Category: Article of the Month

Author: Octavian Ianc, University of Bucharest, Romania

Imagine that you are a researcher at CERN. Some of you already are, others have this on their schedule a few years in the future. Now, what are you doing there? Among other things, you’re analyzing hundreds of petabytes of experimental data. Assuming that you’re a sane person who doesn’t want to spend the next few million years stuck in front of a computer, you don’t do this by hand. You use some machine learning algorithms¹. These algorithms are artificial, no one has seen classifiers growing in trees. And, if my university lecturer did not make up definitions out of thin air, they are, in a way, intelligent.

Dramatis personae: artificial intelligence and physics.

It all began in the last few years, when lots of commercial AI implementations became available. Actually no, it began in 1997, when Deep Blue beat Kasparov in chess. Or in the 50s, when Turing wrote something about tests and machines. No, even earlier, with those philosophers babbling about formalizing logic and reasoning. It’s almost impossible to come up with a definite answer regarding when the idea of artificial intelligence appeared. What’s certain is that things which someone from 50 years ago would have considered as intelligent are around us. For now, and probably for ever.

We’re physicists: dedicating our lives to studying the different phenomena that surround us. Wouldn’t it be, and this is a huge understatement, absolutely crazy if one of these phenomena turned everything around and started studying us? We’ll get there soon, let’s take it gradually.

Marvin Minsky, one of the pioneers of the field, defines artificial intelligence as “the science of making machines do things that would require intelligence if done by men”². This encompasses a lot of stuff. While not being the first things you think of, sorting a list of numbers or navigating through a network are tasks that would require a fair share of human intelligence. Those are boring, we won’t talk about them. We only want true intelligence here. So we’ll have true intelligence. And also physics. And cats – everyone loves cats.

As you’ve noticed by now, intelligent algorithms are a cornerstone of modern research, both in physics and in most other fields, especially when it comes to finding hidden patterns in huge datasets. An algorithm is better, faster, and does not get bored nearly as quickly as we do. Another important field where AI is a great competitor to traditional approaches is in the modelling of complex systems. When talking about predicting molecular properties³, predicting weather⁴ or, analysing complex economic parameters⁵ (why not?), artificial intelligence is able to do some feats for which classical methods need orders of magnitude more time or are outright incapable of.

This link between physics and the thing called artificial intelligence is however nothing new. From the 70s, researchers worked on something which would later be called a Hopfield network⁶, named after J.J. Hopfield, a physicist who brought this into attention. Until then, everyone thought information had to be stored in a straightforward way: words written on a piece of paper, 1 or 0 bits in a hard drive, and so on. What these guys did was to prove this is not always the case. You can also store information using the connections or couplings between elements of a system. In a nutshell, these networks are very similar to the Ising model for magnetism, in fact they’re inspired from it. You have a grid of tiny magnets which can point either up or down, and are coupled between each other. If the coupling strengths are suitable and the system is left to evolve, it will converge, from any initial state, in one or a few chosen states (“memories” of the system).

Now, memory, intelligence, analyzing tons of data in a blink of an eye, all those don’t sound horrible at all. ChatGPT helping with that pesky programming task sounds even better. Yet, as one famous economist put it, ‘There ain’t no such thing as a free lunch’. All these shenanigans come with their fair share of disadvantages and problems.

First of all, to put it frankly, we have no idea what most of these algorithms are doing, or why they are giving a certain output. This is especially true about deep neural networks, the workhorses of a lot of machine learning applications. As a simple example, we can take any task which has something to do with images (finding faces, classifying cats etc.)⁷. These are, most of the time, accomplished with convolutional neural networks (CNNs). Different operations are sequentially applied to the image’s pixel values, ending up with the desired result (a number, a category, whatever). Let’s say we try to analyze such a system. The first layer or couple of layers are quite easy to understand: they detect edges, gradients, basic image features. Surprisingly, this is extremely close to how our visual system does its job. However, if we try to go past this, we’re more or less stuck⁸. It’s quasi-impossible for a researcher to get even a general idea about why the network does what it does. All we see is some numbers. If you have seen the movie Inception (if not, you definitely should), it’s kind of like that. There, the protagonists are navigating through an intricate world, which interconnects levels of reality with dreams. Understanding a deep neural network is similar, but we’re still stuck in the uppermost levels.

