​[[{“value”:” High-performance AI models that can run at the edge and on personal devices are needed to overcome the limitations of existing large-scale models. These models require significant computational resources, making them dependent on cloud environments, which poses privacy risks, increases latency, and adds costs.… Read More »Mistral AI Introduces Les Ministraux: Ministral 3B and Ministral 8B- Revolutionizing On-Device AI Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have gained widespread adoption due to their advanced text understanding and generation capabilities. However, ensuring their responsible behavior through safety alignment has become a critical challenge. Jailbreak attacks have emerged as a significant threat, using carefully crafted prompts to bypass… Read More »AutoDAN-Turbo: A Black-Box Jailbreak Method for LLMs with a Lifelong Agent Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The most serious challenge regarding IGNNs relates to slow inference speed and scalability. While these networks are effective at capturing long-range dependencies in graphs and addressing over-smoothing issues, they require computationally expensive fixed-point iterations. This reliance on iterative procedures severely limits their scalability, particularly… Read More »IGNN-Solver: A Novel Graph Neural Solver for Implicit Graph Neural Networks Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Quantum computers are a revolutionary technology that harnesses the principles of quantum mechanics to perform calculations that would be infeasible for classical computers. Evaluating the performance of quantum computers has been a challenging task due to their sensitivity to noise, the complexity of quantum… Read More »Google AI Research Examines Random Circuit Sampling (RCS) for Evaluating Quantum Computer Performance in the Presence of Noise Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have evolved to become powerful tools capable of understanding and responding to user instructions. Based on the transformer architecture, these models predict the next word or token in a sentence, generating responses with remarkable fluency. However, they typically respond without… Read More »Thinking LLMs: How Thought Preference Optimization Transforms Language Models to Perform Better Across Logic, Marketing, and Creative Tasks Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Despite the vast accumulation of genomic data, the RNA regulatory code must still be better understood. Genomic foundation models, pre-trained on large datasets, can adapt RNA representations for biological prediction tasks. However, current models rely on training strategies like masked language modeling and next… Read More »Orthrus: A Mamba-based RNA Foundation Model Designed to Push the Boundaries of RNA Property Prediction Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) need to be evaluated within the framework of embodied decision-making, i.e., the capacity to carry out activities in either digital or physical environments. Even with all of the research and applications that LLMs have seen in this field, there is… Read More »Embodied Agent Interface: An AI Framework for Benchmarking Large Language Models (LLMs) for Embodied Decision Making Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The ever-increasing size of Large Language Models (LLMs) presents a significant challenge for practical deployment. Despite their transformative impact on natural language processing, these models are often hindered by high memory transfer requirements, which pose a bottleneck during autoregressive generation. This results in high… Read More »SeedLM: A Post-Training Compression Method that Uses Pseudo-Random Generators to Efficiently Encode and Compress LLM Weights Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The increasing reliance on machine learning models for processing human language comes with several hurdles, such as accurately understanding complex sentences, segmenting content into comprehensible parts, and capturing the contextual nuances present in multiple domains. In this landscape, the demand for models capable of… Read More »Google AI Introduces Gemma-APS: A Collection of Gemma Models for Text-to-Propositions Segmentation Shobha Kakkar Artificial Intelligence Category – MarkTechPost