Automating Data Science: Prospects and Challenges (accepted by the Communications of the ACM) arXiv paper abstract https://arxiv.org/abs/2105.05699v1 arXiv PDF paper https://arxiv.org/pdf/2105.05699v1.pdf Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform the work of data scientists, not to replace them.

The third and final ICML2020 invited talk covered the topic of quantum machine learning (QML) and was given by Iordanis Kerenidis. He took us on a tour of the quantum world, detailing the tools needed for quantum machine learning, some of the first applications, and challenges faced by the field. Iordanis started his talk with a bit of background into quantum computing and why we should be interested in it. He stressed that we should not think of quantum computers as just being a faster processor and providing a blanket speed-up. Crucially, the quantum method is a fundamentally different way of performing computation; it could be much faster for certain tasks, but not all.

A special, peer-reviewed edition of OMICS: A Journal of Integrative Biology, has highlighted the importance of key digital technologies, including Artificial Intelligence (AI), machine learning, and blockchain for innovation in healthcare in response to the challenges posed by COVID-19. Vural Özdemir, MD, PhD, Editor-in-Chief of OMICS, said: "COVID-19 is undoubtedly among the ecological determinants of planetary health. Digital health is a veritable opportunity for integrative biology and systems medicine to broaden its scope from human biology to ecological determinants of health. Articles in the special issue include an interview on'Responsible Innovation and Future Science in Australia' by Justine Lacey, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Erik Fisher, Arizona State University, Tempe, 'Blockchain for Digital Health: Prospects and Challenges' and'Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine.' In'Blockchain for Digital Health: Prospects and Challenges' the article explores the challenges that can be faced with the use of blockchain technology.