as an artificial consequence of the way reddish hues crop up at both ends of the visible spectrum. And Berlin and Kay listed brown after blue. But brown isn’t a spectral colour – it’s a kind of dark yellow/orange, and so can be considered a variant shade of orange. Whether or not you accept those explanations for the discrepancies, this model of language evolution looks set to offer a good basis for exploring factors such as cultural differences and contingencies, like those Jarman discovered, and how language gets transmitted between cultures, often mutating in the process.
Paper: V. Loreto, A. Mukherjee & F. Tria, Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1113347109.
____________________________________________________
The artist Derek Jarman once met a friend on London’s Oxford Street and complimented him on his beautiful yellow coat. His friend replied that he’d bought it in Tokyo, where it wasn’t considered yellow at all, but green.
We don’t always agree about colour. Your red might be my pink or orange. Vietnamese and Korean don’t differentiate blue from green – leaves and sky are both coloured xanh in Vietnam. These overlaps and omissions can seem bizarre if they’re not part of your culture, but aren’t even visible if they are.
But we shouldn’t be too surprised by them. The visible spectrum isn’t like a paint colour chart, neatly separated into blocks of distinct hue, but is a continuum in which each colour blends into the next. Why should we expect to agree on where to set the boundaries, or on which colours are the most fundamental? The yellow band, say, is as wide as the cyan band, so why is yellow considered any more ‘basic’ than cyan?
A new study by physicist Vittorio Loreto at the University of Rome ‘La Sapienza’ and his colleagues argues that this naming and hierarchical ranking of colours isn’t, after all, arbitrary. The researchers say that there is a natural hierarchy of colour terms that arises from the interplay between our innate ability to distinguish one hue from another and the complex cultural negotiation out of which language itself appears.
In essence, their argument pertains to the entire edifice of language: how it is that we come to divide the world into specific categories of object or concept that we can all, within a given culture, agree on. Somehow we arrive at a language that distinguishes ‘cup’, ‘mug’, ‘glass’, ‘bowl’ and so on, without there being well-defined and mutually exclusive ‘natural’ criteria for these terms.
But the researchers have not chosen arbitrarily to focus on colour words. These have long been a focus for linguists, since they offer an ideal multicultural example of how we construct discrete categories from a world that lacks such inherent distinctions. Why don’t we have a hundred basic colour terms like ‘red’, ‘blue’ and so on, given that we can in principle tell apart at least this many hues (think back to those paint charts)? Or why not get by with just four or five colours?
In fact, some cultures do. The Dugerm Dani people of New Guinea, for example, have only two colour words, which can best be translated as ‘black’ and ‘white’, or light and dark. A few other pre-literate cultures recognize only three colours: black, white and red. Others have only a handful more.
The curious thing is that these simplified colour schemes are not capricious. For one thing, the named colours tend to match the ‘basic’ colours of more complex chromatic lexicons: red, yellow, blue and so on. What’s more, the colours seem to ‘arrive’ in a culture’s evolving vocabulary in a universal order: first black and white, then red, then green or yellow (followed by the other of this pair), then blue... So there is no known culture that recognizes, say, just red and blue: you don’t tend to ‘get’ blue unless you already have black, white, red, yellow and (perhaps) green.
This universal hierarchy of colour names was first observed [actually Deutscher shows that this wasn’t the first observation, but a rediscovery of an idea proposed in the nineteenth century by the German philologist Lazarus Geiger] by anthropologists Brent Berlin and Paul Kay in 1969, but there has been no explanation for it. This is what Loreto and colleagues now purport to offer. They use a computer model of language evolution in which new words arise as if through a kind of ‘game’ played repeatedly between pairs of individuals in a population: one the speaker, the other the hearer. The speaker might talk about a particular object – a colour say – using a word that the hearer doesn’t already possess. Will the hearer figure out what the speaker is referring to, and if so, will she then adopt the same word herself, jettisoning her own word for that object or recognizing a new sub-category of such objects? It is out of many interactions of this sort, which may or may not act to spread a word, that the population’s shared language arises.
For colour words, this negotiation is biased by our visual perception. We don’t see all parts of the visible spectrum equally: it is easier for us to see small changes in hue (that is, in the wavelength of the light entering our eyes) in some parts than in others. Loreto and colleagues impose this so-called “just noticeable difference function” of colour perception on the inter-agent interactions in their model. That’s what makes it more likely that some bands of the spectrum will begin to emerge as more ‘deserving’ than others of their own colour word. In other words, the population of agents will agree faster on a word associated with some hues than others.
This speed at which a consensus arises about a colour word with an agreed meaning specifies the resulting hierarchy of such words. And the order in which this happens in the computer experiments – red first, then violet, green/yellow, blue, orange and then cyan – is very close to that identified by Berlin and Kay. (Black and white, which aren’t themselves spectral colours, must be assumed at the outset as the crude distinction between dark and light.) Crucially, this sequence can’t be predicted purely from the “just noticeable difference function” – that is, from the physiology of colour vision – but arises only when it is fed into the ‘naming game’.
The match isn’t perfect, however. For one thing, violet doesn’t appear in Berlin and Kay’s hierarchy. Loreto and colleagues explain its emergence in their sequence as an artificial consequence of the way reddish hues crop up at both ends of the visible spectrum. And Berlin and Kay listed brown after blue. But brown isn’t a spectral colour – it’s a kind of dark yellow/orange, and so can be considered a variant shade of orange. Whether or not you accept those explanations for the discrepancies, this model of language evolution looks set to offer a good basis for exploring factors such as cultural differences and contingencies, like those Jarman discovered, and how language gets transmitted between cultures, often mutating in the process.
Paper: V. Loreto, A. Mukherjee & F. Tria, Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1113347109.
