Przejdź do głównej treści

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

Utwórz bezpłatne konto

Ćwicz i monitoruj swoje postępy.

Kontynuuj z GoogleKontynuując, akceptujesz nasz Warunki użytkowania, nasz Polityka prywatności i że Twoje dane są przechowywane w USA.

Koduj razem z nami na

Code Along ↗
Zobacz slajdy ↗

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

Tematy

Artificial IntelligenceMachine LearningDataLabPython

Powiązane

The Case for Responsible AI

blog

The Case for Responsible AI

Zobacz więcej →