Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.
翻译:机械学习(ML)似乎是使人类目前完成的一些复杂任务,如驾驶车辆、承认声音等,部分或完全自动化的最有希望的解决办法之一。它也是在传统执行技术之外实施和嵌入新能力的一个机会。然而,ML技术带来了新的潜在风险。因此,这些技术只应用于其好处被认为值得增加风险的系统。在实践中,ML技术提出了多重挑战,可能防止其在提交认证限制的系统中的使用。但实际挑战是什么?它们能否通过选择适当的ML技术,或者通过采用新的工程或认证做法加以克服?这些都是作为DEEL项目的一部分,由Saint Exup\'ery de Touluuse技术研究所(IT)设立的ML认证3工作组(工作组)处理的一些问题。