Category Added in a WPeMatico Campaign
In recent research, a team of researchers from IEIT Systems has developed Yuan 2.0-M32, a sophisticated model built using the Mixture of Experts (MoE) architecture. Similar in base design to Yuan-2.0 2B, it is distinguished by its use of 32 experts. The model has an efficient computational structure because only two of these experts are…
Artificial intelligence is continually evolving, focusing on optimizing algorithms to improve the performance and efficiency of large language models (LLMs). Reinforcement learning from human feedback (RLHF) is a significant area within this field, aiming to align AI models with human values and intentions to ensure they are helpful, honest, and safe. One of the primary…
Hugging Face has introduced FineWeb, a comprehensive dataset designed to enhance the training of large language models (LLMs). Published on May 31, 2024, this dataset sets a new benchmark for pretraining LLMs, promising improved performance through meticulous data curation and innovative filtering techniques. FineWeb draws from 96 CommonCrawl snapshots, encompassing a staggering 15 trillion tokens…
Large language models (LLMs) possess advanced language understanding, enabling a shift in application development where AI agents communicate with LLMs via natural language prompts to complete tasks collaboratively. Applications like Microsoft Teams and Google Meet use LLMs to summarize meetings, while search engines like Google and Bing enhance their capabilities with chat features. These LLM-based…
Mathematical reasoning has long been a critical area of research within computer science. With the advancement of large language models (LLMs), there has been significant progress in automating mathematical problem-solving. This involves the development of models that can interpret, solve, and explain complex mathematical problems, making these technologies increasingly relevant in educational and practical applications.…
Numerous groundbreaking models—including ChatGPT, Bard, LLaMa, AlphaFold2, and Dall-E 2—have surfaced in different domains since the Transformer’s inception in Natural Language Processing (NLP). Attempts to solve combinatorial optimization issues like the Traveling Salesman Problem (TSP) using deep learning have progressed logically from convolutional neural networks (CNNs) to recurrent neural networks (RNNs) and finally to transformer-based…
The capacity to quickly store and analyze highly related data has led to graph databases’ meteoric popularity in the past few years. Applications like social networks, recommendation engines, and fraud detection benefit greatly from graph databases, which differ from conventional relational databases’ ability to depict complicated relationships between elements. What are Graph Databases? Graph databases…
With its cutting-edge hardware and toolkits, Intel has been at the forefront of AI advancements. Its AI courses offer hands-on training for real-world applications, enabling learners to effectively use Intel’s portfolio in deep learning, computer vision, and more. This article lists top Intel AI courses, including those on deep learning, NLP, time-series analysis, anomaly detection,…
Deep learning foundation models revolutionize fields like protein structure prediction, drug discovery, computer vision, and natural language processing. They rely on pretraining to learn intricate patterns from diverse data and fine-tuning to excel in specific tasks with limited data. The Earth system, comprising interconnected subsystems like the atmosphere, oceans, land, and ice, requires accurate modeling…
Large Language Models (LLMs) have made significant advancements in natural language processing but face challenges due to memory and computational demands. Traditional quantization techniques reduce model size by decreasing the bit-width of model weights, which helps mitigate these issues but often leads to performance degradation. This problem gets worse when LLMs are used in different…