The Paradox of Fine-Tuning AI, Navigating Large Language Models, Chinese Mourners Turn to AI, and Shallow Cross-Encoders for Low-Latency Retrieval

Can benign data undermine AI safety? This paper from Princeton University explores the paradox of machine learning fine-tuning. Plus, a study navigates large language model pre-training with downstreaming capability analysis. Chinese mourners turn to AI to remember and 'revive' loved ones. And researchers at the University of Glasgow propose shallow cross-encoders as an AI-based solution for low-latency information retrieval. Join us as we delve into these fascinating topics at Simply A.I.

Sources:
https://www.marktechpost.com/2024/04/03/can-benign-data-undermine-ai-safety-this-paper-from-princeton-university-explores-the-paradox-of-machine-learning-fine-tuning/
https://www.marktechpost.com/2024/04/03/this-ai-study-navigates-large-language-model-llm-pre-training-with-down-streaming-capability-analysis/
https://www.theguardian.com/technology/2024/apr/04/chinese-mourners-turn-to-ai-to-remember-and-revive-loved-ones
https://www.marktechpost.com/2024/04/03/researchers-at-the-university-of-glasgow-propose-shallow-cross-encoders-as-an-ai-based-solution-for-low-latency-information-retrieval/

Outline:
(00:00:00) Introduction
(00:00:45) Can Benign Data Undermine AI Safety? This Paper from Princeton University Explores the Paradox of Machine Learning Fine-Tuning
(00:03:42) This AI Study Navigates Large Language Model (LLM) Pre-training With Down-streaming Capability Analysis
(00:06:37) Chinese mourners turn to AI to remember and ‘revive’ loved ones
(00:09:09) Researchers at the University of Glasgow Propose Shallow Cross-Encoders as an AI-based Solution for Low-Latency Information Retrieval