Ceasefire Magazine » Science

Science Quantum Computing: The end of privacy?

hich — Wed, 20 Apr 2011 23:00:29 +0000

By Sebastian Meznaric

Computing with machines has had a very long history, stretching back to antiquity and the use of abacuses. More recently, in the first half of the 20th century, electric computers started an exponential explosion of computing power that has continued to this very day.

Thus, for the past few decades computing power has been doubling every 18 months, meaning a quadrupling every 36 months and so on, an exponential increase better known as Moore’s law, named after Gordon Moore, a co-founder of microchip giant Intel, who first predicted it.

All modern computers work according to the same model, known as the Turing machine. A Turing machine is a mathematical model describing every computation that can be done on a typical computer. For this reason it is also known as the ‘Universal Computer’.

However, it is precisely on this point that quantum computers break with tradition. Indeed, they can execute algorithms that can not be run on any Turing machine. Many of these algorithms render problems that would take a ‘normal’ computer a very long time to run much easier to solve.

The way quantum computers work is by making use of small particles that obey the laws of quantum mechanics and can thus be in multiple states at the same time. Whereas today’s computers work with transistors that can only adopt the binary values of 0 or 1 (also known as ‘bits’), quantum computers can be in both ‘state 0′ and ‘state 1′ at the same time and to different extents. If we had multiple bits, then on a classical (normal) computer they could, for example, be in a state such as 01101110. On a quantum computer, however, they could be in a superposition of states, 011100011 and 011110010 for instance. Multiple bits being combined in such ‘superpositions’ is a concept also known as ‘entanglement’.

These superpositions are essential to new types of algorithms. As an example, consider that you are given a table with a large number n of entries and you needed to find a particular entry. You would have to go through each of these entries until you find the one you are looking for – and this is exactly what a classical computer would do. It would therefore take an average of n/2 steps to find a particular entry.

A quantum computer, however, would construct a superposition of all entries in the database, and then operate on these elements. It would then perform a measurement which, after several runs, would give as the result the searched-for element within the database. It would on average only require √n steps to find the result. This algorithm is known as the ‘Grover’s search’ algorithm. No classical algorithm can achieve this.

However, quantum computers could also be dangerous. Most modern encryption methods rely on the principle that factoring large numbers is very difficult. Thus, if one could factor numbers quickly communications that are considered secure today would suddenly cease to be so. Yet this is exactly what quantum computers are able to do.

Communication between you and your bank, between intelligence agencies or activist networks, and pretty much any hitherto secured confidential information would suddenly become accessible to anyone with a quantum computer.

The implications of this are immense, not to mention that the time it will take us to transition to a new kind of encryption would be long enough that for months, possibly years, there would be essentially no secure method of communication anywhere in the world. Period.

In fact, it now seems we may be on the brink of exactly such an encryption-beating machine. The major obstacle so far in constructing a quantum computer has been that quantum states are very, very delicate. Any form of interaction with the environment can disturb them and make them unusable. To make matters worse, the more of these states we have together, the quicker they become unusable – a phenomenon known as ‘decoherence’ (‘coherence’ is another name for superposition).

A team in California has now announced, however, that they may be able to turn some of these interactions off almost completely. UCSB’s John Martinis said this to BBC News: “It’s a problem I’ve been thinking about for three or four years now, how to turn off the interactions. Now we’ve solved it, and that’s great – but there’s many other things we have to do.” The solution to this problem had evaded researchers for more than a decade, and so, unsurprisingly, the research effort conducted by Prof Martinis’s group has been named as the breakthrough of the year by the journal Science.

If their method of turning these interactions off really turns out to be as comprehensive as it is currently believed then we may finally be on the verge of constructing these powerful computers. However, don’t go throwing your Intel or AMD away just yet. They are still likely to be useful in conjunction with quantum devices for many years to come.

Sebastian Meznaric is a theoretical physicist and doctoral reseracher at the University of Oxford. His areas of interests include the study of information theory in quantum mechanics. He is also a keen observer of politics and current affairs.

For those who would like more details, the original article can be found here (Unfortunately, access to the journal is required to view it)

Science Man Vs Machine: Whose intelligence is it anyway?

hich — Sat, 12 Mar 2011 23:00:25 +0000

Sebastian Meznaric

Many tasks that are very simple for most humans to do are extremely difficult to solve for a computer. Conversely, quite a few that appear intractable for most of us happen to be very easy for machines – a paradox known as Moravec’s paradox.

