Présentation de la société : CEA
Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.
Implanté au cœur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.
Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :
• La conscience des responsabilités
• La coopération
• La curiosité
Missions
To address the issue of AI system verification, validation, and certification, existing work define a design solution combining AI algorithms and conventional algorithms. AI algorithms include all complex and/or black box algorithms that need to be certified. The conventional algorithms include functions that monitor and supervise AI code execution. These monitors are derived from safety and requirement analysis. With this particular combination, we hope to benefit from both approaches: a system that can handle complex decision processes through the use of artificial intelligence and that can provide high confidence by leveraging a monitor written in conventional code. Meanwhile, evaluating the robustness of such AI-oriented systems often involves understanding the impact of fine-grain perturbation regarding the computations. The ability to study high-level and fine grain aspects at the same time requires a significant runtime instrumentation capacity.
To evaluate the safety and robustness of such an AI-oriented system with its HW and SW parts, we think of a methodological approach based on preliminary safety assessment results, functional testing on a virtual platform, fault injection, and uncertainty measurement. Concretely, the evaluation framework will be based on:
- Risk assessment providing some kind of envelope of permissible behavior for the AI system without compromising safety and list of faults and failures with their effects on the system
- An embedded system simulator capable of injecting fault at functional and hardware levels
- Online (at runtime from devices) and offline (from testbench dataset) fault injection (HW/SW) technique to stress the system with regard to faulty scenarios
- uncertainty measures with an estimation of their confidence level
- runtime monitoring for the tracking of fault propagation, and study of their impact at the algorithmic level
The goal of the project is to implement and validate such an approach on a use case.
Profil recherché
You are passionate about science and novel technologies. You are preparing a Bac+4 or Bac+5 diploma in the field of computer science or alike. You have knowledge of AI, programming languages, embedded systems, simulation, safety assessment.