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

2024-09-23 · 64 min · episode 702 · 13 entities

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→ appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

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Stealing Part of a Production Language Model with Nicholas Carlini - #702
→ hosted by Sam Charrington person

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Feed author/publisher: Sam Charrington
→ discusses Science concept

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Feed category: Science
→ discusses Technology company

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→ hosted by TWIML company

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Feed author/publisher: TWIML

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mentioned Tech News concept

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Feed category: Tech News
discusses Science concept

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evidence rules-v4
Feed category: Science
discusses News concept

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evidence rules-v4
Feed category: News
discusses Tech News concept

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Feed category: Tech News

companys 4

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Feed author/publisher: TWIML
mentioned Technology company

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hosted by TWIML company

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mentioned Nicholas Carlini person

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Stealing Part of a Production Language Model with Nicholas Carlini - #702
mentioned Sam Charrington person

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evidence rules-v4
Feed author/publisher: Sam Charrington
hosted by Sam Charrington person

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evidence rules-v4
Feed author/publisher: Sam Charrington

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appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

0.68

evidence rules-v4
Stealing Part of a Production Language Model with Nicholas Carlini - #702

Episode description as stored

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.