Let’s get a bit more intellectual, calling in some philosophy. At its core, any software piece we could refer to as intelligent is nothing more than a set of mathematical rules that is applied to data collected from the real world. When something like this is capable of doing independent research, what happens exactly?

A point can be made even aiming at the fundamentals of the scientific method. Suppose we analyze experimental data using an AI. We obtain predictions, we can verify those predictions. However, what we’re doing is that we’re morphing an unknown, the physical phenomenon we’re trying to study, into a different one, the model that was trained on that data. Although we can, in a sense, predict the real phenomenon, we still don’t have the vaguest of ideas about the governing laws. We just have a black box that supposedly can predict it. Although we have some results, is this still science? This looks similar to the differences between science and engineering. Very broadly, a scientist is interested in understanding phenomena, while an engineer aims to harness these phenomena and provide useful results, while still keeping at least a general understanding of the process at hand. When using an AI model to analyze data and make predictions, we’re most of the time losing even that general understanding. One might dare to say this is neither science, nor engineering.

We’ve traditionally referred to mathematics as a man-made tool, which we use to harness the unpredictability of the world around us. The weirdness happens when this math starts creating other math. The mere idea that it is a tool, stemming from our minds and being entirely dependent on it, begins to shatter when this math starts doing stuff without us. It’s as if a hammer would start building by itself. Or as if a cat from your dream would scratch you in real life. There’s even more to this. Gödel’s incompleteness theorems⁹, important results of mathematical logic, state that if you start with a finite number of assumptions (axioms), you would be unable to prove all true propositions of that logical system. As a consequence, you won’t be ever able to prove or disprove that system as consistent. For our AI-scientist, this would mean that it, by the virtue of its own existence, places hard limits on its abilities. For us humans, limitations come mostly from the physical world. I can’t run at 500 km/h because the muscles in my legs are not strong enough, due to air resistance etc. For mathematics (and, as a consequence, physics), the mere fact that it exists creates a constraint on itself.

To sum it all up, the marriage of artificial intelligence and physics has and will continue to revolutionize the way we do research, and not only that. In the midst of these scientific and philosophical ponderings, we find ourselves both awe-inspired and cautious, marvelling at the possibilities while recognizing the need for responsibility.

References:

1. “AI at CERN | sparks.web.cern.ch.”

2. Minsky, “Semantic Information Processing.”

3. Wiercioch and Kirchmair, “DNN-PP.”

4. Hickey, “Using Machine Learning to ‘Nowcast’ Precipitation in High Resolution. | ai.googleblog.com.” 

5. Bickley, Chan, and Torgler, “Artificial Intelligence in the Field of Economics.”

6. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities

7. “Deep Learning (Adaptive Computation and Machine Learning Series): Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron: 9780262035613: Amazon.Com: Books.”

 8. Zhang et al., “A Survey on Neural Network Interpretability.”

9. Gödel, “On Formally Undecidable Propositions of Principia Mathematica And Related Systems.”

Zofia Dziekan, University of Warsaw, Poland

The ability to create physical objects using 3D printers has taken the manufacturing industry by storm and opened up new ways for innovation in a variety of fields (1). But as impressive as it is to print a functional bicycle or a complex medical implant, some researchers have been pushing the limits of this technology in a different direction: down to a nanoscale. With nanoscale printing, we can create structures that are smaller than the width of a human hair, with intricate details and unique properties. In this article, we will explore the history of nanoscale printing, the underlying physics of this process, and the exciting possibilities it offers for the future.

Fig. 1 Size comparison between (A) regular (2) and (B) nano-scale 3D printed object (3).

