Tool Use as a Gauge for Machine Intelligence. The ability to effectively use tools has long been considered
a hallmark of human intelligence. We contend that the tool learning performance can serve as a next-generation
gauge for measuring machine intelligence, offering several advantages over traditional evaluation metrics.
Tool use evaluation requires AI systems to go beyond memorization and use their acquired knowledge
to accomplish speciﬁc tasks, which better aligns with real-world applications and the notion of practical
intelligence (Sternberg, 1999). Hence, evaluating tool use performance is more closely aligned with human
subjective perceptions of intelligence. Researchers can better assess the progress of AI systems in terms of
their ability to assist human decision-making, collaborate with humans in solving problems, and contribute to
a wider range of real-world applications.
Tool Learning with Foundation Models