Deep learning techniques lead to fundamentally non-interpretable decisions made by the machine. Although such choices do not have an explanation, they impact the users in significant ways. If the ultimate innovator is a machine, what is the meaning of responsible conduct? I argue in a recent book that the capacity to extract an AI system from human judgment, by reducing transparency in favor of opacity, is an essential value in machine ethics. This can be achieved through the use of randomness, illustrated on several examples including the trolley dilemma. Methodologically, a comparison of common motives between technological setups and mythological narratives is used to achieve ethical insights.

Energy systems are facing a revolution and many challenges. On the one hand, electricity production is moving to more intermittency and complexity with the increase of renewable energy and the development of small distributed production units such as photovoltaic panels or wind farms. On the other hand, consumption is also changing with e.g. plug-in (hybrid) electric vehicles, heat pumps, the development of new technologies such as smart phones, computers, storage devices. To maintain the electricity quality, energy stakeholders are developing smart grids, the next generation power grid including advance communication networks and associated optimisation and forecasting tools. Exploiting the smart grid efficiently requires advanced data analytics to improve forecasting at different geographical scale. We will present recent development in the field of online learning and probabilistic forecasting done at EDF in this context.