The Physics of Light-Matter Interactions

In 1930, a young mathematician – Maria Göppert-Mayer attended a Max Born seminar at the University of Göttingen (4). Mesmerized by quantum mechanics, she dedicated herself to the pursuit of theoretical physics, eventually becoming one of four women awarded the Nobel prize in Physics. While today she is best known for her work in the Manhattan Project and her postulation of the nuclear shell model, it is her earlier work that is of interest to our story. Göppert-Mayer’s groundbreaking research into molecular excitation, explored in her doctoral dissertation, demonstrated that molecules can be excited by the simultaneous absorption of two photons with energies smaller than the difference between the excited and ground state (Fig. 2A). Despite the lack of high-intensity light sources to test her theory at the time, Göppert-Mayer’s work laid the foundation for future discoveries. Three decades later, the invention of the laser finally provided the tools necessary to observe two-photon excited fluorescence for the first time in CaF₂ crystal doped with europium atoms (5).

Fig. 2 (A) Energy levels involved in one-photon and two-photon absorption (6). 

(B) One-photon and two-photon absorption of fluorescent die (7).

The Pulsed Lasers

The two-photon absorption process involves two photons instead of one, making the probability of absorption proportional to the intensity squared (5). As a result, increasing laser power has been crucial in the development of application for two-photon absorption. Pulsed lasers have been a game-changer in this regard, with their ability to release high-intensity bursts of energy that can be precisely controlled in terms of duration and frequency. Unlike their continuous-wave counterparts, pulsed lasers can vaporize materials without causing thermal damage, making them an indispensable tool for surgery and laser material removal (8). 

3D Printing Through Direct Laser Writing

In the late 1980s, researchers started investigating the potential of using pulsed laser technology to create nano-scale 3D printers (5). One promising technique that emerged in this process was direct laser writing (DLW), a form of 3D printing in which a focused laser beam scans over the sample in three dimensions until it solidifies the polymer solution into the desired shape. To fabricate structures below the diffraction limit, the intensity, duration and frequency of the laser pulses must be precisely controlled to achieve two-photon absorption that would initiate polymerization. The material is polymerized only in the focal spot of the beam where its intensity is the highest as stated previously, and the probability of the process grows with intensity squared (Fig. 2B). This small volume in the focal spot of the beam is known as a voxel and it serves as a building block of any 3D print in DLW (7). 

By moving the laser beam, it is possible to polymerize photosensitive material point-by-point, creating complex structures that are just several microns in size. Just imagine tree-lined avenues, dozens of miniature buildings and little polymer people comfortably sitting on a single strand of hair! It is truly incredible that there is no other method that allows printing on this scale. The resolution of the process is limited mainly by basic properties of an optical setup and material properties. Since its inception, nano-scale printing has come a long way and has found numerous applications in various fields, from micro-robots to drug delivery systems (9 – 10) (Fig. 3). While the technology still faces challenges, including a limited range of building materials and slow printing speed, continued research and development promise more exciting applications in the future. The history of nano-scale printing is a testament to human ingenuity and the power of scientific discovery – and who knows what incredible breakthroughs we’ll see in the years to come!

Fig. 3  SEM images of objects fabricated using DLW. (A) Medical imagining system build by inserting into a needle an optical fiber with 3D printed lenses (9). (B) Light-fueled robot that can walk and jump, placed on a human hair for scale (10). (C) Microfluidic chip designed for the fabrication of drug carrier nanoparticles (11).

References

1. S. Mohr and O. Khan. “3D printing and its disruptive impacts on supply chains of the future.” Technology Innovation Management Review 5.11 (2015): 20

2. [Internet] Available from: 

www.3dbenchy.com/3dbenchy-a-small-giant-in-the-world-of-3d-printing. 

3. Doherty RP, Varkevisser T, Teunisse M, Hoecht J, Ketzetzi S, Ouhajji S, et al. Catalytically propelled 3D printed colloidal microswimmers. Soft Matter. 2020 Dec 14; 16(46):10463–9. 

4. Sachs R.G. Maria Goeppert Mayer – A biographical memoir. 1978. 

5. Liao C, Wuethrich A, Trau M. A material odyssey for 3D nano/microstructures: two photon polymerization based nanolithography in bioapplications. Vol. 19, Applied Materials Today. Elsevier Ltd; 2020. 

