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- W4387670901 abstract "From an evolutionary perspective, creativity is seen as an adaptive mechanism allowing species to cope with changing environments, devise solutions to problems, and thus enhance survival. Large language models (LLMs) and Computer Vision (CV) systems, while products of human innovation, can be framed within the context of this evolutionary understanding of creativity. LLMs, such as GPT-4, can generate novel text based on patterns from vast datasets, simulating a form of linguistic creativity. CV systems can process, recognize, and sometimes even imagine visual information, mirroring aspects of visual creativity. By learning, adapting, and generating content, these artificial systems demonstrate a semblance of creativity, akin to evolutionary adaptations. This abstract conceptualizes the creativity of LLMs and CV as an analog to biological evolution, suggesting that our technological advances might be a continuation of the age-old process of adaptation and innovation, perhaps understanding the concept of desire." @default.
- W4387670901 created "2023-10-17" @default.
- W4387670901 creator A5081007218 @default.
- W4387670901 date "2023-10-15" @default.
- W4387670901 modified "2023-10-17" @default.
- W4387670901 title "How large language model and Computer Vision demonstrate Creativity in an evolutionary perspective: Can AI Understand Desire?" @default.
- W4387670901 doi "https://doi.org/10.31234/osf.io/ejrvk" @default.
- W4387670901 hasPublicationYear "2023" @default.
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