Modal’s Post

In a new paper Distilling Vision-Language Models on Millions of Videos, a research team from Google and University of Texas introduces a straightforward yet highly effective method to adapt image-based vision-language models (VLMs) to video. The approach involves generating high-quality pseudo-captions for millions of videos, outperforming state-of-the-art methods across various video-language benchmarks. #llms #chatgpt4 #gemini #visionai #datascience #ai https://lnkd.in/dyBSbG-K

📅 July 10, 2024 AIBuzzWorld Daily Newsletter! Dive into the fascinating world of Artificial Intelligence and be the first to learn about the latest AI news: 1. **LLMs Struggle with Book-Length Text**      • Current long-context LLMs struggle to understand and reason over book-length texts.   • Researchers created NOCHA, a dataset to test LLM's comprehension of lengthy narratives.   • Even advanced LLMs like GPT-4o achieved only 55.8% accuracy.   • Readmore: https://lnkd.in/g2tFiH5g 2. **Quora’s Poe Introduces Artifacts-Like Feature**      • Quora’s Poe now has a feature to create custom web apps within the chat.   • This feature works well with LLMs that excel at coding.   • Users can create interactive experiences like games and data visualizations.   • Readmore: https://lnkd.in/gSJffbB9 3. **New Features in Ollama 0.2**      • Ollama 0.2 now supports multiple chat sessions and running various models simultaneously.   • Users can load different models for tasks like RAG and running agents.   • This update improves efficiency and multitasking.   • Readmore: https://lnkd.in/g-BCN4zH 4. **Wheebot for Quick Landing Page Creation**      • Wheebot allows users to create and edit landing pages via WhatsApp.   • Users can describe their requirements in plain English.   • Wheebot generates and updates sites instantly through encrypted chats.   • Readmore: https://lnkd.in/gsRtzGrd #AI #ArtificialIntelligence #MachineLearning #LLM #Microsoft #Quora #ClaudeAI #Wheebot #TechNews #Innovation #AIUpdates #FutureTech

VentureBeat writes about a new proposed prompting technique for large language models with the potential to outperform existing ones - a self-discover framework for large language models - https://lnkd.in/eaaFcJx2. #promptengineering #largelanguagemodels #artificialintelligence #venturebeat

https://lnkd.in/gSf4ijhh This project marks the initial steps in exploring advanced LLM techniques for building intelligent question-answering systems. It serves as a foundational project for understanding language models and LLM applications. #LLM #projectbasedlearning #NLP #continuouslearning #Artificialintelligence

In the future, how will we get the best search results on a company’s website? Combine a large language model (which understands natural language) + a user’s search history (including what pages they’ve viewed) + multi-query. Read more here: https://lnkd.in/g3zTiQwk #search #multiquery #llm #generativeai

Dive into the world of Vector Search with Large Language Models (LLMs), where semantic understanding meets intelligent information retrieval. Discover how these technologies revolutionize search engines, enhance AI-driven applications, and offer personalized experiences. Explore the synergy between Vector Search and LLMs, uncovering deeper insights and semantic precision for users navigating the digital landscape. Full article here: https://lnkd.in/g_rKnHW2 #largelanguagemodels #generativeai

"Introducing EventFormer: A groundbreaking model for Video Corpus Moment Retrieval (VCMR) that leverages event reasoning and hierarchical event encoding to revolutionize video retrieval. EventFormer achieves new state-of-the-art results in VCMR, demonstrating its effectiveness and efficiency. This innovation promises to reshape the landscape of video retrieval in the world of AI and ML. #AI #MachineLearning #VCMR #EventFormer"

🚨 New Research: AI Hallucinations Still a Major Issue 🚨 A recent study reveals that even the latest AI models, like GPT-4o and Claude 3 Opus, struggle with generating accurate information. Despite claims of improvement, hallucinations remain prevalent, especially on non-Wikipedia topics. Currently, even the best models can generate hallucination-free text only about 35% of the time. Some models avoided hallucinations by refusing to answer questions they would get wrong, but this trade-off raises questions about usability. The key takeaway? We can't fully trust AI outputs yet. It’s crucial to advance fact-checking and citation methods to mitigate these inaccuracies. #AI #MachineLearning #AIResearch #LegalTech #Innovation https://lnkd.in/guuEC2xV

Having a solid data pipeline is super important for any project. In Verba, we've built our ingestion pipeline to be fully modular, so it can easily scale. This means you can easily add different file types, chunking methods (semantic, recursive, fixed), and embedding models (Ollama, HuggingFace). In Verba v2, we're adding even more steps for metadata processing and natural language processing, making it easy to experiment with new RAG features in the pipeline. The process is pretty simple: we start by reading files provided through the frontend, chunk them into pieces, vectorize them, and then load them into Weaviate. You have complete freedom to configure each step however you like. GitHub: https://lnkd.in/d3sRrBW9

With the contextual windows of large language models (LLMs) getting bigger, some think this might be the end of Retrieval-Augmented Generation (RAG). In my latest story, I look into this question and explain why RAG is still necessary. Even with larger context windows, RAG plays a crucial role in improving the accuracy and relevance of generated content. Find out why RAG is important for the future of AI and how it works with the expanding capabilities of LLMs. https://lnkd.in/dwfuyz2Y #rag #vectordatabases #llms