It is widely believed that the scientific enterprise of theoretical linguistics and the engineering of language applications are separate endeavors with little for their techniques and results to contribute to each other at the moment. In this talk, we explore the possibility that machine learning approaches to natural-language processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language in one area at least, language acquisition, and that, in practice, existing results may offer initial tentative support for this prospect.