axe-methodes-formelles - Contributions [fr]

Synthèse des prospectives 2019

2019-03-06T21:36:54Z

Mechenim :

== Introduction ==

Computing devices have become ubiquitous in everyday life, and this has led to growing concerns about their reliability. Because errors or malfunctions in these can have dramatic consequences, it is expected that these devices should be guaranteed to be safe, trusted, and have an explainable behavior. The goal of the teams in the "Formal methods, models and languages" research area is to understand the underlying semantic models and develop tools that can be used all the way from the rigorous design of computing devices to the formal proof that they behave as expected.

== Societal challenges ==

=== System (software, hardware and cyber-physical) safety ===

Guaranteeing the safety of systems is a critical task in many domains. Due to the increasing heterogeneity and specialization of systems, their developers are faced with more and more complex and conflicting design issues, concerning the efficiency, inter-operability, trustability, and security of these systems. In particular, it has become near impossible to obtain an acceptable level of safety guarantees for a system by solely running a set of tests. Therefore, an increasing number of developers rely on formal tools to obtain this guarantee. Formal methods are also recommended and/or required for certain levels of certification. The rise of quantum technology has also led to new problems on program design, error correction and security issues.

=== Confidentiality ===

An increasing number of applications --especially those used in the construction of AI systems-- are data-driven, and it is necessary to ensure that the data used by these applications does not compromise the privacy of individuals.

=== Adapting digital technology to the ecological transition ===

Digital technology needs to be adapted to the major challenge that is the ecological transition:
* Methods to better understand the environmental and societal impact of digital technology
* More important need for resiliency
* Need to recreate systems and infrastructures that require less energy, resources, maintainance, etc.

== Challenges (Problems to solve) ==

=== Certified construction of systems ===

* use of formal methods
** modeling and verification
** proof
** test case generation

* integration in an industrial workflow

=== Quantum information science ===

* A better understanding of possible correlations and how to exploit them.
* Circuit optimization
* How to model quantum control of operations
* How to use imperfect canals to improve tools for error detection and correction.

=== Explainability and accountability of embarked and cyber-physical systems ===

The increasing complexity and autonomy of CPS have made accountability and explainability requirements more crucial: in order to be socially acceptable, the behaviors of CPS must be explainable. This is particularly the case for systems in which decision making is based on AI, but it is not limited to this case. Along with safety, the respect - and violation - of properties such as security and privacy must be explainable. This explainability should in particular be used to understand were the blame lies from a legal point of view when a CPS malfunctions.

== Research themes ==

=== Concepts, languages and tools for modelling and verifying complex systems ===

* Graph rewriting
* Automated reasoning
* Modern Process Calculi
* Model-Checking, Equivalence Checking
* rapid prototyping
* Automated test case generation
* performance evaluation/prediction

=== Embedded IA ===

"Embedded IA" and more specifically "embedded machine learning". There are now FPGA and ASIC implementations for ML and CNN (convolutional neural networks), as well as multicore deployments.

=== Proof formalisation in applied mathematics and theoretical computer science ===

E.g.:
* Development of libraries of certified definitions
* Theorems and algorithms for financial mathematics, logic, etc.

=== Typed high-level languages for data science ===

The preparation of data (clean-up, requests, distributed handling, etc.) is of paramount importance in the construction of AI applications. Construction of type systems for the analysis and handling of data. Construction of analysis methods and optimized compilation for these languages; in particular for requests.

Synthèse des prospectives 2019

2019-03-06T21:18:15Z

Mechenim : /* System (software, hardware and cyber-physical) safety */

== Introduction ==

Computing devices have become ubiquitous in everyday life, and this has led to growing concerns about their reliability. Because errors or malfunctions in these can have dramatic consequences, it is expected that these devices should be guaranteed to be safe, trusted, and have an explainable behavior. The goal of the teams in the "Formal methods, models and languages" research area is to understand the underlying semantic models and develop tools that can be used all the way from the rigorous design of computing devices to the formal proof that they behave as expected.

