함께 코드 따라하기
코드 따라하기 ↗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
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Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.관련

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The Case for Responsible AI
We recently released a report co-written by DataRobot’s VP of Trusted AI Ted Kwartler, Global AI Ethicist Haniyeh Mahmoudian, and Managing Director of AI Ethics Sara Khatry. Here’s a run-down of what to expect.

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Building Trustworthy AI with Alexandra Ebert, Chief Trust Officer at MOSTLY AI
Richie and Alexandra explore the importance of trust in AI, what causes us to lose trust in AI systems and the impacts of a lack of trust, AI regulation and adoption, AI decision accuracy and fairness, privacy concerns in AI and much more.

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Trust and Regulation in AI with Bruce Schneier, Internationally Renowned Security Technologist
Richie and Bruce explore the definition of trust, how AI mimics social trust, AI and deception, AI regulation, why AI is a political issue and much more.


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.
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