"Exploring the Frontiers of Artificial Intelligence Research"
Artificial intelligence (AI) is rapidly evolving, and two prominent paradigms are shaping its future: generative AI and agentic AI. While both fall under the umbrella of AI, they possess distinct characteristics, capabilities, and implications. This blog post will delve into the key differences between these two approaches, shedding light on their potential impact across various…
Introduction: Large Language Models (LLMs) like GPT, T5, and BERT have shown remarkable performance in NLP tasks. However, fine-tuning these models on downstream tasks can be computationally expensive. Parameter-Efficient Fine-Tuning (PEFT) approaches aim to address this challenge by fine-tuning only a small number of parameters while freezing most of the pretrained model. In this blog…
Introduction: In recent years, Generative AI has witnessed a paradigm shift with the introduction of transformer models. These models, characterized by their attention mechanisms, have revolutionized natural language processing (NLP) and other generative tasks. In this blog post, we’ll explore the transformer architecture, its applications in NLP, and its extension to other creative domains. Understanding…
Named Entity Recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities, such as names of people, organizations, and locations, within a text. NER plays a crucial role in various applications, including information retrieval, question answering, and text summarization. In this blog post, we’ll dive into the world of…