== Societal challenges ==

=== System (software, hardware and cyber-physical) safety ===

Guaranteeing the safety of systems is a critical task in many domains. Due to the increasing heterogeneity and specialization of systems, their developers are faced with more and more complex and conflicting design issues, concerning the efficiency, inter-operability, trustability, and security of these systems. In particular, it has become near impossible to obtain an acceptable level of safety guarantees for a system by solely running a set of tests. Therefore, an increasing number of developers rely on formal tools to obtain this guarantee. Formal methods are also recommended and/or required for certain levels of certification. The rise of quantum technology has also led to new problems on program design, error correction and security issues.

=== Confidentiality ===

An increasing number of applications --especially those used in the construction of AI systems-- are data-driven, and it is necessary to ensure that the data used by these applications does not compromise the privacy of individuals.

=== Adapting digital technology to the ecological transition ===

Digital technology needs to be adapted to the major challenge that is the ecological transition:
* Methods to better understand the environmental and societal impact of digital technology
* More important need for resiliency
* Need to recreate systems and infrastructures that require less energy, resources, maintainance, etc.

== Challenges (Problems to solve) ==

=== Certified construction of systems ===

* use of formal methods
** modeling and verification
** proof
** test case generation

* integration in an industrial workflow

=== Error correction ===

=== Explainability and accountability of embarked and cyber-physical systems ===

The increasing complexity and autonomy of CPS have made accountability and explainability requirements more crucial: in order to be socially acceptable, the behaviors of CPS must be explainable. This is particularly the case for systems in which decision making is based on AI, but it is not limited to this case. Along with safety, the respect - and violation - of properties such as security and privacy must be explainable. This explainability should in particular be used to understand were the blame lies from a legal point of view when a CPS malfunctions.

== Research themes ==

=== Concepts, languages and tools for modelling and verifying complex systems ===

* Graph rewriting
* Automated reasoning
* Modern Process Calculi
* Model-Checking, Equivalence Checking
* rapid prototyping
* Automated test case generation
* performance evaluation/prediction

=== Embedded IA ===

"Embedded IA" and more specifically "embedded machine learning". There are now FPGA and ASIC implementations for ML and CNN (convolutional neural networks), as well as multicore deployments.

=== Proof formalisation in applied mathematics and theoretical computer science ===

E.g.:
* Development of libraries of certified definitions
* Theorems and algorithms for financial mathematics, logic, etc.

=== Quantum information science ===

=== Typed high-level languages for data science ===

The preparation of data (clean-up, requests, distributed handling, etc.) is of paramount importance in the construction of AI applications. Construction of type systems for the analysis and handling of data. Construction of analysis methods and optimized compilation for these languages; in particular for requests.

Synthèse des prospectives 2019

2019-02-22T10:19:20Z

Mechenim : /* System (software, hardware and cyber-physical) safety */

== Introduction ==

Computing devices have become ubiquitous in everyday life, and this has led to growing concerns about their reliability. Because errors or malfunctions in these can have dramatic consequences, it is expected that these devices should be guaranteed to be safe, trusted, and have an explainable behavior. The goal of the teams in the "Formal methods, models and languages" research area is to develop tools that can be used all the way from the rigorous design of computing devices to the formal proof that they behave as expected.

== Societal challenges ==

=== Adapting digital technology to the ecological transition ===

Digital technology needs to be adapted to the major challenge that is the ecological transition:
* Methods to better understand the environmental and societal impact of digital technology
* More important need for resiliency
* Need to recreate systems and infrastructures that require less energy, resources, maintainance, etc.

=== Confidentiality ===

An increasing number of applications --especially those used in the construction of AI systems-- are data-driven, and it is necessary to ensure that the data used by these applications does not compromise the privacy of individuals.

=== System (software, hardware and cyber-physical) safety ===

Guaranteeing the safety of systems is a critical task in many domains. Due to the increasing heterogeneity and specialization of systems, their developers are faced with more and more complex and conflicting design issues, concerning the efficiency, inter-operability, trustability, and security of these systems. In particular, it has become near impossible to guarantee the safety of a system by running an exhaustive set of tests, which is why an increasing number of developers rely on formal tools to obtain this guarantee.

