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The AI landscape is exploding in size, with some early winners emerging, but RAG reigns supreme for enterprise LLM systems. Check out our roundup of the top generative AI and LLM articles for May 2024.
FirstMark Capital has identified 2,000+ companies (!!!) competing in the ML, AI, and Data world. Read an in-depth analysis to understand all the nuances in this complex space: https://mattturck.com/mad2024/
RAG underpins many enterprise LLM systems. This recent paper consolidates existing research on RAG, explores its evolution, and suggests new areas for innovation and development: https://arxiv.org/abs/2404.10981
AI-native enterprise apps have begun to find success, with the winners scaling even quicker than top SaaS companies. Menlo Ventures identified 7 common traits among these winners, providing a guide for all GenAI companies: https://menlovc.com/perspective/7-golden-rules-for-generative-ai-apps-a-playbook-from-early-winners/
Climate change has made extreme weather events more common. Google's moonshot factory X is using AI to analyze disaster scenes in seconds, identify critical infrastructure, and create labeled maps to improve disaster response: https://x.company/case-study/bellwether-diu/
Unlike double stuff oreos, LLMs can have too many layers! Using unique pruning techniques, researchers removed half of the layers in an LLM with minimal impact on performance: https://arxiv.org/abs/2403.17887
The "Retrieval" part of RAG is often overlooked. Learn some advanced retriever techniques to help you build a highly effective RAG system: https://towardsdatascience.com/advanced-retriever-techniques-to-improve-your-rags-1fac2b86dd61
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