Turing-like tests for language and vision demonstrate a narrowing gap between humans and AI

As artificial intelligence (AI) becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. Here we systematically benchmark AI's ability to imitate humans in three language tasks (image captioning, word association and conversation) and three vision tasks (colour estimation, object detection and attention prediction), collecting data from n = 636 humans and 37 AI agents. We also conducted 72,191 Turing-like tests with 1,916 human judges and 10 AI judges. Current AIs are approaching the ability to convincingly impersonate humans and deceive human judges in both language and vision. Even simple AI judges outperformed humans in distinguishing AI from human responses. Imitation ability showed minimal correlation with conventional AI performance metrics, suggesting that passing as human is an important independent evaluation criterion. The large-scale Turing-like datasets and metrics introduced here offer valuable benchmarks for assessing human-likeness in AI and highlight the importance of rigorous, quantitative imitation tests for AI development.