JENKINS Jade

Engineer

Inria

jade.jenkins [at] inria.fr

Short bio

  • MSc Gerontology Research with distinction from the University of Southampton, UK
  • BS magna cum laude from the University of New Orleans, USA

Research project

Hyperscanning and social cognition in middle childhood.

Short abstract

BONNAIRE Julie

Engineer

Inria

julie.bonnaire [at] inria.fr

Short bio

Master degree in Systems Biology at Sorbonne University

Research project

Study of the neural correlates of social skills.

Short abstract

My work aims to better understand the development of social skills. In this vein, I use hyperscanning techniques, which allow the recording of the brain activity from at least two individuals engaged in social exchange, providing a novel type of neural correlate: the inter-brain synchrony. This study contributes to a deeper understanding of how the social brain develops and how rapport management occurs by looking at moments of high or low rapport with neural synchrony.

Cognition

Since the earliest days of Artificial Intelligence, the study of human cognition has played an important role. The very first working code presented at the Dartmouth Summer workshop in 1956 where the term “artificial intelligence” was first used, was a model of how humans learn, implemented by Herb Simon and Alan Newell. Since then the study of human intelligence (and, more broadly, human cognition) and the study of “machine cognition” or artificial intelligence, have been mutually beneficial.

How people learn continues to be one of the important topics addressed in this thematic area, using more contemporary AI tools such as deep learning algorithms and large language models to model human cognitive abilities. In this context a number of  projects address how infants learn language. Here, unsupervised and self-supervised algorithms model the process of infant language learning in diverse contexts, and using diverse data sources including text, raw audio and audio+video. In turn, these studies of infants can lead to more flexible and data efficient machine learning algorithms. A number of other projects address the learning of pragmatics (the use of language, rather than its structure) by children in middle childhood. Here multimodal deep learning algorithms are given both the language and the nonverbal behaviors (such as eye gaze shifts and facial expressions) of pairs of individuals as input into models of the building of interpersonal rapport, or social bonds. In turn, these models are used to implement embodied conversational agents that can successfully build rapport with their users. The most recent set of studies of rapport-building behavior add additional evidence from the neuroscience technique called hyperscanning, where pairs of individuals are scanned simultaneously while working alone and while collaborating, and evidence is gathered about their inter-brain synchrony in these different contexts. Studies such as these can contribute to building truly collaborative AI systems, and also to better understanding the role of the brain in natural human interaction.  

SAUTY DE CHALON Benoit

PhD student

INRIA

benoit.sauty-de-chalon [at] inria.fr

Short bio

Diplôme ingénieur Ecole Polytechnique

Thesis title

Multimodal modelling of neurodegenerative diseases.

Short abstract

The goal is to find quantitative links between the decay of structural properties of the brain, shown through imaging techniques such as MRI/Pet scans/etc and the decay of cognitive abilities of the patients, shown through cognitive assessment tests. The research focuses on Alzheimer and Parkinson patients.

DE SEYSSEL Maureen

PhD student

L’Ecole normale supérieure - PSL

Short bio

  • MSc in Speech and Language Processing – University of Edinburgh (United Kingdom)
  • BSc in Psychology – City, University of London (United Kingdom)

Thesis title

Does multilingual input help or hinder early language acquisition? A computational modelling approach.

Short abstract

Experimental studies in bilingual language acquisition are based on the assumption that children separate languages at birth or within months, and that this early ability is essential for successful learning. This would prevent children from mixing languages and learning a multilingual representation that does not correspond to any specific language. This project will test this hypothesis following a reverse-engineering approach by using computational models, which aim to model the ideal learner when faced with input data whose number of languages is a priori unknown. This approach will directly test two aspects of the hypothesis : (1) the premise that it is possible to separate languages before learning them, and (2) the justification that separation is necessary for learning several languages in parallel.

LOUKATOU Georgia

Postdoctoral researcher

L'Ecole normale supérieure - PSL

georgialoukatou [at] gmail.com

Short bio

PhD, École Normale Supérieure

Research project

Diversity and learnability in early language acquisition.

Short abstract

My research addresses issues of language learnability in cross-linguistic and cross-cultural settings. I follow an interdisciplinary approach, implementing computational modelling, corpus analysis and experimental methods.

Emmanuel Dupoux

DUPOUX Emmanuel

Human inspired machine learning

emmanuel.dupoux [at] gmail.com

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.

Team

DE SEYSSEL Maureen
DE SEYSSEL Maureen
PhD student

PhD student


Justine Cassell

CASSELL Justine

Dialogue & HCI

justine.cassell [at] inria.fr

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). Since January 2021 a member of CNNUM (Conseil National du Numérique) – French National Digital Council.

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.

Team

ABULIMITI Alafate
ABULIMITI Alafate
PhD student
Master degree in Big Data Management and Analytic, University of Tours Engineer degree in Computer Science, Polytech Tours Bachelor degree in Applied Physics, Beijing Institute of Technology

BONNAIRE Julie
BONNAIRE Julie
Engineer
Master degree in Systems Biology at Sorbonne University

MOHAPATRA Biswesh
MOHAPATRA Biswesh
PhD student
Integrated Master of Technology in Computer Science Engineering from IIIT Bangalore

JENKINS Jade
JENKINS Jade
Engineer
MSc Gerontology Research with distinction from the University of Southampton, UK BS magna cum laude from the University of New Orleans, USA

GILMARTIN Emer
GILMARTIN Emer
Postdoctoral researcher
Ph.D, Trinity College Dublin, Ireland. M.Phil, Trinity College Dublin, Ireland B.E (Mech), NUIG, Ireland