== Challenges (Problems to solve) ==

=== Certified construction of systems ===

=== Error correction ===

=== Explainability and accountability of embarked and cyber-physical systems ===

The increasing complexity and autonomy of CPS have made accountability and explainability requirements more crucial: in order to be socially acceptable, the behaviors of CPS must be explainable. This is particularly the case for systems in which decision making is based on AI, but it is not limited to this case. Along with safety, the respect - and violation - of properties such as security and privacy must be explainable. This explainability should in particular be used to understand were the blame lies from a legal point of view when a CPS malfunctions.

== Research themes ==

=== Concepts, languages and tools for modelling and verifying complex systems ===

* Graph rewriting
* Automated reasoning

=== Embedded IA ===

"Embedded IA" and more specifically "embedded machine learning". There are now FPGA and ASIC implementations for ML and CNN (convolutional neural networks), as well as multicore deployments.

=== Proof formalisation in applied mathematics and theoretical computer science ===

E.g.:
* Development of libraries of certified definitions
* Theorems and algorithms for financial mathematics, logic, etc.

=== Quantum information science ===

=== Typed high-level languages for data science ===

The preparation of data (clean-up, requests, distributed handling, etc.) is of paramount importance in the construction of AI applications. Construction of type systems for the analysis and handling of data. Construction of analysis methods and optimized compilation for these languages; in particular for requests.

Synthèse des prospectives 2019

2019-02-22T10:11:08Z

Mechenim : /* Confidentiality */

== Introduction ==

Computing devices have become ubiquitous in everyday life, and this has led to growing concerns about their reliability. Because errors or malfunctions in these can have dramatic consequences, it is expected that these devices should be guaranteed to be safe, trusted, and have an explainable behavior. The goal of the teams in the "Formal methods, models and languages" research area is to develop tools that can be used all the way from the rigorous design of computing devices to the formal proof that they behave as expected.

== Societal challenges ==

=== Adapting digital technology to the ecological transition ===

Digital technology needs to be adapted to the major challenge that is the ecological transition:
* Methods to better understand the environmental and societal impact of digital technology
* More important need for resiliency
* Need to recreate systems and infrastructures that require less energy, resources, maintainance, etc.

=== Confidentiality ===

An increasing number of applications --especially those used in the construction of AI systems-- are data-driven, and it is necessary to ensure that the data used by these applications does not compromise the privacy of individuals.

=== System (software, hardware and cyber-physical) safety ===

== Challenges (Problems to solve) ==

=== Certified construction of systems ===

=== Error correction ===

=== Explainability and accountability of embarked and cyber-physical systems ===

The increasing complexity and autonomy of CPS have made accountability and explainability requirements more crucial: in order to be socially acceptable, the behaviors of CPS must be explainable. This is particularly the case for systems in which decision making is based on AI, but it is not limited to this case. Along with safety, the respect - and violation - of properties such as security and privacy must be explainable. This explainability should in particular be used to understand were the blame lies from a legal point of view when a CPS malfunctions.

== Research themes ==

=== Concepts, languages and tools for modelling and verifying complex systems ===

* Graph rewriting
* Automated reasoning

=== Embedded IA ===

"Embedded IA" and more specifically "embedded machine learning". There are now FPGA and ASIC implementations for ML and CNN (convolutional neural networks), as well as multicore deployments.

=== Proof formalisation in applied mathematics and theoretical computer science ===

E.g.:
* Development of libraries of certified definitions
* Theorems and algorithms for financial mathematics, logic, etc.

=== Quantum information science ===

=== Typed high-level languages for data science ===

The preparation of data (clean-up, requests, distributed handling, etc.) is of paramount importance in the construction of AI applications. Construction of type systems for the analysis and handling of data. Construction of analysis methods and optimized compilation for these languages; in particular for requests.