Monday, July 09, 2012
Who's in charge?
When I was asked to write a piece for the Guardian about the GSK scandal, my first thought was that it would be nice to know Richard Sykes’ response to the court decision, given that at least some of what GSK is being punished for happened under his watch. Lacking the time to hunt him down, I hoped someone else might do that subsequently. They have. The result is quite astonishing. As the Observer also reports this weekend, he tells us that “I have not had a chance to read the newspapers and have not a clue as to what is going on.”
Is this a joke? Sykes is a busy man, but we are being asked to believe that a law case that has been dragging on for years, involving extremely serious malpractice and resulting in a $3 bn settlement, against the company of which he was chairman during at least part of the relevant period, has somehow passed him by, so that he’s now in the same position as the rest of us in having to read all about it in the papers – and that he hasn’t quite got round to that yet. If there is any truth in this – and what do I know about how these things work? – that is all the more shocking. I really do struggle to imagine a situation in which Sykes has managed to shut out all knowledge of this case, has not been called upon during its course, and now lacks the motivation or the sense of obligation to get up to speed on it. And even if all this were somehow plausible, could he not even at least come up with the kind of blandishments offered by the current GSK CEO about having now put things right? The company has pleaded guilty, for goodness’ sake, it is not even as though he can refuse to comment on the question of guilt and culpability. So Murdoch knew nothing, Diamond knew nothing, now Sykes knew nothing. Is there actually anyone in charge of the world?
Is this a joke? Sykes is a busy man, but we are being asked to believe that a law case that has been dragging on for years, involving extremely serious malpractice and resulting in a $3 bn settlement, against the company of which he was chairman during at least part of the relevant period, has somehow passed him by, so that he’s now in the same position as the rest of us in having to read all about it in the papers – and that he hasn’t quite got round to that yet. If there is any truth in this – and what do I know about how these things work? – that is all the more shocking. I really do struggle to imagine a situation in which Sykes has managed to shut out all knowledge of this case, has not been called upon during its course, and now lacks the motivation or the sense of obligation to get up to speed on it. And even if all this were somehow plausible, could he not even at least come up with the kind of blandishments offered by the current GSK CEO about having now put things right? The company has pleaded guilty, for goodness’ sake, it is not even as though he can refuse to comment on the question of guilt and culpability. So Murdoch knew nothing, Diamond knew nothing, now Sykes knew nothing. Is there actually anyone in charge of the world?
Wednesday, July 04, 2012
The drugs aren't working
The Guardian seems to be keeping me busy at the moment. Here’s a piece published today about the GlaxoSmithKline scandal. It was apparently lightly edited for ‘legal’ reasons, but I’m not sure that Coca-Cola is really libelled here. Mind you, in Britain it’s hard to tell. Perhaps I’m safe so long as I don’t mention chiropractors.
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Perhaps the most shocking thing about the latest GlaxoSmithKline drug scandal is that malpractice among our overlords still has the ability to shock at all. Yet despite popular cynicism about doctors being in the pockets of the drug companies, there remains a sense that the people responsible for our healthcare are more principled and less corruptible than expenses-fiddling politicians, predatory bankers, amoral media magnates and venal police.
If this were a junk-food company lying about its noxious products, or a tobacco company pushing ciggies on schoolkids, we’d be outraged but hardly surprised. When a major pharmaceutical company is found to have been up to comparable misdemeanours – bad enough to warrant an astonishing $3 bn fine – it seems more of a betrayal of trust.
This is absurd, of course, but it shows how the healthcare industry benefits from its apparent proximity to the Hippocratic Oath. “Do more, feel better, live longer”, GSK purrs. How can we doubt a company that announces as its priorities “Improving the health and well-being of people around the world”, and “Being open and honest in everything we do”?
Now GSK admits that, in effect, it knowingly risked damaging the health of people around the world, and was secretive and fraudulent in some of what it did. Among other things, it promoted the antidepressant drug Paxil, approved only for adults, to people under 18; it marketed other drugs for non-approved uses; it suppressed scientific studies that didn’t suit (for example over the heart-attack risks of its diabetes drug Avandia), and over-hyped others that did; and it hosted outings for doctors in exotic locations and showered them with perks, knowing that this would boost prescriptions of its drugs.
I’m incensed not because this vindicates a conviction that pharmaceutical companies are staffed by profit-hungry liars and cheats, but precisely because I know that they are not: that so many of their scientists, and doubtless executives and marketers too, are decent folk motivated by the wish to benefit the world. We were bamboozled, but they have been degraded.
And it is precisely because Big Pharma really has benefitted the world, making life a great deal more tolerable and advancing scientific understanding, that the industry has acquired the social capital of public trust that GSK has been busy squandering.
But it’s time we accepted that it is a business like any other, and does not operate on a higher, more altruistic plane than Coca-Cola. It will do whatever it can get away with, whether that means redacting scientific reports, bribing academics and physicians, or pushing into ‘grey’ markets without proper consent or precaution. After all, this has happened countless times before. All the giants – AstraZeneca, Bristol-Myers Squibb, Merck, Eli Lilly – have been investigated for bribery. One of the most notorious episodes of misconduct involved Merck’s anti-inflammatory drug Vioxx, withdrawn in 2004 after the company persistently played down its risk of causing cardiovascular problems. History suggests that GSK CEO Andrew Witty’s assurances that lessons have been learnt are meaningless.
As with the banking scandals, GSK’s downfall is partly a failure of management – those at the top (some of the malparactice predates Witty’s incumbency) weren’t watching. It’s partly a failure of culture: the jollies and bribes came to seem normal, ethically unproblematic, even an entitlement, to both the donors and recipients.
