Déda et al. use neural networks for flow analysis... & control in nonlinear fluid systems, showcasing accuracy & stabilization in complex dynamics. From the Lorenz system to confined cylinder flows, neural networks shine as effective models & controllers.show more

Do you have experience in microscopy, image analysis, fluid... dynamics, network theory, electrophysiology, and/or machine learning? Join us as we track the flows & structure of mycorrhizal networks. We have a biophysics post-doc position open in Amsterdamshow more

SuiRWA is training its investment AI agents on extensive... data, using neural networks for pattern recognition. They learn via supervised & unsupervised methods, with fine-tuning for market analysis, ensuring accuracy and minimizing bias. A revolution in investing might be here.show more

In nature, researchers from Google Quantum AI, TU München,... & UoN Physics & Astronomy simulated a (2+1)D lattice gauge theory and visualized string dynamics, revealing the deconfined-to-confined excitation transition as the effective electric field increases. →show more

The theory of higher order topological dynamics, which combines... multilevel interactions between discrete topology and nonlinear dynamics, has the potential to enhance our understanding of complex systems such as the functions of the nervous system, the development of next-generation machine learning and the creation of advanced nodal processing algorithms. An important and unexpected collective behavior of signal processing in multilevel nodal networks has been observed to lead to a synchronization and diffusion of the irrotational and the solenoidal components of the systems revealing a deep relation of these mechanisms with the complexity of discrete topology. The perspective of the preliminary study linked here offers insights into how topology morphs dynamics, how dynamics stem from topology and how topology evolves dynamically. 🔗show more

It's been incredible to see neural networks working so... well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocksshow more

Thrilled to share UniRig from Tripo Tripo 🔜 gamescom... VAST AI Research: an AI model for rapid automatic 3D rigging. Rig diverse models—from humans, animals, to sci-fi creatures—in seconds! UniRig has the potential to streamline animation workflows with unparalleled accuracy & versatility.show more

🚨🇷🇺🇨🇳 Russia and China develop breakthrough AI-powered earthquake forecasting... method The new AI model predicts earthquakes by tracking changes in the Earth’s crust with superior accuracy—at a fraction of traditional computing costs. 🔸 Geoacoustic emissions are used — sounds produced as tectonic stress builds up underground — to pinpoint possible earthquake precursors 🔸The new system combines Physics-Informed Neural Networks (PINNs) with Kolmogorov-Arnold Networks (KANs) to process seismic data 🔸 The team proposes using human-made noise as seismic sonar to reveal underground details that natural signals miss 🔸 The approach slashes computing costs while delivering sharper 2D and 3D geological maps Beyond earthquake forecasting, the same technology could also aid mineral exploration and infrastructure planning—by revealing subsurface structures with unprecedented clarityshow more

Columbo couldnt say no to Kos' big ol puppy... eyes! Just a short I got the idea to do that features my chosen voice actors for em! Tom Kenny as Kosmo & Peter Falk as Peter Fox/Columbo! 🔊is from SpongeBob "Squid Noir" & The In-Laws(1979)! ...Surprised I didnt use Columbo audio? xDshow more

Here are more results from #RigidFormer: predicting physical dynamics... with purely neural simulators — an attempt to learn physical dynamics in a scalable manner. 🤖 1) Controllable Articulated Body Simulation — More Results Additional Unitree G1 humanoid rollouts under controlled motion. Each sample uses a different initial state and control signal (direction and velocity). 🏺 2) Object Fragmentation Simulating the cracking and fragmentation process of objects. Thanks Žiga Kovačič for suggesting this experiment! 🎬 3) Combining Rigidformer with Diffusion-as-Shader for controllable video generation. Note: the meshes shown here are only for visualization — the network takes point clouds as input and predicts the updated state of each point.show more

