World Models Will Soon Be Inescapable. Here's What You Need to Know Moving AI from text to 3D space, world models simulate real-world physics to teach robots and autonomous cars how to navigate reality. On the prowl, you're swiping on a smartphone to catch that elusive Snorlax and grow your collection in . You, alongside hundreds of millions of other players, are enjoying a mixed-reality game built around lovable characters. What you don't realize is that these scans of your environment will one day help robots navigate obstacles and interact with objects.

OpenAI has announced that its AI models have made several scientific discoveries in mathematics, computer science and physics. The AI giant said on Monday that the three new discoveries concerned the fields of harmonic analysis, complexity theory and quantum field theory. Researchers in harmonic analysis, a branch of mathematics that studies how complex signals and functions can be broken down into elementary waves, had previously only proved the theory in three dimensions. Alex Iosevich, a professor of mathematics at the University of Rochester who specialises in harmonic analysis, said OpenAI appeared to have "cracked" the conjecture in a "historic development". "The conjecture was formulated by my PhD adviser, Chris Sogge, so it is near and dear to my heart, and the news was a shock at first," Iosevich told Al Jazeera.

Antimatter is not known for being easy to get ahold of . That's part of why these particles remain so cryptic. But recently, a team of physicists at Northwestern University managed to trap a single antimatter particle for months. Cooling it to temperatures near absolute zero, they could control its quantum states with great precision without even touching it. The experiment, published in Physical Review Letters represents a breakthrough in our understanding of one of the great mysteries of physics.

It will take a better understanding of underground physics, but volcanologists believe it may be possible. In the summer of 1991, Pinatubo, a volcano in the Philippines, self-destructed. The eruption started on June 12, and three days later it culminated in a tremendous explosion . By the time pyroclastic flows--incandescent avalanches of molten rock and gas--tumbled down its sterilized slopes, Pinatubo's peak had been obliterated and replaced by a 2.5-kilometer-wide chasm. The eruption killed more than 800 people, mainly because roofs, weighed down by rain-saturated ash, collapsed. But it could have been so much worse: About 250,000 people, across multiple cities and a sprawling US Air Force base, lived in the volcano's shadow. When Pinatubo started convulsing and belching steam in April of that year, scientists from the US and the Philippines deployed an array of instruments that tracked the volcano's inner tumult. "We didn't know much about that volcano, and so there was this really rapid geological assessment. And the assessment said, 'Oh, crap, when this thing erupts, it only erupts big,'" says Mike Poland, current scientist in charge at the US Geological Service's Yellowstone Volcano Observatory.

Is It Even Possible to Understand Quantum Mechanics? Common sense is of no help in studying reality at the atomic scale. A big problem with physics is that it's invented by humans. We see stuff around us and use our observations to build a mental model of the world. Children do this, and of course physicists keep right on doing it.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. There was nothing much happening in Jerome and Benno Leuer's yard in Hamel, Minn. on August 11, 1948.

Check your subscription status, update your details and more. The controversial idea that consciousness has a quantum origin is facing a new challenge, after a mathematical analysis appears to show that a long-known law of physics makes it impossible. What physical process makes us conscious, and whether we could observe and analyse it, is one of the great open questions of modern science. In the 1990s, mathematician Roger Penrose and anesthesiologist Stuart Hameroff proposed that classical physics can't explain consciousness alone and so it must arise in the quantum realm. The idea was received with scepticism and remains controversial, but attempts to bring quantumness into explanations of consciousness have persisted since.

Does time come from the entire universe running computations? Explaining the passage of time has been a gnarly problem in physics basically forever, but physicist and computer scientist Stephen Wolfram has a radical proposal for where it comes from. What if the universe is just one big computer? My colleagues and I have a running joke: time isn't real. Oh, you thought that deadline was tomorrow, but it's actually today?

This work presents a new bidirectional autoregressive latent diffusion approach for predicting the evolution of multiple fields (mass density, pressure, velocity, and magnetic field components) for magnetohydrodynamics. We show that this bidirectional flow can be used as a self-supervised consistency metric for uncertainty and error estimation, which enables the model to estimate test-time uncertainty and error without access to ground truth, by comparing how closely flowing forwards and backwards in time returns to the same predicted fields. We also demonstrate this methods's potential to serve as a non-invasive plasma diagnostic, and show how adaptive feedback can be used to make the model more robust based on sparse diagnostics or limited views/measurements.

The integration of big data, physical laws, and machine learning algorithms has shown potential to improve the estimation and understanding of complex realworld systems. However, effectively incorporating physical laws with uncertainties into machine learning algorithms remains understudied. In this work, we bridge this gap by developing the Partial Physics Informed Diffusion Model (PPIDM), a novel framework that integrates known physical principles through a physics operator while reducing the impact of unknown dynamics by minimizing related discrepancies. We showcase PPIDM's capabilities using ocean surface chlorophyll concentration data, which are influenced by both physical and biological processes, while the latter is poorly constrained. Experimental results reveal that PPIDM achieves substantially improved prediction accuracy and stability, significantly outperforming baseline methods that either neglect physics entirely or impose incomplete physical constraints under the assumption of completeness. Code will be available here.