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Certification

Certification: AI Safety & Ethics in Machine Learning by Safe.AI Founder

Upgrade your CV with a certification led by the founder of Safe.AI, covering essential principles of AI safety and ethics in machine learning—stand out by demonstrating your commitment to responsible and informed AI solutions.

Exam of 10 to 20 questionsCertificate for LinkedInIntermediate · Expert · Technical
IT and development
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The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

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What the exam covers

2 questions from each of the 15 chapters of the course

20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01Introduction11:09
  2. 02Deep learning review41:32
  3. 03Risk decomposition and accident models46:41
  4. 04Black swans19:23
  5. 05Adversarial robustness30:55
  6. 06Black swan robustness23:16
  7. 07Anomaly detection42:36
  8. 08Uncertainty, transparency and trojans47:20
  9. 09Emergent behaviour and honest models37:14
  10. 10Machine ethics52:02
  11. 11ML for decisions and cyberdefence32:52
  12. 12Cooperative AI33:33
  13. 13Existential risk overview14:43
  14. 14AI and evolution49:52
  15. 15Safety-capabilities balance and conclusion31:12
About the course

The Certification: AI Safety & Ethics in Machine Learning by Safe.AI Founder provides a comprehensive exploration of critical safety and ethical considerations in artificial intelligence and machine learning. Participants gain essential skills such as improved decision-making, adaptability, and a competitive advantage by learning to identify and mitigate AI risks. Enroll today to develop expertise that will help ensure the responsible and secure deployment of intelligent systems.

This certification covers the following topics:

  • Foundations of Deep Learning and Neural Networks
  • AI Safety: Foundational Concepts and Risk Analysis
  • Adversarial Robustness
  • Anomaly Detection (Out-of-Distribution Detection)
  • Interpretability and Transparency
  • Hidden Functionality and Backdoors (Trojans)
  • Emergent Capabilities and Goals
  • Goodhart's Law
  • Deception and Lying in AI
  • Ethical Considerations and Value Alignment
  • Imposing Ethical Constraints and Moral Guidance
  • Long-Term AI Safety and Existential Risk