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Meet CoLLaVO: KAIST’s AI Breakthrough in Vision Language Models Enhancing Object-Level Image Understanding

AI NewsMarch 1, 2024136Views0Likes0Comments
The evolution of Vision Language Models (VLMs) towards general-purpose models relies on their ability to understand images and perform tasks via natural language instructions. However, it must be clarified if current VLMs truly grasp detailed object information in images. The analysis shows that their image understanding correlates strongly with zero-shot performance on vision language tasks.…

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This AI Research from Google DeepMind Unlocks New Potentials in Robotics: Enhancing Human-Robot Collaboration through Fine-Tuned Language Models with Language Model Predictive Control

RoboticsMarch 1, 2024199Views0Likes0Comments
In robotics, natural language is an accessible interface for guiding robots, potentially empowering individuals with limited training to direct behaviors, express preferences, and offer feedback. Recent studies have underscored the inherent capabilities of large language models (LLMs), pre-trained on extensive internet data, in addressing various robotics tasks. These tasks range from devising action sequences based…

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Leveraging Large Language Models for Business Efficiency | by Benoît Courty | Mar, 2024

Data ScienceMarch 1, 2024154Views0Likes0Comments
In the rapidly evolving landscape of technology, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal forces driving innovation, efficiency, and competitive advantage across industries. For Chief Technology Officers, IT Directors, Tech Project Managers, and Tech Product Managers, understanding and integrating these technologies into business strategies is no longer optional; it’s imperative. It’s…

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Apple Researchers Propose MAD-Bench Benchmark to Overcome Hallucinations and Deceptive Prompts in Multimodal Large Language Models

AI NewsMarch 1, 2024147Views0Likes0Comments
Multimodal Large Language Models (MLLMs), having contributed to remarkable progress in AI, face challenges in accurately processing and responding to misleading information, leading to incorrect or hallucinated responses. This vulnerability raises concerns about the reliability of MLLMs in applications where accurate interpretation of text and visual data is crucial. Recent research has explored visual instruction…

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Improving LLM Inference Speeds on CPUs with Model Quantization | by Eduardo Alvarez | Feb, 2024

Data ScienceFebruary 29, 2024148Views0Likes0Comments
Image Property of Author — Create with NightcafeDiscover how to significantly improve inference latency on CPUs using quantization techniques for mixed, int8, and int4 precisions One of the most significant challenges the AI space faces is the need for computing resources to host large-scale production-grade LLM-based applications. At scale, LLM applications require redundancy, scalability, and…

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