We're deciding what to build next. 🏗️ We'd rather hear what matters to you most than guess. 10 minutes of your time will directly shape the courses and technical frameworks you want us to make next. Take the survey: #MachineLearning# #AI# #DeepLearningAI#

Deploying a machine learning model successfully means more than just building it well. I learned this the hard way when my first model crashed production overnight. — @meisshaily #ArtificialIntelligence# #AI# #MachineLearning# #Tech# #TechNews# #Technology# #DataScience#

🔁 A way to train "think longer, get smarter" models without the gradients exploding. Title: Thinking with Looped Flows URL: Looped models that recurrently update hidden states to "think" have long struggled with unstable BPTT (backprop through time). Looped Flows fixes this by borrowing training principles from diffusion models. Here are 3 highlights. 🧠 Training recurrence without BPTT By training each step with a local loss at gradually decreasing noise levels, the model learns to keep "thinking" stably, without vanishing or exploding gradients. ⏱ More compute at inference, for free Just using a finer time grid at test time boosts accuracy, from 74.5% at 8 steps to 97.9% at 128 steps on Sudoku, with no retraining needed. 🏆 Beats prior looped models on ARC-AGI It reaches 58.8% on ARC-AGI-1 and 12.2% on ARC-AGI-2, outperforming previous looped-model approaches on both. A neat new take on test-time compute scaling: thinking longer at inference genuinely pays off. #LLMInference# #MachineLearning#

We love seeing our learners reach new milestones! 🚀 Huge congratulations to Omar Wael for completing the Machine Learning Specialization! We’re thrilled to see such thoughtful reflections on their journey—take a look at this highlight from Omar's recent post below. Read Omar's full post on our forum to hear more about their experience: Reflections on completing the Machine Learning Specialization #DeepLearningAI# #MachineLearning# #LearnerSpotlight# #Education# #AICommunity#

We were honored to host Dr. Nadia Brancati(NRCI), for a guest seminar on May 28th! 🇮🇹 She shared incredible insights from her cutting-edge work in Computer Vision, Machine Learning, and Medical Imaging. Thank you for an inspiring talk! #ComputerVision# #MedicalAI# #MachineLearning#

The WTR AI Private Pools Index climbed another 14.8% M/M through May 11, pushing total tracked private AI market value to $2.20 trillion across 38 active constituents. 🔹 Anthropic drove the move higher, adding +$276.8B in value to reach a ~$900.5B valuation, while OpenAI remained the second-largest constituent at ~$839.0B 🔹 The index is now up 64.1% YTD, underscoring continued investor appetite for AI infrastructure, foundation models, and enterprise AI platforms 🔹 Harvey, Figure AI, and ElevenLabs were among the largest monthly gainers, while Cursor saw pressure amidst competition from Anthropic’s Claude Code and other AI coding tools Read James Kisner, CFA's full report for more detail on private AI valuation trends, index composition, top movers, and secondary market activity shaping the AI ecosystem. #AI# #ArtificialIntelligence# #PrivateMarkets# #Anthropic# #OpenAI# #GenerativeAI# #MachineLearning# #Technology# #Investing# #WaterTowerResearch#

The Vesuvius Challenge is still a CHALLENGE! 🧠📜 We’re teaming up with @kaggle to launch a $100K Surface Detection competition on 3D CT scans of the Herculaneum scrolls: . Train topology-aware models, climb the leaderboard, and help us read an ancient library! #VesuviusChallenge# #kaggle# #computervision# #machinelearning#

Tabular foundation models have mostly just been predicting a single target variable. A new paradigm learns the relationships across an entire table instead. Title: LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence URL: 📝 Overview Contextual Mechanism Networks (CMN) learn the full joint distribution p(x, y) across a table, not just the target y, letting a single model handle classification, regression, missing-value imputation, and causal discovery. ❗ Problem it solves Existing PFN-based methods specialize in conditional prediction of the target only, without explicitly modeling the dependency structure between variables. ⚙️ Methodology Cell-level embeddings feed a 24-layer dual-axis transformer, trained via "Context-Conditional Masked Modeling" that masks part of the query rows and jointly predicts both features and targets. It's pretrained at scale on synthetic data generated from structural causal models. 📊 Results On TabArena (51 datasets), it hits an Elo of 1935 vs. 1818 for the baseline; on BCCO (156 datasets), cumulative wins are 2.06x higher. It also took 1st place on F1 score for causal skeleton recovery on all 6 tested datasets, all while using 4x fewer parameters than TabFM. 🔬 Use cases Since the same model handles classification, regression, and causal discovery, it could replace much of a data analysis pipeline with a single foundation model. #TabularData# #MachineLearning#

📘 Introducing the Zhihu AI Cookbook (CN) Saved dozens of AI articles but still not sure where to begin? We’ve organized some of Zhihu’s best technical discussions into clear, structured learning paths. Over the past few years, Zhihu has become home to a wealth of in-depth AI writing from researchers, engineers and practitioners. The Zhihu AI Cookbook brings together some of the most useful and enduring pieces in one place. The first edition brings together 80+ selected posts from 68 contributors across three tracks: 🖥️ AI Infra 🧠 Reinforcement Learning for LLMs 🤖 Embodied AI It’s not another textbook. Think of it as a map connecting explainers, source-code walkthroughs, project retrospectives, interview insights and open-source tutorials. 🧭 How to use it: 1️⃣ Start with the introductory guide to understand the key concepts and prerequisites. 2️⃣ Choose the chapter that matches what you need right now. 3️⃣ Read each selection with a guiding question, then use the self-check prompts to test your understanding. 4️⃣ Follow the links to the original Zhihu posts for full derivations, implementation details and community discussions. 5️⃣ Put the ideas into practice with hands-on projects and open-source tutorials. Whether you’re exploring a new research direction, working through a project or preparing for technical interviews, the Zhihu AI Cookbook can help you find the right place to start and build a more complete understanding. 🤝 With open-source tutorials contributed by our content partner, @datawhale2018. 🌏 This edition is currently available in Chinese. An English version is coming soon. 🚀This is only the first edition. More topics and learning paths are on the way… 🔗 Explore the Zhihu AI Cookbook (CN): 💻 View it on GitHub: If you find it useful, give the repo a star and let us know what topic we should cover next. 🌟 #AI# #LLM# #EmbodiedAI# #AIInfrastructure# #ReinforcementLearning# #MachineLearning#