6. Lavocat J.C. Active Photonic Devices Based on Liquid Crystal Elastomers. Dec 2013.

7. [Internet] Available from: www.l3dw.com/an-introduction-to-direct-laser-writing-dlw.

8. Shirk MD, Molian PA. A review of ultrashort pulsed laser ablation of materials. 1998. 

9. Gissibl T, Thiele S, Herkommer A, Giessen H. Two-photon direct laser writing of ultracompact multi-lens objectives. Nat Photonics. 2016 Aug 1; 10(8):554–60. 

10.    Zeng H, Wasylczyk P, Parmeggiani C, Martella D, Burresi M, Wiersma DS. Light-Fueled Microscopic Walkers. Advanced Materials. 2015 Jul 1;27(26):3883–7. 

11. Erfle P, Riewe J, Bunjes H, Dietzel A. Goodbye fouling: a unique coaxial lamination mixer (CLM) enabled by two-photon polymerization for the stable production of monodisperse drug carrier nanoparticles. Lab Chip. 2021 Jun 7; 21(11):2178–93. 

Ali Mohammed Redha and Asif Bin Ayub, University of Bahrain, Bahrain

1 Introduction: The Question

One day, as I was driving my car back to my house after a long day at the university. A question popped up in my mind that caught my attention for the whole drive: “Can we feel speed?” Being a physicist, I am used to thinking of questions like this for hours straight. But this, this was different. I began thinking of the topic from multiple perspectives. How would a physicist answer this question? How would a non-physicist answer this question?

Figure 1: Shows a search result of the word “speed” on Pixabay. Can you feel speed from that? Source: Pixabay

The question was so intriguing to me that I decided to resort to the most scientific method of questionnaires, Instagram polls. I asked in the poll, “Can we feel speed?” and 59% of the respondents said YES, the other 41% of course answered NO. What was most interesting about the poll is that physicists following me took both sides. Some said YES while the remaining said NO. I thought the answer would be obvious to physicists, NO is definitely the answer, right? That made me think deeper about the question, particularly about the word feel.   

Figure 2: The scientific poll conducted. The question is: “Do we feel speed?” 27 participated with 16 answering YES and the remaining 11 answered NO. The participators are all from Bahrain with various backgrounds and specialties. Source: Self-made.

2 What Do We Mean by “Feel”?

To feel is to experience something emotionally or physically [1]. We can feel emotions: happiness and joy, love and passion, sadness and sorrow – which are abstract constructs of our mind. But the type of “feel”-ing we are interested in is rooted in physical experience: such as touch, heat, and texture. In this physical notion of “feel”, which we might call “sensing”, can we truly sense speed?

We sense our physical surroundings using our five senses: vision, hearing, taste, touch, and smell. Think about them as detectors. Thus, to see if humans can sense speed, we need to see if these senses can detect motion. Imagine yourself driving a car and applying each of your senses separately. Which ones can detect speed?

Figure 3: Shows the senses of a normal human. Us physicists, we do have 6th, 7th, 8th, and 9th senses but we don’t talk about those… Source: Self-made

Identifying speed with vision is no problem. And although speed does not have any sound in and of itself, minuscule collisions, friction, engine sound and other interactions generate sound and can be heard. Combine both visions and hearing together and you get a good motion detection apparatus.

However, take vision and hearing away, and we lose almost all notion of speed. As far as we know, speed cannot be identified by smell or taste – though, it would be interesting to see how an avant-garde chef might imagine the flavor of speed. As for touch, it counterintuitively, cannot sense speed. This is one way that nature tricks us into thinking we are sensing something when we are sensing something else entirely. Our inability to sense speed does not stem from a flaw in the human sensory apparatus, rather, it is a consequence of a fundamental principle of nature. 

3 Finally, Some Physics 

There is no physical way to distinguish between a moving object at a constant speed and a stationary object. This is known as the (special) principle of relativity and was first theorized by Galileo Galilei in his theory of relativity [2]. Formally, an object/observer that is moving at a constant speed or is stationary is known as an inertial frame of reference. Therefore, according to the (special) principle of relativity, there is no fundamentally preferred inertial frame of reference [2].

