DeepSeek AI: Understanding Limitations, Risks, and Controversies

As open-weights AI models gain rapid popularity, understanding their real-world limitations and security risks is essential for responsible deployment. This text-based course guides you through the technical challenges, ethical dilemmas, and operational constraints of using DeepSeek models. You will transition from a casual user to an informed evaluator, capable of identifying model biases, security vulnerabilities, and data privacy concerns while implementing effective mitigation strategies. What you'll learn: - Analyze the architectural boundaries and reasoning limits of DeepSeek models. - Identify potential biases, censorship patterns, and alignment challenges in generated outputs. - Evaluate data privacy risks and security considerations for local and cloud deployments. - Compare open-weights architectures with closed proprietary models on safety and performance. - Apply practical prompt engineering and guardrail techniques to mitigate model weaknesses. - Understand the broader ethical and geopolitical controversies surrounding modern AI development. Starting with foundational AI safety concepts, you will read through structured analyses of model behavior, explore real-world case studies of failure modes, and learn how to set up robust evaluation frameworks. This course is designed for beginners, developers, and AI enthusiasts, requiring no prior programming experience or advanced technical background. Equip yourself with the critical knowledge needed to navigate the evolving landscape of open-weights AI safely and effectively.