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Simplifying complex ideas in sketches

What would you see if you tried to travel alongside a light wave at the speed of light? And suppose you held a mirror in front of you as you zipped along. What would you see in the mirror? This and similar thought experiments were posed by the young Albert Einstein to himself in his teens. It’s come to be known as Einstein’s Mirror and is also the title of a popular book on relativity. It would at first seem that light, reflected off your face, could never reach the mirror to, in turn, reflect back into your eyes to see it. So what would you see? It was only years later that Einstein developed a theory that answered this puzzle. And it required some fundamental adjustments to how we understood the world, which still bend my mind to think about them. These include: You can’t travel at the speed of light. Time is not fixed; it is relative. The speed of light is a universal constant—it is the same, independent of the motion of the source. Einstein wrote: “After ten years of reflection, such a principle resulted from a paradox upon which I had already hit at the age of sixteen: If I pursue a beam of light with the velocity c [the velocity of light in a vacuum], I should observe such a beam of light as a spatially oscillatory electromagnetic field at rest. However, there seems to be no such thing…” — Autobiographical notes, 1949 I’ll try to explain a little as I understand it. Our usual experience is that velocities are additive. Suppose I am on a moving train carriage and I throw a ball from the back of the carriage to the front. For an observer outside the train, that ball moves at the speed of the train plus the speed of the ball relative to me. But light behaves differently. As you approach the speed of light, the energy required to keep accelerating approaches infinity. In effect, you can’t reach the speed of light. So an observer of a flying Einstein wouldn’t see light travelling from him to the mirror at twice the speed of light. What changes is time. For the high-speed Einstein, the light would appear to travel away from him to the mirror and back at its usual immense speed. However, for an observer, what would only seem a moment for the high-speed Einstein might take years for the rest of us—the experience of time changes with velocity. It’s a remarkable turn for a simple and fascinating question. It’s amazing to me that the young Einstein would both pose this question, continue work on it, and then think to question some of the most self-evident facts of our world as we experience it: that time is not fixed, that a speed cannot be reached, and of course, ultimately, that energy is matter. The book Einstein’s Mirror is co-authored by my Dad (respect!). It’s full of photographs, fascinating stories, and the characters that moved physics forward. It includes the people, events and science central to another of Christopher Nolan’s films, Oppenheimer. Perhaps Christopher read it 🤔 Related Ideas to Einstein’s Mirror Also see: Laplace’s Demon Redshift Looking back in time The Doppler Effect Sonic Boom The most beautiful equation — Earlier this year, we attended a showing of Christopher Nolan’s Interstellar at the Royal Albert Hall in London with Hans Zimmer’s soundtrack played by a live orchestra. It was a fantastic way to experience a remarkable film—a film that manages to make black holes, wormholes, and time slippage both understandable (largely) and part of the plot. It strikes me as an astonishing achievement for a mainstream film.

Battleship-trained AI learns to ask sharper questions, boosting win rate from 8% to 82%

In 2026, the hype for artificial intelligence agents is louder than ever before. These semi-autonomous programs can “think” and execute well-defined tasks in areas like customer service and software development, typically using language models (LMs). But fields like medical diagnosis and scientific discovery require them to inquire about a vast range of solutions in uncertain environments which LMs struggle with.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Harvard University’s School of Engineering and Applied Sciences (SEAS) peered deeper into LMs to understand their main issues in high-stakes settings. Their test: Battleship, a classic guessing game that’s helped cognitive scientists study how humans seek information.

CSAIL and SEAS scholars added a twist by reframing the game around asking and answering natural language questions. In their “Collaborative Battleship” game, one participant is a “captain” who inquires about where hidden ships are, while their teammate plays the “spotter” by responding to those questions in real time.

Death Toll In Missing Scientists Case Rises… | TMZ

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Paul Vitányi

Consider teaching a computer how to read by giving it billions of books. You don’t teach it grammar rules or logic; you simply ask it to play a game: “Look at these words, and guess what word comes next.” To win this game at a world-class level, the computer can’t just memorize phrases. It has to start figuring out how the world works. If it’s reading a mystery novel, it needs to deduce who the killer is to guess the final sentence. If it’s reading a math textbook, it has to understand addition to predict the answer to a problem. This is the core idea explored in a recent scientific paper titled “Algorithmic Compression via Pretrained Neural Networks.”*The researchers look under the hood of today’s Large Language Models (LLMs)—like the AI assistants we use every day—to explain a fascinating mystery: Why does a machine trained merely to predict the next word end up looking like it can think, reason, and solve complex problems? Think about how a ZIP file works on your computer. If you have a massive text file filled with the word “apple” repeated a million times, a compression program won’t save all million words. It will compress it into a short rule: “Repeat ‘apple’ 1,000,000 times.” It turns a massive mountain of data into a tiny, elegant recipe. (learning how to learn). Because the AI is fed a massive, diverse diet of information, it can’t just memorize everything. Instead, it is forced to find the underlying “recipes” or rules behind the data it sees. When you type a prompt into an AI, it doesn’t just look up an answer in a database. It looks at your text, infers the “generative algorithm” (the underlying pattern or logic of what you are asking), and uses that pattern to compress the problem and generate the correct response. In essence, it deduces the hidden rules of the game on the fly. * Discover Complex Logic: When given a sequence of chess moves, the AI doesn’t just guess random moves; it actually reconstructs the abstract rules and evaluations of a chessboard in its digital “mind.” While this framework helps explain why AI is getting so smart, it also opens up big new questions. We know these models are compressing data and finding rules, but we still don’t fully understand the absolute limits of this approach. How close can a practical AI get to that theoretical “perfect” intelligence? What happens when the AI runs out of human data to learn from?


Vitányi was appointed professor of computer science at the University of Amsterdam, and researcher at the National Research Institute for Mathematics and Computer Science in the Netherlands (CWI, initially Mathematical Centre [MC]) where he is currently a CWI Fellow. He was guest professor at the University of Copenhagen in 1978; research associate at the Massachusetts Institute of Technology in 1985/1986; Gaikoku-Jin Kenkyuin (councilor professor) at INCOCSAT at the Tokyo Institute of Technology in 1998; visiting professor at Boston University in 2004, at Monash University in 1996 and at the National ICT of Australia NICTA at University of New South Wales in 2004/2005; visiting professor at and adjunct professor of computer science at the University of Waterloo from 2005.

Why ‘football’ beats ‘shamrock’ when your brain is dismantling every word at lightning speed

Before you even know what a word means, your brain is already playing a rapid-fire game of linguistic LEGO. Discover how our minds secretly dissect words, piece by orthographic piece, in the blink of an eye.

Imagine catching a flash of the word football on a screen. Before you even register its meaning (“a game” or “a ball”), your brain may have already parsed it into “foot” + “ball.” A clever new experiment used red-and-blue anaglyph glasses and split-second word flashes to probe this. It found that real compound words (like football) are recognized much faster than lookalikes (like shamrock), suggesting our eyes and brain latch onto word form almost instantly.

In the lab, volunteers wore 3D-style red/blue glasses while words appeared for just 60 milliseconds under a mask. Each word was painted half red and half blue, splitting it either at a meaningful break or in the middle of a syllable. For example, “FOOT” might be blue and “BALL” red, or vice versa, sending “foot” to one hemisphere and “ball” to the other. Participants then quickly reported if what they saw was a real word or a made-up one.

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