With the rapid advancement of AI and machine learning, we’re now generating and analyzing data at unprecedented speeds. While this opens exciting possibilities, it also raises an important concern: are we beginning to rely too heavily on algorithmic outputs, potentially sidelining the critical role of human insight, creativity, and intuition in research? Scientific discovery has always been driven not just by data, but by the questions we ask and the meaning we derive from patterns. How can researchers ensure that AI remains a tool to enhance, not replace, human understanding? What strategies or frameworks can we adopt to maintain the balance between data-driven automation and human-led inquiry?
With the advanced AI and ML really its though to control automation and security. The way that shows hope are the standards from ISO, OGC and BIS in India. For example :ISO/IEC 42001 it sets out a structured way to manage risks and opportunities associated with AI, balancing innovation with governance. Following these standards should be made mandatory by implementing Policies and acts from the government.
AI depends on organized data, and it cannot be compared with human observation, intuition, and creativity because human memory is unorganized, while computer memory is organized. Unorganized memory or gut feelings provide humans with a new vision to see things at random.
- AI can generate patterns, but it cannot replace the human ability to frame meaningful questions and interpret context;
- Researchers should setting goals, validating results, and applying domain expertise to interpretation;
- human-in-the-loop frameworks, emphasize interdisciplinary training, and promote ethical, help integrate AI while preserving human judgment.