​[[{“value”:” The rapid growth of large language models (LLMs) has brought impressive capabilities, but it has also highlighted significant challenges related to resource consumption and scalability. LLMs often require extensive GPU infrastructure and enormous amounts of power, making them costly to deploy and maintain. This… Read More »Microsoft Open-Sources bitnet.cpp: A Super-Efficient 1-bit LLM Inference Framework that Runs Directly on CPUs Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” This post is co-written Rodrigo Amaral, Ashwin Murthy and Meghan Stronach from Qualcomm. In this post, we introduce an innovative solution for end-to-end model customization and deployment at the edge using Amazon SageMaker and Qualcomm AI Hub. This seamless cloud-to-edge AI development experience will… Read More »Train, optimize, and deploy models on edge devices using Amazon SageMaker and Qualcomm AI Hub Rodrigo Amaral AWS Machine Learning Blog

​[[{“value”:” The study investigates the emergence of intelligent behavior in artificial systems by examining how the complexity of rule-based systems influences the capabilities of models trained to predict those rules. Traditionally, AI development has focused on training models using datasets that reflect human intelligence, such… Read More »Emergence of Intelligence in LLMs: The Role of Complexity in Rule-Based Systems Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Omni-modality language models (OLMs) are a rapidly advancing area of AI that enables understanding and reasoning across multiple data types, including text, audio, video, and images. These models aim to simulate human-like comprehension by processing diverse inputs simultaneously, making them highly useful in complex,… Read More »Google DeepMind Introduces Omni×R: A Comprehensive Evaluation Framework for Benchmarking Reasoning Capabilities of Omni-Modality Language Models Across Text, Audio, Image, and Video Inputs Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have revolutionized how machines process and generate human language, but their ability to reason effectively across diverse tasks remains a significant challenge. Researchers in AI are working to enable these models to perform not just language understanding but also complex… Read More »Salesforce AI Introduces ReGenesis: A Novel AI Approach to Improving Large Language Model Reasoning Capabilities Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have gained significant traction in recent years, with fine-tuning pre-trained models for specific tasks becoming a common practice. However, this approach needs help in resource efficiency when deploying separate models for each task. The growing demand for multitask learning solutions… Read More »Model Kinship: The Degree of Similarity or Relatedness between LLMs, Analogous to Biological Evolution Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Multimodal AI models are powerful tools capable of both understanding and generating visual content. However, existing approaches often use a single visual encoder for both tasks, which leads to suboptimal performance due to the fundamentally different requirements of understanding and generation. Understanding requires high-level… Read More »DeepSeek AI Releases Janus: A 1.3B Multimodal Model with Image Generation Capabilities Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Maintaining the model’s capacity to manage changes in data distribution, i.e., the ability to function effectively even when presented with data that is different from what it was trained on, is essential when modifying a pre-trained foundation model for certain downstream tasks. Because retraining… Read More »DaWin: A Training-Free Dynamic Weight Interpolation Framework for Robust Adaptation Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Vision-Language Models (VLMs) struggle with spatial reasoning tasks like object localization, counting, and relational question-answering. This issue stems from Vision Transformers (ViTs) trained with image-level supervision, which often fail to encode localized information effectively, limiting spatial understanding. Researchers from Stanford University propose a novel… Read More »Researchers at Stanford University Propose Locality Alignment: A New Post-Training Stage for Vision Transformers ViTs Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Research on the robustness of LLMs to jailbreak attacks has mostly focused on chatbot applications, where users manipulate prompts to bypass safety measures. However, LLM agents, which utilize external tools and perform multi-step tasks, pose a greater misuse risk, especially in malicious contexts like… Read More »Assessing the Vulnerabilities of LLM Agents: The AgentHarm Benchmark for Robustness Against Jailbreak Attacks Sana Hassan Artificial Intelligence Category – MarkTechPost