Predicting the next word "only" is sufficient for language... models to learn a large body of knowledge that enables then to code, answer questions, understand many topics, chat, and so on. This is clear to many researchers now, and there are nice tutorials on why this works by Ilya Sutskever resorting to compression ( ) and by Geoffrey Hinton ( ). However, the emergence of types of understanding is not unique to language models. In by Misha Denil and Brandon Amos the authors trained models to predict the next few time stems of over a hundred robot hand sensors (Touch, Gyro, Accelerometer, Joint Info, Actuator Info, etc.). They ten found out that they could regress the shape of the thing the hand was touching from the activations of the neural networks using probes. That is, the model developed an internal representation of shapes even though it was simply used to predict "only" the next few senses. Awareness follows from simple predictions and interaction with the world.show more

🇨🇳🇺🇸 China's military may be learning from America's AI,... without building it from scratch Why spend billions training a frontier AI model when you can let someone else do the expensive part? Chinese military-linked researchers have repeatedly used outputs from OpenAI and Anthropic models to train smaller domestic AI systems for surveillance, cyber operations, drone targeting, battlefield decision-making, and software analysis. The trick is called distillation: instead of copying the model itself, researchers use its answers, and more importantly, its reasoning, to teach a smaller model. The result? AI that isn't as powerful as the original, but is cheap, runs on local hardware, and can be deployed on drones, military networks, and tactical systems without relying on U.S technology. The AI arms race isn't just about who builds the smartest model anymore; it's about who can squeeze the most military value out of everyone else's. Source: Reuters / Writer: Ianshow more

This fact absolutely shatters evolution. ...And makes the intelligent... design in Life certain. All the systems in Life that make things function - like everything that enables birds to fly - require a whole set of complex interconnected parts. Nothing works in isolation. Something evolutionists overlook is how the nature of all these systems are intricately interconnected. Every part relies on something else to make it work. You need the whole system, down to the DNA itself, or the function fails. This coordinattion between so many systems makes step-by-step evolution all but impossible. For instance... A wing isn’t just bones and feathers (or membrane). A wing requires a certain type of hollow bones, which the alleged ancestors of birds didn't have. And wings by themselves are useless - what reproductive benefit would wings have without the accompanying systems that lead to actual flight (the simultaneous requirements for bone pneumatization, muscle attachment sites, feather or membrane development, and the regulatory changes that control all of them)? Wings without these features would be harmful. Hollow bones without flight would be detrimental. The step-by-step path leading from non-bird to flying birds is filled with easily killed, unselectable intermediates. This is where evolutionists constantly fail - they look at modification in isolation, without considering that these systems are large, interconnected networks of functional parts all working together. And it all starts at the genetic level, from the DNA itself. DNA genes code for proteins. Regulatory information organizes when these proteins are built and how much should be made. Further regulatory systems coordinate where these proteins go after they’re built. And yet more informational networks place the proteins into the final system in coordination with other proteins. Then these protein systems are fit together to make entire organs, tissues, bones, etc. All of these systems and processes are coordinated from the DNA - which means major modification like turning a fish fin into a bird wing requires many coordinated DNA changes across the entire network. One change at a time simply cannot lead to the coordination of an entire multi-part coordinated system like this. A mutation that changes one protein would need another coordinated mutation to fit that mutated protein into the right place, which would require other proteins to be mutated to fit together into that system, which would also require yet more regulatory mutations to keep everything coordinated... Nothing works in isolation. It's all connected. Which is why it can't evolve one step at a time. One step at a time does nothing. Life requires all or nothing systems. Only intelligence can engineer all or nothing systems.show more

🚨 SCIENTISTS ARE USING AI TO MAP A HIDDEN... “CLEANING SYSTEM” INSIDE THE HUMAN BRAIN AND IT COULD CHANGE HOW WE UNDERSTAND SLEEP, AGING, AND NEURODEGENERATIVE DISEASE. It’s called the glymphatic system a vast network of fluid channels that flushes toxic waste from the brain while you sleep. For years it was almost impossible to observe in detail. Now AI-powered imaging is revealing it like a living galaxy of microscopic rivers and pathways inside neural tissue. Why this matters: Your brain produces toxic proteins constantly. The glymphatic system is one of its primary waste-clearance mechanisms clearing the very proteins linked to Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. But here’s the unsettling part: This cleaning system becomes dramatically less efficient with age, poor sleep, stress, and brain injury. Sleep may not just “rest” your mind it may literally wash your brain. The deeper implication is staggering: The brain isn’t just a computer. It’s a dynamic, living network that constantly rebuilds and cleanses itself in real time. The more we map it, the more it looks like an entire universe of connected energy flows inside our skulls. What if some neurological diseases begin when the brain can no longer properly “wash” itself at night? Follow for more frontier neuroscience and future technology.show more

