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Source channel @githubtrending · Post #14909 · Jul 3

#other#agent#llm#rag Happy-LLM is a free, open-source learning project that helps you deeply understand large language models (LLMs) from basics to advanced training and applications. It teaches you key concepts like NLP, Transformer architecture, pretraining, and how to build and train your own LLaMA2 model step-by-step. You also learn practical skills like fine-tuning and using cutting-edge techniques such as Retrieval-Augmented Generation (RAG) and intelligent agents. This project is ideal if you know some Python and deep learning, and it offers both theory and hands-on code to help you master LLM development and apply it in real-world AI tasks. This can boost your skills and confidence in AI model building and research. https://github.com/datawhalechina/happy-llm

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AI & Law

@ai_and_law · Post #212 · 01/12/2024, 08:04 AM

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NIST Issues Urgent Report on Escalating Threat of AI Attacks Hello, dear subscribers! The National Institute of Standards and Technology (NIST) has released a critical report titled "Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations," sounding the alarm on the intensifying threat landscape targeting artificial intelligence systems. In the face of increasingly powerful yet vulnerable AI systems, the report outlines the technique of adversarial machine learning, wherein attackers manipulate AI systems through subtle tactics with potentially catastrophic consequences. The document categorizes these attacks based on attackers' goals, capabilities, and knowledge of the target AI system. Concerns include "data poisoning" and "backdoor attacks," exploiting vulnerabilities in AI system development and deployment. #NIST#AIAttacks#AISecurity#ThreatLandscape#MachineLearning**

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