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… →

Large language models (LLMs) have shown their potential in many natural language processing (NLP) tasks, like summarization and question answering using zero-shot and few-shot prompting approaches. However, prompting alone is not enough to make LLMs work as agents who can navigate environments to solve complex and multi-step. Fine-tuning LLMs for these tasks is also impractical… →

Vision-and-language (VL) representation learning is an evolving field focused on integrating visual and textual information to enhance machine learning models’ performance across a variety of tasks. This integration enables models to understand and process images and text simultaneously, improving outcomes such as image captioning, visual question answering (VQA), and image-text retrieval. A significant challenge in… →

Current methods for aligning LLMs often match the general public’s preferences, assuming this is ideal. However, this overlooks the diverse and nuanced nature of individual preferences, which are difficult to scale due to the need for extensive data collection and model training for each person. Techniques like RLHF and instruction fine-tuning help align LLMs with… →