Space & Physics
Pioneers of modern Artificial Intelligence

The 2024 Nobel Prize for Physics has been a trend breaker with computer scientists being awarded the prestigious prize.
Geoffrey Hinton, one of this year’s laureates, was previously awarded the 2018 Turing Prize, arguably the most prestigious prize in computer science.
John Hopfield, the other laureate, and Hinton, were amongst the early generation of computer scientists in the 1980s, who’d set the foundations for machine learning, a technique used to train artificial intelligence. These techniques shaped modern AI models, to take up the mantle from us, to discover patterns within reams of data, which otherwise would take humans arguably forever.
Until the last mid-century, computation was a task that required manual labor. Then, Alan Turing, the British inventor and scientist, who’d rose to fame during World War 2, having helped break the Enigma code, would conceive the theoretical basis for modern computers. It was when he tried to push further, he came up with, arguably a thought, that led to publication of “Can machines think?” Seemingly an innocuous question, but with radical consequences if it really took shape, Turing, through his conceptions of algorithms, laid the foundation of artificial intelligence.
Why the physics prize?
Artificial neural networks, particularly, form the basis for today’s much popular OpenAI’s ChatGPT, and numerous other facial, image and language translational software. But these machine learning models have broken the ceiling with regards to their applications in numerous disciplines: from computer science, to finance to physics.
Physics did form the bedrock in AI research, particularly that of condensed matter physics. Particularly of relevance is spin glass – a phenomena in condensed matter physics, that involves quantum spins behaving randomly when it’s not supercooled, when it rather becomes orderly. Their applications to AI is rather foundational.
John Hopfield and Geoff Hinton are pioneers of artificial neural networks. Hopfield, an American, and Hinton, from Britain, came from diverse disciplines. Hopfield trained as a physicist. But Hinton was a cognitive psychologist. The burgeoning field of computer science, needed interdisciplinary talent, to attack a problem that no single physicist, logician, mathematician could solve. To construct a machine that can think, it will have to learn to make sense of reality. Learning is key, and computer scientists took inspiration from across statistical and condensed matter physics, psychology and neuroscience to come up with the neural network.
Inspired by the human brain, it involves artificial neurons, that holds particular values. This takes shape when the network would be initially fed data as part of a training program before it’s trained further on unfamiliar data. These values would update upon subsequent passes with more data; forming the crux of the learning process. The potential for this to work happened though with John Hopfield constructing a simple neural network in 1982.

Hopfield network, with neurons forming a chain of connections. Credit: Wikimedia Commons
Neurons pair up with one another, to form a long chain. Hopfield would then feed an image, training it by having these neurons passing along information, but only one-way at a time. Patterns of neurons that fire together, wire together, responding to particular patterns that it formerly trained with. Known as the Hebbian postulate, it actually forms the basis for learning in the human brain. It was when the Hopefield network was able to identify even the most distorted version of the original image, did AI take its baby steps. But then to train the network to learn robustly across a swathe of more data, required additional layers of neurons, and wasn’t an easy goal to achieve. There was a need for an efficient method of learning.

Artificial neural network, with neurons forming connections. The information can go across in both directions (though not indicated in the representation). Credit: Wikimedia Commons
That’s when Geoff Hinton entered the picture at around the same timeframe, helping conceive backpropagation, a technique that’s now mainstream and is the key to machine learning models that we use today. But in 2000, Hinton conceived the multi-layered version of the “Boltzmann machine”, a neural network founded on the Hopfield network. Geoff Hinton was featured in Ed Publica‘s Know the Scientist column.
Space & Physics
This Sodium-Fuelled Clean Energy Breakthrough Could Electrify Aviation and Shipping
The innovation offers more than triple the energy density of today’s lithium-ion batteries — potentially clearing a major hurdle for electric-powered aviation, rail, and maritime travel

