How a founder's battle with eczema led to a data-driven healthtech startup
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @fabiomoioli @pascal_bornet @alliekmiller @mattshumer_ @OfficialLoganK @jeremyphoward @GaryMarcus
MACHINE LEARNING
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Founder’s Eczema Battle Inspires Data-Driven Healthtech Startup
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Magistral Models Achieve Major Leap on Artificial Analysis Intelligence Index
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Our latest Magistral models just made a massive leap on the Artificial Analysis Intelligence Index—competing with models much larger in sizes.
→ View original post on X — @arthurmensch, 2025-09-21 16:18 UTC
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Databricks Free Edition: Explore AI, ML, and Data Analytics
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With Databricks Free Edition, you can explore the latest technologies at no cost — in the same trusted environment used daily by millions of professionals.
— Databricks (@databricks) 21 septembre 2025
Build AI agents and applications, collaborate on ML projects, run SQL queries, create dashboards, and more.
👉 Get started… pic.twitter.com/AuecT9o8lAWith Databricks Free Edition, you can explore the latest technologies at no cost — in the same trusted environment used daily by millions of professionals. Build AI agents and applications, collaborate on ML projects, run SQL queries, create dashboards, and more. Get started
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Survey on Retrieval and Structuring Augmented Generation for LLMs
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8. A Survey on Retrieval and Structuring Augmented Generation with LLMs This survey reviews Retrieval and Structuring (RAS) Augmented Generation, which combines external retrieval and structured knowledge to mitigate LLM issues like hallucinations.
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In-Context Learning: Formal Analysis of Capabilities and Limitations
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5. Is In-Context Learning Learning? This large study argues yes in a formal sense, then shows where it works and where it breaks.
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Physics Foundation Model: Transformer Learning Dynamics from PDEs
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4. Towards a Physics Foundation Model A transformer-based “neural differentiator + numerical integrator” that learns governing dynamics from short spatiotemporal prompts and predicts next states across varied PDE systems.
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K2-Think: 32B Model Rivals Larger Models on Math
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2. K2-Think A 32B-parameter system built on Qwen2.5 that rivals or beats far larger models on hard math by combining long CoT SFT, RL with verifiable rewards, lightweight test-time scaffolding, and inference optimization.
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Traditional RL Researchers and Their Opposition to Supervised Learning
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Traditional RL folks were for some weird reason against supervised learning and LLMs. Have they learned the bitter lesson?
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Standard Maximum Likelihood Expectation Maximisation EM Interpretation
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In fact, this may be interpreted as the standard maximum likelihood expectation maximisation (EM).
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Single-step RL in Language Models: Differentiability and Token Generation
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+1 to @TacoCohen on differentiability. Moreover it is important to remember that most RL for LMs involves only one step of RL where the state (question, instruction) is provided by the environment and where RL must generate a single sequence of tokens (1 action). This results in