The theoretical

Michele Diego

A world in perpetual pursuit of the enormous volume of data  that it produces; and to pursuit that data, fast computational tools and countless scientists  more than a few brilliant brains  are needed.

A few years ago, while at a public meeting in a bookstore in the centre of Trieste, I asked writer Claudio Magris if he agreed with me that, during the course of history, there has been a noticeable decline in the nurture of culture, thus allowing more and more people to possess at least a piece of it and, at the same time, causing the extinction of great thinkers who were once holders of extensive knowledge. He agreed and added that he further believed in a society made up of one hundred average educated men capable of critical judgement of the world, rather than ninety-nine illiterates and a lonely Goethe.
Certainly, this type of society is the same one that is on the increase globally. If, for example, one was to compare the Italy of today with that portrayed in Pasolini’s documentary Love Meetings (1963), one would realise that the tones of the ‘Bel paese’ have softened; there are no longer the legitimately ignorant peasants who struggle to express themselves, yet at the same time one feels the lack of characters such as Ungaretti, Moravia or Pasolini himself. The more we look back in time, the more the gap seems to have widened: can we imagine, today, a character like Leopardi – a nineteenth-century uniqueness who emerged from a society that counted for about seventy percent of illiterates? [1]
In the field of sciences, a scientist’s position throughout history does not contradict the trend described so far. If in the past a scientist was an all-embracing thinker – so much so that until the nineteenth century one could not even distinguish between a philosopher and a scientist, whereas today practising science, in most cases, is like any other job: Its rhythm of research and the deadlines to be met make the average scientist an ultra-specialized employee, almost unaware even of what they are trying to discover in the laboratory next door.

Yet, discussing this trend with several physicists, I seem to find a general agreement with Claudio Magris’ opinion in the scientific field as well. In the world of physics, the school of thought according to which one hundred ‘average’ scientists make a greater contribution to society than a single brilliant scientist prevails. Moreover, the somehow romantic and stereotypical idea of a solitary brilliant scientist, locked in their office writing revolutionary equations, is almost stigmatized. This is because such an individual, no matter how intellectually valuable, has not able to become a pioneer of their field, i.e. to build a network of events, conferences, winning competitions, awarded scholarships, students seeking out supervisors looking for theses, interns and trainees – all things that keep a research group in good shape and therefore, more generally, help science to advance in contemporary society. We are far away from Lev Landau’s model – a great Soviet physicist, who had created a school of theoretical physics whose entrance exam, called the theoretical minimum, required knowledge of vector analysis, tensor algebra and more generally of everything that Landau knew [2]; so much so that, to be clear, only forty-three physicists in thirty years managed to pass the exam.
All of this is in the peace of mind of that group of somewhat reactionary dreamers who see in this trend the fragmentation of knowledge, the decline of the highest figure of the intellectual and therefore a form of decadence rather than progress. Those who belong to this group of cultural elitists clash with a contemporary world in which in a few years an amount of data is produced equal to what has been done in the whole of human history up to that moment [3], when all the big high tech companies collect and analyse the enormous amount of information regarding our researches on the web, in which the figure of the ‘data scientist’ (i.e. the one who processes large amounts of data) is increasingly required, in which new materials are being studied to be used as smaller and larger memories (it is of these days the news of a 500000 Gigabyte glass disk [4]), in which Google claims that its quantum computer has been able to perform a calculation in a few minutes that would have taken a classic supercomputer ten thousand years [5]. In short, a world in perpetual pursuit of the enormous volume of data that it produces; and to pursuit that data, fast computational tools and countless scientists more than a few brilliant brains are needed.
As an answer to all this, a new frontier of science appears: the so-called citizen science, in which the citizen becomes a participant in scientific research. Let’s imagine for example, that a substantial part of European citizens actively provide data about the number of stars visible from their homes, this could be useful data for scientists who wish to study pollution in various parts of the continent and that would be very difficult to obtain on their own – this is but one of the many examples of citizen science. Another one is provided by Stanford University that with the project Folding@home [6] uses the computers of more than one hundred thousand volunteers, exploiting their unused computing power to process data related to the dynamics of molecules in various fields, including pharmaceuticals (currently also studying cures and therapies for Covid-19). In this case, citizens voluntarily make their computers available, making themselves useful to science in a passive way.
But there are even more subtle and ingenious cases. The Discovery project [7] has integrated image recognition exercises into an online video game (EVE Online) in order to characterise the cellular structure of millions of images produced by a fluorescence microscope. In doing so, we are now able to overcome the natural barrier due to the goodwill of citizens who offer part of their time or their means to science out of pure generosity, and instead a real relationship of ‘do ut des‘ is established, for which the game is useful both to the player as to science. In essence, while having fun the user is performing the task that would otherwise be destined to a scientist or an automated recognition program. If you multiply the process by the total number of users (a few hundred thousand), you can get significant results. Furthermore, it is interesting to note that the results of the players were comparable to and, in some specific respects, even superior to those of tools normally used for automatic recognition of cell images [8]. Therefore, if a large group of people completely neophyte in cell structure, only thanks to statistics, can classify cells with results comparable to the best specific tools we have available, it is natural to ask ourselves: for how many and for what other problems the opinion, statistically mediated, of hundreds of thousands of people randomly is more authoritative than the opinion of a single expert? And what would be the consequences on the role of the expert, of the educated, of the intellectual, of the awareness that statistics can beat him or her without knowing anything about the subject to which he or she has devoted his or her life?

From Lev Landau’s theoretical minimum to science played online, culture seems to be increasingly democratising itself by pushing up those who are at the bottom and forcing down those who are at the top. In such a world, where everyone becomes a gear aimed at their utility, but is not more useful than the another, finding time to acquire any culture might seem immoral, and reading La Livella Magazine may seem to be a subversive act.

[2] Leonard Susskind & George Hrabovsky, The Theoretical Minimum, Basic Books (2013)

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