Synthèse des prospectives 2019

2019-02-22T10:10:21Z

Mechenim : /* Confidentiality */

Synthèse des prospectives 2019

2019-02-22T10:07:41Z

Mechenim :

Synthèse des prospectives 2019

2019-02-11T10:10:02Z

Mechenim :

== Societal challenges ==

=== Adapting digital technology to the ecological transition ===

Digital technology needs to be adapted to the major challenge that is the ecological transition:
* Methods to better understand the environmental and societal impact of digital technology
* More important need for resiliency
* Need to recreate systems and infrastructures that require less energy, resources, maintainance, etc.

=== Confidentiality ===

=== System safety (software, hardware and cyber-physical) ===

== Problems to solve ==

=== Certified construction of systems ===

=== Error correction ===

=== Explainability and accountability of embarked and cyber-physical systems ===

The increasing complexity and autonomy of CPS have made accountability and explainability requirements more crucial: in order to be socially acceptable, the behaviors of CPS must be explainable. This is particularly the case for systems in which decision making is based on AI, but it is not limited to this case. Along with safety, the respect - and violation - of properties such as security and privacy must be explainable. This explainability should in particular be used to understand were the blame lies from a legal point of view when a CPS malfunctions.

== Research themes ==

=== Concepts, languages and tools for modelling and verifying complex systems ===

* Graph rewriting
* Automated reasoning

=== Embedded IA ===

"Embedded IA" and more specifically "embedded machine learning". There are now FPGA and ASIC implementations for ML and CNN (convolutional neural networks), as well as multicore deployments.

=== Proof formalisation in applied mathematics and theoretical computer science ===

E.g.:
* Development of libraries of certified definitions
* Theorems and algorithms for financial mathematics, logic, etc.

=== Quantum information science ===

=== Typed high-level languages for data science ===

The preparation of data (clean-up, requests, distributed handling, etc.) is of paramount importance in the construction of AI applications. Construction of type systems for the analysis and handling of data. Construction of analysis methods and optimized compilation for these languages; in particular for requests.

Synthèse des prospectives 2019

2019-02-11T09:47:46Z

Mechenim :

== Societal challenges ==

=== Adapting digital technology to the ecological transition ===

Digital technology needs to be adapted to the major challenge that is the ecological transition:
* Methods to better understand the environmental and societal impact of digital technology
* More important need for resiliency
* Need to recreate systems and infrastructures that require less energy, resources, maintainance, etc.

=== Confidentialité ===

=== Sûreté des systèmes (logiciels, matériels, et cyperphysiques) ===

== Problématiques ==

=== Construction certifiée de systèmes ===

=== Correction d'erreurs ===

=== Explicabilité et accountability des systèmes embarqués et cyber-physiques ===

Avec la complexité et l'autonomie croissantes des CPS les besoins d'accountability et d'explicabilité deviennent cruciaux : pour être socialement acceptables les comportements des CPS doivent être explicables. Ce besoin touche particulièrement les systèmes dont la prise de décisions est basée sur l'intelligence artificielle, mais il n'est pas limité à l'IA. Outre la sûreté, le respect - et la violation - de propriétés comme la sécurité et la vie privée doivent être rendus explicables. Cette explicabilité doit servir, en particulier, à départager les responsabilités juridiques entre acteurs en cas de dysfonctionnements des CPS.

== Thèmes de recherche ==

=== Concepts, langages et outils pour la modélisation et vérification de systèmes complexes ===

* réécriture de graphes
* raisonnement automatisé, etc.

=== Embedded IA ===

"Embedded IA" et plus specifiquement "embedded machine learning". On voit ainsi emerger des implementations FPGA et ASIC pour faire du ML et des CNN (convolutional neural networks), ainsi que du deploiement sur des multi-coeurs.

=== Formalisation de preuve en mathématiques appliquées et en informatique théorique ===

e.g.:
* développement de corpus certifiés de définitions
* théorèmes et algorithmes pour les mathématiques financières, la logique, etc.

=== Informatique quantique ===

=== Langages de haut niveau typés pour la science des données ===

Importance prépondérante de la préparation des données (nettoyage,
requêtage, manipulation distribuée des données, etc.) dans la
construction d'applications en intelligence artificielle. Construction de systèmes de types pour l'analyse de la manipulation de données. Construction de méthodes d'analyse et de compilation optimisante pour ces langages et en particulier pour le requêtage.