A new type of fuel cell developed by MIT researchers could represent a pivotal breakthrough in the race to decarbonize heavy transportation. Designed around liquid sodium metal, the innovation offers more than triple the energy density of today’s lithium-ion batteries — potentially clearing a major hurdle for electric-powered aviation, rail, and maritime travel.
Unlike traditional batteries that require time-consuming recharging, this system operates like a fuel cell that can be refueled quickly using liquid sodium — a cheap, abundant substance derived from salt. The technology, which uses air as a reactant and a solid ceramic electrolyte to facilitate the reaction, was tested in lab prototypes and demonstrated energy densities exceeding 1,500 watt-hours per kilogram — a level that could enable regional electric flight and clean shipping.
“We expect people to think that this is a totally crazy idea,” said Professor Yet-Ming Chiang, lead author and Kyocera Professor of Ceramics, in a media statement. “If they didn’t, I’d be a bit disappointed because if people don’t think something is totally crazy at first, it probably isn’t going to be that revolutionary.”
Chiang explained that current lithium-ion batteries top out at around 300 watt-hours per kilogram — far short of the 1,000 watt-hours needed for electric aircraft to become viable at scale. The new sodium-based cell meets that benchmark, which could enable 80% of domestic flights and drastically reduce aviation’s carbon footprint.
Moreover, the sodium-fueled system offers environmental benefits beyond zero emissions. Its chemical byproduct, sodium oxide, reacts spontaneously in the atmosphere to capture carbon dioxide and convert it into sodium bicarbonate — better known as baking soda — which may help counteract ocean acidification if it ends up in marine environments.
“There’s this natural cascade of reactions that happens when you start with sodium metal,” Chiang said. “It’s all spontaneous. We don’t have to do anything to make it happen, we just have to fly the airplane.”
The team has already created two functioning lab-scale prototypes: one vertical and one horizontal model. In both, sodium gradually reacts with oxygen from air to generate electricity, and a moist air stream improves the process by allowing liquid byproducts to be expelled more easily.
Karen Sugano, one of the MIT doctoral students on the project, noted, “The key was that we can form this liquid discharge product and remove it easily, as opposed to the solid discharge that would form in dry conditions,” she said in a media statement.
The researchers have founded a startup, Propel Aero, housed in MIT’s startup incubator The Engine, to scale the technology. Their first commercial goal: a brick-sized fuel cell capable of powering a large agricultural drone — expected to be ready within a year.
Chiang emphasized the economic and safety benefits of using sodium, which melts just below 100°C and was once mass-produced in the U.S. for leaded gasoline production. “It reminds us that sodium metal was once produced at large scale and safely handled and distributed around the U.S.,” he said.
Critically, the fuel cell design also avoids many safety concerns of high-energy batteries by physically separating the fuel and oxidizer. “If you’re pushing for really, really high energy density, you’d rather have a fuel cell than a battery for safety reasons,” Chiang said.
By reviving and reimagining sodium-metal chemistry in a practical, scalable form, the MIT team may have lit the path toward clean, electrified transportation systems — from the skies above to the oceans below.
Space & Physics
Is Time Travel Possible? Exploring the Science Behind the Concept
Subtle forms of time travel — such as time dilation — do occur and have practical implications in science and technology.

Everyone is, in a way, a time traveller. Whether we like it or not, we are constantly moving through time — one second per second. From one birthday to the next, we travel through time at a steady pace, just like walking one foot per footstep. However, when we talk about “time travel,” we often imagine something much more dramatic — traveling faster (or even backward) through time, as seen in science fiction movies and novels. But is such a thing truly possible?
From Fiction to Science
The concept of time travel first gained widespread attention through literature, particularly with H.G. Wells’ 1895 novel The Time Machine. In it, time is described as the fourth dimension, akin to space, and the protagonist travels forward and backward in time using a specially built machine. Interestingly, this idea predates Albert Einstein’s theory of relativity, which would later reshape how we understand space and time.

