UFO,PhD☀️

生物的复杂度比材料高至少3个数量级，why？ 我写一个软界面的非平衡态物理，最多20组方程 若把蛋白的控制方程都搞清楚，比湍流方程展开还要复杂，我只能退而求其次，用粗化动力学逼近细胞长期的真实行为 自从玩 $PYTHIA 后，真入坑生物物理了🧠 专注软界面物理 (材料，生物，社会，金融，治理)

Los villanos siempre tienen doctorado. Dr. Doom, el Dr. Octopus, el Dr. Doofenshmirtz, Hannibal Lecter o el Dr. No. Incluso el Dr. Frankenstein o el Dr. Evil. En cambio, los buenos suelen quedarse en la maestría, como el maestro Yoda, el Maestro Roshi, el Maestro Splinter, el Maestro Miyagi, Shifu o el mismísimo Luke Skywalker. Los estudios de posgrado corrompen el alma.

@SergioParra_ Oh 😂

@0xWayne_light @Orama__Desci 对😆

@ufobtc27 @Orama__Desci 搞定央视了啊？

央视CCTV3正在报道，脑机接口动物实验，你看看这是什么？👀🧠🐁 $PYTHIA @Orama__Desci

@CryptoApprenti1 兄弟，有空回香港了，天道信托坐坐啊

有谁能说一句，教授牛逼？

@rise6982 @Orama__Desci 冲

@ufobtc27 @Orama__Desci 大的来了老师

@wangelssd @Orama__Desci 可以

@ufobtc27 @Orama__Desci 牛逼

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

@Orama__Desci @0xMax_8282 @SobottaProtocol @Floa_AI @CROsDAO @zenotech_ai Nice

✋Who is in HK meeting with our partners? @SobottaProtocol @Floa_AI @CROsDAO @zenotech_ai

太有牌面了，700人在香港半山共聚 $PYTHIA 🧠 全球啟動！ @Orama__Desci

@muditha96 @OpenBCI Yeah

@ufobtc27 @OpenBCI If AI vibes all hard science first, then mind is the real singularity test.

AI will “Vibe” Physics, Math, Matter, Bio, then almost each Engineering Only Meta-Physics, Humanity, Culture and their operational system: MIND may be the last thing not “Vibed” before SINGULARITY 🧠 Brain is the end game #OpenBCI @OpenBCI doi.org/10.1002/adma.2…

🧠 the Digital Brain Project is now live: $5M total · up to $500k per selected team Let's open-source the modeling of the human brain brain activity! ➡️Apply on: digitalbrainproject.org

@HaoYin20 @BiologyAIDaily this

Quantitative rational design of microcapsules/microparticles😎 How to do it? 1⃣Build a Chemical-Process-Structure-Performance database 😱 All literature related to Microencapsulation by interfacial polymerization 303 experimental batches x 44 input features 2⃣Interpretable machine learning Methods: SHAP, permutation, ALE Performance metrics: Encapsulation efficiency, mean particle diameter, shell thickness-to-radius ratio 3⃣Extraction of hidden descriptors for high-throughput virtual screening Core LogP, emulsification variables, Mayr reactivity parameters...... 4⃣A virtual formulation space of 40k candidate microcapsules ➡️Programmable design (rather than recipe-based optimization)😌 I feel that I am reading a book, instead of a research paper!?😳 (👉凝聚态风水大师👹) #AdvMater 2026 @AdvPortfolio advanced.onlinelibrary.wiley.com/doi/abs/10.100…

@HaoYin20 Bro , u could make some with BioPhysics 😆

My continued biological education Physiological aging (& senescence) in three dimensions Cell type-specific aging trajectories of 3D genome folding, A/B compartment switch/score variation/interactions, TAD boundary alteration, enhancer-promoter rewiring Very straightforward educational illustrations!😎 @TrendsCellBio 2026 sciencedirect.com/science/articl…

@tig88411109 😂哈哈哈，老师麻烦关注下订阅者，我以前那个DrU账号没了，这是新的

推特鱼龙混杂还有很多AI 机器人，我无法鉴别/回复/关注 请我的订阅者多提问、发言和评论，我基本上都会及时答疑解惑。 这也是我最喜欢的部分，上网的动力在于和真实的人互动。 ❤️

We are building autonomous scientific discovery. And it runs 370x faster than a human scientist. Our AI agents index millions of scientific publications and learn what an entire field has done in the time it takes a researcher to read one paper. Then we bring it to the lab to verify and further fuel learnings. 10 years to discover a material? Not here.

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks. Try the demo and learn more here: go.meta.me/tribe2

@HaoYin20 Yeah

Soon we all yield to AI...👹

@KKaWSB 这个不太算零样本了，700人的数据以及拟合的很好

Meta发布”读脑”AI：TRIBE v2 给你看任何画面、听任何声音，它能预测你大脑怎么反应。 700多人、500多小时fMRI数据训练出的”神经活动数字孪生”。 关键是：零样本预测。 新的人、新语言、新任务，直接能预测。