This presentation provides a comprehensive overview of Large Language Models (LLMs), starting with the history of AI from the 1940s through major breakthroughs like ELIZA, Deep Blue, and the transformer architecture revolution in 2017.
It explains that LLMs are neural network-based structures that can predict and generate language, built using a complex training process that requires massive computational resources.
The presentation details how LLMs work through transformers architecture and neural networks, discusses their current capabilities (like text generation, translation, and code assistance), and acknowledges their limitations (including hallucinations, bias, and ethical concerns).
It concludes with a forward-looking perspective on LLM development over the next 5+ years, emphasizing both the technology's potential for augmenting human capabilities and the importance of responsible development with proper ethical considerations. Throughout, it maintains a balance between technical explanation and accessibility, using analogies and examples to make complex concepts understandable to beginners.