Einstein’s Contribution: Relativity and Time Dilation
In the early 20th century, Albert Einstein introduced a revolutionary idea through his theory of relativity. He proposed that space and time are interconnected, forming a four-dimensional continuum called space-time. According to his theory, the speed of light (186,000 miles per second) is the ultimate speed limit in the universe. But how does this relate to time travel?
Einstein’s theory states that as you move faster — especially at speeds approaching the speed of light — time slows down relative to someone who is stationary. This phenomenon, known as time dilation, has been proven through various experiments. One famous example involved two synchronized atomic clocks — one placed on Earth and the other onboard a high-speed jet. When the plane returned, the onboard clock showed slightly less time had passed compared to the one on the ground. This demonstrates that, at very high speeds, time passes more slowly.
Astronaut Twins and Time
A notable example of time dilation involved twin astronauts Scott and Mark Kelly. Scott spent 520 days aboard the International Space Station, while Mark spent only 54 days in space. Due to the effects of time dilation, Scott aged slightly less than Mark — by about 5 milliseconds. Though this difference is minuscule, it is real and measurable, showing that time can indeed “bend” under certain conditions.
The GPS Example
Surprisingly, even GPS satellites experience time differently than we do on Earth. These satellites orbit at altitudes of about 20,200 kilometers and travel at speeds of roughly 14,000 km/h. Due to both their speed (special relativity) and weaker gravitational pull at high altitudes (general relativity), time ticks slightly faster for the satellites than for devices on Earth. This discrepancy is corrected using Einstein’s equations to ensure precise positioning. Without these adjustments, GPS systems could be off by several miles each day.
Science Fiction vs. Scientific Reality
Science fiction has long explored imaginative time travel — characters jumping into machines and traveling decades into the future or past. Stories often depict them altering historical events or witnessing the far future. However, there is no scientific evidence that anyone has travelled backward or forward in time in such a dramatic way.
Renowned physicist Stephen Hawking addressed this idea humorously in 2009. He hosted a party for time travellers — but only announced it afterward, reasoning that if time travel were possible, people from the future would show up. No one came. Hawking took this as a tongue-in-cheek sign that backward time travel may not be feasible.
Could Wormholes Be the Key?
Theoretical physics does suggest possibilities like wormholes — shortcuts through space-time. According to Einstein’s equations, these could, in theory, connect distant places and times. A wormhole might allow someone to enter at one point in space and exit at another, potentially in a different time. However, this remains purely speculative. The extreme gravitational forces within black holes or wormholes could destroy anything attempting to pass through.
Moreover, the idea of backward time travel introduces major paradoxes — such as the classic “grandfather paradox,” where someone goes back in time and prevents their own existence. Such contradictions challenge our understanding of causality and logic.
The Limitations of Current Science
At present, building a time machine capable of transporting people backward or forward in time by centuries remains outside the realm of scientific possibility. It’s a concept best enjoyed in novels and films for now. However, subtle forms of time travel — such as time dilation — do occur and have practical implications in science and technology.
While we may not have DeLoreans or TARDISes at our disposal, time travel — at least in small, measurable ways — is a part of our reality. The interplay of speed, gravity, and time demonstrates that our universe is far more flexible than it appears. And who knows? In some distant corner of the cosmos, nature might already be bending time in ways we are only beginning to imagine.
Until then, we’ll keep moving forward — one second per second.
Space & Physics
MIT Physicists Capture First-Ever Images of Freely Interacting Atoms in Space
The new technique allows scientists to visualize real-time quantum behavior by momentarily freezing atoms in motion and illuminating them with precisely tuned lasers

In an intriguing advancement for quantum physics, MIT researchers have captured the first images of individual atoms freely interacting in space — a feat that until now was only predicted theoretically.
The new imaging technique, developed by a team led by Professor Martin Zwierlein, allows scientists to visualize real-time quantum behavior by momentarily freezing atoms in motion and illuminating them with precisely tuned lasers. Their results, published in Physical Review Letters, reveal how bosons bunch together and fermions pair up in free space — phenomena crucial to understanding superconductivity and other quantum states of matter.
“We are able to see single atoms in these interesting clouds of atoms and what they are doing in relation to each other, which is beautiful,” said Zwierlein in a press statement.
Using their method — called “atom-resolved microscopy” — the team was able to trap atom clouds with a loose laser, briefly immobilize them with a lattice of light, and then image their positions via fluorescence. This approach allowed the researchers to observe quantum behaviors at the level of individual atoms for the first time.
The MIT group directly visualized sodium atoms (bosons) bunching together in a shared quantum wave — a vivid confirmation of the de Broglie wave theory — and lithium atoms (fermions) pairing up despite their natural repulsion, a key mechanism underlying superconductivity.
“This kind of pairing is the basis of a mathematical construction people came up with to explain experiments. But when you see pictures like these, it’s showing in a photograph, an object that was discovered in the mathematical world,” said co-author Richard Fletcher in a press statement.
Two other research teams — one led by Nobel laureate Wolfgang Ketterle at MIT, and another by Tarik Yefsah at École Normale Supérieure — also reported similar quantum imaging breakthroughs in the same journal issue, marking a significant moment in the experimental visualization of quantum mechanics.
The MIT team plans to expand the technique to probe more exotic quantum behaviors, including quantum Hall states. “Now we can verify whether these cartoons of quantum Hall states are actually real,” Zwierlein added. “Because they are pretty bizarre states.”
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