Synthèse des prospectives 2019

2019-02-11T09:13:39Z

Mechenim :

== Défis sociétaux ==

=== Adaptation du numérique à la transition écologique ===

Adaptation du numérique au défi majeur que constitue la transition écologique ou aux conséquences de notre échec à relever ce défi : méthodes pour mieux comprendre l'impact environnemental et sociétal du numérique, besoins accrus en résilience, nécessité de recréer des systèmes et des infrastructures économes en énergie, ressources, maintenance, etc.

=== Confidentialité ===

=== Sûreté des systèmes (logiciels, matériels, et cyperphysiques) ===

== Problématiques ==

=== Construction certifiée de systèmes ===

=== Correction d'erreurs ===

=== Explicabilité et accountability des systèmes embarqués et cyber-physiques ===

Avec la complexité et l'autonomie croissantes des CPS les besoins d'accountability et d'explicabilité deviennent cruciaux : pour être socialement acceptables les comportements des CPS doivent être explicables. Ce besoin touche particulièrement les systèmes dont la prise de décisions est basée sur l'intelligence artificielle, mais il n'est pas limité à l'IA. Outre la sûreté, le respect - et la violation - de propriétés comme la sécurité et la vie privée doivent être rendus explicables. Cette explicabilité doit servir, en particulier, à départager les responsabilités juridiques entre acteurs en cas de dysfonctionnements des CPS.

== Thèmes de recherche ==

=== Concepts, langages et outils pour la modélisation et vérification de systèmes complexes ===

* réécriture de graphes
* raisonnement automatisé, etc.

=== Embedded IA ===

"Embedded IA" et plus specifiquement "embedded machine learning". On voit ainsi emerger des implementations FPGA et ASIC pour faire du ML et des CNN (convolutional neural networks), ainsi que du deploiement sur des multi-coeurs.

=== Formalisation de preuve en mathématiques appliquées et en informatique théorique ===

e.g.:
* développement de corpus certifiés de définitions
* théorèmes et algorithmes pour les mathématiques financières, la logique, etc.

=== Informatique quantique ===

=== Langages de haut niveau typés pour la science des données ===

Importance prépondérante de la préparation des données (nettoyage,
requêtage, manipulation distribuée des données, etc.) dans la
construction d'applications en intelligence artificielle. Construction de systèmes de types pour l'analyse de la manipulation de données. Construction de méthodes d'analyse et de compilation optimisante pour ces langages et en particulier pour le requêtage.

Synthèse des prospectives 2019

2019-02-11T09:13:18Z

Mechenim :

Synthèse des prospectives 2019

2019-02-11T09:12:38Z

Mechenim :

Synthèse des prospectives 2019

2019-02-11T09:06:41Z

Mechenim :

== Défis sociétaux ==

=== Sûreté des systèmes (logiciels, matériels, et cyperphysiques) ===

=== Adaptation du numérique à la transition écologique ===

Adaptation du numérique au défi majeur que constitue la transition écologique ou aux conséquences de notre échec à relever ce défi : méthodes pour mieux comprendre l'impact environnemental et sociétal du numérique, besoins accrus en résilience, nécessité de recréer des systèmes et des infrastructures économes en énergie, ressources, maintenance, etc.

=== Confidentialité ===

== Problématiques ==

=== Explicabilité et accountability des systèmes embarqués et cyber-physiques ===

Avec la complexité et l'autonomie croissantes des CPS les besoins d'accountability et d'explicabilité deviennent cruciaux : pour être socialement acceptables les comportements des CPS doivent être explicables. Ce besoin touche particulièrement les systèmes dont la prise de décisions est basée sur l'intelligence artificielle, mais il n'est pas limité à l'IA. Outre la sûreté, le respect - et la violation - de propriétés comme la sécurité et la vie privée doivent être rendus explicables. Cette explicabilité doit servir, en particulier, à départager les responsabilités juridiques entre acteurs en cas de dysfonctionnements des CPS.

