### Key Points about Deep Seek and DeepSeeker R1: 1. **Introduction of New Models**: The models Deep Seek and DeepSeeker R1 are creating a significant buzz due to their potential to challenge the dominance of established AI firms. 2. **Large Language Models (LLMs)**: LLMs are large transformer-based neural networks designed for next-word prediction, primarily training on vast amounts of text data sourced from the internet. 3. **Generative AI Types**: There are two primary forms of generative AI: diffusion models for image generation and transformers for text generation. 4. **Training Models**: - Massive hardware resources (hundreds of thousands of GPUs) typically make training LLMs costly (up to billions). - The Deep Seek model has been trained for as little as $5 million, showcasing a more efficient approach. 5. **Mixed Models**: - **Mixture of Experts**: This approach activates only relevant parts of a large model depending on the task, significantly reducing computational costs. - Instead of using the entire model, it selectively engages only the relevant sub-models for specific tasks. 6. **Distillation**: A technique where a smaller model is trained based on the output of a larger one, enabling efficient utilization of capabilities without extensive resource requirements. 7. **Mathematical Efficiency**: Recent advancements have been made in reducing the number of computations needed in network processing, further lowering costs. 8. **Chain of Thought (CoT)**: - Introduced in the R1 model, CoT involves step-by-step reasoning, improving problem-solving capabilities for complex logic and mathematical tasks. - R1 uses reinforcement learning to refine its internal monologue based on problem-solving outcomes. 9. **Open Source Efforts**: DeepSeeker R1's public training data and models are a notable shift towards transparency in AI development, potentially leveling the playing field against closed-source models from larger firms. 10. **Impacts on AI Industry**: - The advent of these models could disrupt existing business models based on proprietary large models, pushing toward more accessible and open AI development. - Companies like Nvidia may face challenges if smaller entities can produce effective AI models without the expensive infrastructure. 11. **Future of AI**: This development suggests a potential move away from closed-source domination and promotes innovation among researchers and startups with limited resources, democratizing access to advanced AI technology. These insights highlight the evolving landscape of AI and the implications of new models on both technology and industry accessibility.