I was thinking: how do we separate established knowledge from emerging insights, especially when both are valuable? The introduction of generative AI and Language Models (LLM) adds another layer of complexity to this challenge.
Established knowledge, derived from reputable sources and expert opinions, acts as our anchor. It’s been thoroughly vetted and is generally accepted as accurate. Yet, in a fast-evolving world, can we afford to rely solely on this?
New knowledge, on the other hand, is like the uncharted waters of innovation and discovery. It’s exciting but comes with its own risks, primarily the uncertainty of its accuracy.
Generative AI and LLMs have emerged as tools that act as if they are guiding us through this area. They can provide direct answers and direct perspectives, bypassing traditional information gatekeepers. But there’s a hitch: their training data includes both reliable and unreliable information. How then can we trust the guidance they offer?
To ensure that AI enabled tools that we build we could incorporate source credibility indicators and peer-review mechanisms into their design. This would help them differentiate between reliable and questionable information. Furthermore, recognizing knowledge as a continuum rather than a binary classification can refine their navigational capabilities.
Valuing the stability of established knowledge while being open to new insights is a tough balancing act as we further develop and refine our usage of AI technologies. Our focus should remain on enhancing their ability to discern the quality of information. This balance isn’t just about leveraging technology; it’s about fostering a culture of critical thinking and continuous exploration