The above picture is from Friedrich, Bretislav, and Dudley Herschbach. “Stern and Gerlach: How a Bad Cigar Helped Reorient Atomic Physics.” Physics Today 56, no. 12 (December 1, 2003): 53–59. https://doi.org/10.1063/1.1650229.
During the formative years of quantum mechanics (early 1900s), the spin and orbital angular momentum of atoms were found to be quantized by theoretical arguments. Experimental proof was lacking.
Stern-Gerlach experiment provided the first experimental proof in 1922. They took a beam of neutral silver atoms and deflected them through an inhomogeneous magnetic field.
Silver atoms have an unpaired electron in their outermost orbit. If they were to obey quantum mechanics, they should exhibit a spin of +1/2 or -1/2. When subjected to an external magnetic field, the electrons with +1/2 or -1/2 should spatially split into two. That is exactly what Stern and Gerlach observed, and below is the first picture of the same.
To quote the authors:
Gerlach’s postcard, dated 8 February 1922, to Niels Bohr. It shows a photograph of the beam splitting, with the message, in translation: “Attached [is] the experimental proof of directional quantization. We congratulate [you] on the confirmation of your theory.” (Courtesy AIP Emilio Segrè Visual Archives.)
This experiment was one of the most important observations in quantum mechanics and further confirmed the quantization of spins, which is now common knowledge in physics.
Superstition is a belief system or behavior of an individual that cannot be justified with evidence and logic. It is usually associated with people who do not (or do not want to) think critically. From the history and philosophy of science, we learn that a few famous thinkers of the past had some form of belief that can be termed superstitious. Of course, they were products of their times and environments, but it is always interesting to learn about the contradictions.
Take, for example, Kepler, Galileo and Newton. They were 3 important figures who laid the foundation of classical mechanics (along with many other things). But they also had their pet beliefs that were neither logical nor scientific.
In the preface of his book, Karl Popper has to say about the superstition of the 3 individuals mentioned:
Each of the three intellectual giants was, in his own way, caught up in a superstition. (‘Superstition’ is a word we should use only with the greatest caution, knowing how little we know and how certain it is that we too, without realizing it, are caught up in various forms of superstition.) Galileo most deeply believed in a natural circular motion – the very belief that Kepler, after lengthy struggles, conquered both in himself and in astronomy. Newton wrote a long book on the traditional (mainly biblical) history of mankind, whose dates he adjusted in accordance with principles quite clearly derived from superstition. And Kepler was not only an astronomer but also an astrologer; he was for this reason dismissed by Galileo and many others.
Of course, I am bringing this up not to justify any superstition. But to highlight the fact that people whom we call ‘heroes’ are humans and have their beliefs and flaws. We may derive inspiration from their work, but not all aspects of their character may be suitable for emulation.
We will have to adapt what is good and discard what is not. You may ask: what is the definition of ‘good’? Well, that is a topic for a different debate, but in this context, I would say ‘good’ are the ideas and methods developed by the abovementioned that are testable and falsifiable. Karl Popper may be happy with that definition.
There is a new book (88 pages) on the philosophy of science that discusses the demarcation problem between science and pseudoscience. The topics look interesting, and have relevance in a day and age where science has been appropriated for various purposes, including spirituality.
One will have to ask how to differentiate science from something that may sound like science but, with further exploration, turns out to be a hoax?
This book tries to address this issue from a philosophical viewpoint.
Our world is a place with complex ideas superimposed on people with ever-changing attention. Complex ideas are complex because they depend on multiple parameters. If something changes in the world, then that change can occur due to multiple reasons.
Unlike a carefully designed physics experiment, there are too many ‘hidden variables’ in human life and behavior, especially when they act collectively. In such a situation, it is pertinent to search for models to understand the complex world. Models, by definition, capture the essence of a problem and do not represent the complete system. They are like maps, zoomed out, but very useful if you know their limitations. I keep searching for mental models that will help me understand the complex world in which I live, interact, and comprehend.
Among many models, one of them that I use extensively is the follow-the-money model. This model explains some complex processes in a world where one does not have complete information about a problem.
Take, for example, the incentives to choose a research project. This is a task that as scientists, we need to do very often. In the process of choosing a project to work on, researchers have to factor in the possibility of that research being funded prior to the start of the project. This is critical for scientific research that is dependent on infrastructure, such as experimental sciences, including physics, chemistry, and biology. Inherently, as researchers, we tend to pick a topic that is at the interface of personal interest, competence, relevance, and financial viability.
The viability is an important element because sustained funding plays a critical role in our ability to address all the contours of a research project. Thus, as scientists, we need to follow the money and ask ourselves how our research can be adapted to the financial incentives that a society creates. A case in point is research areas such as AI, where many people are aware of its potential and, hence, support from society and an opportunity to utilize the available incentive.
