🌌 Star-Universe-90后🙃 🤖 AI 未来× Web3 观察者× 自发秩序。 📐 相信理性、节制与涌现。 🧠 从“生产”走向“创造”的社会。 ✍️ 奇韬者，以自身处境抒写未来，百无禁忌，也终将为此负责，谢谢。
Star-Universe
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Haha, it seems that AI is powerless against encryption technology
AI has made zero progress in breaking modern encryption such as AES-128. And if you need safe key-exchange to use shared session keys with AES-128, the possibility of quantum computers is bigger threat than AI. The least bad option seems to be technique called X25519MLKEM768.
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Ok let me explain how cool this is from my perspective , please RT! I developed one of the first methods to probe RNA structure in high throughput inside living cells, and it takes tons of time and $$ to apply on thousands of viruses - it’s practically impossible. This ML approach is just as good and works in seconds for nearly free ! Not only that, it highlights the MOST IMPORTANT structures that track with function. It’s the future of RNA strucute prediction and discovery across all families 🔥 biorxiv.org/content/10.64898…
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Gene-editing techniques could soon allow researchers to replace entire genes and engineer complex cellular circuits — if the systems can be delivered safely into cells
go.nature.com/3VD02mS
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In light of the progress in mathematics, we at Edison Scientific and FutureHouse have assembled a set of Millennium Problems for Biology. They are chosen to be very hard to solve but very easy to validate in a simple laboratory environment. Any of these, if solved, would mark a major advance in biotechnology, and most of them would contribute materially towards curing disease. These are, in some sense, the “last reasonable eval” for AI in biology. This was work primarily by @MichaelaThinks and myself, with contributions from many others.
Short descriptions below. The full descriptions of the problems with acceptance criteria are at the Bio Millennium Problems website, linked in the next post. Share more if you have ideas. If they meet our criteria, we’ll add them to our list (with attribution and permission).
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"JEPA-Anything: Learning Predictive Models across Different Worlds"
This research proposes one world-modeling framework that works across vision, biology, control, molecules, physics, weather, and clinical data.
They used something called Orthogonal Predictive Factorization, which splits a single JEPA latent state into complementary factors that predict different parts of the world and recombine into a complete state.
This improves all 10 matched dynamics tasks and even produces factors that recover Kepler’s scaling law and nominate a biologically validated intervention.
alphaxiv.org/abs/2609.20800
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Repetitive negative thinking is linked to Alzheimer’s protein buildup in the brain.
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