You can also use it to: • Search for like-minded talent to collaborate with • Analyse other members' profiles to see their real skills • Discover new opportunities via AI-powered reasoning
MACHINE LEARNING
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World Models and the Future of Autonomous Robotics
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This is why the haters who pointed out a human would be embodied inside the robot to do many tasks don't matter. The world model will learn tasks quickly, and within a few years I doubt the human will be needed much at all.
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Skepticism on MIT Paper Production Claims and Memory Limitations
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1) The paper was just published 2 weeks by MIT researchers, did DeepMind really already put it into production? I can't believe that's true. 2) I think it's a great paper and promising method, but perfect memory is a bit far fetched. It's essentially just chunking up the
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RoPE vs NoPE: Ablation Studies in LLM Architecture Design
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I think the problem is that we don't have ablation studies: how would the same architecture and training run do with RoPE vs NoPE?
That being said, I'd say Kimi Linear would be an example where NoPE worked well. -
Neo humanoid robot to be powered by a world model
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Neo humanoid robot will be run by a world model. https://t.co/nW23xR6IUI
— Robert Scoble (@Scobleizer) 12 janvier 2026Neo humanoid robot will be run by a world model.
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Optimizing SWE-bench Performance Through Shared Resource Provisioning
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4/5 The fix was a mindset shift: stop provisioning per run. Provision per SWE-bench instance, then reuse it. Shared per instance: repo at the right commit, MCP server, dependencies. Unique per run: execution path, files changed, commands, patch. We keep runs isolated with
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Agent Orchestration Explained: AI Systems Coordination
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Agent Orchestration Explained https://
buff.ly/i8bGeGy
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
The Kaggle Book: Master Data Science Competitions with Machine Learning
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The Kaggle Book — Master Data Analysis and #DataScience Competitions with #MachineLearning, GenAI, and LLMs [2nd Edition]: http://
amzn.to/4pxJpTC v/ @PacktDataML Table of Contents: Introducing Data Science Competition Organizing Data with Datasets Work & Learn with -
Scikit-learn Cookbook: 80+ Python Machine Learning Recipes Edition 3
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Scikit-learn Cookbook — 80+ recipes for #MachineLearning in Python with scikit-learn [3rd Edition]: http://
amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
Dimensionality -
Build Data Pipelines and Machine Learning Models with Snowpark Python
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Design robust data pipelines, develop efficient data workloads, deploy mature #MachineLearning models, and build secure data apps with Snowpark using #Python — get this Ultimate Guide: http://
amzn.to/3x6gjoI
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#DataScience #DataScientist #CDO #DataEngineer #ML #AI