For instance, computers are able to integrate, differentiate, solve differential equations, memorize large amounts of data, all in a fraction of the time that any human genius would need just to get started on any of these tasks. On the other hand, despite decades of concerted efforts by computer scientists, machines are still unable to, for instance, speak and/or understand human communication.

The machine understanding of a natural language has long been the holy grail of research into Artificial Intelligence. All the way back in the 1950s, Alan Turing proposed the now famous ‘Turing test’ to determine whether a computer is as intelligent as a human. The test proceeds by having the computer communicate with a number of human ‘judges’. These judges are not told whether they are ‘talking’ (essentially via text-based exchanges) to a computer or to a fellow human. If, at the end of the test, the judges are unable to reliably determine whether they had been talking to a machine or a human, then the machine is deemed to be as “intelligent” as a human.

Solving the problem of “understanding” human/natural language would enable machines to undertake most of the tasks that are performed with relative ease by humans, from translation and writing, to summarising texts and conducting interviews. They could combine their natural strengths (memorisation and complex problem solving) with the understanding of language to create a virtual genius. It would enable us to build a computer not unlike the one in Star Trek, where Captain Picard asks the computer questions as if to a human, which the machine duly, and swiftly, answers.

It is in an effort to produce computer systems like this that IBM has ventured to create a machine that could compete at Jeopardy, the popular TV game. Jeopardy, a long-standing staple of American TV quiz shows, involves questions touching on various topics ranging from pop culture and sport to history, literature and science. Its unique feature, however, is that contestants are presented with an ‘answer’ to which they must find a corresponding question, in other words, the answers are questions. For instance, the presenter might say “He is the 44th President of the United States”, to which a contestant should answer “Who is Barack Obama?”.

Over a course of several years, IBM built a computer consisting of 2880 Power7 processors. They named it ‘Watson’, after the founder and first president of the company. Having made the machine read the human equivalent of 200 million pages of text, they decided to pit it against former human champions. One of them was Brad Rutter, Jeopardy’s biggest ever money winner. The other contestant was Ken Jennings, who had previously won 75 consecutive matches and $2.5 million. At the end of the tournament, Watson came ahead with about double the score of his two rivals.

So what does this mean for the future of computing? And for our notions of human intelligence? IBM have said that, for their part, they hope to create marketable spin-off systems that will do anything, from analysing financial data and helping the government, to answering simple trivia questions. The home PCs of today are nowhere near computationally powerful enough to perform anything close to what IBM’s Watson can do, but with computing power doubling every 1.5 years (an observation known as ‘Moore’s Law’), this is bound to change within the next 10-15 years.

Looking further ahead, we can expect to see computers that can perform virtually every task that humans can perform to the equal or better standard. What we decide to do with this kind of power is going to shape the future of mankind to levels not seen before. Beyond the ethical arguments around Artificial Intelligence, the technological leaps and achievements that have already been notched up, and those predicted, are surely bound to change humanity’s conception of itself forever. Whether this would be for the better or not is, of course, a question nobody can answer, not even Watson.

Book Review True Enough: Learning to live in a post-fact society

hich — Sun, 17 Oct 2010 21:18:48 +0000

By Sebastian Meznaric

Most people rely on mainstream media institutions to deliver the news to them on a wide range of topics. Of course we all want, above all, to consume news that is free of bias and as close to the truth as possible. Could it be, however, that it is partly our own behaviour and desire for truth that actually drives the media to give us the news that paints the world in a biased way? Well, in True Enough, journalist and author Farhad Manjoo examines the driving forces behind the media presentation of news and calling upon a number of scientific studies that had been undertaken on the relevant psychological phenomena.

One such study concerns what psychologists call “biased assimilation”. It is the idea that people tend to understand any new information they consume in a way that conforms to their pre-established beliefs. Lee Ross and Mark Lepper, from Stanford University, got hold of two empirical studies concerning capital punishment. One set of data indicated that the existence of capital punishment had a detrimental effect on the murder rates, while the other study indicated the exact opposite.

