Abstract: Recent years have been marked by computer science achievements suggesting that artificial intelligence, robots or autonomous machines will invest more and more in our daily environment. As a result of interacting more and more with humans, an interest in designing moral or ethical autonomous agents has been raised. In this talk, I will investigate first what kind of ethical issues autonomous agents may raise, and what could be an ethical autonomous agent. Then I will present logical architectures that may be useful to design autonomous agents embedded with explicit ethical reasoning capabilities, such as attributing causality and responsibilities, judging, deciding and acting according to ethical principles.

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Abstract: With the advent of connected devices with computation and storage capabilities, it becomes possible to run machine learning on-device to provide personalized services. However, the currently dominant approach is to centralize data from all users on an external server for batch processing, sometimes without explicit consent from users and with little oversight. This centralization poses important privacy issues in applications involving sensitive data such as speech, medical records or geolocation logs.

In this talk, I will discuss an alternative setting where many agents with local datasets collaborate to learn models by engaging in a fully decentralized peer-to-peer network. We introduce and analyze asynchronous algorithms that allow agents to improve upon their locally trained model by exchanging information with peers that have similar objectives. I will then describe how to make such algorithms differentially private to avoid leaking information about the local datasets, and analyze the resulting privacy-utility trade-off. I will demonstrate the benefits our approach compared to competing techniques on synthetic and real datasets.

See others videos of the MLAI workshop on www.mlai-workshop.org

Abstract: The success of machine learning models is in part due to their capacity to train on large amounts of data. Distributed systems are the common way to process more data than one computer can store, but they can also be used to increase the pace at which models are trained by splitting the work among many computing nodes. In this talk, I will study the corresponding problem of minimizing a sum of functions which are respectively accessible by separate nodes in a network. New centralized and decentralized algorithms will be presented, together with their convergence guarantees in deterministic and stochastic convex settings, leading to optimal algorithms for this particular class of distributed optimization problems.

See others videos of the MLAI workshop on www.mlai-workshop.org

Les enseignants se sont distingués parmi 10 finalistes de renom international par leur approche novatrice de l’enseignement en ligne de l’informatique.

Les 7 MOOC en anglais permettent un apprentissage progressif depuis les fondements du Langage C sous Linux pour les débutants, jusqu’à devenir utilisateurs expérimentés de ce système d’exploitation puissant. Les MOOC utilisent des outils innovants développés par France ioi, qui révolutionnent l’apprentissage de la programmation en ligne :
• Codecast : tutoriel proposant une interface interactive de code commentée en direct par l'enseignant ;
• Taskgrader : outil d’évaluation automatique du code produit par l’apprenant.

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🏫 Bienvenue à Télécom Paris !
3 étudiants uruguayens María Belén, Rodrigo, Santiago partagent leur expérience à Télécom Paris.

🏫 Welcome to Telecom Paris!
3 Uruguayan students María Belén, Rodrigo, Santiago share their experience at Telecom Paris.

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Membre fondateur de l’Institut Polytechnique de Paris, école de l’IMT (Institut Mines-Télécom), Télécom Paris se positionne comme le collège de l’innovation par le numérique du plateau de Saclay.

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