Wednesday, November 14, 2018

Paris ML E#2 S#6: Conscience, Code Analysis, Can a machine learn like a child?

Pour ce deuxieme meetup régulier de la saison, nous parlerons au moins de conscience, de santé et de code et de comment les machines apprennent comme les enfants... Merci à Samsung de nous accueillir !

Voici le programme pour l'instant:

+ Presentation Gilles Mazars, AI Labs Samsung, 

From my recent paper published in Brain:] Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. ... Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.

During this talk Eiso Kant demonstrates how different machine learning techniques can be used to learn from source code and provide developers with novel insights into their code. This talk includes several demos that show the power of MLonCode.
+ Autonomous developmental learning: can a machine learn like a child? , Pierre Oudeyer

Résumé: Current approaches to artificial intelligence and machine learning are still fundamentally limited in comparison with autonomous learning capabilities of children. Even impressive systems like AlphaGo require huge amounts of trial and error and the help of an engineer to deal with other games or tasks. On the contrary, children learn fast and robustly a wide and open-ended repertoire of skills, without needing any form of intervention by an engineer. I will present a research program that has studied computational modeling of child development and learning mechanisms in the last decade. I will explain approaches to model curiosity-driven autonomous learning, with algorithmic models enabling machines to sample and explore their own goals, self-organizing a learning curriculum without any external supervision. I will show how this has helped scientists understand better aspects of human development, and how this has opened novel approaches to address the current limits of machine learning. I will illustrate this research with experiments where robots learn autonomously repertoires of complex tasks. I will then conclude by illustrating how these approaches can be applied successfully in the domain of educational technologies, enabling to personalize sequences of exercises for human learners, while maximizing both learning efficiency and intrinsic motivation.

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