Taking the two studies together, it seems difficult to use them to conclude either way. And yet, when the two sets of results were presented to groups of students, one supporting and the other opposing capital punishment, the majority of the students did in fact reach a conclusion. As you might have guessed, the students interpreted the results in a way that agreed with their preconceived notions. More than that, the presented data reinforced their views. In the same situation, a dispassionate person who might have held preconceived opinions would be expected to have moderate their views. What’s even more important, is that the people in the study did not realise they had interpreted the data in a skewed way – they believed themselves to be objective and that it was a fact that the studies supported their view.

If, as suggested above, each of us considers our own view of the world to be objective, then we must also view anyone who disagrees with us as being either biased, unreasonable or brainwashed. In order to verify this hypothesis, Ross and Lepper decided to do another study. In 1982 a Lebanese Maronite Christian political party, the Phalangists, commited a massacre killing hundreds of refugees in the Sabra and Shatila refugee camps in Beirut. At the time, the city was under Israeli occupation and both camps were in fact surrounded by Israeli forces when the massacre took place.

Immediately after the massacre many of the details surrounding the events were rather murky and it was not clear who to hold responsible (indeed, the debate continues to this day).

To investigate the way we perceive news, Ross and Lepper recruited 144 Stanford students from three different groups – a pro-Arab student society, a pro-Israeli student society and people from the introductory psychology courses (this group was presumed to be neutral on the issue).

The students were shown a series of news clips from a variety of news networks covering the event. The selection of the clips was made so that they were, in the eyes of researchers, as neutral as possible. The participants were then asked to rate the fairness and objectivity of the reports. The results were rather interesting. The neutral group did indeed consider the clips to be rather objective. The other two groups, however, did not agree.

The pro-Israeli group found that the accounts focused too much on the Israeli involvement and too little on the responsibility of the other parties. They consequently felt that the programs blamed Israel when they would have excused some other country. The students believed that whoever had produced the news was probably very pro-Arab and that a neutral viewer would be convinced by the clips to turn against Israel.

The students from the pro-Arab society experienced the exact opposite. This is how Ross described the results: “If I see the world as all black and you see the world as all white, and some person comes along and says it’s partially black and partially white, we both are going to be unhappy.”

The media owners and executives are very well aware of these psychological phenomena. In our society, different groups of people are served by different types of media. For example, 64% of the Daily Telegraph readers intended to vote for the Conservative party in 2005, while for the Guardian the numbers were 48% for the Labour party and 34% for the Liberal Democrats. This tells you that in order to maintain their credibility, these publishing houses must provide news that is coloured in a way their readers will expect. If they do not, they will be seen as biased, unreasonable and generally bad at reporting. The media are therefore quick to grab anything that can easily be interpreted to their readers’ liking.

Manjoo finds that facts are actually not what determines our belief systems. With the development of technology, everyone can publish and be heard. This has led to views such as “AIDS is not caused by HIV”, “Bush planned 9/11” and so on to spread more widely than ever before. Everyone can find people who, like them, believe the same nonsense as they do.

It is becoming more difficult than ever to distinguish facts from fabrications, be they intentional or not; and the book leaves us none the wiser in this respect. However, it does address deep and important questions about the society we live in as well as about the ways we consume information. Perhaps more importantly, it shines some light on the process through which information is gathered and eventually delivered for our viewing. No matter what your political views are, this book is an essential read for any observer of politics, the media and international affairs.

True Enough
pp 256 pages, Wiley (March 17, 2008)
Is available at Amazon

Flying without wings in China: the future of train travel

Omayr Ghani — Thu, 09 Sep 2010 18:02:30 +0000

By Omayr Ghani

Mention magnet trains and one immediately conjures up images of the Shinkansens (bullet trains) of Japan. However, the majority of these are conventional rail vehicles, and the only commercial high-speed train service to use magnetic levitation (maglev) is China’s Transrapid service from Shanghai’s Pudong Airport to the city’s expat-dominated apartment complexes on Longyang road. It’s a 30km journey through the centre of town, which takes the Shanghai Transrapid just 7m 20s, at a top speed of 431 km/h (268mph).

The Shinkansens’ dominance has also been eclipsed by foreign competitors. Its 1997 commercial record of 300km/h (186mph) record was smashed by one of China’s CRH trains when the 922km (571m) Guangzhou-Wuhan line travelling at a top speed of 350 km/h (217mph) opened on boxing day of last year. Today, it completes the mammoth journey in less than 3 hours, forcing airlines to slash the prices of flights between the two cities by half.

