#SIGGRAPHAsia2024 🔥 Replace your Marching Cubes code with our... Occupancy-Based Dual Contouring to reveal the "real" shape from either a signed distance function or an occupancy function. No neural networks involved. Web:show more

I've been digging into what Roblox is working on... with Terrain. There's an ongoing effort to switch from an occupancy model to a SDF model (signed distance field) In practice voxels aren't stored as a [0 to 1] value anymore, it's a [-1 to 1] value.show more

🔥 TypeScript Tip 🔥 Here, we build a function... that creates zod schemas from your own interfaces, with full autocomplete. The lesson? It's often really hard to get autocomplete when your function _accepts the results of another function_.show more

This video, created by my dear coauthor Mahdi E... Kahou for our teaching and papers, shows how overparameterized neural networks produce smooth function approximations even in the context of the Runge phenomenon. Some background. Imagine you want to approximate the Runge function using polynomial interpolation at equally spaced points. It is well known that, despite targeting an infinitely differentiable function, such a polynomial approximation produces oscillatory behavior that worsens with the degree of the polynomial. In other words, higher-degree polynomial approximations might not improve accuracy. Instead, approximate the Runge function with a neural network (here, two layers are just to make the example concrete; nothing fundamental depends on it). As you increase the number of parameters well above the 11 training points (in our example, a two-layer neural network with 128 nodes each), you nicely converge to the target, without wild oscillations. Yes, this has much to do with double descent and benign overparameterization, but the main punchline of this post is that neural networks are really very different types of animals than polynomial approximations. And yes, Chebyshev nodes and splines exist, and in this case, they will prevent the oscillations. But that's not the point. Chebyshev nodes and splines still confront Faber’s theorem, which states that for any system of polynomial interpolation nodes, there exists a continuous function whose sequence of interpolating polynomials diverges as the number of nodes grows to infinity. Faber’s theorem does not apply to neural networks because they are not polynomials. The notebook, if you want to check the details, is here: Stay tuned for more on this 👀show more

we live in a $TAO simulation in a few... months we went from a whitepaper, a concept, a dream (kinda) to now generating 3d simulations based on our own neural network (that we’re training using Celium btw) this is what Score - Subnet 44 is about: > collecting visual data > creating visual models > generating a ‘real time’ positional value function aren’t you entertained? I love this game!show more

Bangladesh polls At the moment, Bangladesh is supposed to... function on the basis of its existing constitution, which is the 1972 Constitution. There is absolutely no provision in it for either a referendum or an interim authority: Subir Bhaumik | Senior Journalist & Author #NewsToday | Rajdeep Sardesaishow more

Function calling is a powerful way to extend the... capabilities of LLMs and AI agents by letting them use external tools. Our new short course Function calling and Data Extraction with LLMs, created with @NexusflowX and taught by Jiantao Jiao and Venkat, demonstrates how to prompt LLMs to form calls to external functions. You'll work with NexusRavenV2-13B, a 13B parameter open-source model that excels in function calling tasks while still being small enough to host locally. Learn to use function calling to extract structured data from unstructured text and access web APIs, and build an end-to-end application that processes customer service transcripts. You'll learn how to build LLM-powered applications that can analyze feedback, automate data entry, and enhance search. Please get started here:show more

Let's start the new year with an Airdrop based... on your 𝕏 account age coming to $SOL!💧 🟢 Claim Now: 🎁 Get your Mystery Code: Leave a comment and send your comment link in our DM to get your code. (First 1000 people)show more

How to Create a Spinning Globe 🌍 with Your... Own Data Layers (No Coding Required) Learn how to create a spinning 3D globe map without writing any code using a simple web-based tool. In this step-by-step geospatial tutorial, you'll see how to build an interactive spinning globe, overlay raster and vector data layers, and visualize datasets directly in your browser or in a Jupyter Notebook environment. Web-based demo: Jupyter notebook demo: Video tutorial: #geospatial #opensource #python #jupyter #maplibreshow more

We’re excited to be integrating SERV Reasoning by OpenServ... to bring our no-code robotics solution to market, and had the pleasure of joining their closed beta. Advanced robotics used to require a full team of specialists. Now you can build real behavior in our no-code studio — this Unitree G1 hand grasp function was entirely written by the serv-standard model inside ROBA's no-code studio. This is just a small glimpse of what can be done in our platform. In our benchmarks across real builder tasks, SERV's models matched the quality bar we needed while cutting AI costs by over 80%. SERV Reasoning will now be the default inference engine for the upcoming release of our ROBA no-code studio on June 1st.show more

Sabine Hossenfelder On The Double-Slit Experiment: "You either have... to accept a faster-than-light update of the wave function… or you have to accept that the future is fully determined already." Einstein called it "spooky action at a distance." The wave goes through both slits. Measure at one slit, the other slit updates instantly, even if the particle never hits your detector. The particle somehow “knows” you’re trying to measure it.show more

Introducing OpenReview An AI code review system I'm building... to learn how real systems work. No noise. No generic comments. Just reviews that actually understand your code. Open source. Currently in build. Everything I'm exploring: • AI PR reviews with real context • Full repo indexing (embeddings + vector search) • Inline comments + detailed summaries • Similar code retrieval across your codebase • Smart diff-based re-indexing • Scalable workers + queue-based architecture … and a lot more.show more

As we all know, Valve is working on an... AI based Anticheat called VACNet however there are already AI based Aimbots ruining the game experience. According to the dev, these aimbots doesn't modify the game files or interfere with the game at all. Here is a working AI based Aimbot in CS2. It works by real-time object detection with neural networks and recognizes the patterns similar to human movements. I am sure with the amount of data Valve has, they will be able to ban these aimbots however it's gonna be a tough battle to fight. What do you think about this? 🤔show more

One cup of blueberries a day could be the... simplest way to supercharge your brain An analysis of 11 studies suggests blueberry consumption can improve certain aspects of memory, executive function, and psychomotor function across various ages, from children to older adults The power players in blueberries are these compounds called anthocyanins—powerful flavonoids that boost vascular function and brain blood flow If you're not a fan of blueberries, try supplementing with CocoaVia — a cacao powder high in similar polyphenols It's been shown in clinical studies to improve blood flow to the brain as well as cognition & memory in older adultsshow more

New short course: Building Code Agents with Hugging Face... smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!show more

We’re excited to announce that our integration with Patreon... is live! Now you can add Patreon as a destination in either Streamlabs Desktop or the Plugin for OBS, and take advantage of your favorite Streamlabs features like multistream, dual output, and more!show more