AI Proving Ground

Generative AI is a cutting-edge technology that has been making waves across various industries in recent years. Unlike traditional AI systems that are designed to analyze and interpret existing data, generative AI has the remarkable ability to create entirely new content, from text and images to music and even code. This revolutionary technology leverages complex machine learning algorithms and neural networks to understand patterns and structures in vast amounts of data, enabling it to generate original and often highly realistic outputs. As generative AI continues to evolve and improve, it promises to transform creative processes, problem-solving, and innovation across numerous fields, from art and entertainment to scientific research and product development. Learn about what Generative AI is and how it works, in this Learning Path.

In this learning path you will gain a fundamental understanding of AI security by exploring topics such as the AI development lifecycle, data flow during this lifecycle, as well as attack surfaces present in the AI development lifecycle. You will also learn about topics such as the attacks that AI models face and the risks of user data and privacy when it comes to AI.

This Learning Path aims to provide a fundamental understanding of AI, from it's earliest beginnings, to current state, and its potential future developments, making it accessible to readers with varying levels of technical expertise.

In this learning path, you will gain a fundamental understanding of Large Language Model (LLM) security. Through a series of articles and videos you will explore topics like what is an LLM, data security risks, the OWASP Top 10 for LLMs, strategies for defending LLM systems, and the critical role users play in maintaining security.

RAG, or Retrieval-Augmented Generation, is an AI solution that combines the power of Generative AI with external data sources to provide more accurate and up-to-date responses. This Learning Path explains the concept of RAG, its origins, and how it addresses key limitations of traditional GenAI systems. You'll learn about the benefits of RAG, including enhanced accuracy, reduced hallucinations, and its flexibility in integrating real-time information without the need for constant model retraining.