As technology advances at an unprecedented pace, so does the development of Machine Learning (ML) and Artificial Intelligence (AI) systems. These systems have grown exponentially in size, capability, and are now being deployed in critical and high-stakes environments. As with any powerful technology, safety becomes an important concern.

We just published a full course on the YouTube channel that addresses the important issue of AI Safety.

Dan Hendrycks developed this course. Dan is a machine learning researcher and has a Ph.D. in Computer Science. He serves as the director of the Center for AI Safety.

This course is designed to equip researchers and ML enthusiasts with the knowledge and skills to ensure the safe development and deployment of AI systems. The course recognizes the importance of prioritizing safety as AI systems continue to evolve, and it aims to steer the AI development process towards a safer and more controlled direction.

The course covers an array of technical topics that aim to mitigate existential risks (X-Risks) associated with powerful AI systems. The curriculum is structured around four main pillars:

  1. Robustness: In this section, the course delves into techniques and strategies to withstand potential hazards in AI systems. You will learn how to fortify ML models against adversarial attacks and uncertainties, ensuring the AI is resilient and reliable even in challenging scenarios.
  2. Monitoring: Identifying hazards is a crucial aspect of building safe AI. In this section, you will learn various monitoring techniques that help researchers detect potential issues and anomalies within AI systems. By proactively recognizing risks, you will be better equipped to address them before they escalate.
  3. Control: Reducing inherent hazards in ML systems is of utmost importance. You will learn how to implement effective control mechanisms that govern the behavior of AI systems, mitigating the chances of unintended harmful actions.
  4. Systemic Safety: This section focuses on reducing systemic hazards in AI deployments. You will learn how AI systems interact with their environment, stakeholders, and other systems, and explore strategies to ensure the overall safety and reliability of the AI ecosystem.

Towards the end, the course takes a step back and discusses abstract existential hazards. You will gain insights into potential far-reaching implications of AI development and explore ways to enhance safety without unintended consequences. You will gain a better understanding of the broader ethical and societal impacts of AI.

To make the most out of this course, you should have a solid background in Machine Learning and Deep Learning. A familiarity with key concepts, algorithms, and practical applications will ensure a smooth learning experience.

You can watch the full course on the YouTube channel (9-hour watch).