Data is at the heart of any AI-led business transformation—whether developing new products and services or elevating customer experience—and Generative AI is no exception. The variety, volume, and veracity of data impacts the outcomes and efficacy of the Generative AI models. This article explores how data quality, governance, and engineering critically determine the value of outcomes in enterprise applications.
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What does it mean for artificial intelligence to be ethical? This analysis explores how Hierarchical Bayesian Program Learning (HBPL) offers a framework for understanding what "Ethical AI" means and how different conceptions of AI ethics can be understood as variations within a unified computational architecture.
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Exploring how Indian cultural values and philosophical traditions can inform ethical AI development and deployment in today's rapidly evolving technological landscape.
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Recent breakthroughs in Artificial General Intelligence and their potential impact on society, science, and the future of human-AI collaboration.
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How AI technologies are transforming healthcare delivery in rural India, bridging accessibility gaps, and revolutionizing medical diagnostics in remote areas.
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How smart farming solutions powered by AI are enhancing agricultural productivity, sustainability, and resource optimization across India.
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Exploring adaptive educational platforms that customize learning experiences based on individual student needs, learning styles, and progress.
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Exploring frameworks for aligning AI systems with Indian cultural values, ethical principles, and social norms to ensure responsible AI development.
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Generative AI is today the most talked about topic, almost everywhere from board rooms to children in schools. The reason is that it serves one of the fundamental needs of all, that of creating relevant & realistic content based on multiple inputs from contextual sources in different forms.
Data is at the heart of any AI led business transformation -- whether developing new products and services or elevating customer experience -- and Generative AI is no exception. The variety, volume, and veracity of data impacts the outcomes and efficacy of the Generative AI models too. This article helps understand the critical aspects of Data for Generative AI - how it helps train the Large Language Models (LLMs), that critically determines the value of outcomes, how policies and prompts can remove biases, ensure privacy and security as well as how outcomes can be evaluated with the right datasets to ensure the efficacy.
Volumes of data allows the generative AI model to capture a wider dimensionality and a broader range of patterns in data that will enable the model to generate more versatile and apt outputs. Leveraging data, not only from the enterprise, but also from the ecosystem in which it participates as well as from the universe in which the organization could be impacted with (e.g., public or open datasets) will improve the value of the generated outcomes.
