Using a novel simulation model based on machine learning, an international research team at GSI/FAIR has succeeded in gaining a deeper understanding of element formation in stellar events such as neutron star mergers. For the first time, the scientists used deep learning with a neural network to model the energy release during r-process nucleosynthesis in hydrodynamic simulations. The results are published in the journal Physical Review D.
Many of the chemical elements we know are created in massive stellar events such as exploding stars or neutron star mergers. These events release incredible amounts of energy, allowing for the production of heavy nuclides. One key nuclear production process is the so-called rapid neutron-capture process, or r-process, in which free neutrons are captured by existing nuclei and converted into protons—thus creating larger, heavier atomic nuclei.
“Researchers around the world strive to make these complex reactions understandable through theoretical simulations. However, modeling all parameters requires incredible computing power, which is why the models often have to be simplified,” said Dr. Oliver Just, first author of the publication and a researcher in the Nuclear Astrophysics & Structure Department at GSI/FAIR. “Our new model, RHINE, which uses artificial intelligence, offers an efficient alternative.”
Ray Kurzweil’s Six Epochs of Intelligence maps the entire history of the universe as a story of accelerating information processing, from subatomic particles to a future merger of human and artificial intelligence.
Each epoch operates on a dramatically compressed timescale compared to the one before, driven by what Kurzweil calls the Law of Accelerating Returns.
We trace the journey from atoms forming after the Big Bang, through the emergence of DNA and the Cambrian Explosion, to the rise of brains, technology, and what Kurzweil predicts comes next.
By 2029, he believes AI will pass the strong Turing test, opening the door to brain-computer interfaces that link our neocortices directly to the cloud.
The final epoch envisions intelligence spreading throughout the cosmos, though critics like Michael Shermer argue this collides with the laws of physics.
Chapters.
00:00 — Intro.
Lovecraft’s ultimate amorphous, shape-shifting horror. Far more than just a monster, this protoplasmic nightmare from At the Mountains of Madness is a creature of pure, terrifying potential—a slave race that violently found its own mind.
We’re dissecting the Shoggoth’s anatomy and dark origins, but more importantly, we are exploring why this hundred-year-old biological horror is the perfect modern metaphor for Large Language Models (LLMs) and A.I.
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Detailed Timestamps.