To put this simply, this means that everyone has the right to claim themselves to be stationary. Me, I see myself as always stationary so I can claim to be always stationary, “I am the center of the universe”. You, dear reader, always see yourself as stationary and so you have the right to claim that you are always stationary and that you are the center of the universe. No one is wrong here; we are both correct. Someone else might also jump in and claim they are stationary, and we are not, and they will still be right. All of these are physically identical, just seen from different perspectives.

This explains why we cannot sense speed physically by touch. After all, I am always stationary according to myself. There is nothing changing about me, whether I was sitting down, having a walk or driving a car. Everything else around me is moving, but me? No, I am always stationary (keyword: according to me). 

What humans physically sense are forces, we feel forces (more accurately, energy transmission). If you hit a wall, then you get hurt by it, because of the force the wall enacts on you. When driving a car, the car vibrates due to friction and minuscule collisions which act as forces on our bodies. Add to that the force exerted by the seat belt and through this nature tricks us into thinking we sense speed when we in fact sense forces. A more in-depth dive regarding our sense of touch is found in [3]. 

It is important to note that Galileo’s relativity does not give the full picture, and one should resort to Einstein’s relativity for a more accurate representation of reality. Einstein’s relativity agrees with Galileo’s principle of relativity in the case of inertial frames. However, when forces are involved the frames of reference become non-inertial. In that case, there would be preferred frames of reference and that has many implications [4]. Still, how does that answer our question? 

4 Conclusion: The Answer

This question goes beyond just science, just answering the question with physics does not feel right. The answer to it is heavily dependent on how we, humans, function. It is always awe-inspiring how complex the human system is. Not just biologically, but socially and psychologically as well. I mentioned how scientifically, we always think of ourselves as being stationary and everything around us to be moving. In the theory of relativity, you are the center of the universe. Go ahead and apply this way of thinking to the social norm and things would be confusing and overly complicated. We sacrifice accuracy for easiness. We pick perceptions and logical systems that would lead us to the simplest, most straightforward path. Everyone is ready to throw away their title of “center of the universe” to live harmonically and in symphony. Ultimately, it does not matter if we believe that we can feel speed or not. What matters is that we can communicate our situation in a way that others would feel. So, is the answer YES, or NO? Well… whatever makes you feel better.

5 Acknowledgement

Many thanks to my friend and colleague Asif Bin Ayub for his help in writing this article. He had a large input and helped me in it throughout. 

6 References 

[1] Cambridge University Press. Meaning of feel in English [Internet]. Cambridge Dictionary; cited 2023 Apr 27. Available from: https://dictionary.cambridge.org/dictionary/english/feel. 

[2] Wikipedia contributors. Principle of relativity [Internet]. Wikipedia, The Free Encyclopedia; 2023 Mar 27, 20:46 UTC [cited 2023 Apr 27]. Available from: https://en.wikipedia.org/wiki/Principle_of_relativity.  

[3] Fulkerson M. Touch, Edward. The Stanford Encyclopedia of Philosophy; 2020 June 21 [cited 2023 May 21]. Available from: https://plato.stanford.edu/archives/sum2020/entries/touch/.[4] Wikipedia contributors. Preferred frames [Internet]. Wikipedia, The Free Encyclopedia; 2022 Feb 16, 21:04 UTC [cited 2023 May 21].  Available from: https://en.wikipedia.org/wiki/Preferred_frame.

Author: Andrea Arlette ESPAÑA-TINAJERO

Universidad Autónoma de San Luis Potosí, México

Aix-Marseille Université, France

When talking about the synchronization phenomenon, the most common thing is to think of biological events where it is possible to observe it with our own eyes, for example, when dozens of birds fly in the sky and form various patterns with choreographies that seem very well rehearsed. Also, some types of fish exhibit this type of behavior when swimming; it seems that they do it harmoniously and without colliding with each other [1]. 

In our case, we will think of the synchronization phenomenon in the following way: it will be a process in which a set of agents interacts, and by allowing a long enough period to pass, then all of them will have the same state, which we will call synchronized state. In the bird example, the synchronized state might be that they are all flying north, and in the fish example, that they are all swimming south.