When Japan’s National Rail was privatised in 1987, many saw that as the end of what promised to be a future of infinite possibilities. This sense of opportunities lost is being highlighted once more by China’s plans to extend its Maglev line from Shanghai to Hangzhou, allowing the 169km (105m) trip to be made in 27 minutes. China is also building 42 more lines of the same specification as the ones of the Guangzhou-Wuhan track, in the hope of connecting all its major cities by rail. China is also working with South Africa on a high speed Johannesburg-Durban line which, assuming it is completed before 2015 (when Morocco’s plans to unveil its own Tangier-Casablanca line,) will be Africa’s first high-speed-rail line. As ambitious as these developments seem, they pale in comparison to the most recent project, unveiled this month by China: Vacuum Trains.

Whilst Magnetic Levitation trains are able to surpass the top speeds of conventional rail, even at very short distances, through the elimination of wheel resistance, there is another way to increase speeds still further. This is done through the elimination of air by laying Maglev track through a series of vacuum-pumped tubes or tunnels, allowing the trains to move without friction. Though the idea was first conceived by liquid-fuelled-rocket inventor Robert Goddard in the 1910s, his blueprints weren’t discovered until after his death, in 1945. And the technology was not patented until 1999. The first proposed use of this technology, variously dubbed “flying without wings” and “space travel on earth”, was for Switzerland’s Gotthard Base Tunnel in 2005, though the plan fell through due to lack of political support, . as conventional high-speed-rail preferred. China, on the other hand, became interested in the proposal and this month revealed that researchers are currently working on a prototype for a vacuum train (Vactrain) that will be completed and capable of travelling at up to 1000km/h (621mph) speeds in two to three years. Moreover, Vactrains are scheduled to be available for commercial use in China within 10 years.

They have also found that using evacuated tunnels only increases costs by 10-20% per mile, in comparison to the Shanghai Maglev which, despite its short track, obscure route, limited operating hours, experimental nature and cheap tickets, has proved itself not only to be economically viable (with very little government subsidy) but able to expand its line at a third of the cost of the original track. Even if we use the upper estimate of 20%, that would still make Vacuum trains cheaper than conventional high-speed rail. According to figures by project and cost management consultancy Faithful+Gould, Maglev only costs £30m per kilometre of line, as opposed to £60m for High Speed Rail. The reasons for this are Maglev’s narrower track, ability to scale steeper gradients and elevated track that allows land underneath it to continue to be used.

As fast as 621mph may seem, it is not the limit for how fast these trains are able to go. With an increased length of track, which allows more time for acceleration than the prototype being developed, speeds of up to 4000mph can easily be achieved, making Vactrains over 7 times faster than commercial aeroplanes while using 25% as much energy.

Channel Tunnel pioneer Frank Davidson has long advocated the use of this technology for a transatlantic tunnel, supported by its own buoyancy, that would be able to get from London to New York in under an hour. He insisted that such a project requires no further scientific advances but only “getting used to new realities”. However, if we take into account Britain’s corporatist system of rail franchising, erratic investment, uniquely unnecessary levels of oversight and, in the case of the last transport secretary, outright political corruption, such realities are a long way from being understood by the government without a large increase in public awareness and activism in relation to public transport.

Given our increasing reliance on trains for long-distance travel, especially following recent volcanic eruptions, airline strikes, growing concerns about the effects of aviation-related emissions and ever-rising fuel prices, to advocate the use of a form of transport that independent academic reports have found to be “unaffected by any extremes in weather conditions… has low maintenance and operation costs”, causes no direct pollution and is four times as energy efficient as aeroplanes, seems like an intuitive way to ensure the transition from air to track becomes a step forward rather than a regressive lurch. A retired MIT professor of ocean engineering recently stated, in relation to the ultimate step of building a transatlantic Vactrain: “From an engineering point of view there are no serious stumbling blocks.” Chinese researchers have proved the same from an economic point of view yet the same cannot be said, in this country at least, of the political battle.
It is thus up to us, the public, to fight the political stumbling blocks of corruption and corporatism that are standing in the way. The sooner we do it, the better we’ll serve our communities and the environment.

Omayr Ghani is Ceasefire‘s Political Editor. He also likes trains, a lot.

