Evaluating LLM Compression: Balancing Efficiency, Trustworthiness, and Ethics in AI-Language Model Development

Researchers at the Clinic of Radiology and Nuclear Medicine at University Hospital Basel have developed a deep learning model called TotalSegmentator that can automatically segment anatomical structures in CT images. The model has been trained on…

Generative AI is playing a growing role in business operations and customer service. According to Salesforce research, 61% of workers either use or plan to use generative AI, with 68% confident that it will enhance customer…

Open-Sora by HPC AI Tech: Democratizing Video Production Open-Sora 1.0 and 1.1 Open-Sora, an initiative by HPC AI Tech, aims to make advanced video generation techniques accessible to everyone. Open-Sora 1.0 laid the groundwork for video…

Current Limitations of Multimodal Retrieval-Augmented Generation (RAG) Most existing benchmarks for RAG focus mainly on text for answering questions, which can be limiting. In many cases, it’s easier and more useful to retrieve visual information instead…

Large Language Models (LLMs) must judge textual qualities consistently for reliability. Inconsistency in evaluations leads to untrustworthy results. Universal Self-Consistency (USC) improves LLM consistency across diverse tasks. Integrating external knowledge increases reasoning accuracy. Seeded sampling aids…

Researchers challenge the belief that Vision Transformers (ViTs) outperform Convolutional Neural Networks (ConvNets) with large datasets. They introduce NFNet, a ConvNet architecture pre-trained on the JFT-4B dataset. NFNet performs comparably to ViTs, showing that computational resources…

Understanding Vision-and-Language Models (VLMs) Vision-and-language models (VLMs) are powerful tools that use text to tackle various computer vision tasks. These tasks include: Recognizing images Reading text from images (OCR) Detecting objects VLMs approach these tasks by…

Salesforce Research has proposed MoonShot, a breakthrough AI model for video generation. It addresses the limitations of existing techniques by allowing conditioning on both text and image inputs, leading to improved accuracy and performance. MoonShot’s Multimodal…

The fourth chapter of “A Bird’s Eye View of Linear Algebra” focuses on how matrix multiplication and its inverse play a fundamental role in building many simple machine learning models. The chapter discusses systems of linear…

Microsoft is introducing its AI assistant called “Microsoft 365 Copilot” which integrates with ChatGPT and will be available in their office software. The AI tool can generate meeting summaries, draft emails, create Word documents, design PowerPoint…

The study delves into the impact of reasoning step length on the Chain of Thought (CoT) performance in large language models (LLMs). It finds that increasing reasoning steps in prompts improves LLMs’ reasoning abilities, while shortening…

Pinokio 2.0: Redefining Offline Web and AI Apps Offline web and AI apps often pose challenges, requiring users to navigate multiple steps for app setup and customization. These processes can be confusing and time-consuming, especially for…

Challenges in Emotion Recognition Emotion recognition from video poses various complex challenges. Models relying solely on visual or audio signals often overlook the intricate relationship between these modalities, resulting in misinterpretation of emotional content. A significant…

Understanding the Challenges of AI Language Models Creating language models that mimic human understanding is a tough task in AI. A key challenge is achieving a balance between computational efficiency and the ability to perform a…

Leveraging Linguistic Expertise in NLP: A Deep Dive into RELIES and Its Impact on Large Language Models With the significant advancement in the fields of Artificial Intelligence (AI) and Natural Language Processing (NLP), Large Language Models…

This article discusses three measures of distance: Earth Mover’s Distance (EMD) for image search, Word Mover’s Distance (WMD) for document retrieval, and Concept Mover’s Distance (CMD) for analyzing concepts within texts. The measures progress from tangible…

Multimodal Generative Models: Advancing AI Capabilities Enhancing Autoregressive Models for Image Generation Multimodal generative models integrate visual and textual data to create intelligent AI systems capable of various tasks, from generating detailed images from text to…