Abstract :

Artificial Intelligence (AI) And Machine Learning (ML) Represent A Paradigm Shift In The Way Technology Interacts With Human Society. AI Broadly Refers To The Capability Of Machines To Mimic Intelligent Human Behavior Such As Reasoning, Problem-solving, Learning, And Adaptation. ML, A Significant Subdomain Of AI, Provides Systems The Ability To Automatically Learn From Data, Identify Patterns, And Make Decisions With Minimal Human Intervention. Together, These Technologies Are Rapidly Changing The Operational Dynamics Of Virtually Every Sector, From Healthcare And Education To Transportation, Agriculture, Business, And Governance. This Research Paper Aims To Provide An In-depth Exploration Of The Theoretical Foundations, Technical Methodologies, Practical Implementations, And Future Prospects Of AI And ML. It Begins With A Historical Overview, Tracing The Origin Of AI From Early Symbolic Systems To Contemporary Advancements Such As Deep Learning And Generative Models. Key Concepts Like Supervised And Unsupervised Learning, Reinforcement Learning, Neural Networks, Natural Language Processing, And Computer Vision Are Examined To Highlight How Machines Learn From Data And Make Informed Decisions.Beyond The Technical Dimension, The Paper Critically Investigates Contemporary Debates Surrounding The Ethical Use Of AI, Data Protection, Algorithmic Accountability, And The Potential For Job Displacement Due To Automation. It Emphasizes The Necessity Of Integrating Human Values Into AI Design, Promoting Explainability, Fairness, And Transparency In AI Systems. The Global Policy Landscape Is Also Reviewed, With Particular Attention To Frameworks Proposed By Governments And International Bodies To Ensure Responsible AI Development. To Bridge Theory With Public Perception, The Study Incorporates A Survey-based Analysis Conducted Among Diverse Demographic Groups. This Survey Assesses General Awareness, Frequency Of AI/ML Usage, Trust In AI-driven Decision

Published:

15-7-2025

Issue:

Vol. 25 No. 7 (2025)

Page Nos:

367 - 379

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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