Sponsored by Intel
As AI becomes more pervasive, the question of how to build, test, and maintain risk-averse systems grows too. Join experts from the field to learn how they analyze and handle privacy, risk, and safety in their work.
About the AI Superstream Series: This four-part series is packed with insights from some of the brightest minds in AI. You’ll get a deeper understanding of the latest tools and technologies that can help keep your organization competitive and learn to leverage AI to drive real business results.What you’ll learn and how you can apply it
- Understand what secure AI does (and doesn’t) include
- See what secure AI might look like from design through deployment
- Discover real-world technical applications of secure AI
- You're a machine learning engineer or data scientist interested in the challenges of building secure AI and machine learning tools.
- You want to better understand how industry experts handle security.
- You want to learn how to improve your AI and machine learning systems.
- Read AI and the Law (report)
Table of contents
- Introduction and Tendai Gomo: Keynote—The Devil Is in the Details: Thoughts on Securing Machine Learning and Artificial Intelligence Systems and Infrastructure
- Jason Dai: Privacy-Preserving Machine Learning (PPML) for Big Data AI (Sponsored by Intel)
- Mohammed M. Alani: AI Under Attack—Recent Attacks on Machine Learning That You Should Know About
- Patricia Thaine: Privacy-Enhancing Technologies in AI Security
- Arijit Sengupta: Solving Bias and Privacy Challenges in AI by Design (Sponsored by Intel)
- Ethan Jackson: Practical Use Cases for Privacy Enhancing Techniques in Deep Learning
- Natalie Dullerud: CaPC—Confidential and Private Collaborative Learning
- Title: AI Superstream Series: Securing AI
- Release date: December 2021
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920672296
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