=== Construction certifiée de systèmes ===

=== Correction d'erreurs ===

== Thèmes de recherche ==

=== Langages de haut niveau typés pour la science des données ===

Importance prépondérante de la préparation des données (nettoyage,
requêtage, manipulation distribuée des données, etc.) dans la
construction d'applications en intelligence artificielle. Construction de systèmes de types pour l'analyse de la manipulation de données. Construction de méthodes d'analyse et de compilation optimisante pour ces langages et en particulier pour le requêtage.

=== Concepts, langages et outils pour la modélisation et vérification de systèmes complexes ===

* réécriture de graphes
* raisonnement automatisé, etc.

=== Formalisation de preuve en mathématiques appliquées et en informatique théorique ===

e.g.:
* développement de corpus certifiés de définitions
* théorèmes et algorithmes pour les mathématiques financières, la logique, etc.

=== Informatique quantique ===

=== Embedded IA ===

"Embedded IA" et plus specifiquement "embedded machine learning". On voit ainsi emerger des implementations FPGA et ASIC pour faire du ML et des CNN (convolutional neural networks), ainsi que du deploiement sur des multi-coeurs.

Synthèse des prospectives 2019

2019-02-11T09:04:31Z

Mechenim : Page créée avec « == Prospective pour l'evaluation de 2019 == === Défis sociétaux === ==== Sûreté des systèmes (logiciels, matériels, et cyperphysiques) ==== ==== Adaptation du num... »

== Prospective pour l'evaluation de 2019 ==

=== Défis sociétaux ===

==== Sûreté des systèmes (logiciels, matériels, et cyperphysiques) ====

==== Adaptation du numérique à la transition écologique ====

Adaptation du numérique au défi majeur que constitue la transition écologique ou aux conséquences de notre échec à relever ce défi : méthodes pour mieux comprendre l'impact environnemental et sociétal du numérique, besoins accrus en résilience, nécessité de recréer des systèmes et des infrastructures économes en énergie, ressources, maintenance, etc.

==== Confidentialité ====

=== Problématiques ===

==== Explicabilité et accountability des systèmes embarqués et cyber-physiques ====

Avec la complexité et l'autonomie croissantes des CPS les besoins d'accountability et d'explicabilité deviennent cruciaux : pour être socialement acceptables les comportements des CPS doivent être explicables. Ce besoin touche particulièrement les systèmes dont la prise de décisions est basée sur l'intelligence artificielle, mais il n'est pas limité à l'IA. Outre la sûreté, le respect - et la violation - de propriétés comme la sécurité et la vie privée doivent être rendus explicables. Cette explicabilité doit servir, en particulier, à départager les responsabilités juridiques entre acteurs en cas de dysfonctionnements des CPS.

==== Construction certifiée de systèmes ====

==== Correction d'erreurs ====

=== Thèmes de recherche ===

==== Langages de haut niveau typés pour la science des données ====

Importance prépondérante de la préparation des données (nettoyage,
requêtage, manipulation distribuée des données, etc.) dans la
construction d'applications en intelligence artificielle. Construction de systèmes de types pour l'analyse de la manipulation de données. Construction de méthodes d'analyse et de compilation optimisante pour ces langages et en particulier pour le requêtage.

==== Concepts, langages et outils pour la modélisation et vérification de systèmes complexes ====

* réécriture de graphes
* raisonnement automatisé, etc.

==== Formalisation de preuve en mathématiques appliquées et en informatique théorique ====

e.g.:
* développement de corpus certifiés de définitions
* théorèmes et algorithmes pour les mathématiques financières, la logique, etc.

==== Informatique quantique ====

==== Embedded IA ====

"Embedded IA" et plus specifiquement "embedded machine learning". On voit ainsi emerger des implementations FPGA et ASIC pour faire du ML et des CNN (convolutional neural networks), ainsi que du deploiement sur des multi-coeurs.

