Dear students of science, especially physics. As you study a scientific concept, ask:
How does it connect to a measurement?
This is the first step in expanding your thinking into the realm of experiments, other branches of science, engineering, economics & society.
I am not saying that you should become an expert in engineering or economics, but you should have a basic understanding of the outward connection of the concept. You will be surprised to know how ‘context’ can bring a new perspective to your studies.
Even if you want to become a theoretical physicist/scientist, this way of thinking will help you to expand your intuition. Your theories/models may find relevance beyond your immediate peers. Importantly, this exposure will bring feedback – vital when you are exploring the unknowns.
Many scientists, including some great theoretical physicists, have taken this approach. For example, Feynman, Einstein, Bethe, and Penrose come to my mind. The 2024 Nobel winners in Physics, especially John Hopfield, is an excellent contemporary example of this way of thinking.
An unanticipated consequence of this way of thinking is that you may end up contributing significantly to a field of research that you were not associated with initially. Guess what: most of the creativity is about the connection of ideas. A useful connection has a deep impact on the outward world: other branches of science, engineering, economics & society.
Pratidhvani (ಪ್ರತಿಧ್ವನಿ/प्रतिध्वनि) means reflection or resonance (of sound). Here, the aim of the podcast is to resonate with knowledge & humanize science. The podcast has two themes: 1) History & Philosophy of Physical Sciences & Technology, 2) Conversations with people related to their intellectual journey & themes mentioned in 1) Below is the link to all the episodes. Italicized ones are solo episodes
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.
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.
In scientific research, comparative analysis is an excellent way to objectively quantify two measurable entities. The recent Google quantum chip (named Willow) does that efficiently as it compares its capability with today’s fastest supercomputers. The comparison note on Google’s blog is worth reading.
In scientific analysis, such comparison teaches us three things:
a) how a scientific boundary is claimed to be pushed?
b) how a benchmark problem is used to achieve comparison?
c) what is the current state-of-the-art in that research area?
Some further observations on the work:
The theme of the Nature paper reporting this breakthrough is mainly on error correction. Technically, it shows how error tolerance is measured for a quantum device. This device is based on superconducting circuits, which were tested first on a 72-qubit processor and then on a 105-qubit processor.
Interestingly, as the authors mention in the paper, the origins of the errors are not understood well.
The paper is quite technical to read, and, to my limited understanding as an outsider, it makes a good case for the claim. The introduction and the outlook of the paper are written well, and give more technical information that can be appreciated by a general scientific audience.
There is more to come ! It looks like Google has further plans to expand on this work, and it will be interesting to see in which direction they will take the capability. The Google blog shows a roadmap and mentions their ambition as follows: “The next challenge for the field is to demonstrate a first “useful, beyond-classical” computation on today’s quantum chips that is relevant to a real-world application. We’re optimistic that the Willow generation of chips can help us achieve this goal.”
In the past 12 months or so, there has been a lot of buzz related to AI tools (thanks to GPTs, Nobels and perplexities :-), which are mainly in the realm of software theoretical development. This breakthrough in the realm of ‘hardware’ tells us how the physical world is still important!
More to learn and explore…interesting times ahead..
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.
