Technology · proposed 1 month ago
The next generation of AI should learn from human experience, context, and wisdom—not only from massive datasets.
आज AI मुख्यतः विशाल datasets से patterns सीखता है, लेकिन मानव intelligence केवल data पर आधारित नहीं है। उसमें experience, context, failure, intuition, culture और generations की accumulated wisdom भी शामिल है। Future AI systems को ऐसा होना चाहिए जो verified human experiences और diverse perspectives से continuously learn करें, जबकि यह सुनिश्चित किया जाए कि misinformation, bias और manipulation system में प्रवेश न करें। इससे AI केवल information-processing machine नहीं रहेगा, बल्कि humanity की collective intelligence को amplify करने वाला tool बन सकता है।
Proposed by Kewal Singh
Surfaced by AYVA's assistant
Considerations to engage — not conclusions.
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Has this existed before?
Expert systems, case-based reasoning, and oral traditions all tried encoding experiential wisdom; they struggled with scale, verification, and context-transfer—similar challenges likely recur here.
Where could it fail?
Defining 'verified human experience' is subjective; curation bottlenecks could slow learning or silently encode curators' own biases as 'truth'.
Who could exploit it?
Gatekeepers deciding which experiences count as 'wisdom' could shape AI values to favor specific cultures, ideologies, or corporate interests under guise of neutrality.
What are the unintended consequences?
Prioritizing 'verified' wisdom may marginalize minority or dissenting experiences, ironically reducing diversity while claiming to amplify collective intelligence.
Does it work across cultures?
Wisdom is culturally embedded; what counts as intuition or context in one society may conflict with another, risking homogenization or hidden favoritism.
Does it survive generations?
Wisdom evolves as circumstances change; static 'verified' datasets could ossify outdated norms unless continuously renegotiated across generations.
Does it work in scarcity?
Collecting diverse, verified human experience requires resources—compute, review, trust infrastructure—which poorer communities may lack, skewing whose wisdom gets included.
Does it work when machines do most productive labor?
If humans work less, lived 'experience' input may shrink or shift meaning, changing what data reflects human judgment versus automated simulation of it.
Openings you might build on
Expert systems in the 1980s tried encoding human expertise directly; they failed to scale or adapt, offering cautionary lessons for experience-based AI today.
Filtering for 'verified' wisdom risks centralizing authority over truth, potentially suppressing minority viewpoints under the banner of preventing misinformation.
Instead of pre-verifying experiences, systems could expose provenance and uncertainty transparently, letting users weigh context rather than gatekeepers deciding validity.
If AI absorbs 'accumulated wisdom' unevenly, it may entrench dominant cultural narratives while marginalized experiential knowledge remains underrepresented or lost.
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