With this concept of synchronization that has just been defined, we can now think of another type of phenomenon, in particular, the following: consider an empty, impermeable box, without a lid (to be able to observe what happens inside), in which we will pour a liquid (none in particular, we can think of water or oil), with a volume large enough to cover the entire surface of the box. Intuition tells us that when we finish pouring the liquid, each unit of surface area of the box will be the basis of the same volume of liquid. This would be the synchronized state, and this process is more commonly called diffusion.

The way in which the liquid fills the box is not a very surprising mechanism; that is, if the liquid begins to pour at a point, then a circular neighborhood around that point is expected to fill uniformly at the same time, until the entire area of the box is covered, and then, we will see how its volume increases, until the liquid runs out.

On the other hand, what happens if the box is not initially empty? Suppose that on the surface of the box there are drops of different volumes, and the liquid begins to pour at a point where there is no drop. Diffusion occurs as in the case of the empty box, but when the liquid interacts with a drop, then the circular neighborhood changes its shape to become a kind of deformed number 8, and so on with the drops that it finds in its path. What happens is a kind of agglutination phenomenon, that is, how one drop sticks to another, just before the area of the box fills up and begins to increase in volume until the liquid runs out.

So, for the case where there are drops, how could we describe the way the box is filled? First, we can identify that this phenomenon depends on some factors, the first, how many drops there are in the box at the beginning and what volume each of them has. You can even think of the effect if the box is not square or has holes inside it (without considering liquid losses, each of the holes would have a barrier that would prevent liquid loss).

Despite these new obstacles, the ways in which the synchronized state is reached are accurately described using mathematics, thanks to a new combinatorial approach. In this approach, the configurations of the droplets are encoded together with their volume and the total volume of the liquid. In this way, its route towards synchronization is fully determined [2].

The kind of mathematical objects that are used to describe and code the paths to synchronization are very simple to understand. They are called discrete increasing functions that go above the diagonal and below the constant. They take a list of length N, considering that in each place a number greater than or equal to the one on the left and less than the one on the right is placed. For example, when N=5, the diagonal is (1,2,3,4,5), the constant is (5,5,5,5,5) and two functions between them would be (2,2,3,4,5) and (2,2,4,4,5). These mathematical objects have been extensively studied; multiple characteristics and properties of them are known.

In physics, knowing how to use the tools provided by mathematics, which usually focus on calculus, differential equations, probability, and statistics, have allowed us to solve many problems in an elegant, useful, and educational way. In this case, using combinatorial and number theory tools allowed us to make an exact description of what happens before reaching synchronization, which in turn is a widely observed and studied phenomenon. No tool is left over, one day it could help us to graduate with a doctorate.

References:

1. A. Pikovsky, M. Rosenblum, J. Kurths, Synchronization – a universal concept in nonlinear sciences, in: Cambridge Nonlinear Science Series, 2001.
2. A. España, X. Leoncini, E. Ugalde, Combinatorics of the paths towards synchronization, 2022. doi:10.48550/ARXIV.2205.05948. URL: https://arxiv.org/abs/2205.05948

Author: Aikaterini Nikou, University of Edinburgh, UK

Happy New Year! This is the first article in an exciting new series. Every month we hope to showcase a scientific article written by an undergraduate or postgraduate physics student. Is there a topic you would like to write about? Just email your article to jiaps@iaps.info . 

(Word limit – 1000 words. For guidance on how to write an article, see http://iaps.ovh/wp-content/uploads/2019/01/How-to-write-an-article.pdf and http://iaps.ovh/wp-content/uploads/2022/04/jIAPS-Submission-Guidelines.pdf )

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Social Sciences; Let’s get… physical!

In the orbits of stars, in particle collisions, in chemical reactions, in vehicles’ machines… physics is everywhere. Notoriously, physics is also in human behaviours, human interactions, and social dynamics. Have you ever considered how elegantly physics could describe social phenomena?