And it’s partly a failure of regulation. The US Food and Drugs Administration has seemed at times not just toothless but actually collusive. Meanwhile, some American academics, having enjoyed Big Pharma’s kickbacks for decades, are now shrieking about the Physician Payments Sunshine Act, a part of the ObamaCare package which would make it mandatory for physicians to declare any perks or payments received from drug companies greater than $10, whether as speaker fees, theatre tickets or Hawaiian holidays. The protestors claim they will drown in bureaucracy. In reality they will be forced to reveal how much these things supplement their already healthy income. Harvard physician Thomas Stossel claimed in the Wall Street Journal that the backhanders don’t harm patients. The GSK ruling shows otherwise.
But the problems are still deeper. You don’t have to be an anti-capitalist to admit the inadequacies of relying solely on market forces for our drugs – not least for those that, being urgently needed mostly by poor countries, will never turn a profit. Incentives for Global Health, a non-profit organization at Yale University, have argued the case for a global, public-sector drug development agency, funded for example by a Tobin tax. In the unlikely event that our leaders should dare to demand such genuine recompense for the moral bankruptcy of the financial world, there would be few better uses for it – and freedom from the corrupting influence of the profit margin adds another argument to this already compelling case.
One way or another, some rethinking of how drugs are discovered, developed, sold and used is needed, before the noble art of medicine comes to look more like Mr Wormwood selling a dodgy motor for whatever he can get away with.
______________________________________________________________
Perhaps the most shocking thing about the latest GlaxoSmithKline drug scandal is that malpractice among our overlords still has the ability to shock at all. Yet despite popular cynicism about doctors being in the pockets of the drug companies, there remains a sense that the people responsible for our healthcare are more principled and less corruptible than expenses-fiddling politicians, predatory bankers, amoral media magnates and venal police.
If this were a junk-food company lying about its noxious products, or a tobacco company pushing ciggies on schoolkids, we’d be outraged but hardly surprised. When a major pharmaceutical company is found to have been up to comparable misdemeanours – bad enough to warrant an astonishing $3 bn fine – it seems more of a betrayal of trust.
This is absurd, of course, but it shows how the healthcare industry benefits from its apparent proximity to the Hippocratic Oath. “Do more, feel better, live longer”, GSK purrs. How can we doubt a company that announces as its priorities “Improving the health and well-being of people around the world”, and “Being open and honest in everything we do”?
Now GSK admits that, in effect, it knowingly risked damaging the health of people around the world, and was secretive and fraudulent in some of what it did. Among other things, it promoted the antidepressant drug Paxil, approved only for adults, to people under 18; it marketed other drugs for non-approved uses; it suppressed scientific studies that didn’t suit (for example over the heart-attack risks of its diabetes drug Avandia), and over-hyped others that did; and it hosted outings for doctors in exotic locations and showered them with perks, knowing that this would boost prescriptions of its drugs.
I’m incensed not because this vindicates a conviction that pharmaceutical companies are staffed by profit-hungry liars and cheats, but precisely because I know that they are not: that so many of their scientists, and doubtless executives and marketers too, are decent folk motivated by the wish to benefit the world. We were bamboozled, but they have been degraded.
And it is precisely because Big Pharma really has benefitted the world, making life a great deal more tolerable and advancing scientific understanding, that the industry has acquired the social capital of public trust that GSK has been busy squandering.
But it’s time we accepted that it is a business like any other, and does not operate on a higher, more altruistic plane than Coca-Cola. It will do whatever it can get away with, whether that means redacting scientific reports, bribing academics and physicians, or pushing into ‘grey’ markets without proper consent or precaution. After all, this has happened countless times before. All the giants – AstraZeneca, Bristol-Myers Squibb, Merck, Eli Lilly – have been investigated for bribery. One of the most notorious episodes of misconduct involved Merck’s anti-inflammatory drug Vioxx, withdrawn in 2004 after the company persistently played down its risk of causing cardiovascular problems. History suggests that GSK CEO Andrew Witty’s assurances that lessons have been learnt are meaningless.
As with the banking scandals, GSK’s downfall is partly a failure of management – those at the top (some of the malparactice predates Witty’s incumbency) weren’t watching. It’s partly a failure of culture: the jollies and bribes came to seem normal, ethically unproblematic, even an entitlement, to both the donors and recipients.
And it’s partly a failure of regulation. The US Food and Drugs Administration has seemed at times not just toothless but actually collusive. Meanwhile, some American academics, having enjoyed Big Pharma’s kickbacks for decades, are now shrieking about the Physician Payments Sunshine Act, a part of the ObamaCare package which would make it mandatory for physicians to declare any perks or payments received from drug companies greater than $10, whether as speaker fees, theatre tickets or Hawaiian holidays. The protestors claim they will drown in bureaucracy. In reality they will be forced to reveal how much these things supplement their already healthy income. Harvard physician Thomas Stossel claimed in the Wall Street Journal that the backhanders don’t harm patients. The GSK ruling shows otherwise.
But the problems are still deeper. You don’t have to be an anti-capitalist to admit the inadequacies of relying solely on market forces for our drugs – not least for those that, being urgently needed mostly by poor countries, will never turn a profit. Incentives for Global Health, a non-profit organization at Yale University, have argued the case for a global, public-sector drug development agency, funded for example by a Tobin tax. In the unlikely event that our leaders should dare to demand such genuine recompense for the moral bankruptcy of the financial world, there would be few better uses for it – and freedom from the corrupting influence of the profit margin adds another argument to this already compelling case.
One way or another, some rethinking of how drugs are discovered, developed, sold and used is needed, before the noble art of medicine comes to look more like Mr Wormwood selling a dodgy motor for whatever he can get away with.
