AI-powered tools have rapidly grown in popularity over the past few years. Many organizations, regardless of their understanding of the technology, are adopting these tools. This shift brings inevitable challenges, missteps, and pain points as individuals and teams navigate the complexities of emerging AI technologies.
This week, we’re featuring insightful posts that address common AI pain points head-on. From dealing with privacy nightmares to vulnerabilities in AI applications, these articles offer practical solutions and approaches to tackle AI challenges effectively.
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Why GenAI Is a Data Deletion and Privacy Nightmare
“Trying to remove training data once it has been baked into a large language model is like trying to remove sugar once it has been baked into a cake.” Cassie Kozyrkov analyzes the privacy issues related to training models on user data and the challenges in resolving them post-training.
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Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT
Kenneth Leung explores the security risks posed by LLM-based products and demonstrates the use of the open-source ARTKIT framework to evaluate LLM security vulnerabilities.
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Choosing Between LLM Agent Frameworks
Aparna Dhinakaran’s overview discusses the challenges in selecting the right AI agent framework for specific tasks and workflows.
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How I Deal with Hallucinations at an AI Startup
Tarik Dzekman shares insights on avoiding costly mistakes in AI applications by leveraging grounding methods and utilizing smaller models.