Biomni Lab lets biologists collaborate with AI agents to... finish complex tasks end-to-end. Here are 15 popular use cases, each link is a full replay so you can watch the agent work through every step: 1. Spatial transcriptomics analysis: map gene expression across tissue architecture from spatial transcriptomics data, with spatial clustering and neighborhood analysis. 2. Binder design: design de novo protein binders against a target structure using computational protein design tools. 3. Biomarker panel design: identify and optimize a multi-marker diagnostic or prognostic panel from omics data. 4. Clinical trial landscaping: search and summarize the trial landscape for a disease area, mapping phase, endpoints, and sponsor activity. 5. Survival analysis: pull clinical and expression data, fit Cox models, generate Kaplan-Meier curves, and identify prognostic markers. 6. scRNA-seq processing and annotation: from raw counts to UMAP clustering, marker gene detection, and automated cell type labeling. 7. Cell-cell communication: infer ligand-receptor interactions between cell types from single-cell data and map intercellular signaling networks. 8. Primer design for novel Cas13: analyze a putative Cas13 protein from a metagenomic screen—verify the ORF, identify HEPN domains, and design cloning primers with restriction sites and a FLAG 9. Proteomics differential expression: normalize mass spec data, run statistical tests, and visualize differentially abundant proteins. 10. Gene regulatory network inference: reconstruct transcription factor-target gene networks from expression data and identify key regulators. 11. Gene co-expression network analysis: build weighted co-expression networks, identify gene modules, and correlate them with phenotypic traits. 12. Microbiome analysis: process 16S/metagenomic sequencing data to profile microbial communities, diversity, and differential abundance. 13. Polygenic risk scores: compute and evaluate PRS from GWAS summary statistics against a target cohort. 14. Variant annotation: annotate genetic variants with functional predictions, allele frequencies, and clinical significance. 15. Fine-mapping: narrow GWAS loci to credible causal variants using statistical fine-mapping methods. Each of these would normally take days to weeks of scripting, debugging, and iteration. In Biomni Lab, the agent handles the full execution while you steer the science. Learn more:show more

Do you ever wonder what happens inside the mind... of an elite athlete? I absolutely love this perspective from gold medal freeskier Eileen Gu on the power of our mindset and belief system: "You can control how you think. And therefore, you can control who you are." Athletes don’t just magically create routines and habits. They’re supported by coaches who use the principles of behavioral science to set them up for success. That means using techniques to reduce decision fatigue and lower stress. Through neuroplasticity, the small choices we repeat every day can strengthen our neural circuits and rewire our brains to make certain behaviors more automatic, which is exactly what Gu has trained her mind to do. As Gu said, “With neuroplasticity on my side, I can become exactly who I want to be.” What was your favorite moment of the Winter 2026 Olympics? Let me know in the comments!show more

