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Stanford Researchers Introduce BIOMEDICA: A Scalable AI Framework for Advancing Biomedical Vision-Language Models with Large-Scale Multimodal Datasets
The development of VLMs in the biomedical domain faces challenges due to the lack of large-scale, annotated, and publicly accessible multimodal datasets across diverse fields. While datasets have been ...
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Meet OmAgent: A New Python Library for Building Multimodal Language Agents
Understanding long videos, such as 24-hour CCTV footage or full-length films, is a major challenge in video processing. Large Language Models (LLMs) have shown great potential in handling multimodal ...
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CrewAI: A Guide to Agentic AI Collaboration and Workflow Optimization with Code Implementation
CrewAI is an innovative platform that transforms how AI agents collaborate to solve complex problems. As an orchestration framework, it empowers users to assemble and manage teams of specialized AI ...
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Purdue University Researchers Introduce ETA: A Two-Phase AI Framework for Enhancing Safety in Vision-Language Models During Inference
Vision-language models (VLMs) represent an advanced field within artificial intelligence, integrating computer vision and natural language processing to handle multimodal data. These models allow ...
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Salesforce AI Research Proposes PerfCodeGen: A Training-Free Framework that Enhances the Performance of LLM-Generated Code with Execution Feedback
Large Language Models (LLMs) have become essential tools in software development, offering capabilities such as generating code snippets, automating unit tests, and debugging. However, these models ...
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What is?
Enabling artificial intelligence to navigate and retrieve contextually rich, multi-faceted information from the internet is important in enhancing AI functionalities. Traditional search engines are ...
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Enhancing Retrieval-Augmented Generation: Efficient Quote Extraction for Scalable and Accurate NLP Systems
LLMs have significantly advanced natural language processing, excelling in tasks like open-domain question answering, summarization, and conversational AI. However, their growing size and ...
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Chat with Your Documents Using Retrieval-Augmented Generation (RAG)
Imagine having a personal chatbot that can answer questions directly from your documents—be it PDFs, research papers, or books. With Retrieval-Augmented Generation (RAG), this is not only possible but ...
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Agentic AI
The study of artificial intelligence has witnessed transformative developments in reasoning and understanding complex tasks. The most innovative developments are large language models (LLMs) and ...
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Researchers from Meta AI and UT Austin Explored Scaling in Auto-Encoders and Introduced ViTok: A ViT-Style Auto-Encoder to Perform Exploration
Modern image and video generation methods rely heavily on tokenization to encode high-dimensional data into compact latent representations. While advancements in scaling generator models have been ...
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Microsoft Presents a Comprehensive Framework for Securing Generative AI Systems Using Lessons from Red Teaming 100 Generative AI Products
The rapid advancement and widespread adoption of generative AI systems across various domains have increased the critical importance of AI red teaming for evaluating technology safety and security.
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NVIDIA AI Introduces Omni-RGPT: A Unified Multimodal Large Language Model for Seamless Region-level Understanding in Images and Videos
Multimodal large language models (MLLMs) bridge vision and language, enabling effective interpretation of visual content. However, achieving precise and scalable region-level comprehension for static ...