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Large Language Models (LLMs) are trained on vast datasets using the Transformer architecture, forming the core of AI agents for planning and reasoning. Input is tokenized into subwords or characters, converted to IDs via a multilingual vocabulary, and transformed into high-dimensional embeddings that capture semantic relationships. Transformer layers, including encoders with self-attention for context processing and decoders with masked attention for output generation, enable understanding of long-range dependencies and multilingual text. Positional encodings and feed-forward networks enhance sequence handling. Training demands significant compute resources and techniques like RLHF for safety.

Large language models (LLMs) evolved from dense architectures, like GPT-3 with 175 billion parameters, where all parameters activated for every query, causing slow inference, high compute demands, and scalability issues. By 2025, Mixture of Experts (MoE) architectures emerged, dividing models into specialized sub-networks with a router activating only relevant experts (e.g., 2–8 out of hundreds) for tasks, reducing active parameters and enabling efficiency. This supports agentic AI systems that plan, reason, and act autonomously, with benefits including faster responses, lower costs, and domain-specific fine-tuning. Future trends point to modular, collaborative systems mimicking human cognition, though routing accuracy remains a challenge.

This professional guide dives into Physical AI and Humanoid Robotics, covering design, simulation, and deployment using tools like ROS 2 and AI integrations. It emphasizes practical setups with Docusaurus for easy content management, allowing developers to focus on core robotics development. Built on React, the guide supports customizable layouts while ensuring consistency in navigation. Key themes include comprehensive humanoid robot creation through simulation environments, AI-driven behaviors, and real-world deployment strategies, making it an essential resource for engineers blending physical systems with intelligent automation.