We are at NeurIPS Conference for our 4th #MyoChallenge... and 7th #MyoSymposium! What started as a discussion with Vittorio Caggiano is a global community now MyoSuite 💪 In 2022, we started with two key hypotheses - 1⃣𝑺𝒄𝒂𝒍𝒊𝒏𝒈 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔: can we scale data driven learnings to achieve human level motor control? 2⃣𝑬𝒎𝒃𝒐𝒅𝒊𝒎𝒆𝒏𝒕 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔: Akin to Neurons's inspiration behind NN, are there embodied priors that will form the critical substrate to get to human performance? After 3 years, both these hypotheses are running strong. But in different ways than we anticipated -scaling hypothesis predicted vanilla RL algorithms (developed over OpenAI Gym and robotics tasks) will scale & realize human level motor control. RL did scale with better simulation & compute infrastructure but the curse of dimensionality became the limiter for high dimensional MSK systems. This is where our 2nd Embodiment Hypothesis kicked in. Spatial as well as morphological embodied priors facilitated development of next generation of algorithms at the intersection of representation and reinforcement learning - (DepRL from Pierre Schumacher et al, MyoDex & SAR from Cameron Vittorio Caggiano et al, Kinesis from Alberto Chiappa, muscleVAE from Yusen et al, etc) Current #MyoChallenge result evaluations phase was humbling realization - what started with a team of two evolved as a global community, the entry barriers has been lowered enough for even high school students, and underrepresented groups with limited resources to participate. It's incredible to realize the progress we have seen in 3 years. All the same time idiosyncrasies of the behaviors leave us quite unsatisfied and wanting more. Ahead of us there are exciting challenges on all frontiers -- embodiment, proprioceptive+exteroceptive sensing and control, validation -- presenting large real world potentials in health, wellness, sports, robotics. While there is a lot for us to be proud of, open challenges in understanding, as well as emulating human level motor intelligence remains. Join MyoSuite team for the awaited #MyoSymposium in discussing these frontiers on Saturday, Dec. 6th from 8-11 am: Ballroom 6D.show more

I believe the "Pit" described in the book of... Revelation is a Galactic Federation prison deep under Antarctica, where "Satan" and "Lucifer" are already confined. From Pleiadian contact we know that the primary leader of the dark forces on Earth is a reptilian with four arms named Pidkozox. He's from the planet Uranus (Nebulac) in our solar system, but in a different dimension. Pidkozox is the father of two Sirian sons, Ashtar and Oppisheklio. Elder Ashtar is the leader of Sirius, a close friend to Elder Ikai, and a respected Galactic Federation commander. Oppisheklio is Earth's greatest deceiver, who played Good Cop to his father's Bad Cop. They were known by many names, including Enlil and Enki, Zeus and Apollo, Baal and Yahweh. They can't harm us directly and rely on human free will to carry out their plans. In blood rituals, they give commands to elites, who then control networks of puppets worldwide. We're in a system with oversight by Galactic Federation which allows dark forces to deceive us without harming us directly, and allows humans to harm each other with free will. Until the Shift, we're dealing with dark plans carried out by humans, as well as spiritual warfare, but we don't have to worry about physical encounters with negative extraterrestrials or defeating the dark forces.show more

What is Chainlink CCIP? Chainlink's (Chainlink) Cross-Chain Interoperability Protocol,... or CCIP, is designed to let applications communicate across different blockchain networks. Put simply, CCIP acts as a secure messaging and transfer layer between otherwise disconnected blockchains. Here's how it works: (1) It moves data between blockchains CCIP allows smart contracts on one blockchain to send messages to smart contracts on another network. That means an application can trigger an action on a different chain without requiring users to manually move between ecosystems. (2) It can transfer tokens across networks CCIP also supports cross-chain token transfers. Projects can use token pools and other mechanisms to move assets between supported chains while maintaining controlled supply across networks. (3) It lets you combine messaging and asset movement A major feature of CCIP is that developers can send arbitrary messages, transfer tokens, or do both in a single cross-chain transaction, rather than needing separate systems for each. (4) It uses Chainlink's decentralized oracle infrastructure CCIP relies on Chainlink's decentralized oracle network to validate and deliver cross-chain messages. The system uses multiple independent components to help verify transactions and protect against failures or manipulation. (5) It adds programmable token transfers CCIP is not limited to simply sending an asset from one chain to another. Developers can attach instructions to transfers, allowing receiving applications to automatically perform actions when tokens arrive. This could make cross-chain lending, payments, trading, and other DeFi applications easier to build. (6) It is designed for multiple blockchain environments CCIP supports communication across different blockchain ecosystems rather than forcing applications to operate within a single network. That matters as liquidity, users, and applications become increasingly fragmented across chains. The bigger idea is simple. Blockchains were originally built as separate networks, but users and capital increasingly need to move between them. CCIP is Chainlink's attempt to provide the infrastructure for that movement. If cross-chain applications continue expanding, secure interoperability could become one of the most important layers in the blockchain stack.show more