​[[{“value”:” Machine learning (ML) has revolutionized wireless communication systems, enhancing applications like modulation recognition, resource allocation, and signal detection. However, the growing reliance on ML models has increased the risk of adversarial attacks, which threaten the integrity and reliability of these systems by exploiting model… Read More »Adversarial Machine Learning in Wireless Communication Systems Mahmoud Ghorbel Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In the evolving field of artificial intelligence, a major challenge has been building models that excel in specific tasks while also being capable of understanding and reasoning across multiple data types, such as text, images, and audio. Traditional large language models have been successful… Read More »Mistral AI Releases Pixtral Large: A 124B Open-Weights Multimodal Model Built on Top of Mistral Large 2 Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In today’s increasingly interconnected world, effective communication across languages is essential. However, many natural language processing (NLP) models still struggle with less common languages. This challenge is particularly evident for low-resource languages such as Thai, Mongolian, and Khmer, which lack the data and processing… Read More »Meet Xmodel-1.5: A Novel 1-Billion-Parameter Multilingual Large Model Pretrained on Approximately 2 Trillion Tokens Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:”This paper was accepted at the Machine Learning and Compression Workshop at NeurIPS 2024. Compressing Large Language Models (LLMs) often leads to reduced performance, especially for knowledge-intensive tasks. In this work, we dive into how compression damages LLMs’ inherent knowledge and the possible remedies. We… Read More »Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications Apple Machine Learning Research

​Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents of various lengths and then chunking them into sequences of a predetermined target length (concat-and-chunk). Recent attention implementations mask cross-document attention, reducing the… Read More »Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum Apple Machine Learning Research

​[[{“value”:”This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. Tensor parallelism provides an effective way to increase server large language model (LLM) inference efficiency despite adding an additional communication cost. However, as server LLMs continue to scale… Read More »Towards Low-Bit Communication for Tensor Parallel LLM Inference Apple Machine Learning Research

​[[{“value”:” The development of vision-language models (VLMs) has faced challenges in handling complex visual question-answering tasks. Despite substantial advances in reasoning capabilities by large language models like OpenAI’s GPT-o1, VLMs still struggle with systematic and structured reasoning. Current models often lack the ability to organize… Read More »Meet LLaVA-o1: The First Visual Language Model Capable of Spontaneous, Systematic Reasoning Similar to GPT-o1 Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In recent years, the development of large language models has significantly advanced natural language processing (NLP). These models, trained on extensive datasets, can generate, understand, and analyze human language with remarkable proficiency. However, building such models requires substantial amounts of data, and access to… Read More »Pleias Introduces Common Corpus: The Largest Multilingual Dataset for Pretraining Language Models Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Today, we are happy to announce the availability of Binary Embeddings for Amazon Titan Text Embeddings V2 in Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless. With support for binary embedding in Amazon Bedrock and a binary vector store in OpenSearch Serverless, you can… Read More »Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases Shreyas Subramanian AWS Machine Learning Blog

​[[{“value”:” Cloud technologies are progressing at a rapid pace. Businesses are adopting new innovations and technologies to create cutting-edge solutions for their customers. However, security is a big risk when adopting the latest technologies. Enterprises often rely on reactive security monitoring and notification techniques, but… Read More »Automate cloud security vulnerability assessment and alerting using Amazon Bedrock Shikhar Kwatra AWS Machine Learning Blog