Название: Data Centric Artificial Intelligence: A Beginner’s Guide
Автор: Parikshit N. Mahalle, Gitanjali R. Shinde, Yashwant S. Ingle
Издательство: Springer
Год: 2023
Страниц: 137
Язык: английский
Формат: pdf (true), epub
Размер: 12.4 MB

This book discusses the best research roadmaps, strategies, and challenges in data-centric approach of Artificial Intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.

Artificial Intelligence (AI) is a multidisciplinary field that aims to create intelligent systems which are able to perform tasks that typically need human intelligence. These systems rely on a variety of building blocks that work together to enable AI capabilities. In this explanation, we will explore the fundamental building blocks of AI, including Machine Learning (ML), Natural Language Processing (NLP), computer vision, and robotics.

Natural Language Processing (NLP): Natural Language Processing enables computers to understand, interpret, and generate human language. It involves the development of models and algorithms that can process and analyze text, speech, and other forms of natural language data. NLP techniques include tasks like language translation, sentiment analysis, text summarization, and question-answering systems. NLP algorithms utilize techniques from machine learning, computational linguistics, and linguistics to extract meaning and context from human language.

Data-centric AI empowers decision-making by providing actionable insights and recommendations based on data analysis. By leveraging large datasets, AI systems can identify patterns, correlations, and trends that may not be comprehensive to humans. These insights can inform strategic business decisions, optimize processes, and unlock new opportunities for innovation and growth.

Hence, the motivation for data-centric AI is driven by the recognition that high-quality and diverse data is essential for developing accurate, unbiased, and robust AI systems. By prioritizing data collection, curation, and utilization, data-centric approaches aim to improve performance, address bias and fairness concerns, enhance user experiences, and provide valuable decision support and insights in various domains.

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