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Responsible AI

Applying AI Principles with Google Cloud

Google Cloud Training

Learn how to operationalize responsible AI in your organization with this course. Discover best practices and lessons from Google Cloud on building and implementing responsible AI frameworks. Enroll today!

What's inside

Syllabus

If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers industry-standard techniques and tools for responsible AI implementation
Taught by Google Cloud Training, a well-established provider of AI courses
Designed for professionals interested in operationalizing responsible AI in their organizations
Provides practical guidance and lessons learned from Google Cloud on building and implementing responsible AI frameworks
May require prior knowledge or experience in AI and machine learning

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Career center

Learners who complete Responsible AI: Applying AI Principles with Google Cloud will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Responsible AI: Applying AI Principles with Google Cloud.
Provides a comprehensive overview of the field of deep learning, covering a wide range of topics from neural networks to reinforcement learning. It valuable resource for anyone who wants to learn more about the latest advances in deep learning.
Explores the potential risks and benefits of artificial intelligence, and argues that we need to develop new ways to align AI with human values.
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Provides a comprehensive overview of the safety and security risks associated with the development and use of artificial intelligence. It valuable resource for anyone who wants to learn more about how to mitigate these risks.
Provides a rigorous framework for developing ethical algorithms. It covers a wide range of topics, from fairness to transparency.
Explores the potential risks and benefits of superintelligence. It argues that we need to develop new strategies to ensure that superintelligence is used for good.
Explores the problem of control in artificial intelligence. It argues that we need to develop new ways to ensure that AI systems are aligned with human values.
Explores the history and future of machine learning. It argues that machine learning will eventually lead to the development of a master algorithm that will be able to solve any problem.

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