DUPOUX Emmanuel

Human inspired machine learning

emmanuel.dupoux [at] gmail.com

Emmanuel Dupoux

Short bio

Emmanuel Dupoux is full professor at the Ecole des Hautes Etudes en Sciences Sociales (EHESS), directs the Cognitive Machine Learning team at the Ecole Normale Supérieure (ENS) in Paris and INRIA (www.syntheticlearner.com) and is currently a part time scientist at Facebook AI Research. His education includes a PhD in Cognitive Science (EHESS), a MA in Computer Science (Orsay University) and a BA in Applied Mathematics (Pierre & Marie Curie University, ENS). He is the recipient of an Advanced ERC grant, the organizer of the Zero Resource Speech Challenge (2015, 2017, 2019) and the Intuitive Physics Benchmark (2019).

Topics of interest

Speech perception, language and cognitive development in infant, low resource language technology, automatic speech recognition, unsupervised and self supervised learning

Project in Prairie

Emmanuel Dupoux aims at reverse engineering how young children between 1 and 4 years of age learn from their environment, and construct machine learning algorithms that are more data efficient and flexible than current ones. He will develop unsupervised representation learning algorithms from raw audio or video, and evaluates them with cognitive developmental tests. He will study the inductive biases of neural architectures for language by studying how neural agents can develop communicative protocols. He will use these algorithms applied to naturalistic data to conduct quantitative studies of how infants learn across diverse environments.

Quote

Reverse engineering the ability of young children to learn languages is key to constructing machine learning algorithms that are more data efficient and flexible than current ones. It is also key to understanding how infants learn as a function of their input and to constructing predictive models for early diagnosis of developmental disorders.

CASSELL Justine

Dialogue & HCI

justine.cassell [at] inria.fr

Justine Cassell

Short bio

Professor and former Associate Dean, School of Computer Science, Carnegie Mellon University (2010-). Chaire Blaise Pascale and Chaire Sorbonne (2017-2018). On leave from CMU, at Inria since fall 2019. ACM Fellow (2017), Fellow Royal Academy of Scotland (2016), AAAS Fellow (2012), Anita Borg Women of Vision Award (2009). AAMAS test-of-time award (2017). Chair, World Economic Forum Global Agenda Council on Robotics & Smart Devices (2011-2014).

Topics of interest

Natural language processing, human-computer interaction, autonomous and virtual agents, social AI

Project in Prairie

Justine Cassell will address issues at the intersection of NLP, AI, Cognitive Science, and Human-Computer Interaction, employing methods from each of these traditions, and developing new interdisciplinary methods. Her goal is to develop theories, architectures, algorithms, and implementations of embodied conversational agents capable of engaging people in natural dialogue, including both task and social components, language and non-verbal behavior. She will participate in the PSL AI graduate school.

Quote

There is a need for a more human-centered design of AI systems so that they may act as partners and teammates to people rather than their replacements. My work in Social AI attempts to address these design challenges by basing AI agent behavior on a close study of human collaboration and teamwork, thereby working towards fulfilling their societal promise, as well as advancing fundamental areas of AI as diverse as natural language generation and transparency in machine learning.