Nowadays, distributed systems are more and more versatile. Computing units can join, leave or move inside a global infrastructure. These features require the implementation of dynamic systems that can cope autonomously with changes in their structure. It therefore becomes necessary to define, develop, and validate distributed algorithms able to manage such dynamic at a large scale.Â
Failure detection is a prerequisite to failure mitigation and a key component to build distributed algorithms requiring resilience. Â We introduce the problem of failure detection in asynchronous network where the transmission delay is not known. We show how distributed failure detector oracles can be used to address fundamental problems such as consensus, k-set agreement, or mutual exclusion. Then, we focus on new advances and open issues for taking into account the dynamic of the infrastructure.
The recent rise of modern Artificial Intelligence has been supported by large scale operational deployments of machine learning algorithms. The dominant technology today in this field is Deep Learning, the modern name of an older technology – Artificial Neural Networks. Is there something special about these methods that make them different from alternative machine learning or statistical techniques? What are the future evolutions of this domain? Is it only a new episode of the Neural Network saga or is it the sign of a deeper and definitive evolution of AI? I will draw an historical perspective on the domain, introducing the main challenges, concepts and evolutions of the field. I will describe some of the recent advances and try to put in evidence some future challenges. This will be illustrated via several application domains in the field of semantic data analysis.