Tuesday, July 03, 2012
Introducing Iamus
This story was in yesterday’s Guardian in slightly edited form. It was accompanied by a review of some of Iamus’s music by Tom Service, who was not terribly impressed. It’s a shame that Tom had only Hello World! to review, since that was an early piece by Iamus and so very much a prototype – things have moved on since then. I think his review was quite fair, but I had a sense that, knowing it was made by computer, he was looking out for the “computer-ness” in it. This bears on the final part of my story below, for which there was no room in the Guardian. I think one can detect a certain amount of ‘anti-computer prejudice’ in the Guardian comments thread too, though that is perhaps no stronger than the general ‘anti-modernist’ bias. I’d be interested to see what Tom Service makes of the CD when it appears later this year. I carry no torch for Iamus as a composer, but I must admit that I’m growing fond of it and certainly feel it is a significant achievement. Anyway, there will be more on this soon – I’m writing a different piece on the work for Nature, to appear in August.
_______________________________________________________
As soon as you see the title of Iamus’s composition “Transits – Into an Abyss”, you know it’s going to be challenging, modernist stuff. The strings pile up discords, now spooky, now ominous. But if your tastes run to Bartók, Ligeti and Penderecki, you might like it. At least you have to admit that this bloke knows what he’s doing.
But this bloke doesn’t know anything at all. Iamus is a computer programme. Until the London Symphony Orchestra was handed the score, no human had intervened in preparing this music.
“When we tell people that, they think it’s a trick”, says Francisco Vico, leader of the team at the University of Malaga who devised Iamus. “Some say they simply don’t believe us, others say it’s just creepy.” He anticipates that when Iamus’s debut CD is released in September, performed by top-shelf musicians including the LSO, it is going to disturb a lot of folk.
You can get a taste of Iamus’s oeuvre before then, because on 2 July some of Iamus’s compositions will be performed and streamed live from Malaga. The event is being staged to mark the 100th anniversary of the birth of Alan Turing, the man credited with more or less inventing the concept of the computer. It was Turing who devised the test to distinguish humans from artificial intelligence made famous by the opening sequence of Ridley Scott’s Blade Runner. And the performance will itself be a kind of Turing test: you can ask yourself whether you could tell, if you didn’t know, that this music was made by machine.
Iamus composes by mutating very simple starting material in a manner analogous to biological evolution. The evolving compositions each have a kind of musical core, a ‘genome’, which gradually becomes more complex. “Iamus generates an initial population of compositions automatically”, Vico explains, “but their genomes are so simple that they barely develop into a handful of notes, lasting just a few seconds. As evolution proceeds, mutations alter the content and size of this primordial genetic material, and we get longer and more elaborated pieces.” All the researchers specify at the outset is the rough length of the piece and the instruments it will use.
“A single genome can encode many melodies”, explains composer Gustavo Díaz-Jerez of the Conservatory of the Basque Country in San Sebastian, who has collaborated with the Malaga team since the outset and is the pianist on the new recordings. “You find this same idea of a genome in the Western musical canon – that’s why the music makes sense.”
The computer doesn’t impose any particular aesthetic. Although most of its serious pieces are in a modern classical style, it can compose in other genres too, and for any set of instruments. The ‘darwinian’ composition process also lends itself to producing new variations of well-known pieces [PB: I’ve been sent some great variants of the Nokia ringtone] or merging two or more existing compositions to produce offspring – musical sex, you might say.
Using computers and algorithms – automated systems of rules – to make music has a long history. The Greek composer Iannis Xenakis did it in the 1960s, and in the following decade two Swedish composers devised an algorithm for creating nursery-rhyme melodies in the style of Swedish composer Alice Tegnér. In the 1980s computer scientist Kemal Ebcioglu created a program that harmonised chorales in the style of Bach.
As artificial intelligence and machine learning became more sophisticated, so did the possibilities for machine music: now computers could infer rules and guidelines from real musical examples, rather than being fed them to begin with. Computer scientist John ‘Al’ Biles devised an algorithm called GenJam that learns to improvise jazz. A trumpeter himself, Biles performs live alongside GenJam under the name the Al Biles Virtual Quintet, but admits that the algorithm is a rather indifferent player. The same is true of GenBebop, devised by cognitive scientists Lee Spector and Adam Alpern, which improvises solos in the style of Charlie Parker by ‘listening’ to him and iterating its own efforts under the ultimately less-than-discerning eye of an automated internal critic.
One of the most persuasive systems was the Continuator, devised by François Pachet at Sony’s Computer Science Laboratory in Paris. In a Turing test where the Continuator traded licks with an improvising human pianist, expert listeners were mostly unable to guess whether it was the human or the computer playing.
But these efforts still haven’t shown that a computer can make tolerable music from scratch. One of the best known attempts is ‘Emily Howell’, a programme created by music professor David Cope. Yet Howell’s bland, arpeggiated compositions sound like a technically skilled child trying to ape Beethoven or Bach, or like Michael Nyman on a bad day: fine for elevators but not for the concert hall.
This is why Iamus – named after the mythical son of Apollo who could understand the language of birds – is different. This seems to be the first time that music composed purely by computer has been deemed good enough for top-class performers to play it. Díaz-Jerez admits that the LSO were “a little bit sceptical at the beginning, but were very surprised” by the quality of what they were being asked to play. The soprano Celia Alcedo, he says, “couldn’t believe the expressiveness of some of the lines” she was given to sing.
Lennox Mackenzie, the LSO’s chairman, had mixed feelings about the orchestral pieces. “I felt it was like a wall of sound”, he says. “If you put a colour to it, this music was grey. It went nowhere. It was too dense and massive, no instrument stuck out at any point. But at the end of it, I thought it was quite epic.”
