Best Practices for Reliable and Resposible Generative AI | Large Language Models | Community Webinar скачать в хорошем качестве

Best Practices for Reliable and Resposible Generative AI | Large Language Models | Community Webinar

Generative AI models and applications are being rapidly deployed across several industries, but some ethical and social considerations need to be addressed. These concerns include lack of interpretability, bias, and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. In this talk, we first provide a brief overview of Generative AI, motivate the need for adopting responsible AI principles when developing and deploying LLMs and other generative AI models, and provide a roadmap for thinking about responsible AI for generative AI in practice. We'll also focus on real-world LLM use cases, such as evaluating LLMs for robustness, security, bias, and more. By providing real-world generative AI use cases, lessons learned, and best practices, this talk will enable practitioners to build more reliable and trustworthy generative AI applications. Table of Contents: 0:00 Introduction 1:56 New Categories in AI 3:01 Generative AI Overview 7:37 Trustworthiness Challenges in Generative AI 8:13 Hallucinations in AI 12:22 Robustness in Input and Adversarial Perturbations 14:00 Prompt Injection and Data Poisoning Attacks 16:24 Privacy and Copyright Concerns in LLMs 22:39 Bias in Generative AI 28:01 Transparency in LLMs 33:59 Monitoring LLM Quality 36:17 Enterprise Concerns in Generative AI 42:36 Deploying Trustworthy Generative AI in Practice Here's more to explore in Large Language Models: 💼 Learn to build LLM-powered apps in just 40 hours with our Large Language Models bootcamp: https://hubs.la/Q01ZZGL-0 #llm #largelanguagemodels #generativeai #ai #artificialintelligence #ethicalai