🤖 How Small AI Chatbots Work in 5 Surprising Steps (Explained Without Code)

Small Language Models (SLMs) are trained to understand and generate human-like text, but are optimized to run locally on devices like phones, earbuds, and watches. They are designed to be small and smart, rather than big and powerful like models like GPT-4. SLMs work in five simple steps: tokenization, building the AI brain, training the model, optimization, and testing. Tokenization breaks language into smaller chunks called tokens, allowing the model to process language more efficiently. The AI brain is designed with fewer parameters and simplified architectures to reduce memory usage. Training the model involves feeding it task-specific data and using techniques like transfer learning and trimming unnecessary complexity. Optimization involves reducing precision, removing unnecessary connections, and compressing big model knowledge into a smaller model. The final step is testing the model's performance to ensure it can respond well and run locally and fast. Small AI chatbots have many real-world uses, including voice commands, real-time translation, and customer service. The future of AI might be smaller, not bigger, with SLMs redefining where AI can go and what it can do.