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​[[{“value”:” The integration of generative AI agents into business processes is poised to accelerate as organizations recognize the untapped potential of these technologies. Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will… Read More »Creating asynchronous AI agents with Amazon Bedrock Aaron Sempf AWS Machine Learning Blog

​[[{“value”:” The Qwen 2.5 multilingual large language models (LLMs) are a collection of pre-trained and instruction tuned generative models in 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B (text in/text out and code out). The Qwen 2.5 fine tuned text-only models are optimized for multilingual dialogue… Read More »How to run Qwen 2.5 on AWS AI chips using Hugging Face libraries Jim Burtoft AWS Machine Learning Blog

​[[{“value”:” This post is cowritten with Harrison Hunter is the CTO and co-founder of MaestroQA. MaestroQA augments call center operations by empowering the quality assurance (QA) process and customer feedback analysis to increase customer satisfaction and drive operational efficiencies. They assist with operations such as… Read More »Revolutionizing customer service: MaestroQA’s integration with Amazon Bedrock for actionable insight Carole Suarez AWS Machine Learning Blog

​[[{“value”:” DeepSeek-R1, developed by AI startup DeepSeek AI, is an advanced large language model (LLM) distinguished by its innovative, multi-stage training process. Instead of relying solely on traditional pre-training and fine-tuning, DeepSeek-R1 integrates reinforcement learning to achieve more refined outputs. The model employs a chain-of-thought… Read More »Optimize hosting DeepSeek-R1 distilled models with Hugging Face TGI on Amazon SageMaker AI Pranav Murthy AWS Machine Learning Blog

​[[{“value”:” Emotion recognition from video involves many nuanced challenges. Models that depend exclusively on either visual or audio signals often miss the intricate interplay between these modalities, leading to misinterpretations of emotional content. A key difficulty is reliably combining visual cues—such as facial expressions or… Read More »Alibaba Researchers Introduce R1-Omni: An Application of Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-Multimodal Large Language Model Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Long-horizon robotic manipulation tasks are a serious challenge for reinforcement learning, caused mainly by sparse rewards, high-dimensional action-state spaces, and the challenge of designing useful reward functions. Conventional reinforcement learning is not well-suited to handle efficient exploration since the lack of feedback hinders learning… Read More »From Sparse Rewards to Precise Mastery: How DEMO3 is Revolutionizing Robotic Manipulation Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In this tutorial, we implement a Bilingual Chat Assistant powered by Arcee’s Meraj-Mini model, which is deployed seamlessly on Google Colab using T4 GPU. This tutorial showcases the capabilities of open-source language models while providing a practical, hands-on experience in deploying state-of-the-art AI solutions… Read More »Building an Interactive Bilingual (Arabic and English) Chat Interface with Open Source Meraj-Mini by Arcee AI: Leveraging GPU Acceleration, PyTorch, Transformers, Accelerate, BitsAndBytes, and Gradio Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) models primarily depend on their internal knowledge, which can be inadequate when handling real-time or knowledge-intensive questions. This limitation often leads to inaccurate responses or hallucinations, making it essential to enhance LLMs with external search capabilities. By leveraging reinforcement learning,… Read More »This AI Paper Introduces R1-Searcher: A Reinforcement Learning-Based Framework for Enhancing LLM Search Capabilities Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Transformers have revolutionized natural language processing as the foundation of large language models (LLMs), excelling in modeling long-range dependencies through self-attention mechanisms. However, as these models grow deeper and more complex, training stability presents a significant challenge that directly impacts performance. Researchers face a… Read More »HybridNorm: A Hybrid Normalization Strategy Combining Pre-Norm and Post-Norm Strengths in Transformer Architectures Sajjad Ansari Artificial Intelligence Category – MarkTechPost