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Debbie Reynolds에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Debbie Reynolds 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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The Data Diva E258 - Terry Bollinger and Debbie Reynolds

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Manage episode 513515561 series 2897113
Debbie Reynolds에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Debbie Reynolds 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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Episode 258 – Terry Bollinger: Understanding the Limits of Artificial Intelligence

In this episode of The Data Diva Talks Privacy Podcast, Debbie Reynolds, The Data Diva, speaks with Terry Bollinger, retired technology analyst at MITRE, about the limits of artificial intelligence and the growing risks of relying on systems that only mimic human understanding. They discuss how large language models operate as mimicry machines, imitating intelligence rather than achieving it, and how this design choice leads to fundamental weaknesses in trust, accuracy, and accountability. Terry explains that AI models based on probability and pattern replication erase uniqueness, creating false confidence in their results. He warns that by averaging data rather than analyzing meaning, these systems blur important distinctions, making it difficult to detect errors, anomalies, or malicious activity. Debbie and Terry explore why true privacy and security depend on identifying outliers —the small deviations that reveal hidden threats, rather than relying on average trends.

Terry describes how traditional security systems are built on clearly defined boundaries, data paths, and verification processes, while modern AI systems often remove those controls. He emphasizes that when data is distributed, reweighted, and stored probabilistically, it becomes nearly impossible to verify what has been learned, lost, or leaked. The conversation examines the risks of utilizing LLMs in sensitive environments, where transmitting confidential data to remote commercial systems can compromise containment and integrity. Terry discusses how interpolation, or the act of filling in the blanks when data is missing, leads AI to generate convincing but incorrect answers, what he calls “random noise masquerading as insight.” Debbie and Terry also examine why intelligence, wisdom, and comprehension cannot be replicated through scale or speed. The episode concludes with a reflection on the importance of human judgment, accountability, and boundary control in an era where automation is expanding faster than understanding.

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Become an insider, join Data Diva Confidential for data strategy and data privacy insights delivered to your inbox.


💡 Receive expert briefings, practical guidance, and exclusive resources designed for leaders shaping the future of data and AI.


👉 Join here:
http://bit.ly/3Jb8S5p

Debbie Reynolds Consulting, LLC

  continue reading

264 에피소드

Artwork
icon공유
 
Manage episode 513515561 series 2897113
Debbie Reynolds에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Debbie Reynolds 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Send us a text

Episode 258 – Terry Bollinger: Understanding the Limits of Artificial Intelligence

In this episode of The Data Diva Talks Privacy Podcast, Debbie Reynolds, The Data Diva, speaks with Terry Bollinger, retired technology analyst at MITRE, about the limits of artificial intelligence and the growing risks of relying on systems that only mimic human understanding. They discuss how large language models operate as mimicry machines, imitating intelligence rather than achieving it, and how this design choice leads to fundamental weaknesses in trust, accuracy, and accountability. Terry explains that AI models based on probability and pattern replication erase uniqueness, creating false confidence in their results. He warns that by averaging data rather than analyzing meaning, these systems blur important distinctions, making it difficult to detect errors, anomalies, or malicious activity. Debbie and Terry explore why true privacy and security depend on identifying outliers —the small deviations that reveal hidden threats, rather than relying on average trends.

Terry describes how traditional security systems are built on clearly defined boundaries, data paths, and verification processes, while modern AI systems often remove those controls. He emphasizes that when data is distributed, reweighted, and stored probabilistically, it becomes nearly impossible to verify what has been learned, lost, or leaked. The conversation examines the risks of utilizing LLMs in sensitive environments, where transmitting confidential data to remote commercial systems can compromise containment and integrity. Terry discusses how interpolation, or the act of filling in the blanks when data is missing, leads AI to generate convincing but incorrect answers, what he calls “random noise masquerading as insight.” Debbie and Terry also examine why intelligence, wisdom, and comprehension cannot be replicated through scale or speed. The episode concludes with a reflection on the importance of human judgment, accountability, and boundary control in an era where automation is expanding faster than understanding.

Support the show

Become an insider, join Data Diva Confidential for data strategy and data privacy insights delivered to your inbox.


💡 Receive expert briefings, practical guidance, and exclusive resources designed for leaders shaping the future of data and AI.


👉 Join here:
http://bit.ly/3Jb8S5p

Debbie Reynolds Consulting, LLC

  continue reading

264 에피소드

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