Frontiers of Science

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.

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.

An Antarctic expedition is among the most dangerous and adventurous journeys in the world. Modern technology has made these expeditions more feasible, but the spirit of adventure and harsh realities remain. Such exploration required sacrifices, including the loss of lives and failed attempts. This raises key questions: why are humans so drawn to these perilous journeys? How has exploration turned into a scientific and strategic goal to access this remote, icy continent? This article discusses Antarctica’s discovery, the global interest it sparked, and the Antarctic Treaty—a milestone in international cooperation. It traces human engagement from the Heroic Age to the present, highlighting efforts by both developed and developing nations to protect and study this unique environment, particularly India’s growing scientific and environmental role.

DeepSeek’s rapid ascent in artificial intelligence (AI) has disrupted the sector, challenging industry norms and intensifying global competition. This analysis by the Editorial Board highlights the company’s breakthroughs, notably its cost-effective, high-performance AI model "R1" and open-source large language models (LLMs) "DeepSeek-V3" and "DeepSeek-R1".

In our relentless quest to understand the Universe, we often overlook the most intricate and profound object we have: the human brain. Weighing just 1.5 kilograms, this soft, wet tissue is a marvel of complexity, capable of solving problems, creating art, and exploring the depths of existence. Throughout history, we have sought to unravel the mysteries of thought and reasoning, constantly drawing parallels between our minds and the advanced technologies of our time. From clockwork mechanisms to modern computers, these analogies reflect our evolving understanding of intelligence and cognition. Written by one of the leading experts on AI, this article takes a journey through the lens of chess—a game long considered a benchmark for intelligence—tracing the development of Artificial Intelligence over the past 75 years. As we examine how machines have learned to play this strategic game, we will uncover the challenges and breakthroughs that have shaped the AI field, revealing the deep connection between our understanding of the mind and the machines we create.

Electric cars evolved from elite transport in the 1900s to Ford’s Model T revolution. GM’s 1996 EV1 met emissions standards but was controversially scrapped...