It is important for the public to be aware of this aspect of research where the financial incentive to execute a project plays a role in the choice of the project itself. The downstream of this incentive is the opportunity to employ more people. This means large funding projects and programs attract more researchers. More people in the research area generate more data, and more data, hopefully, will result in more knowledge in the chosen research area. This shows how financial incentives play a critical role in propelling a research area. In that sense, the ‘follow the money’ model has a direct correlation with more researchers flocking towards a research area.
The downside of this way of functioning is that it skews people towards certain areas of research at the cost of another research area which may not find financial support from the society. This is a topic that is generally not discussed in science classes, especially at the undergraduate and research level but I think we should discuss with students about this asymmetry as their futures are dependent on financial support that they can garner.
Broadening the scope further, the ‘follow the money’ model is useful to understand why a certain global trend rises or falls. A contemporary global upheaval is the situation of war in Ukraine and Gaza. At first sight, it looks like these wars are based on ideologies, but a closer look reveals that these wars cannot be fought without financial support. Such underpinning of the money running the war reveals patterns in geopolitics that are otherwise not easy to grasp.
Ideologies have the power to act as vehicles of human change, but these vehicles cannot be propelled without the metaphorical fuel – that is, money. The ‘follow-the-money’ model can show some implicit motivation and showcase how ideologies can be used as trojan horses to gain financial superiority either through captured resources or through showcasing the ability to capture that resource. Following money is also a very powerful and useful model for understanding many cultural, sociological and political evolution, even in a complex country like India and other South Asian countries. I leave it as an intellectual assignment for people who want to explore it 😊. You will be surprised how effective it can be in explaining many complex issues, provided we know the limitations of the model.
As I mentioned earlier, a model is like a map. It is limited by resolution, the dimension and the viewpoint. But they are useful for navigating a complex world.
I think that knowledge, especially academic knowledge, should be openly accessible to fellow researchers and the public. Given that most of the academic research in India is public-funded, it is imperative that our work is made available for anyone interested in reading and utilizing the knowledge. This makes a strong case for making our work open to access. Unfortunately, the open-access publication venture has been hijacked by some commercial publishers, who have misused the enthusiasm of academic researchers by converting it into a money-making opportunity via so-called ‘gold’ open-access models, where authors pay exorbitant article processing charges (APCs) to publish their work in the journals.
I have been searching literature to understand the philosophy and economics of open-access publishing models, and recently, by chance, I came across a couple of books by Peter Suber.
First is an open-access book on Open Access. Peter Suber has been a philosopher & open-access advocate for a long time. In this book, he explains what it really means to have open access to knowledge & the related philosophy, including its economics. Link to the book.
Second is a collection of writings on open-access publication (link), and as the online blurb says :
Influential writings make the case for open access to research, explore its implications, and document the early struggles and successes of the open access movement.
The green open-access model is very interesting and pertinent to countries such as India, and as per wiki it is defined as :
Green OA, is the practice of placing a version of an author’s manuscript into a repository, making it freely accessible for the scientific community.
The primary motivation of Open Access was to provide Open Access to Knowledge to the READER of Publications and; to allow Open Access to AUTHORS Publication (unbiased publication of Knowledge)
Open access in the true sense, should neither have barriers to knowledge consumption nor to knowledge generation and dissemination. Therefore, APCs are a major hurdle to researchers and authors who do not have monetary support. This is most of the global south, and hence, a fair policy is needed to make it more democratic.
A contemporary question of interest: How can artificial intelligence (AI) influence writing?
Writing has two consequences – 1) a writer processing information and communicating it to an audience; 2) a reader processing the author’s information.
The first part has an element of personal touch, just like any art or craft (for example, pottery). One does write (or create a pot) partly because it gives some pleasure and helps one to understand something in the process. There is a gain of knowledge in writing. This pleasure and wisdom through writing cannot be replaced by an external agency like AI. This is because external tools like AI are assistants of thought, not internal replacements of thought. In that sense, no external tool can replace any amateur activity because something is done for the sake of the process. Writing as a tool of self-reflection cannot be replaced by something external.
So, where is the threat? Actually, it is professional writing which is under partial threat from AI. Wherever the end product is more important than the process of writing, AI can gain prominence, provided it is accurate. It is still a partial threat because a professional writer can create questions and combinations that may arise out of individual experiences. Those lived experiences are derived from “life“, and AI cannot be a substitute for such an internal experience.
Writing, like many human endeavors, is both internal and external. The former makes us human, and that is hard to replace. After all, the A in AI stands for artificial.
