Use a real-life example of an AI system to discuss some impacts of cyber attacks : Categories of ML Tasks and Attacks

The video explores vulnerabilities in various machine learning tasks, including classification, regression, generative models, clustering, dimensionality reduction, and reinforcement learning. It highlights how adversarial attacks can exploit these vulnerabilities, affecting tasks like spam detection, malware analysis, and face detection. The video concludes that all categories of machine learning tasks are susceptible to such attacks, emphasizing the need for safety considerations.

What are the common vulnerabilities found in machine learning algorithms?

How do adversarial examples affect regression tasks in machine learning?

What role do generative models play in the context of adversarial attacks?

In what ways can dimensionality reduction techniques be exploited in machine learning?

What is the significance of reinforcement learning in the context of adversarial attacks?