Tim Hwang, Global Public Policy Lead on AI and Machine Learning for Google, discusses topics such as developing AI systems, privacy, bias in machine learning, adversarial data, the potential of machine learning APIs, preparing for a shift in AI, impact on the economy, and advancements in machine learning and art.
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Quick takeaways
Developing policies around AI involves complex trade-offs between diverse data collection and privacy concerns.
The future of AI will require attention to skill development, domain knowledge, and the changing nature of programming.
Deep dives
Governments and AI
The podcast episode discusses how governments are approaching and thinking about the implications of AI. Tim Wong, the global public policy lead on AI and machine learning for Google, explains his role in educating governments on AI and helping to shape Google's policies.
Policy Challenges in AI
Tim Wong delves into the challenges of developing policies around AI. He provides the example of fairness in machine learning systems, explaining the complex trade-offs between collecting diverse data and privacy concerns when debiasing systems.
Security and Machine Learning
The episode explores the importance of security in machine learning systems. Tim Wong highlights the need for understanding adversarial examples and the vulnerabilities that can arise in computer vision systems. He also mentions the emerging field of federated learning, where training of machine learning models is done locally on devices.
Future of AI and Education
The podcast episode discusses how the future of AI will impact education and the workforce. Issues such as skill development, domain knowledge, and the changing nature of programming are mentioned as areas that need attention as AI continues to progress.