This article presents an extended dialogue between Angshuman Guha, a researcher with hands-on experience in neural networks dating back to 1993, and Google’s large language model (LLM), Gemini. It explores a set of closely connected questions at the intersection of AI and cognitive science. Are modern LLMs genuine reasoning systems, or are they sophisticated Stochastic Parrots that recombine language without understanding? Do the so-called “emergent abilities” of large models reflect a real shift in machine capability, or are they artifacts of how we measure performance? And perhaps most fundamentally: can a system trained entirely on human-generated text be said to produce anything like “original thought”?
Most intelligent tasks we perform in our lives, we learn those skills through examples rather than being told step-by-step how to do them. For example, no one told us how to recognize the numbers, but showed us many examples, and our minds figured out some subconscious rules to distinguish a “1” from a “2” and all the other digits. Scientists quickly realized that if machines must do complex tasks, they cannot be taught algorithmically, with step-by-step instructions, as we ourselves may not know these logical steps, but rather by showing many examples of the correct behavior. Although that was the holy grail of AI, machine learning was a hard task.
This article is an overview of the oldest electromagnetic radiation in the Universe, known as the Cosmic Microwave Background (CMB), representing a snapshot of the cosmos approximately 380,000 years after the Big Bang. The author discusses how precise measurements from COBE, WMAP, and Planck telescopes have given us clues about when and how the CMB formed, and helped us refine our understanding of the Universe's composition, age, and geometry.
What if a painting were a hidden map of the heavens? In recent years, scientists have begun to treat famous canvases as puzzles to be solved with telescopes and software. Consider Vincent van Gogh’s The Starry Night: its swirling stars and glowing crescent moon might look like pure imagination, but astronomers and art historians discovered that the sky in the painting closely matches the real night sky on June 19, 1889. In fact, Venus appears in exactly the position Van Gogh painted it. Likewise, Johannes Vermeer’s View of Delft is more than a cityscape; researchers measured the angles of sunlit patches and shadows in the painting and found they align with the Sun’s position on a clear Dutch morning around 8 a.m. on September 3 or 4, 1659. It’s as if these masterpieces are cosmic records. In this article, the author plays detective with science and art, using celestial clues – star charts, sun positions, historical maps – to decode the hidden details of Van Gogh’s and Vermeer’s worlds. What emerges is a story of wonder: art created in the real light of the sky, waiting for modern "astronomers as sleuths" to unlock its secrets.
Meet Professor Anthony James Leggett, Nobel laureate in Physics (2003) and titan of low-temperature physics, whose groundbreaking Leggett-Garg inequality — born from his 1985 collaboration with Anupam Garg — unlocks laboratory tests of Schrödinger’s Cat paradox, challenging realism itself. This piece emerges from an intimate conversation Anindya De shared with the professor during his 80th birthday conference at Raman Research Institute, Bengaluru.
The Physicist Who Drew to Think
Werner Heisenberg took up artistic pursuits as a young man, aligning with the broad cultural education expected of Germans...
This article presents an extended dialogue between Angshuman Guha, a researcher with hands-on experience in neural networks dating back to 1993, and Google’s large language model (LLM), Gemini. It explores a set of closely connected questions at the intersection of AI and cognitive science. Are modern LLMs genuine reasoning systems, or are they sophisticated Stochastic Parrots that recombine language without understanding? Do the so-called “emergent abilities” of large models reflect a real shift in machine capability, or are they artifacts of how we measure performance? And perhaps most fundamentally: can a system trained entirely on human-generated text be said to produce anything like “original thought”?
Gravity‑assist flybys, or gravitational slingshots, are essential to modern deep‑space exploration because they allow spacecraft to gain heliocentric energy without expending propellant. By passing behind a moving planet, a probe undergoes an exchange of linear momentum that increases its Sun‑centered velocity while leaving its speed relative to the planet nearly unchanged. This fuel‑free transfer of orbital energy is indispensable for reaching the outer Solar System, where vast travel distances and the prohibitive propellant demands of direct propulsion make conventional trajectories unworkable. Gravity assists provide the momentum needed to access high‑value astrobiological targets such as Europa and Enceladus — icy moons whose subsurface oceans, sustained by tidal heating, make them prime candidates in the search for extraterrestrial life. By reshaping trajectories through planetary and lunar encounters, mission designers can conserve fuel for complex scientific operations, including plume sampling and close‑range reconnaissance, thereby enabling ambitious exploration that would otherwise be impossible.
When John Bell graduated from Queen’s in mid-1949, he was lucky that, following the invention of radar and the atomic bomb in the Second World War, physics was very much in favor with the powers-that-be. In particular, under the leadership of John Cockcroft, the Atomic Energy Research Establishment at Harwell was just getting into its stride. Doubtlessly, many physicists would have liked to get a job there, so it was remarkable that a new graduate without experience. and without even a PhD, was appointed, and more than that, there was even competition for his services.
The organization's aim was fundamental research in atomic physics, with no commercial or defence work, and John Bell was highly valued.
Intra-uterine fetal surgery is relatively new and a costly procedure. It requires a large, collaborative team in which each member is aware of their co-worker's role. Though considered a unique speciality, intra-uterine fetal surgery evolved alongside advances in ultrasonography, the establishment of fetal medicine, and the increased acceptance of fetoscopy for diagnosis and therapy in desperate situations.
