Watermarking Large Language Models to Fight Plagiarism with Tom Goldstein - 621

Watermarking Large Language Models to Fight Plagiarism with Tom Goldstein - 621

Today we’re joined by Tom Goldstein, an associate professor at the University of Maryland. Tom’s research sits at the intersection of ML and optimization and has previously been featured in the New Yorker for his work on invisibility cloaks, clothing that can evade object detection. In our conversation, we focus on his more recent research on watermarking LLM output. We explore the motivations behind adding these watermarks, how they work, and different ways a watermark could be deployed, as well as political and economic incentive structures around the adoption of watermarking and future directions for that line of work. We also discuss Tom’s research into data leakage, particularly in stable diffusion models, work that is analogous to recent guest Nicholas Carlini’s research into LLM data extraction.

Jaksot(779)

AI for Materials Discovery with Greg Mulholland - TWiML Talk #148

AI for Materials Discovery with Greg Mulholland - TWiML Talk #148

In this episode I’m joined by Greg Mulholland, Founder and CEO of Citrine Informatics, which is applying AI to the discovery and development of new materials. Greg and I start out with an exploration ...

7 Kesä 201842min

Data Innovation & AI at Capital One with Adam Wenchel - TWiML Talk #147

Data Innovation & AI at Capital One with Adam Wenchel - TWiML Talk #147

In this episode I’m joined by Adam Wenchel, vice president of AI and Data Innovation at Capital One, to discuss how Machine Learning & AI are being integrated into their day-to-day practices, and how ...

4 Kesä 201845min

Deep Gradient Compression for Distributed Training with Song Han - TWiML Talk #146

Deep Gradient Compression for Distributed Training with Song Han - TWiML Talk #146

On today’s show I chat with Song Han, assistant professor in MIT’s EECS department, about his research on Deep Gradient Compression. In our conversation, we explore the challenge of distributed traini...

31 Touko 201846min

Masked Autoregressive Flow for Density Estimation with George Papamakarios - TWiML Talk #145

Masked Autoregressive Flow for Density Estimation with George Papamakarios - TWiML Talk #145

In this episode, University of Edinburgh Phd student George Papamakarios and I discuss his paper “Masked Autoregressive Flow for Density Estimation.” George walks us through the idea of Masked Autoreg...

28 Touko 201834min

Training Data for Computer Vision at Figure Eight with Qazaleh Mirsharif - TWiML Talk #144

Training Data for Computer Vision at Figure Eight with Qazaleh Mirsharif - TWiML Talk #144

For today’s show, the last in our TrainAI series, I'm joined by Qazaleh Mirsharif, a machine learning scientist working on computer vision at Figure Eight. Qazaleh and I caught up at the TrainAI confe...

25 Touko 201821min

Agile Data Science with Sarah Aerni - TWiML Talk #143

Agile Data Science with Sarah Aerni - TWiML Talk #143

Today we continue our TrainAI series with Sarah Aerni, Director of Data Science at Salesforce Einstein. Sarah and I sat down at the TrainAI conference to discuss her talk “Notes from the Field: The Pl...

24 Touko 201838min

Tensor Operations for Machine Learning with Anima Anandkumar - TWiML Talk #142

Tensor Operations for Machine Learning with Anima Anandkumar - TWiML Talk #142

In this episode of our TrainAI series, I sit down with Anima Anandkumar, Bren Professor at Caltech and Principal Scientist with Amazon Web Services. Anima joined me to discuss the research coming out ...

23 Touko 201834min

Deep Learning for Live-Cell Imaging with David Van Valen - TWiML Talk #141

Deep Learning for Live-Cell Imaging with David Van Valen - TWiML Talk #141

In today’s show, I sit down with David Van Valen, assistant professor of Bioengineering & Biology at Caltech. David joined me after his talk at the Figure Eight TrainAI conference to chat about his re...

22 Touko 201837min

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