Machine learning is not ready to tackle the unconscious—but here are some ways it could help
Dream researchers can help in the vital process of identifying the "unconscious" biases of AI systems.
But asking dream experts to distinguish human from AI-generated dream reports would be a gimmick, with no research value.tools can be trained on a set of texts, then take a prompt and create new texts that are semantically similar to the original texts. As researchers experiment with different applications of this technology, the question naturally arises as to what would happen if these tools were trained with a collection of dream reports.
Yes, there are some potential benefits here for the study of dreams and for efforts to improve the AI systems. However, we will first need to overcome the negative effects of an inevitable but ultimately dead-end experiment. This experiment will present a collection of texts to a panel of dream experts and ask them to identify the human vs computer-generated dreams. The almost-certain result will be that the experts cannot reliably distinguish between real and fake dream reports.
What would this mean? One might easily conclude from such an experiment that dream research as a whole is untrustworthy and self-deluded in its claims. The results would seem to demonstrate a fundamental lack of objectivity in the study of dreams. This may seem sensible, but it reflects a lack of familiarity with actual dream research. For most researchers, the question of how to distinguish between real and fake dream reports is not something they worry about. Why not? The reason is simple: The virtually infiniteof dreams means that there is NO linguistic marker that can absolutely and consistently distinguish a genuine dream report from another kind of text.
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