Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner - Dupoux, Emmanuel (Cognition, 2018)
•Key theoretical puzzles of infant language development are still unsolved.•A roadmap for reverse engineering infant language learning using AI is proposed.•AI algorithms should use realistic input (little or no supervision, raw data).•Realistic input should be obtained by large-scale recording in ecological environments.•Machine/Human comparison should be run on a benchmark of psycholinguistic tests. Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which ’reverse engineering’ language development, i.e., building an effective system that mimics infant’s achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results.