[[{“value”:” The rapid expansion of data in today’s era has brought with it both possibilities and difficulties. Businesses handle and use this data to their advantage with the help of some techniques. With their own unique architecture, capabilities, and optimum use cases, data warehouses and… Read More »Big Data vs Data Warehouse Tanya Malhotra Artificial Intelligence Category – MarkTechPost
[[{“value”:” Foundation models (FMs) and large language models (LLMs) are revolutionizing AI applications by enabling tasks such as text summarization, real-time translation, and software development. These technologies have powered the development of autonomous agents that can perform complex decision-making and iterative processes with minimal human… Read More »This AI Paper Explores AgentOps Tools: Enhancing Observability and Traceability in Foundation Model FM-Based Autonomous Agents Nikhil Artificial Intelligence Category – MarkTechPost
[[{“value”:” Large Language Models (LLMs) have advanced exponentially since the last decade. However, LLMs still need to improve regarding deployment and utilization, particularly in the areas of computational cost, latency, and output accuracy. This limits the accessibility of LLMs to smaller organizations, degrades the user… Read More »OptiLLM: An OpenAI API Compatible Optimizing Inference Proxy which Implements Several State-of-the-Art Techniques that can Improve the Accuracy and Performance of LLMs Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost
[[{“value”:” Drug discovery is a costly, lengthy process with high failure rates, as only one viable drug typically emerges from a million screened compounds. Advanced high-throughput (HTS) and ultra-high-throughput screening (uHTS) technologies allow rapid testing of large compound libraries, enabling Pharma and Biotech companies to… Read More »VirtuDockDL: A Deep Learning-Powered Platform for Accelerated Drug Discovery through Advanced Compound Screening and Binding Prediction Sana Hassan Artificial Intelligence Category – MarkTechPost
[[{“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