The mysterious Amaterasu particle may not be a proton at all. New research suggests that some of the most extreme cosmic rays could be ultraheavy atomic nuclei, heavier than iron, which are better able to retain their energy while traveling through space. This idea could help explain how these rare particles reach Earth and provide new clues about the powerful cosmic explosions that create them.
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===== My name is Artem, I’m a neuroscience PhD student at Harvard University. 🌎 Website and Social links: https://kirsanov.ai/ 📥 \.
Michal Irani (Weizmann Institute)
https://simons.berkeley.edu/talks/mic…
Topics in Intelligence: World Models and Social Reasoning.
In this talk I will present my vision of how combining the power of Brains & Deep-Networks (DNNs) can lead to significant breakthroughs in both domains and potentially bridge the gap between Brains & Machines. I will show how combining the power of Multiple Brains (“the Wisdom of a Crowd of Brains”) may lead to new breakthrough discoveries in Brain-Science, allow mapping of information between different brains (with NO shared data), and lead to new ways of training and interpreting artificial DNNs.
Stars shine because atoms fuse in their interiors, releasing energy. When a very massive star has exhausted its nuclear fuel, radiation pressure can no longer provide sufficient counterforce to gravity. The star then collapses under its own mass until only a single point remains: the singularity.
While the formation of a black hole appears plausible, black holes themselves continue to pose major challenges for science. How can 10 billion solar masses concentrate at a single tiny point? How can spacetime be curved infinitely at that point, the singularity? At this stage, the laws of physics break down, making it impossible to predict what happens. Moreover, black holes conceal all information from observation: Everything, including light, disappears irretrievably beyond the event horizon.
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Your brain is running on twenty watts right now. The power of a dim lightbulb. And yet it contains the entire eight-hundred-million-year history of life’s most improbable experiment — the experiment of intelligence itself. In this episode, we follow that experiment from its very beginning: from the first bacterium that navigated a chemical gradient in the ancient ocean, through the nerve nets of jellyfish, the distributed arms of the octopus, the tool-making crow, the grieving elephant, the dreaming mammalian brain — all the way to the only creature that has ever turned its intelligence on the question of where intelligence came from. This is not a story about the human brain. It is a story about what matter does when evolution pressures it long enough and hard enough. It is the deepest origin story you have.
/ @theevolutionoflife2026 Subscribe to the channel and join us — there is much more of this story still to tell.
Lotteries and tickets are often used as a didactical analogy to explain the success of overparameterized neural networks: “larger networks succeed because they more likely contain a well-initialized subnetwork that can learn the task in isolation, much like buying more tickets increases the chances of winning a lottery.”
This explanation is intuitive but misleading: it suggests that subnetworks can be treated in isolation from the rest of the network. Following this reasoning leads to interpreting learning in wide networks as a multi-start optimization process, where gradient descent simply conducts a parallel search over subnetworks. We argue that this view is flawed since, among other reasons, winning tickets can be made to fail by perturbing the rest of the network.
Problem-solving using novel solutions without explicit training is often considered a hallmark of cognitive flexibility. We investigated whether bumble bees (Bombus terrestris) could solve a novel object manipulation task spontaneously. Bees trained to associate a blue ring (“flower”) on the floor with a reward successfully moved a ball underneath a flower relocated to the ceiling to reach the flower. In control experiments in which the flower was out of sight when ball movement began and remained hidden during transport, bees still succeeded in the task. These results suggest that these were goal-directed actions rather than reinforcement-based associations driven by perceptual feedback. Our findings provide evidence that bumble bees can exhibit spontaneous problem-solving, challenging the notion that such advanced cognitive abilities are exclusive to large-brained vertebrates.
