​[[{“value”:” Zyphra has officially released Zamba2-7B, a state-of-the-art small language model that promises unprecedented performance in the 7B parameter range. This model outperforms existing competitors, including Mistral-7B, Google’s Gemma-7B, and Meta’s Llama3-8B, in both quality and speed. Zamba2-7B is specifically designed for environments that require… Read More »Zyphra Releases Zamba2-7B: A State-of-the-Art Small Language Model Asif Razzaq Artificial Intelligence Category – MarkTechPost

​Time series data are inherently functions of time, yet current transformers often learn time series by modeling them as mere concatenations of time periods, overlooking their functional properties. In this work, we propose a novel objective for transformers that learn time series by re-interpreting them… Read More »Generalizable Autoregressive Modeling of Time Series Through Functional Narratives Apple Machine Learning Research

​While server-side Large Language Models (LLMs) demonstrate proficiency in tool integration and complex reasoning, deploying Small Language Models (SLMs) directly on devices brings opportunities to improve latency and privacy but also introduces unique challenges for accuracy and memory. We introduce CAMPHOR, an innovative on-device SLM… Read More »CAMPHOR: Collaborative Agents for Multi-Input Planning and High-Order Reasoning On Device Apple Machine Learning Research

​[[{“value”:” LLMs leverage the transformer architecture, particularly the self-attention mechanism, for high performance in natural language processing tasks. However, as these models increase in depth, many deeper layers exhibit “attention degeneration,” where the attention matrices collapse into rank-1, focusing on a single column. These “lazy… Read More »Inheritune: An Effective AI Training Approach for Developing Smaller and High-Performing Language Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The problem with efficiently linearizing large language models (LLMs) is multifaceted. The quadratic attention mechanism in traditional Transformer-based LLMs, while powerful, is computationally expensive and memory-intensive. Existing methods that try to linearize these models by replacing quadratic attention with subquadratic analogs face significant challenges:… Read More »Stanford Researchers Propose LoLCATS: A Cutting Edge AI Method for Efficient LLM Linearization Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Language models (LMs) are widely utilized across domains like mathematics, coding, and reasoning to handle complex tasks. These models rely on deep learning techniques to generate high-quality outputs, but their performance can vary significantly depending on the complexity of the input. While some queries… Read More »This AI Paper by MIT Introduces Adaptive Computation for Efficient and Cost-Effective Language Models Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” With recent advances in large language models (LLMs), a wide array of businesses are building new chatbot applications, either to help their external customers or to support internal teams. For many of these use cases, businesses are building Retrieval Augmented Generation (RAG) style chat-based… Read More »Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs Emmett Goodman AWS Machine Learning Blog

​[[{“value”:” Amazon Bedrock Agents enable generative AI applications to perform multistep tasks across various company systems and data sources. They orchestrate and analyze the tasks and break them down into the correct logical sequences using the reasoning abilities of the foundation model (FM). Agents automatically… Read More »Design secure generative AI application workflows with Amazon Verified Permissions and Amazon Bedrock Agents Ram Vittal AWS Machine Learning Blog

​[[{“value”:” Home Table of Contents Photogrammetry Explained: From Multi-View Stereo to Structure from Motion Technique #1: Multi-View Stereo Technique #2: Structure from Motion Example: COLMAP Summary and Next Steps Next Steps Citation Information Photogrammetry Explained: From Multi-View Stereo to Structure from Motion In this blog… Read More »Photogrammetry Explained: From Multi-View Stereo to Structure from Motion Jeremy Cohen PyImageSearch

​[[{“value”:” Current multimodal retrieval-augmented generation (RAG) benchmarks primarily focus on textual knowledge retrieval for question answering, which presents significant limitations. In many scenarios, retrieving visual information is more beneficial or easier than accessing textual data. Existing benchmarks fail to adequately account for these situations, hindering… Read More »Researchers from UCLA and Stanford Introduce MRAG-Bench: An AI Benchmark Specifically Designed for Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models Asif Razzaq Artificial Intelligence Category – MarkTechPost