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Optimizing Imitation Learning: How X‑IL is Shaping the Future of Robotics

Designing imitation learning (IL) policies involves many choices, such as selecting features, architecture, and policy representation. The field is advancing quickly, introducing many new techniques and increasing complexity, making it difficult to explore all possible designs and understand their impact. IL enables agents to learn through demonstrations rather than reward-based approaches. The increasing number of…

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IBM and Hugging Face Researchers Release SmolDocling: A 256M Open-Source Vision Language Model for Complete Document OCR

Converting complex documents into structured data has long posed significant challenges in the field of computer science. Traditional approaches, involving ensemble systems or very large foundational models, often encounter substantial hurdles such as difficulty in fine-tuning, generalization issues, hallucinations, and high computational costs. Ensemble systems, though efficient for specific tasks, frequently fail to generalize due…

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Google DeepMind’s Gemini Robotics: Unleashing Embodied AI with Zero-Shot Control and Enhanced Spatial Reasoning

Google DeepMind has shattered conventional boundaries in robotics AI with the unveiling of Gemini Robotics, a suite of models built upon the formidable foundation of Gemini 2.0. This isn’t just an incremental upgrade; it’s a paradigm shift, propelling AI from the digital realm into the tangible world with unprecedented “embodied reasoning” capabilities. Gemini Robotics: Bridging…

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When Algorithms Dream of Photons: Can AI Redefine Reality Like Einstein? | by Manik Soni | Jan, 2025

In 1905, Albert Einstein published a paper on the photoelectric effect — a deceptively simple observation that light could eject electrons from metals. This work, which later won him the Nobel Prize, didn’t just explain an oddity in physics. It shattered classical mechanics, birthing quantum theory and reshaping our understanding of reality. But here’s a…

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This AI Paper Introduces MAETok: A Masked Autoencoder-Based Tokenizer for Efficient Diffusion Models

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly when operating directly on high-dimensional pixel data. Researchers have been investigating ways to optimize latent space representations to improve efficiency without compromising image quality. A critical problem in diffusion models is…

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