Recently, Artificial Intelligence has been able to perform tasks by imitating humans. With the development of extensive language models like ChatGPT and DALL-E and the rise in popularity of generative AI, generating content like a human is no longer a dream. Everything is now possible, from answering questions, completing code, and generating content from a textual description to generating an image from text and an image from an image. AI has been matching the creativity of humans lately. He has even proven to be better than a human at games like chess.
In a recent research paper, some researchers have compared the ideas that have been produced by a human being with those generated by generative Artificial Intelligence. The six generative AI chatbots that researchers have used for comparison are alpa.ai, Copy.ai, ChatGPT (versions 3 and 4), Studio.ai, and YouChat. To determine the similarities and differences between the creativity of AI-generated and human-generated ideas, both the quality and quantity of ideas were independently assessed. They have been accessed by both humans and an AI trained explicitly for this purpose.
The team has compared the ideas and the creativity that compose them using the Test of Alternative Uses (AUT28). The alternative uses test assesses divergent thinking skills and lists not-so-obvious and creative uses for a common object. The team applied AUT on 100 human participants and five generative AIs. The test required humans and artificial intelligence to develop several unique uses for five common objects: pants, ball, tire, fork, and toothbrush. These five objects were called indications.
The team evaluated the responses generated based on their originality and fluency. They have used an intuitive human evaluation (consensual evaluation technique) and a specifically trained AI to evaluate AUT-trained large language models to score the originality of responses. To determine reliability between the six human raters, the team calculated intraclass correlations using the R package irr33, the results of which indicated that the human raters generally agreed which answers were original.
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For comparison, two mixed-effects linear models with random intercepts and random slopes for the five indications have been used. Using the first model in which human-rated responses were the dependent variable, no difference was found between human-generated and generative AI ideas. The second model, in which AI-rated responses acted as the dependent variable, also found no differences between responses. However, human-rated responses for forks and AI-rated responses for toothbrushes outperformed generative AI.
Since GPT-4 was released in mid-March 2023, the researchers conducted additional analysis. GPT-4 completed the AUT, with the responses parsed only by the AI, as the human testers might be biased knowing that the responses were not human. GPT-4 outperformed all five other GAIs, except for fastball, where he ranked second. When comparing GPT-4 performance to humans, only two humans were more creative than the most creative AI for message: pants, 29 were more creative for message: ball, none were more creative for message: tire, three they were more creative. for – fork and 13 were more creative for – tooth” Overall, 9.4 humans were more creative than GPT-4 across all indications. Consequently, there was not much of a significant difference in creativity between humans and AI in terms of originality and fluency, except for a small percentage of human participants who turned out to be more creative.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