Decoding Reality: The Universe as Quantum Information

hich — Fri, 20 Aug 2010 22:35:50 +0000

Vlatko Vedral

by Sebastian Meznaric

What is information and how do we quantify it? Can we, through genetics, use the concept of information to describe living organisms? How about the stock market and social changes? Decoding reality makes a compelling case that the answer to all of the above is yes, and goes even further by introducing the reader to quantum mechanics in the context of the Information Theory. In addition, it deals with many of the philosophical implications of modern physics, such as the question of whether nature is fundamentally unpredictable and random (also known as the determinism question), whether something can come from nothing and, indeed, if nothing can again arise from something. As such, the book deals with many topics and so in this review I will address some of them in more detail, namely: Information Theory, Quantum Mechanics and determinism.

Information theory is a branch of mathematics that deals with quantifying information. So how does one go about quantifying a concept that appears vague and ambiguous at best? Imagine the toss of a fair coin, where the probability of it landing on heads or tails is exactly equal. Then one bit of information is defined to be exactly the amount that you learn if I tell you that the coin will land on either heads or tails. On the other hand, if one knows with certainty and in advance that the coin is going to land on heads, then learning about the future outcome from someone else imparts exactly zero bits of information. In between the two extremes, the amount of information one gains depends on the probabilities one assigns in advance to the different outcomes.

But of course, one might argue, reality cannot be described simply as a collection of coin tosses. The events in the real universe often have multiple possible outcomes, sometimes infinitely many. And the outcomes themselves might not even have equal probabilities of occurrence. The book informs the reader, in a simple to grasp way, about how information about such events might be quantified and further postulates that reality as a whole can be described using such quantities.

Notice that the concept of uncertainty is essential to the above discussion. Indeed, if one knows with certainty everything that is going to occur, then one has effectively ‘decoded’ the universe. However, the modern physical theory of Quantum Mechanics has a few surprises for us here. In fact, the standard interpretation of quantum mechanics tells us that it is in fact impossible to predict all future events with certainty. Imagine a spinning atom. In our everyday understanding of the world, the atom can spin either to the left or to the right. However, this is not so in quantum mechanics – the atom can exist in a state whereby it spins both to the left and to the right at the same time!

This concept seemed so incredible and counter-intuitive to the physicists at the dawn of quantum mechanics that many rejected the idea outright. However, experiments have been conducted that demonstrate that such states indeed do exist and are, in fact, very common in the world of things as small as atoms.

Now imagine one has a device that is capable of finding out only whether the atom is spinning to the right or to the left. It is not capable of measuring anything in between. According to the standard interpretation of quantum mechanics, it is impossible to predict whether the device will find the atom spinning to the right or to the left. The only thing that we may predict are the probabilities of either one or the other. Furthermore, the act of measurement will disturb the atom in a way that will force it to spin either to the left or to the right immediately after the measurement.

Does it then follow that the world can never be decoded, and that our future is unknowable? Well, another group of physicists believes that it is in fact possible to predict whether the device will find the atom spinning to the left or to the right. Their crucial argument lies in the idea that quantum mechanics does not only apply to things as small as atoms but also to large things, like footballs, cars and indeed everything. Now, when the atom is undisturbed by large measurement devices, its state changes in a way that is completely predictable. It then stands to reason that if the entire universe is governed by quantum mechanics then so are the devices which we use to measure the way the atoms spin. And since the universe cannot be disturbed by anything external to itself, it is then entirely predictable. These two viewpoints have been argued over for many decades so that the problem has even been given its own name – the measurement problem – and the final resolution of the argument has not yet been achieved.

However, determinism is not important merely as a philosophical issue. On a daily basis, many bankers attempt to predict stock market fluctuations in order to stay ahead of the competition. Their calculations lead them to a probability that the price of the stock will go up or down by tomorrow and by how much. The information theory can then tell us what is an optimal portfolio based on these probabilities.

Equally, information theory can be used in biology. Our genetic code (DNA) is composed of a sequence of bases – adenine (A), cytosine (C), guanine (G) and thymine (T). A group of three bases forms an amino acid which means that our DNA can encode 43 = 64 amino acids.
However, only 20 of these possibilities are found in living organisms. In other words, there are more than three times as many symbols (amino acids) than we need to encode. In other words, nature has implemented a natural redundancy so that, in case an error is made in some of the bases, the DNA is still usable. Information theorists have developed a very similar error-toleration system for computers and, in fact, such systems are still a topic of active research in quantum computation.