“The other thing that struck me”, Mackenzie adds, “was that it was festooned with expression marks, which just seemed arbitrary and meaningless. My normal inclination is to delve into music and find out what it’s all about. But here I don’t think I’d find anything.” But he’s far from discouraging. “I didn’t feel antipathy towards it. It does have something. They should keep trying, I’d say.”
What is slightly disconcerting is that Iamus can produce this stuff endlessly: thousands of pieces, all valid and musically plausible, all fully notated and ready to play, faultless from a technical point of view, and “many of them great”, according to Díaz-Jerez. Such profligacy feels improper: if it’s that easy, can the music really be any good? Yet Díaz-Jerez thinks that the pieces are often better in some respects than those produced by some avant-garde composers, which might revel in their own internal logic but are virtually impossible to play. And crucially, different people have different favourites – it’s not as though the programme just occasionally gets lucky and turns out something good.
How does a performer interpret these pieces, given that there’s no “intention” of the composer to look for? “Suppose I found a score in a library without knowing who wrote it”, says Díaz-Jerez. “I approach these pieces as I would that one – by analysing the score to see how it works.” In that respect, he sees no difference from deducing the structure of an intricate Bach fugue.
You can compare it with computer chess, says philosopher of music Stephen Davies of the University of Auckland in New Zealand. “People said computers wouldn't be able to show the same original thinking, as opposed to crunching random calculations. But now it’s hard to see the difference between people and computers with respect to creativity in chess. Music too is rule-governed in a way that should make it easily simulated.”
However, Iamus might face deeply ingrained prejudice. Brain-scanning studies by neuroscientists Stefan Koelsch and Nikolaus Steinbeis have shown that the same piece of music played to listeners elicits activity in the parts of the brain responsible for ascribing intentions to other humans if they are told that it was composed by a human but not if they are told it was computer-generated. In other words, it matters to our perceptions of expressiveness how we think the music was made. Perhaps Iamus really will need to be marketed as a pseudo-human to be taken seriously.
_______________________________________________________
As soon as you see the title of Iamus’s composition “Transits – Into an Abyss”, you know it’s going to be challenging, modernist stuff. The strings pile up discords, now spooky, now ominous. But if your tastes run to Bartók, Ligeti and Penderecki, you might like it. At least you have to admit that this bloke knows what he’s doing.
But this bloke doesn’t know anything at all. Iamus is a computer programme. Until the London Symphony Orchestra was handed the score, no human had intervened in preparing this music.
“When we tell people that, they think it’s a trick”, says Francisco Vico, leader of the team at the University of Malaga who devised Iamus. “Some say they simply don’t believe us, others say it’s just creepy.” He anticipates that when Iamus’s debut CD is released in September, performed by top-shelf musicians including the LSO, it is going to disturb a lot of folk.
You can get a taste of Iamus’s oeuvre before then, because on 2 July some of Iamus’s compositions will be performed and streamed live from Malaga. The event is being staged to mark the 100th anniversary of the birth of Alan Turing, the man credited with more or less inventing the concept of the computer. It was Turing who devised the test to distinguish humans from artificial intelligence made famous by the opening sequence of Ridley Scott’s Blade Runner. And the performance will itself be a kind of Turing test: you can ask yourself whether you could tell, if you didn’t know, that this music was made by machine.
Iamus composes by mutating very simple starting material in a manner analogous to biological evolution. The evolving compositions each have a kind of musical core, a ‘genome’, which gradually becomes more complex. “Iamus generates an initial population of compositions automatically”, Vico explains, “but their genomes are so simple that they barely develop into a handful of notes, lasting just a few seconds. As evolution proceeds, mutations alter the content and size of this primordial genetic material, and we get longer and more elaborated pieces.” All the researchers specify at the outset is the rough length of the piece and the instruments it will use.
“A single genome can encode many melodies”, explains composer Gustavo Díaz-Jerez of the Conservatory of the Basque Country in San Sebastian, who has collaborated with the Malaga team since the outset and is the pianist on the new recordings. “You find this same idea of a genome in the Western musical canon – that’s why the music makes sense.”
The computer doesn’t impose any particular aesthetic. Although most of its serious pieces are in a modern classical style, it can compose in other genres too, and for any set of instruments. The ‘darwinian’ composition process also lends itself to producing new variations of well-known pieces [PB: I’ve been sent some great variants of the Nokia ringtone] or merging two or more existing compositions to produce offspring – musical sex, you might say.
Using computers and algorithms – automated systems of rules – to make music has a long history. The Greek composer Iannis Xenakis did it in the 1960s, and in the following decade two Swedish composers devised an algorithm for creating nursery-rhyme melodies in the style of Swedish composer Alice Tegnér. In the 1980s computer scientist Kemal Ebcioglu created a program that harmonised chorales in the style of Bach.
As artificial intelligence and machine learning became more sophisticated, so did the possibilities for machine music: now computers could infer rules and guidelines from real musical examples, rather than being fed them to begin with. Computer scientist John ‘Al’ Biles devised an algorithm called GenJam that learns to improvise jazz. A trumpeter himself, Biles performs live alongside GenJam under the name the Al Biles Virtual Quintet, but admits that the algorithm is a rather indifferent player. The same is true of GenBebop, devised by cognitive scientists Lee Spector and Adam Alpern, which improvises solos in the style of Charlie Parker by ‘listening’ to him and iterating its own efforts under the ultimately less-than-discerning eye of an automated internal critic.
One of the most persuasive systems was the Continuator, devised by François Pachet at Sony’s Computer Science Laboratory in Paris. In a Turing test where the Continuator traded licks with an improvising human pianist, expert listeners were mostly unable to guess whether it was the human or the computer playing.
