Zum Hauptinhalt springen

Building Trustworthy AI with Agents

Shingai Manjengwa, the Head of AI Education at Theoriq (ChainML Labs), will discuss the principles of responsible AI and demonstrate how they may be implemented in a world with multiple collaborating agents.
Shingai Manjengwa
Shingai Manjengwa

Erstelle dein kostenloses Konto

Üben und Lernpfad deinen Fortschritt.

Weiter mit GoogleWenn du fortfährst, akzeptierst du unsere Nutzungsbedingungen, unsere Datenschutzrichtlinie und dass deine Daten in den USA gespeichert werden.

Coden mit uns auf

Code-Along ↗
Folien ansehen ↗

The future is agentic! Join us for an exciting code-along session where we dive into the world of personalized AI agents! In this session, you'll learn how to build and evaluate AI agents ensuring they perform tasks reliably. 

In this code-along,  Shingai Manjengwa, the Head of AI Education at Theoriq (ChainML Labs), will discuss the principles of responsible AI and demonstrate how they may be implemented in a world with multiple collaborating agents. Shingai will guide you through the process of developing two distinct AI agents, each offering unique perspectives on the same task and you will learn how to create an evaluator to determine which agent gives the best output based on user criteria.

Key Takeaways:

  • Learn the principles of responsible machine learning and AI.
  • Learn how to create a machine learning agent in Python.
  • Learn a framework for managing multiple agents.

Resources

Themen

Artificial IntelligenceMachine LearningDataLabPython

Ähnlich

The Case for Responsible AI

Blog

The Case for Responsible AI

Mehr anzeigen →