What if black holes weren’t the only things slowly vanishing from existence? Scientists have now shown that all dense cosmic bodies—from neutron stars to white dwarfs—might eventually evaporate via Hawking-like radiation.
Even more shocking, the end of the universe could come far sooner than expected, “only” 1078 years from now, not the impossibly long 101100 years once predicted. In an ambitious blend of astrophysics, quantum theory, and math, this playful yet serious study also computes the eventual fates of the Moon—and even a human.
Plasma—the electrically charged fourth state of matter—is at the heart of many important industrial processes, including those used to make computer chips and coat materials.
Simulating those plasmas can be challenging, however, because millions of math operations must be performed for thousands of points in the simulation, many times per second. Even with the world’s fastest supercomputers, scientists have struggled to create a kinetic simulation—which considers individual particles—that is detailed and fast enough to help them improve those manufacturing processes.
Now, a new method offers improved stability and efficiency for kinetic simulations of what’s known as inductively coupled plasmas. The method was implemented in a code developed as part of a private-public partnership between the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) and chip equipment maker Applied Materials Inc., which is already using the tool. Researchers from the University of Alberta, PPPL and Los Alamos National Laboratory contributed to the project.
A broad systematic review has revealed that quantum computing applications in health care remain more theoretical than practical, despite growing excitement in the field.
The comprehensive study published in npj Digital Medicine, which analyzed 4,915 research papers published between 2015 and 2024, found little evidence that quantum machine learning (QML) algorithms currently offer any meaningful advantage over classical computing methods for health care applications.
“Despite exponential growth in research claiming quantum benefits for health care, our analysis shows no consistent evidence that quantum algorithms outperform classical methods for clinical decision-making or health service delivery,” said Dr. Riddhi Gupta from the School of Mathematics and Physics and the Queensland Digital Health Center (QDHeC) at the University of Queensland.
Science students and academics wrote papers about the mathematics and physics of Ringworld after it was published. Larry Niven discusses whether this would happen if Ringworld was published today. – Learn more ➤ https://www.newscientist.com/article/.…
About New Scientist: New Scientist was founded in 1956 for “all those interested in scientific discovery and its social consequences”. Today our website, videos, newsletters, app, podcast and print magazine cover the world’s most important, exciting and entertaining science news as well as asking the big-picture questions about life, the universe, and what it means to be human.
Jakub Pachocki, OpenAI’s chief scientist since 2024, believes artificial intelligence models will soon be capable of producing original research and making measurable economic impacts. In a conversation with Nature, Pachocki outlined how he sees the field evolving — and how OpenAI plans to balance innovation with safety concerns.
Pachocki, who joined OpenAI in 2017 after a career in theoretical computer science and competitive programming, now leads the firm’s development of its most advanced AI systems. These systems are designed to tackle complex tasks across science, mathematics, and engineering, moving far beyond the chatbot functions that made ChatGPT a household name in 2022.
In the new study, however, these shapes appeared in calculations describing the energy radiated as gravitational waves when two black holes cruised past one another. This marks the first time they’ve appeared in a context that could, in principle, be tested through real-world experiments.
Mogull likens their emergence to switching from a magnifying glass to a microscope, revealing features and patterns previously undetectable. “The appearance of such structures sheds new light on the sorts of mathematical objects that nature is built from,” he said.
These findings are expected to significantly enhance future theoretical models that aim to predict gravitational wave signatures. Such improvements will be crucial as next-generation gravitational wave detectors — including the planned Laser Interferometer Space Antenna (LISA) and the Einstein Telescope in Europe — come online in the years ahead.
Since Nick Bostrom wrote Superintelligence, AI has surged from theoretical speculation to powerful, world-shaping reality. Progress is undeniable, yet there is an ongoing debate in the AI safety community – caught between mathematical rigor and swiss-cheese security. P(doom) debates rage on, but equally concerning is the risk of locking in negative-value futures for a very long time.
Zooming in: motivation selection-especially indirect normativity-raises the question: is there a structured landscape of possible value configurations, or just a chaotic search for alignment?
From Superintelligence to Deep Utopia: not just avoiding catastrophe but ensuring resilience, meaning, and flourishing in a’solved’ world; a post instrumental, plastic utopia – where humans are ‘deeply redundant’, can we find enduring meaning and purpose?
This is our moment to shape the future. What values will we encode? What futures will we entrench?
0:00 Highlights. 3:07 Intro. 4:15 Interview.
P.s. the background music at the start of the video is ’ Eta Carinae ’ which I created on a Korg Minilogue XD: https://scifuture.bandcamp.com/track/.… music at the end is ‘Hedonium 1′ which is guitar saturated with Strymon reverbs, delays and modulation: / hedonium-1 Many thanks for tuning in! Please support SciFuture by subscribing and sharing! Buy me a coffee? https://buymeacoffee.com/tech101z Have any ideas about people to interview? Want to be notified about future events? Any comments about the STF series? Please fill out this form: https://docs.google.com/forms/d/1mr9P… Kind regards, Adam Ford
Google DeepMind’s AlphaEvolve AI system breaks a 56-year-old mathematical record by discovering a more efficient matrix multiplication algorithm that had eluded human mathematicians since Strassen’s 1969 breakthrough.
Large language models (LLMs) are remarkably versatile. They can summarize documents, generate code or even brainstorm new ideas. And now we’ve expanded these capabilities to target fundamental and highly complex problems in mathematics and modern computing.
Today, we’re announcing AlphaEvolve, an evolutionary coding agent powered by large language models for general-purpose algorithm discovery and optimization. AlphaEvolve pairs the creative problem-solving capabilities of our Gemini models with automated evaluators that verify answers, and uses an evolutionary framework to improve upon the most promising ideas.
AlphaEvolve enhanced the efficiency of Google’s data centers, chip design and AI training processes — including training the large language models underlying AlphaEvolve itself. It has also helped design faster matrix multiplication algorithms and find new solutions to open mathematical problems, showing incredible promise for application across many areas.