But these efforts still haven’t shown that a computer can make tolerable music from scratch. One of the best known attempts is ‘Emily Howell’, a programme created by music professor David Cope. Yet Howell’s bland, arpeggiated compositions sound like a technically skilled child trying to ape Beethoven or Bach, or like Michael Nyman on a bad day: fine for elevators but not for the concert hall.
This is why Iamus – named after the mythical son of Apollo who could understand the language of birds – is different. This seems to be the first time that music composed purely by computer has been deemed good enough for top-class performers to play it. Díaz-Jerez admits that the LSO were “a little bit sceptical at the beginning, but were very surprised” by the quality of what they were being asked to play. The soprano Celia Alcedo, he says, “couldn’t believe the expressiveness of some of the lines” she was given to sing.
Lennox Mackenzie, the LSO’s chairman, had mixed feelings about the orchestral pieces. “I felt it was like a wall of sound”, he says. “If you put a colour to it, this music was grey. It went nowhere. It was too dense and massive, no instrument stuck out at any point. But at the end of it, I thought it was quite epic.”
“The other thing that struck me”, Mackenzie adds, “was that it was festooned with expression marks, which just seemed arbitrary and meaningless. My normal inclination is to delve into music and find out what it’s all about. But here I don’t think I’d find anything.” But he’s far from discouraging. “I didn’t feel antipathy towards it. It does have something. They should keep trying, I’d say.”
What is slightly disconcerting is that Iamus can produce this stuff endlessly: thousands of pieces, all valid and musically plausible, all fully notated and ready to play, faultless from a technical point of view, and “many of them great”, according to Díaz-Jerez. Such profligacy feels improper: if it’s that easy, can the music really be any good? Yet Díaz-Jerez thinks that the pieces are often better in some respects than those produced by some avant-garde composers, which might revel in their own internal logic but are virtually impossible to play. And crucially, different people have different favourites – it’s not as though the programme just occasionally gets lucky and turns out something good.
How does a performer interpret these pieces, given that there’s no “intention” of the composer to look for? “Suppose I found a score in a library without knowing who wrote it”, says Díaz-Jerez. “I approach these pieces as I would that one – by analysing the score to see how it works.” In that respect, he sees no difference from deducing the structure of an intricate Bach fugue.
You can compare it with computer chess, says philosopher of music Stephen Davies of the University of Auckland in New Zealand. “People said computers wouldn't be able to show the same original thinking, as opposed to crunching random calculations. But now it’s hard to see the difference between people and computers with respect to creativity in chess. Music too is rule-governed in a way that should make it easily simulated.”
However, Iamus might face deeply ingrained prejudice. Brain-scanning studies by neuroscientists Stefan Koelsch and Nikolaus Steinbeis have shown that the same piece of music played to listeners elicits activity in the parts of the brain responsible for ascribing intentions to other humans if they are told that it was composed by a human but not if they are told it was computer-generated. In other words, it matters to our perceptions of expressiveness how we think the music was made. Perhaps Iamus really will need to be marketed as a pseudo-human to be taken seriously.
Thursday, June 28, 2012
Want to win £1000?
I have a piece in the Guardian online on the Mpemba effect and the RSC’s £1000 prize for explaining it. The article is largely unchanged from what I wrote, but here it is anyway.
The aim here was to stimulate suggestions from readers of how this thing can be explained – or even if there’s a real effect to be explained, though few seem to question that. (One does so with amusing literalism, thinking it implies that hot water will always freeze first whatever the temperature difference. All the same, this reinforces Charles Knight’s point that the phenomenon is too ill defined.) I like too the cute popular notion of “heat loss momentum” – check out Newton’s cooling law, please.
But I’m certainly not going to mock the many confused or just plain wrong suggestions put forward, since the whole point of the exercise is to get people engaged, not to laugh at their errors. However, I can’t help being struck by the inevitable one or two who say, apparently in all seriousness, that the answer is just obvious and everyone but them has been too stupid so far to see it. One chap dispenses with all of the additional ‘mysteries’ in the article this way too. Why can all arms of a snowflake sometimes be identical? “The symmetry comes from the initial nucleation of the crystal. It starts symmetrically and keeps growing symmetrically. And computer simulations have shown this.” I can only assume he/she (probably he) has seen some simulated flakes and failed to read the warning that symmetry was imposed on all six arms. He certainly didn’t think it worth bothering to check out the link to Ken Libbrecht’s page, which makes it clear that (as I said in the piece) the side-branches are, according to the standard theory of dendritic growth, amplified randomness. So the entire form of any given flake is somehow inherent in its initial nucleus? Please. I couldn’t help smiling too at the apparent belief of some readers that the Brazil-nut effect was actually discovered in muesli (leading to a discussion on how muesli gets packaged). Anyway, the comments thread provides a nice little cross-section of how folk think about science. And, I think, somewhat encouraging at that, despite the misconceptions.
________________________________________________________________
If you can explain, before the end of July, why hot water freezes faster than cold, you could bag £1000. That’s what the Royal Society of Chemistry (RSC) is offering for “the most creative explanation” of this phenomenon, known as the Mpemba effect. They say that submissions should be “eye-catching, arresting and scientifically sound”, and may use any media, including film.
At the end of the month the problem will also be put to an international summer school for postgraduate science students called Hermes 2012, convened at Cumberland Lodge in Windsor Great Park to present research in materials science and imbue the participants with skills in science communication. The event, organized by Imperial College and sponsored by the RSC, is timed to coincide with the opening of the Olympic Games as a kind of scientific Olympiad. A presentation of the top entries to the RSC’s competition, alongside the efforts of the meeting attendees, will form a highlight of the event on 30 July.
