Stealing Part of a Production Language Model with Nicholas Carlini - #702

Stealing Part of a Production Language Model with Nicholas Carlini - #702

Today, we're joined by Nicholas Carlini, research scientist at Google DeepMind to discuss adversarial machine learning and model security, focusing on his 2024 ICML best paper winner, “Stealing part of a production language model.” We dig into this work, which demonstrated the ability to successfully steal the last layer of production language models including ChatGPT and PaLM-2. Nicholas shares the current landscape of AI security research in the age of LLMs, the implications of model stealing, ethical concerns surrounding model privacy, how the attack works, and the significance of the embedding layer in language models. We also discuss the remediation strategies implemented by OpenAI and Google, and the future directions in the field of AI security. Plus, we also cover his other ICML 2024 best paper, “Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining,” which questions the use and promotion of differential privacy in conjunction with pre-trained models. The complete show notes for this episode can be found at https://twimlai.com/go/702.

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Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

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Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution...

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Why Models Are AI’s Next Training Dataset with Damian Borth - #772

For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, r...

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How AI Learns to Smell with Alex Wiltschko - #771

How AI Learns to Smell with Alex Wiltschko - #771

In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the sci...

8 Heinä 59min

Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We...

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Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries...

9 Kesä 51min

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep lea...

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How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Masl...

7 Touko 53min

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