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Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. This paradigm, i.e., tool learning with foundation models, combines the strengths of specialized tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field. To this end, we present a systematic investigation of tool learning in this paper. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research into tool-augmented and tool-oriented learning. We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate the generalization in tool learning. Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools. Finally, we discuss several open problems that require further investigation for tool learning. Overall, we hope this paper could inspire future research in integrating tools with foundation models.

Electrical discharge erosion is widely applied in the fabrication process of the metallic particles in liquids. The Ultrasonic aided electrical discharge erosion is based on the spark discharge in pure water. The synthesized colloids were classified in accordance with the nano size and micron size. The higher magnifications of morphology, chemical compositions, the crystal structure of the multi-scale particles were observed and analyzed by SEM, TEM, EDS, and XRD. It is verified that ultrasonic wave influenced the morphology of micro/nanoparticles and the roughness of inner and external surfaces of hollow micro-particles. Besides, based on results of EDS, XRD, and Quantitative phase analysis, it is confirmed that nickel oxide was detected only on the surface of microparticles but the nickel oxide was easily obtained when nanoparticles were formed. In addition, ultrasound wave affected the oxidation reaction in both scales but the reaction was remarkably enhanced on nanoparticles. The DLS and LPSA were used to measure the size distributions for the nano and micron scale, respectively. The D-Values of both conditions shown that the ultrasound has an enhanced effect on decreasing the size distribution in both scales.