All good fun – except that the Mpemba effect seems at first encounter to be scientific nonsense. Let’s have that again: “why hot water freezes faster than cold”. How can that be? In order to freeze, hot water has to lose more heat than cold, so why would that happen faster? Even if the cooling of hot water somehow catches up with that of the colder water, why should it then overtake, if the two have at that point the same temperature?
Yet this effect has been attested since antiquity. Aristotle mentions it, as do two of the fathers of modern science, Francis Bacon and René Descartes in the seventeenth century. The effect is today named after a Tanzanian schoolboy, Erasto Mpemba, who was set the project of making ice cream from milk in the 1960s. The pupils were supposed to boil their milk, let it cool, then put it in the fridge to freeze. But Mpemba worried about losing his space in the fridge, and so put in the milk while it was still hot. It froze faster than the others.
When Mpemba learnt a few years later that this seemed to contradict the theory of heat transfer devised by Isaac Newton, he recalled his experiment and asked his teacher to explain it – only to receive a mocking reply. Undeterred, he carried out his own experiments, and asked a visiting university professor from Dar es Salaam, D. G. Osborne, what was going on. Osborne was more open-minded – he asked his technician to repeat the experiment, and found the same result. In 1969 Osborne published the result in a physics education journal. Coincidentally, that same year a physicist in Canada described the same result, saying that it was already folk wisdom in Canada that a car should be washed with cold water in winter, because hot water froze more quickly.
Yet no one really knows if the Mpemba effect is real. You’d think it should be easy to check, but it isn’t. Ice specialist Charles Knight of the National Center for Atmospheric Research in Boulder, Colorado, says that the claim that “hot water freezes faster than cold” is so ill-defined that it’s virtually meaningless. Does it mean when ice first starts to appear, or when the last bit of water is frozen? Both are hard to observe in any case. And there are so many things you could vary: the amount of water, the shape of the containers, the initial temperature difference, the rate of cooling… Do you use tap water, distilled water, de-aerated water, filtered water? Freezing is notoriously capricious: it can be triggered by tiny scratches on the sides of the flask or suspended dust in the liquid, so it’s almost impossible to make truly identical samples differing only in their starting temperature. For this reason, even two samples starting at the same temperature typically freeze at different times. If such ‘seeding’ sites are excluded, water can be ‘supercooled’ well below freezing point without turning to ice – but here experiments are conflicting. Some find that initially hotter water can be supercooled further, others that it can be supercooled less before it freezes.
There is one trivial explanation for Mpemba’s observations. Hot water would evaporate faster, so if there was no top on the flasks then there could have been less liquid left to freeze – so it would happen faster. Tiny gas bubbles in solution could also act as seeds for ice crystals to form – and hot water holds less dissolved gas than cold.
All this means that a single experiment won’t tell you much – you’ll probably have to do lots, with many different conditions, to figure out what’s important and what isn’t. And you’ve only got a month, so get cracking.
Other mysteries to solve at home:
1. Why do the Brazil nuts gather at the top of the muesli? There’s no complete consensus on the cause of the so-called Brazil nut effect, but current explanations include:
- shaken grains in a tall box circulate like convection currents while the big bits get trapped at the top, excluded from the narrow descending current at the sides
- little landslides in the void that opens up temporarily under a big grain as it is shaken upwards ratchet it ever higher
- it's all to do with the effect of air between the grains
The problem is made harder by the fact that, under some conditions, the big grains can sink to the bottom instead – the ‘reverse Brazil nut effect’.
2. Does the water in a bathtub spiral down the plughole in opposite directions in the Northern and Southern Hemisphere? Cyclones rotate counterclockwise in the north and clockwise in the south, a consequence of the Earth’s rotation called the Coriolis effect. But is the effect too weak to govern a plughole vortex? In 1962 an American engineer named Ascher Shapiro claimed that he consistently observed counterclockwise plughole vortices in his lab, but this result has never been verified. The problem is that it’s really hard to rid a bathtub of water of any residual currents that could bias the outcome.
3. Why are all six arms of a snowflake sometimes (but not always) identical? How does one arm know what the other is doing? The standard theory of snowflake formation explains the ornate branching patterns as amplifications of random bumps on the sides of needle-like ice crystals. But if they’re random, how can one arm look like another? One suggestion is that they listen to one another: acoustic vibrations in the ice crystal set up standing-wave patterns that dictate the shape. But this doesn’t seem to work. Most snowflakes aren’t actually as symmetrical as is often supposed – but the fact that some are is still unexplained.
The aim here was to stimulate suggestions from readers of how this thing can be explained – or even if there’s a real effect to be explained, though few seem to question that. (One does so with amusing literalism, thinking it implies that hot water will always freeze first whatever the temperature difference. All the same, this reinforces Charles Knight’s point that the phenomenon is too ill defined.) I like too the cute popular notion of “heat loss momentum” – check out Newton’s cooling law, please.
But I’m certainly not going to mock the many confused or just plain wrong suggestions put forward, since the whole point of the exercise is to get people engaged, not to laugh at their errors. However, I can’t help being struck by the inevitable one or two who say, apparently in all seriousness, that the answer is just obvious and everyone but them has been too stupid so far to see it. One chap dispenses with all of the additional ‘mysteries’ in the article this way too. Why can all arms of a snowflake sometimes be identical? “The symmetry comes from the initial nucleation of the crystal. It starts symmetrically and keeps growing symmetrically. And computer simulations have shown this.” I can only assume he/she (probably he) has seen some simulated flakes and failed to read the warning that symmetry was imposed on all six arms. He certainly didn’t think it worth bothering to check out the link to Ken Libbrecht’s page, which makes it clear that (as I said in the piece) the side-branches are, according to the standard theory of dendritic growth, amplified randomness. So the entire form of any given flake is somehow inherent in its initial nucleus? Please. I couldn’t help smiling too at the apparent belief of some readers that the Brazil-nut effect was actually discovered in muesli (leading to a discussion on how muesli gets packaged). Anyway, the comments thread provides a nice little cross-section of how folk think about science. And, I think, somewhat encouraging at that, despite the misconceptions.
