Scaling AI for lifelong learning and reasoning requires the ability to transform raw inputs into abstract concepts that can be efficiently composed to form more complex ones. Our lab has a strong focus on few-shot learning for concept acquisition. In recent research, we have enabled large-scale foundation models to incrementally learn new language and visual concepts. Our current efforts extend to recognizing functional and relational concepts, as well as exploring how learned concepts can be composed hierarchically for high-level reasoning. These advancements are key to building AI systems that generalize efficiently and adapt continuously to new tasks.