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Article . 2025 . Peer-reviewed
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Article . 2025
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What can machines teach us in our journey of reproducing human scientific creativity?

Authors: Mazumdar, Anoushka; Karim, Nasir; Banerjee, Soumya;

What can machines teach us in our journey of reproducing human scientific creativity?

Abstract

In the race toward creating a strong AI, we have historicallyfocused on replicating human intelligence. For manyadvanced tasks such as language and image generation, complexclassifications in fields such as medicine, computer visionand other sensor data in self-driving cars, we have beensuccessful. However, for complex behaviours like creativity,we often deem machines incapable. Maybe we are desperateto have something of our own, that machines could never do.Maybe we are too prideful in our own intelligence. What ifwe were tasked to build a truly creative AI capable of intuitionand insight? What should we consider?Would replicating humanabilities be the best option, or could we make somethingeven better?This article holds a mirror up to us and explores scientificcreativity. We first explore the many properties that may allowmachines to surpass humans in creative insight, such asunbounded effort and lack of competition. We should exploitthese, rather than limit them in the attempt to make AI more‘human-like’. In the second half of this article, we realisethere are many traits we have overlooked in ourselves, thatwe should strive to emulate in machines. There is no doubtthat machines someday could mimic human creativity. Thepurpose of this reflection is to realise it is not about what wecan build, but what we should build.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid