<ul data-eligibleForWebStory="true"><li>Artificial Intelligence (AI) is poised to revolutionize technology and society, with Large Language Models (LLMs) playing a crucial role.</li><li>LLMs, powered by transformer architecture, analyze sequences in parallel using self-attention, enabling contextual understanding.</li><li>During training, LLMs consume vast text corpora, tokenize text, convert tokens to numerical vectors, and predict next tokens to learn.</li><li>When prompted, LLMs tokenize input, process through layers, and generate subsequent tokens based on probabilities.</li><li>LLMs benefit hackers in areas like social engineering, code automation, reconnaissance, and obfuscation, but caution is advised due to hallucination.</li><li>A minimalist Python demo is provided for training a nano-LLM, with potential future applications in offensive security.</li><li>As LLMs evolve, potential uses include autonomous network probing, adaptive malware, and counter-LLM warfare.</li><li>It's crucial to validate LLM outputs and adapt quickly in the AI arms race to harness their power effectively.</li></ul>

How Artificial Intelligence (AI) Large Language Models (LLMs) Work, Part 1

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