The Nobel Prize in Physics 2024 was awarded to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks“. There has been much buzz surrounding this prize, especially in the context of whether these discoveries are indeed in the realm of mainstream physics. Many science commentators have questioned the choice and have provocatively dismissed it as ‘not part of mainstream physics’.
This has also brought into focus an important question: What is physics?
This question does not have a simple answer, given the rich history of the subject and its applicability over centuries. What we now call engineering is essentially an extrapolation of thinking in physics. New avenues have branched out from physics that cannot be readily identified as mainstream physics; a case in point is artificial intelligence and machine learning.
One of the aspects of mainstream physics is that the intellectual investment in the contemporary scenario is mainly driven by discoveries happening in the realm of quantum mechanics and general relativity. One of the mainstream problems in physics is to combine quantum mechanics and gravitation, which remains an unresolved task. Therefore, significant attention is paid to understanding these theories and verifying them through experimentation. Other areas and sub-disciplines in physics have become loosely connected to these two important theories.
There is another dimension to physics that is equally important and has vast applications: statistical physics. In statistical physics, the motivation comes from multi-particle systems and their applicability as models to understand our world, including biological systems. One utilizes knowledge from mathematics and statistics, combining them with physical laws to predict, invent and understand new forms and assemblies of matter. This thinking has been extrapolated to abstract assemblies and hence applied to a variety of situations. This approach has led to a revolution in how we can understand the realistic world because a statistical viewpoint is very useful for studying complex systems, such as many-body quantum mechanical aggregates (such as groups of electrons), dynamics of molecules inside a cell and the evolution of the stock market. Statistical physics plays a dominant role in all these situations. It has become a ubiquitous tool, making it difficult to directly connect it to basic principles of physics as taught in college textbooks and classrooms. It reminds me of a saying: if you are everywhere, then you are from nowhere.
This situation leads us back to the question: What is physics? John Hopfield himself offers an interesting definition related to this question, emphasizing that viewpoint is a crucial element. This perspective allows for greater freedom in using physics beyond conventional definitions. Among scientific disciplines, physics is always associated with its depth of understanding. This is a good opportunity to emphasize the breadth of physics, which is equally noteworthy.
In that light, the 2024 Nobel Prize in Physics should be welcomed as an expansion of the horizon of what constitutes physics. In a day and age where basic science has been questioned regarding its applicability to modern-day life and technology, this prize serves as a welcome change to showcase that basic science has played a fundamental role in establishing a contemporary tool of primary importance to society.
This point is particularly important because policymakers and politicians tend to focus on immediate issues and ask how they can influence them by using modern-day technology. Utility is central to this form of thinking. Given that basic sciences are often viewed as ‘not immediately useful’, this viewpoint diminishes the prominence of foundational disciplines: physics, chemistry, biology, and mathematics. In contrast, this prize reinforces the idea that building cutting-edge technology, which holds contemporary relevance and societal impact, has its roots in these foundational disciplines. In that sense, this prize is an important message because, like it or not, the Nobel Prize captures the attention not only of the scientific world but also of the public and, hence, of interest to politicians and policymakers.
Issac Asimov is attributed to have said: “There is a single light of science, and to brighten it anywhere is to brighten it everywhere.” The Nobel Prize in Physics 2024 fits that bill.
The title of this blog is the closing line of an autobiographical essay written by John Hopfield (pictured above), one of the physics Nobel laureates today: “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
In this essay, he retraces his trajectory across various sub-disciplines of physics and how he eventually used his knowledge of physics to work on a problem in neurobiology that further connects to machine learning.
The title of the essay is provocative(see below) but worth reading to understand how physics has evolved over the years and its profound impact on various disciplines.
Thanks to Gautam Menon for bringing the essay to my notice.
By the way, Hopfield and Deepak Dhar shared the 2022 Boltzmann medal, and after the award, he gave a wonderful online talk at IMSc, Chennai. Thanks to Arnab Pal of IMSc for bringing this to my notice on X.
Let me end this post quoting Hopfield from the mentioned essay:
What is physics? To me—growing up with a father and mother who were both physicists—physics was not subject matter. The atom, the troposphere, the nucleus, a piece of glass, the washing machine, my bicycle, the phonograph, a magnet—these were all incidentally the subject matter. The central idea was that the world is understandable, that you should be able to take anything apart, understand the relationships between its constituents, do experiments, and on that basis be able to develop a quantitative understanding of its behavior. Physics was a point of view that the world around us is, with effort, ingenuity, and adequate resources, understandable in a predictive and reasonably quantitative fashion. Being a physicist is a dedication to the quest for this kind of understanding.
