Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner∗ Emmanuel Dupoux EHESS, ENS, PSL Research University, CNRS, INRIA, Paris, France
[email protected], www.syntheticlearner.net Abstract • Machine/humans comparison should be run on a benchmark of psycholinguistic tests. Spectacular progress in the information processing sci- ences (machine learning, wearable sensors) promises to revo- 1 Introduction lutionize the study of cognitive development. Here, we anal- yse the conditions under which ’reverse engineering’ lan- In recent years, artificial intelligence (AI) has been hit- guage development, i.e., building an effective system that ting the headlines with impressive achievements at matching mimics infant’s achievements, can contribute to our scien- or even beating humans in complex cognitive tasks (play- tific understanding of early language development. We argue ing go or video games: Mnih et al., 2015; Silver et al., that, on the computational side, it is important to move from 2016; processing speech and natural language: Amodei et toy problems to the full complexity of the learning situation, al., 2016; Ferrucci, 2012; recognizing objects and faces: He, and take as input as faithful reconstructions of the sensory Zhang, Ren, & Sun, 2015; Lu & Tang, 2014) and promising signals available to infants as possible. On the data side, a revolution in manufacturing processes and human society accessible but privacy-preserving repositories of home data at large. These successes show that with statistical learning have to be setup. On the psycholinguistic side, specific tests techniques, powerful computers and large amounts of data, have to be constructed to benchmark humans and machines it is possible to mimic important components of human cog- at different linguistic levels.