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If you can explain, before the end of July, why hot water freezes faster than cold, you could bag £1000. That’s what the Royal Society of Chemistry (RSC) is offering for “the most creative explanation” of this phenomenon, known as the Mpemba effect. They say that submissions should be “eye-catching, arresting and scientifically sound”, and may use any media, including film.
At the end of the month the problem will also be put to an international summer school for postgraduate science students called Hermes 2012, convened at Cumberland Lodge in Windsor Great Park to present research in materials science and imbue the participants with skills in science communication. The event, organized by Imperial College and sponsored by the RSC, is timed to coincide with the opening of the Olympic Games as a kind of scientific Olympiad. A presentation of the top entries to the RSC’s competition, alongside the efforts of the meeting attendees, will form a highlight of the event on 30 July.
All good fun – except that the Mpemba effect seems at first encounter to be scientific nonsense. Let’s have that again: “why hot water freezes faster than cold”. How can that be? In order to freeze, hot water has to lose more heat than cold, so why would that happen faster? Even if the cooling of hot water somehow catches up with that of the colder water, why should it then overtake, if the two have at that point the same temperature?
Yet this effect has been attested since antiquity. Aristotle mentions it, as do two of the fathers of modern science, Francis Bacon and René Descartes in the seventeenth century. The effect is today named after a Tanzanian schoolboy, Erasto Mpemba, who was set the project of making ice cream from milk in the 1960s. The pupils were supposed to boil their milk, let it cool, then put it in the fridge to freeze. But Mpemba worried about losing his space in the fridge, and so put in the milk while it was still hot. It froze faster than the others.
When Mpemba learnt a few years later that this seemed to contradict the theory of heat transfer devised by Isaac Newton, he recalled his experiment and asked his teacher to explain it – only to receive a mocking reply. Undeterred, he carried out his own experiments, and asked a visiting university professor from Dar es Salaam, D. G. Osborne, what was going on. Osborne was more open-minded – he asked his technician to repeat the experiment, and found the same result. In 1969 Osborne published the result in a physics education journal. Coincidentally, that same year a physicist in Canada described the same result, saying that it was already folk wisdom in Canada that a car should be washed with cold water in winter, because hot water froze more quickly.
Yet no one really knows if the Mpemba effect is real. You’d think it should be easy to check, but it isn’t. Ice specialist Charles Knight of the National Center for Atmospheric Research in Boulder, Colorado, says that the claim that “hot water freezes faster than cold” is so ill-defined that it’s virtually meaningless. Does it mean when ice first starts to appear, or when the last bit of water is frozen? Both are hard to observe in any case. And there are so many things you could vary: the amount of water, the shape of the containers, the initial temperature difference, the rate of cooling… Do you use tap water, distilled water, de-aerated water, filtered water? Freezing is notoriously capricious: it can be triggered by tiny scratches on the sides of the flask or suspended dust in the liquid, so it’s almost impossible to make truly identical samples differing only in their starting temperature. For this reason, even two samples starting at the same temperature typically freeze at different times. If such ‘seeding’ sites are excluded, water can be ‘supercooled’ well below freezing point without turning to ice – but here experiments are conflicting. Some find that initially hotter water can be supercooled further, others that it can be supercooled less before it freezes.
There is one trivial explanation for Mpemba’s observations. Hot water would evaporate faster, so if there was no top on the flasks then there could have been less liquid left to freeze – so it would happen faster. Tiny gas bubbles in solution could also act as seeds for ice crystals to form – and hot water holds less dissolved gas than cold.
All this means that a single experiment won’t tell you much – you’ll probably have to do lots, with many different conditions, to figure out what’s important and what isn’t. And you’ve only got a month, so get cracking.
Other mysteries to solve at home:
1. Why do the Brazil nuts gather at the top of the muesli? There’s no complete consensus on the cause of the so-called Brazil nut effect, but current explanations include:
- shaken grains in a tall box circulate like convection currents while the big bits get trapped at the top, excluded from the narrow descending current at the sides
- little landslides in the void that opens up temporarily under a big grain as it is shaken upwards ratchet it ever higher
- it's all to do with the effect of air between the grains
The problem is made harder by the fact that, under some conditions, the big grains can sink to the bottom instead – the ‘reverse Brazil nut effect’.
2. Does the water in a bathtub spiral down the plughole in opposite directions in the Northern and Southern Hemisphere? Cyclones rotate counterclockwise in the north and clockwise in the south, a consequence of the Earth’s rotation called the Coriolis effect. But is the effect too weak to govern a plughole vortex? In 1962 an American engineer named Ascher Shapiro claimed that he consistently observed counterclockwise plughole vortices in his lab, but this result has never been verified. The problem is that it’s really hard to rid a bathtub of water of any residual currents that could bias the outcome.
3. Why are all six arms of a snowflake sometimes (but not always) identical? How does one arm know what the other is doing? The standard theory of snowflake formation explains the ornate branching patterns as amplifications of random bumps on the sides of needle-like ice crystals. But if they’re random, how can one arm look like another? One suggestion is that they listen to one another: acoustic vibrations in the ice crystal set up standing-wave patterns that dictate the shape. But this doesn’t seem to work. Most snowflakes aren’t actually as symmetrical as is often supposed – but the fact that some are is still unexplained.
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