Ensuring Privacy for Any LLM with Patricia Thaine - #716

Ensuring Privacy for Any LLM with Patricia Thaine - #716

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. The complete show notes for this episode can be found at https://twimlai.com/go/716.

Episoder(781)

From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731

From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731

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How OpenAI Builds AI Agents That Think and Act with Josh Tobin - #730

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Today, we're joined by Nidhi Rastogi, assistant professor at Rochester Institute of Technology to discuss Cyber Threat Intelligence (CTI), focusing on her recent project CTIBench—a benchmark for evalu...

30 Apr 202556min

Generative Benchmarking with Kelly Hong - #728

Generative Benchmarking with Kelly Hong - #728

In this episode, Kelly Hong, a researcher at Chroma, joins us to discuss "Generative Benchmarking," a novel approach to evaluating retrieval systems, like RAG applications, using synthetic data. Kelly...

23 Apr 202554min

Exploring the Biology of LLMs with Circuit Tracing with Emmanuel Ameisen - #727

Exploring the Biology of LLMs with Circuit Tracing with Emmanuel Ameisen - #727

In this episode, Emmanuel Ameisen, a research engineer at Anthropic, returns to discuss two recent papers: "Circuit Tracing: Revealing Language Model Computational Graphs" and "On the Biology of a Lar...

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Teaching LLMs to Self-Reflect with Reinforcement Learning with Maohao Shen - #726

Teaching LLMs to Self-Reflect with Reinforcement Learning with Maohao Shen - #726

Today, we're joined by Maohao Shen, PhD student at MIT to discuss his paper, “Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search.” We dig into...

8 Apr 202551min

Waymo's Foundation Model for Autonomous Driving with Drago Anguelov - #725

Waymo's Foundation Model for Autonomous Driving with Drago Anguelov - #725

Today, we're joined by Drago Anguelov, head of AI foundations at Waymo, for a deep dive into the role of foundation models in autonomous driving. Drago shares how Waymo is leveraging large-scale machi...

31 Mar 20251h 9min

Dynamic Token Merging for Efficient Byte-level Language Models with Julie Kallini - #724

Dynamic Token Merging for Efficient Byte-level Language Models with Julie Kallini - #724

Today, we're joined by Julie Kallini, PhD student at Stanford University to discuss her recent papers, “MrT5: Dynamic Token Merging for Efficient Byte-level Language Models” and “Mission: Impossible L...

24 Mar 202550min

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