Is AI Really as Creative as Humans?

Key Points:

• Recent headlines claim that AI is more creative than humans, particularly in divergent thinking tasks such as the Alternative Uses Task (AUT), Consequences Task (CT), and Divergent Associations Test (DAT).
• These tasks are language-based, measuring semantic distance between words, which AI language models excel at due to their inherent design.
• However, these tests only measure one aspect of creativity, referred to as "creative potential." Creativity also involves the usefulness and appropriateness of ideas, which are difficult to standardize.
• Examples from materials science and workshops on creativity and generative AI show that AI-generated ideas often lack practicality and appeal, despite being technically "creative."
• Human creativity is enhanced by real-world, embodied experiences that are sensory and social, allowing for a better understanding of the micro-causal structure of the world.
• The future of amplifying human creativity with AI goes beyond simply having AI generate lists while humans sift through them. It involves designing tools that allow for increased exploration, playfulness, and guidance during the AI's thinking process.

Recently, several papers have been published with headlines claiming that AI is more creative than humans. Personally, these headlines make me anxious. I deeply value creativity as an expression of individuality and humanity. My bias is to view our entire human endeavor as centered on creativity—not just in art, music, and traditionally creative professions, but also in how we advance knowledge and solve complex problems.

So, my heart sinks a little each time I read another headline suggesting that GPT-4 surpasses all but the most creative humans. Is AI actually more creative? On key measures of creativity, specifically divergent thinking tasks, it seems so. But we shouldn't be surprised by this finding. Divergent creativity tasks like the Alternative Uses Task (AUT), the Consequences Task (CT), and the Divergent Associations Test (DAT) are fundamentally language-based. Therefore, it's not surprising that large language models excel at these tasks, which measure the semantic distance between words generated in the test.

For example, the AUT asks participants (or AI) to generate alternative uses for common objects, such as a fork. The CT asks for potential consequences of hypothetical scenarios, like "What if humans no longer needed to sleep?" The DAT challenges participants to come up with words that are as different as possible from each other. Creativity in these tasks is measured by fluency, originality, and elaboration—all of which are language-driven metrics.

One of the advantages of AI is its ability to precisely measure these aspects. Semantic distance between words or concepts is no longer solely a human judgment. Language models inherently run on this principle, making related metrics much easier to obtain. This is similar to any measurement bias in that once we can measure something accurately, we start to uncover more about it.