CXOTalk
CXOTalk
Michael Krigsman
Top Data Scientists Explain Bad Data, Poisoned Datasets, and Other AI Killers | CXOTalk #896
59 minutes Posted Oct 9, 2025 at 10:30 pm.
πŸ€– Understanding AI Quicksand and the Challenges of Data02:16 ⚠️ The Five Ms of AI Risks and Practical Examples07:12 πŸ’‘ The Disconnect in AI Adoption and the Importance of Purpose09:46 πŸ€– The Challenges and Risks of Generative AI14:28 βš–οΈ Ethical and Intellectual Property Concerns in AI16:30 πŸ›‘οΈ Responsibility and Oversight in Ethical AI20:22 βš–οΈ Liability and Critical Thinking in AI Services22:12 πŸ“œ AI Regulation and Consumer Advocacy25:55 πŸ€– Future Directions in AI Development30:59 πŸ€– Exploring AI's Potential and Limitations33:03 πŸ§ͺ Data Poisoning and Its Implications40:07 ⚠️ Addressing AI Risks and Perception of Reality41:38 πŸ“Š The Evolution of Data Analysis and Critical Thinking44:36 🧠 Teaching Critical Thinking and AI Critique46:36 πŸ€– Embodied AI, Data Misuse, and Global AI Perspectives50:57 πŸ€– The Evolution of Work and AI's Role52:59 🌟 AI's Positive Impact on Healthcare and Society56:23 🌈 Hope and Resilience in the Age of AI#AI #ArtificialIntelligence #DataScience #AIEthics #MachineLearning #DataPoisoning #GenerativeAI #EnterpriseAI #AIStrategy #DigitalTransformation #CyberSecurity #AIRegulation #FutureOfWork #TechLeadership #CXOTalk
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Is your AI built on quicksand? Learn how bad data, poisoned datasets, and deep fakes threaten your AI systems, and what to do about it.In this episode of CXOTalk (#896), AI luminaries Dr. David Bray and Dr. Anthony Scriffignano reveal the hidden dangers lurking in your AI foundations. They share practical strategies for building trustworthy AI systems and escaping the "AI quicksand" that traps countless organizations.🎯 KEY TAKEAWAYS:The Five Ms framework for identifying AI failures before they scaleWhy red teams are essential for AI risk managementHow nation-states are actively poisoning AI training dataCritical questions every leader must ask about their AI initiativesThe future of specialized AI models vs. mega LLMsπŸ”· Show notes and resources: https://www.cxotalk.com/episode/escape-ai-quicksand-data-misuse-misadventures-and-mal-intentπŸ”· Newsletter: www.cxotalk.com/subscribeπŸ”· LinkedIn: www.linkedin.com/company/cxotalkπŸ”· Twitter: twitter.com/cxotalk00:00 πŸ€– Understanding AI Quicksand and the Challenges of Data02:16 ⚠️ The Five Ms of AI Risks and Practical Examples07:12 πŸ’‘ The Disconnect in AI Adoption and the Importance of Purpose09:46 πŸ€– The Challenges and Risks of Generative AI14:28 βš–οΈ Ethical and Intellectual Property Concerns in AI16:30 πŸ›‘οΈ Responsibility and Oversight in Ethical AI20:22 βš–οΈ Liability and Critical Thinking in AI Services22:12 πŸ“œ AI Regulation and Consumer Advocacy25:55 πŸ€– Future Directions in AI Development30:59 πŸ€– Exploring AI's Potential and Limitations33:03 πŸ§ͺ Data Poisoning and Its Implications40:07 ⚠️ Addressing AI Risks and Perception of Reality41:38 πŸ“Š The Evolution of Data Analysis and Critical Thinking44:36 🧠 Teaching Critical Thinking and AI Critique46:36 πŸ€– Embodied AI, Data Misuse, and Global AI Perspectives50:57 πŸ€– The Evolution of Work and AI's Role52:59 🌟 AI's Positive Impact on Healthcare and Society56:23 🌈 Hope and Resilience in the Age of AI#AI #ArtificialIntelligence #DataScience #AIEthics #MachineLearning #DataPoisoning #GenerativeAI #EnterpriseAI #AIStrategy #DigitalTransformation #CyberSecurity #AIRegulation #FutureOfWork #TechLeadership #CXOTalk