Accueil

2019-02-11T09:03:55Z

Mechenim : /* Liste des pages disponibles */

== Liste des pages disponibles ==

* [[Synthèse des prospectives 2019]]

== Démarrer avec MediaWiki ==
* [//meta.wikimedia.org/wiki/Aide:Contenu Guide de l’utilisateur] pour plus d’informations sur l’utilisation de ce logiciel.
* [//www.mediawiki.org/wiki/Manual:Configuration_settings Liste des paramètres de configuration]
* [//www.mediawiki.org/wiki/Manual:FAQ/fr FAQ sur MediaWiki]
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce Liste de discussion sur les distributions de MediaWiki]

Accueil

2019-02-11T09:02:51Z

Mechenim :

== Liste des pages disponibles ==

* [[Synthèse]]

== Démarrer avec MediaWiki ==
* [//meta.wikimedia.org/wiki/Aide:Contenu Guide de l’utilisateur] pour plus d’informations sur l’utilisation de ce logiciel.
* [//www.mediawiki.org/wiki/Manual:Configuration_settings Liste des paramètres de configuration]
* [//www.mediawiki.org/wiki/Manual:FAQ/fr FAQ sur MediaWiki]
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce Liste de discussion sur les distributions de MediaWiki]

Synthèse

2019-02-11T09:02:32Z

Accueil

2019-02-11T09:01:42Z

Mechenim :

== Liste des pages disponibles ==

* [[Synthèse]]

== Prospective pour l'evaluation de 2019 ==

=== Défis sociétaux ===

==== Sûreté des systèmes (logiciels, matériels, et cyperphysiques) ====

==== Adaptation du numérique à la transition écologique ====

Adaptation du numérique au défi majeur que constitue la transition écologique ou aux conséquences de notre échec à relever ce défi : méthodes pour mieux comprendre l'impact environnemental et sociétal du numérique, besoins accrus en résilience, nécessité de recréer des systèmes et des infrastructures économes en énergie, ressources, maintenance, etc.

==== Confidentialité ====

=== Problématiques ===

==== Explicabilité et accountability des systèmes embarqués et cyber-physiques ====

Avec la complexité et l'autonomie croissantes des CPS les besoins d'accountability et d'explicabilité deviennent cruciaux : pour être socialement acceptables les comportements des CPS doivent être explicables. Ce besoin touche particulièrement les systèmes dont la prise de décisions est basée sur l'intelligence artificielle, mais il n'est pas limité à l'IA. Outre la sûreté, le respect - et la violation - de propriétés comme la sécurité et la vie privée doivent être rendus explicables. Cette explicabilité doit servir, en particulier, à départager les responsabilités juridiques entre acteurs en cas de dysfonctionnements des CPS.

==== Construction certifiée de systèmes ====

==== Correction d'erreurs ====

=== Thèmes de recherche ===

==== Langages de haut niveau typés pour la science des données ====

Importance prépondérante de la préparation des données (nettoyage,
requêtage, manipulation distribuée des données, etc.) dans la
construction d'applications en intelligence artificielle. Construction de systèmes de types pour l'analyse de la manipulation de données. Construction de méthodes d'analyse et de compilation optimisante pour ces langages et en particulier pour le requêtage.

==== Concepts, langages et outils pour la modélisation et vérification de systèmes complexes ====

* réécriture de graphes
* raisonnement automatisé, etc.

==== Formalisation de preuve en mathématiques appliquées et en informatique théorique ====

e.g.:
* développement de corpus certifiés de définitions
* théorèmes et algorithmes pour les mathématiques financières, la logique, etc.

==== Informatique quantique ====

==== Embedded IA ====

"Embedded IA" et plus specifiquement "embedded machine learning". On voit ainsi emerger des implementations FPGA et ASIC pour faire du ML et des CNN (convolutional neural networks), ainsi que du deploiement sur des multi-coeurs.

== Démarrer avec MediaWiki ==
* [//meta.wikimedia.org/wiki/Aide:Contenu Guide de l’utilisateur] pour plus d’informations sur l’utilisation de ce logiciel.
* [//www.mediawiki.org/wiki/Manual:Configuration_settings Liste des paramètres de configuration]
* [//www.mediawiki.org/wiki/Manual:FAQ/fr FAQ sur MediaWiki]
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce Liste de discussion sur les distributions de MediaWiki]

Accueil

2019-02-11T08:46:57Z

Mechenim :