Vedral illustrates many more other uses of information theory and provides an introduction to the subject that is both illuminating and useful. But the core of the book is undoubtedly dedicated to the Quantum Physics side, where the strange and non-intuitive ways of nature reveal themselves in all their glory. The questions of determinism and the workings of nature are most likely among the deepest questions that humans have considered. Their answers, as illusive as they may have been, may just now be coming into the realm of modern scientific understanding and this book provides a thought-provoking, and timely, introduction.

Decoding Reality by Vlatko Vedral

Oxford University Press

£16.99

Open Source: the future of science?

hich — Thu, 18 Mar 2010 03:28:46 +0000

Illustration by Xiang Zeng www.freerangedoodle.com

By Sebastian Meznaric

The recent scientific crisis around climate research data leaks has greatly damaged the credibility and respect usually accorded the scientific community. The collected global temperature data was the subject of statistical analysis where the scientists in question used a “trick” to conceal certain decreases in the temperatures measured. The “trick” apparently went unnoticed through the peer review process which ended up leading to the processed data being published in a high profile scientific journal. After failing to obtain the data by other means, the sceptics wishing to analyse the data for themselves were (as they saw it) forced to resort to using the Freedom of Information Act. Their efforts went unrewarded as the requests were routinely rejected and evaded by the researchers. This situation eventually exploded in dramatic style in November with startling news of the public data leaks conducted with the help of hackers based overseas. The damage that was caused to the image and reputation of the scientific community was grave and should lead us to ask: could there be an overarching solution to deal with such problems in the future?

Open source, whether in computing or in broader terms, is a principle advocating free access to the end product’s “source materials”. This may be the source code (in the case of computer software), it may also be the design specifications for a product or it could be the data used for a statistical analysis in a scientific project. The main guiding principle behind it is peer production by collaboration. The end product is made available to the general public at no cost at all.

The creative practice of sharing the source of one’s work is nowhere more appropriate than in science. The work is by its nature collaborative and very often publicly funded. As such, it should be freely available for public examination.

Other than raw data, there are numerous scientific projects where a computer programme is the key part of the project. The need for verification of the results by the scientific community would dictate that the code be made available for inspection and modification. Indeed, if in the climate scandal noted above, the raw collected data had been made available from the beginning, the errors in the analysis could have been noticed and corrected early, benefiting both the integrity of the scientific process and the search for truth. In today’s competitive research environment, however, the data and source code for computer programmes are not always freely available.

The competition among various research groups makes the idea of hiding one’s software code and/or data (we will henceforth simply use “source” for both terms) extremely attractive to most scientists. The implication is that sharing the source would make it easier for other groups to reap the benefits of one’s hard work. However, it would be very easy (and indeed necessary) to give credit to the principal author of the source by making them a co-author of the resulting publication. Indeed, the practice of making people who collected the data and/or wrote the code co-authors of journal articles is actually already well established. For large projects where co-authorship is impractical, like perhaps CERN-related findings, the name of the open source project can simply be referenced in the acknowledgements. Such practices would avoid having a very large number of authors while at the same time give credit where credit is due.

Another commonly used argument is that competition drives the scientific research better than openness. Different competing research groups in the same field might therefore use their own self-written versions of software designed to accomplish very similar tasks. Often, these groups would compete with one another in adding new functionalities and improving the performance of their code in order to publish new results before other competitors. However, as we see with Wikipedia, Linux and other greatly successful open source projects, more “eyes” see better and, more importantly, think better. Scientific collaboration among peers very often leads to ideas that one would not think of in a smaller group or on their own. Indeed, dramatically increasing the group of people working together on a scientific software project often quickly leads to a sky rocketing improvement in performance and applicability. Perhaps even more crucially, researchers would have more time to focus on science rather than coding or collecting data.

The open source concept has been successfully used in the commercial world, notably in the automobile industry, where the patent sharing started by Ford led to automobile design innovations moving faster than ever to the great benefit of the general public. The sharing of technology did not at all reduce the competition among the companies nor their innovative drive.

The adoption of open source models in science would not only foster greater creativity, but would also attract interest in science from programmers and other interested parties, further increasing our global productivity and efficiency.

For instance, the field of biotechnology is fast adapting to the drive for greater openness in the scientific process. Other disciplines will hopefully follow suit to harness the greater efficiency and openness offered by the open source development model. Whether the scientific community at large adopts the open source paradigm remains a matter of speculation but, considering the climate data leak fiasco, the potential benefits are surely beyond dispute.