Experts discuss the risks and evolving security landscape of AI, emphasizing education in managing AI risks and privacy engineering. They explore legal and ethical implications of AI, balance between utility and privacy, and the importance of safeguarding models and data in AI solutions. The conversation delves into memory, learning, legal frameworks, privacy concerns in large models, and Apple's business strategies in AI.
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Quick takeaways
Balancing privacy and security is crucial in AI to address model flaws effectively.
Understanding and managing privacy risks in LM applications is essential for compliance with GDPR laws.
Deep dives
Panel Introduction and Focus on Privacy and Security
The podcast episode features a panel discussion with Diego Oppenheimer, Ads Dawson, Catherine Jarmel, and David Abbair focusing on privacy and security in AI technology. Diego emphasizes the importance of addressing security flaws in models, highlighting the need to balance privacy and security effectively.
Defining Privacy and Security in AI
Katherine explains the distinction between privacy and security in AI, emphasizing the impact of laws like GDPR on machine learning practices. She discusses how privacy and security can sometimes conflict and shares insights on the importance of understanding and managing privacy risks in LM applications.
Privacy Challenges and Risks in LM Training Data
Highlighting privacy challenges in large over-parametized LM models, the panel delves into the issue of training data memorization. The discussion covers concerns about data ownership abuse, copyright infringement, and the need for privacy-enhancing techniques like differential privacy in LM training.
Security Landscape and Risk Management in ML Models
Ads and David explore the evolving security landscape in ML models, emphasizing the need for comprehensive risk management. They discuss the shift from traditional ML security to protecting LM applications, highlighting the importance of vulnerability assessments, security controls, and trust boundaries in securing AI systems.
// Abstract
Diego, David, Ads, and Katharine, bring to light the risks, vulnerabilities, and evolving security landscape of machine learning as we venture into the AI-driven future. They underscore the importance of education in managing AI risks and the critical role privacy engineering plays in this narrative. They explore the legal and ethical implications of AI technologies, fostering a vital conversation on the balance between utility and privacy.
// Bio
Diego Oppenheimer - Moderator
Diego Oppenheimer is a serial entrepreneur, product developer and investor with an extensive background in all things data. Currently, he is a Partner at Factory a venture fund specialized in AI investments as well as a co-founder at Guardrails AI. Previously he was an executive vice president at DataRobot, Founder and CEO at Algorithmia (acquired by DataRobot) and shipped some of Microsoft’s most used data analysis products including Excel, PowerBI and SQL Server.
Diego is active in AI/ML communities as a founding member and strategic advisor for the AI Infrastructure Alliance and MLops.Community and works with leaders to define AI industry standards and best practices. Diego holds a Bachelor's degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University.
Ads Dawson
A mainly self-taught, driven, and motivated proficient application, network infrastructure & cyber security professional holding over eleven years experience from start-up to large-size enterprises leading the incident response process and specializing in extensive LLM/AI Security, Web Application Security and DevSecOps protecting REST API endpoints, large-scale microservice architectures in hybrid cloud environments, application source code as well as EDR, threat hunting, reverse engineering, and forensics.
Ads have a passion for all things blue and red teams, be that offensive & API security, automation of detection & remediation (SOAR), or deep packet inspection for example.
Ads is also a networking veteran and love a good PCAP to delve into. One of my favorite things at Defcon is hunting for PWNs at the "Wall of Sheep" village and inspecting malicious payloads and binaries.
Katharine Jarmul
Katharine Jarmul is a privacy activist and data scientist whose work and research focuses on privacy and security in data science workflows. She recently authored Practical Data Privacy for O'Reilly and works as a Principal Data Scientist at Thoughtworks. Katharine has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany -- implementing data processing and machine learning systems with privacy and security built in and developing forward-looking, privacy-first data strategy.
David Haber
David has started and grown several technology companies. He developed safety-critical AI in the healthcare space and for autonomous flight. David has educated thousands of people and Fortune 500 companies on the topic of AI. Outside of work, he loves to spend time with his family and enjoys training for the next Ironman.
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