A key element of Natural Language Processing (NLP) applications is Named Entity Recognition (NER), which recognizes and classifies named entities, such as names of people, places, dates, and organizations within the text. While specified entity types limit the effectiveness of traditional NER models, they also restrict their adaptability to new or diverse datasets.  On the… →

Reinforcement learning (RL) is a fascinating field of AI focused on training agents to make decisions by interacting with an environment and learning from rewards and penalties. RL differs from supervised learning because it involves doing rather than learning from a static dataset. Let’s delve into the core principles of RL and explore its applications… →

The introduction of Audio Description (AD) marks a big step towards making video content more accessible. AD provides a spoken narrative of important visual elements within a video that are unavailable in the original video track. However, making accurate AD requires a lot of resources, such as special expertise, equipment, and significant time investment. Also,… →

Software vulnerability detection is a critical field focused on safeguarding system security and user privacy by identifying security flaws in software systems. Ensuring software systems are secure against potential attacks is crucial with increasingly sophisticated cyber threats. The application of advanced AI technologies, particularly large language models (LLMs) and deep learning, has become instrumental in… →

Large Language Models (LLMs) have demonstrated remarkable abilities in generating human-like text, answering questions, and coding. However, they face hurdles requiring high reliability, safety, and ethical adherence. Reinforcement Learning from Human Feedback (RLHF), or Preference-based Reinforcement Learning (PbRL), emerges as a promising solution. This framework has shown significant success in fine-tuning LLMs to align with… →

In the quickly changing field of Natural Language Processing (NLP), the possibilities of human-computer interaction are being reshaped by the introduction of advanced conversational Question-Answering (QA) models. Recently, Nvidia has published a competitive Llama3-70b QA/RAG fine-tune. The Llama3-ChatQA-1.5 model is a noteworthy accomplishment that marks a major advancement in Retrieval-Augmented Generation (RAG) and conversational quality… →

Convolutional Neural Networks (CNNs) have become the benchmark for computer vision tasks. However, they have several limitations, such as not effectively capturing spatial hierarchies and requiring large amounts of data. Capsule Networks (CapsNets), first introduced by Hinton et al. in 2017, provide a novel neural network architecture that aims to overcome these limitations by introducing… →

Researchers in computer vision and robotics consistently strive to improve autonomous systems’ perception capabilities. These systems are expected to comprehend their environment accurately in real-time. Developing new methods and algorithms allows for innovations that benefit various industries, including transportation, manufacturing, and healthcare. A significant challenge in this field is enhancing the precision and efficiency of… →