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Ensuring Privacy for Any LLM with Patricia Thaine - 716

The TWIML AI Podcast with Sam Charrington 478 lượt xem 3 months ago
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Today, we're joined by Patricia Thaine, co-founder and CEO of Private AI to discuss techniques for ensuring privacy, data minimization, and compliance when using 3rd-party large language models (LLMs) and other AI services. We explore the risks of data leakage from LLMs and embeddings, the complexities of identifying and redacting personal information across various data flows, and the approach Private AI has taken to mitigate these risks. We also dig into the challenges of entity recognition in multimodal systems including OCR files, documents, images, and audio, and the importance of data quality and model accuracy. Additionally, Patricia shares insights on the limitations of data anonymization, the benefits of balancing real-world and synthetic data in model training and development, and the relationship between privacy and bias in AI. Finally, we touch on the evolving landscape of AI regulations like GDPR, CPRA, and the EU AI Act, and the future of privacy in artificial intelligence.

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📖 CHAPTERS
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00:00 - Introduction
2:06 - Private AI
3:32 - Data flows
13:13 - User requirements
14:06 - Entity recognition
18:39 - Warranties
19:43 - Generalized model
20:42 - Multilingual challenges
22:13 - OCR
25:31 - Synthetic data
28:06 - Multimodality
37:44 - Managing system and product scope
39:30 - Data catalogs and MDM systems
41:43 - The connection between privacy and ethical responsible AI
45:23 - AI regulations
48:07 - Future directions


🔗 LINKS & RESOURCES
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Private AI - https://private-ai.com/en/redact/
Forum Ventures - https://hubs.ly/Q0346ccs0


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