There is a particularly graceful beauty in the notion that social phenomena could be modelled, explained, analysed and predicted using mathematics in a way similar to physical phenomena. This could have a great spectrum of applications including economy (econophysics), pedagogy, tackling pandemics or even… dating. Social physics experiments conducted in the MIT media laboratory have investigated dating and found that it is possible to predict the outcome merely by analysing non-linguistic social signals such as the tone of voice [1]. A similar view could be used to analyse and predict other societal aspects including negotiating.

Social physics is a revolutionising topic in science; however, studying social phenomena through a scientific scope has existed for centuries. The English philosopher Thomas Hobbes mentioned this concept before the term Social Physics or Sociophysics was coined for the first time. He expressed the notion that social phenomena could be represented in terms of the laws of motion of physics and therefore explained through the lens of physics. In his book “De Corpore” (“On the Body”), he described the idea that the behaviour of “material bodies” can be expressed mathematically through the laws of motion invented by Galileo [2]. It seems almost natural to stop for a second and admire the beautiful diachronism of physics, as well as its interdisciplinarity in examining society from a scientific point of view.

Social Physics in Today’s Society

We live in a society where data collection is easier than ever, while there is a great number of datasets that are incredibly large and complex to analyse. Such datasets could be phone call records, web activity and credit card transactions. These datasets hold in their arms mathematical patterns that could reveal behavioural changes and patterns. Social physics can… deal with all – the so called “big-data”. It is a powerful tool that could be used for the blooming of our society. Evidently, data science is at the heart of social physics. Wonderfully, it can also help tackle world issues like the Covid-19 pandemic. A study showed that the multi-wave dynamics of Covid-19 outbreaks was dependent on the differences in responses to social stress [3].

A great benefit of social physics is that big data and exact mathematical tools can be applied in order to, in great accuracy, reflect on human behaviour as well as changes in it. It allows us to notice behavioural patterns and to therefore predict future social trends. These trends could include purchase preferences, shopping behaviour, communication behaviour, mobility or even Covid-19 cases spikes. These can then help us come up with more efficient plans to tackle climate change or urban development and traffic. It is worth noting that we could also observe and mathematically model connections between innovation and patterns of habits and communication which could greatly benefit the evolution of society. In other words, social physics can provide us with a way to more profoundly and accurately understand the mechanism of change of society. This could signal the birth of a new and innovative theory for society.

Social Physics and Machine Learning?

A question worth addressing is whether this analysis could be achieved using machine learning. Machine learning is a great tool for analysing mechanical and physical-driven data. For example, it can be invaluable in monitoring oil drill pumps control data and helping engineers prevent a possible malfunction. What about analysing financial transactions and therefore predicting customers’ preferences? Which type of customer would opt for a specific service for example? Social physics can help here as an appropriate tool for analysing human behaviour data. 

Social Physics and human development 

Moreover, social physics can also help in furthering our understanding of human development processes. Social physics has revealed a connection between the communication of a child and its brain development. The level of engagement (communication with people close to them such as parents or caretakers, inside of the red circle as seen in Fig.3) greatly affects the brain development of a child. Children that have a higher level of engagement and exploration (communication with people not in their close circle) have more developed brains and these children become more successful [4].

Conclusion

Recently, more and more social and societal phenomena are being studied through the lens of physics and mathematics. This interdisciplinary of social physics is particularly powerful. A great number of social physics studies have been conducted bringing to the surface revolutionising ideas, and so many more have yet to be conducted by the next generation of social physicists that could contribute to the blooming of our society. 

References

1. Madan A, Caneel R.  Pentland A”S”. Voices of Attraction. MIT Media Laboratory Technical Note 2004 Sep; No. 584. Available from: https://dam-prod.media.mit.edu/x/files/tech-reports/TR-584.pdf
2. Social Phyics/ Wikipedia [Internet]. Available from https://en.wikipedia.org/wiki/Social_physics 
3. Kastalskiy AI, Pankratova VE, Mirkes ME, Kazantsev BV, Gorban NA. Social stress drives the multi-wave dynamics of COVID-19 outbreaks. Sci Rep. 2021 Nov18;11(1):22497. 
4. MIT TEDxTalk [Intenet] Success through Social Physics Alex “Sandy”; 2014 Dec 13. Available from https://www.youtube.com/watch?v=C-wHdSJM_GI