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
Atoms Speak Out: Physicists Use Electrons as Messengers to Unlock Secrets of the Nucleus
Physicists at MIT have devised a table-top method to peer inside an atom’s nucleus using the atom’s own electrons

Physicists at MIT have developed a pioneering method to look inside an atom’s nucleus — using the atom’s own electrons as tiny messengers within molecules rather than massive particle accelerators.
In a study published in Science, the researchers demonstrated this approach using molecules of radium monofluoride, which pair a radioactive radium atom with a fluoride atom. The molecules act like miniature laboratories where electrons naturally experience extremely strong electric fields. Under these conditions, some electrons briefly penetrate the radium nucleus, interacting directly with protons and neutrons inside. This rare intrusion leaves behind a measurable energy shift, allowing scientists to infer details about the nucleus’ internal structure.
The team observed that these energy shifts, though minute — about one millionth of the energy of a laser photon — provide unambiguous evidence of interactions occurring inside the nucleus rather than outside it. “We now have proof that we can sample inside the nucleus,” said Ronald Fernando Garcia Ruiz, the Thomas A. Franck Associate Professor of Physics at MIT, in a statement. “It’s like being able to measure a battery’s electric field. People can measure its field outside, but to measure inside the battery is far more challenging. And that’s what we can do now.”
Traditionally, exploring nuclear interiors required kilometer-long particle accelerators to smash high-speed beams of electrons into targets. The MIT technique, by contrast, achieves similar insight with a table-top molecular setup. It makes use of the molecule’s natural electric environment to magnify these subtle interactions.
The radium nucleus, unlike most which are spherical, has an asymmetric “pear” shape that makes it a powerful system for studying violations of fundamental physical symmetries — phenomena that could help explain why the universe contains far more matter than antimatter. “The radium nucleus is predicted to be an amplifier of this symmetry breaking, because its nucleus is asymmetric in charge and mass, which is quite unusual,” Garcia Ruiz explained.
To conduct their experiments, the researchers produced radium monofluoride molecules at CERN’s Collinear Resonance Ionization Spectroscopy (CRIS) facility, trapped and cooled them in laser-guided chambers, and then measured laser-induced energy transitions with extreme precision. The work involved MIT physicists Shane Wilkins, Silviu-Marian Udrescu, and Alex Brinson, alongside international collaborators.
“Radium is naturally radioactive, with a short lifetime, and we can currently only produce radium monofluoride molecules in tiny quantities,” said Wilkins. “We therefore need incredibly sensitive techniques to be able to measure them.”
As Udrescu added, “When you put this radioactive atom inside of a molecule, the internal electric field that its electrons experience is orders of magnitude larger compared to the fields we can produce and apply in a lab. In a way, the molecule acts like a giant particle collider and gives us a better chance to probe the radium’s nucleus.”
Going forward, the MIT team aims to cool and align these molecules so that the orientation of their pear-shaped nuclei can be controlled for even more detailed mapping. “Radium-containing molecules are predicted to be exceptionally sensitive systems in which to search for violations of the fundamental symmetries of nature,” Garcia Ruiz said. “We now have a way to carry out that search”
Space & Physics
Physicists Double Precision of Optical Atomic Clocks with New Laser Technique
MIT researchers develop a quantum-enhanced method that doubles the precision and stability of optical atomic clocks, paving the way for portable, ultra-accurate timekeeping.

MIT physicists have unveiled a new technique that could significantly improve the precision and stability of next-generation optical atomic clocks, devices that underpin everything from mobile transactions to navigation apps. In a recent media statement, the MIT team explained: “Every time you check the time on your phone, make an online transaction, or use a navigation app, you are depending on the precision of atomic clocks. An atomic clock keeps time by relying on the ‘ticks’ of atoms as they naturally oscillate at rock-steady frequencies.”
Current atomic clocks rely on cesium atoms tracked with lasers at microwave frequencies, but scientists are advancing to clocks based on faster-ticking atoms like ytterbium, which can be tracked with lasers at higher, optical frequencies and discern intervals up to 100 trillion times per second.
A research group at MIT, led by Vladan Vuletić, the Lester Wolfe Professor of Physics, detailed that their newly developed method harnesses a laser-induced “global phase” in ytterbium atoms and boosts this effect using quantum amplification. Vuletić stated, “We think our method can help make these clocks transportable and deployable to where they’re needed.” The approach, called global phase spectroscopy, doubles the precision of an optical atomic clock, enabling it to resolve twice as many ticks per second compared to standard setups, and promises further gains with increasing atom counts.
The technique could pave the way for portable optical atomic clocks able to measure all manner of phenomena in various locations. Vuletić summarized the broader scientific ambitions: “With these clocks, people are trying to detect dark matter and dark energy, and test whether there really are just four fundamental forces, and even to see if these clocks can predict earthquakes.”
The MIT team has previously demonstrated improved clock precision by quantumly entangling hundreds of ytterbium atoms and using time reversal tricks to amplify their signals. Their latest advance applies these methods to much faster optical frequencies, where stabilizing the clock laser has always been a major challenge. “When you have atoms that tick 100 trillion times per second, that’s 10,000 times faster than the frequency of microwaves,” said Vuletić in the statement. Their experiments revealed a surprisingly useful “global phase” information about the laser frequency, previously thought irrelevant, unlocking the potential for even greater accuracy.
The research, led by Vuletić and joined by Leon Zaporski, Qi Liu, Gustavo Velez, Matthew Radzihovsky, Zeyang Li, Simone Colombo, and Edwin Pedrozo-Peñafiel of the MIT-Harvard Center for Ultracold Atoms, was published in Nature. They believe the technical benefits of the new method will make atomic clocks easier to run and enable stable, transportable clocks fit for future scientific exploration, including earthquake prediction, fundamental physics, and global time standards.
Space & Physics
Nobel Prize in Physics: Clarke, Devoret, and Martinis Honoured for Pioneering Quantum Discoveries
The 2025 Nobel Prize in Physics honours John Clarke, Michel H. Devoret, and John M. Martinis for revealing how entire electrical circuits can display quantum behaviour — a discovery that paved the way for modern quantum computing.

The 2025 Nobel Prize in Physics has been awarded to John Clarke, Michel H. Devoret, and John M. Martinis for their landmark discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit, an innovation that laid the foundation for today’s quantum computing revolution.
Announcing the prize, Olle Eriksson, Chair of the Nobel Committee for Physics, said, “It is wonderful to be able to celebrate the way that century-old quantum mechanics continually offers new surprises. It is also enormously useful, as quantum mechanics is the foundation of all digital technology.”
The Committee described their discovery as a “turning point in understanding how quantum mechanics manifests at the macroscopic scale,” bridging the gap between classical electronics and quantum physics.
John Clarke: The SQUID Pioneer
British-born John Clarke, Professor Emeritus at the University of California, Berkeley, is celebrated for his pioneering work on Superconducting Quantum Interference Devices (SQUIDs) — ultra-sensitive detectors of magnetic flux. His career has been marked by contributions that span superconductivity, quantum amplifiers, and precision measurements.
Clarke’s experiments in the early 1980s provided the first clear evidence of quantum behaviour in electrical circuits — showing that entire electrical systems, not just atoms or photons, can obey the strange laws of quantum mechanics.
A Fellow of the Royal Society, Clarke has been honoured with numerous awards including the Comstock Prize (1999) and the Hughes Medal (2004).
Michel H. Devoret: Architect of Quantum Circuits
French physicist Michel H. Devoret, now the Frederick W. Beinecke Professor Emeritus of Applied Physics at Yale University, has been one of the intellectual architects of quantronics — the study of quantum phenomena in electrical circuits.
After earning his PhD at the University of Paris-Sud and completing a postdoctoral fellowship under Clarke at Berkeley, Devoret helped establish the field of circuit quantum electrodynamics (cQED), which underpins the design of modern superconducting qubits.
His group’s innovations — from the single-electron pump to the fluxonium qubit — have set performance benchmarks in quantum coherence and control. Devoret is also a recipient of the Fritz London Memorial Prize (2014) and the John Stewart Bell Prize, and is a member of the French Academy of Sciences.
John M. Martinis: Building the Quantum Processor
American physicist John M. Martinis, who completed his PhD at UC Berkeley under Clarke’s supervision, translated these quantum principles into the hardware era. His experiments demonstrated energy level quantisation in Josephson junctions, one of the key results now honoured by the Nobel Committee.
Martinis later led Google’s Quantum AI lab, where his team in 2019 achieved the world’s first demonstration of quantum supremacy — showing a superconducting processor outperforming the fastest classical supercomputer on a specific task.
A former professor at UC Santa Barbara, Martinis continues to be a leading voice in quantum computing research and technology development.
A Legacy of Quantum Insight
Together, the trio’s discovery, once seen as a niche curiosity in superconducting circuits, has become the cornerstone of the global quantum revolution. Their experiments proved that macroscopic electrical systems can display quantised energy states and tunnel between them, much like subatomic particles.
Their work, as the Nobel citation puts it, “opened a new window into the quantum behaviour of engineered systems, enabling technologies that are redefining computation, communication, and sensing.”
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