by

->Artificial intelligence towards data science

Probabilistic Machine Learning for Healthcare

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

https://editorialia.com/wp-content/uploads/2020/09/report-on-publications-norms-for-responsible-ai.jpg

Report on Publications Norms for Responsible AI

The history of science and technology shows that seemingly innocuous developments in scientific theories and research have enabled real-world applications with significant negative consequences for humanity.

https://editorialia.com/wp-content/uploads/2020/07/research-and-innovation-in-smart-mobility-and-services-in-europe.jpg

Research and innovation in smart mobility and services in Europe

For smart mobility to be cost-efficient and ready for future needs, adequate research and innovation (R&I) in this field is necessary. This report provides a comprehensive analysis of R&I in smart mobility and services in Europe.

https://editorialia.com/wp-content/uploads/2020/06/the-state-of-a-ethics-report-june-2020.jpg

The State of AI Ethics Report (June 2020)

It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions.

https://editorialia.com/wp-content/uploads/2020/06/machine-learning-in-medicine-a-practical-introduction.jpg

Machine learning in medicine: a practical introduction

Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data

https://editorialia.com/wp-content/uploads/2020/05/techdispatch-1_2020_-contact-tracing-with-mobile-applications-2.jpg

TechDispatch #1/2020: Contact Tracing with Mobile Applications

In public health, contact tracing is the process to identify individuals who have been in contact with infected persons. Proximity tracing with smartphone applications and sensors could support contact tracing. It involves processing of sensitive personal data.

https://editorialia.com/wp-content/uploads/2020/05/standards4quantum_-making-quantum-technology-ready-for-industry_v2.jpg

#Standards4Quantum: Making Quantum Technology Ready for Industry

The Joint Research Center (JRC) in cooperation with the European Committee for Standardization (CEN) and the European Committee for Electrotechnical Standardization (CENELEC), European Commission’s Directorate General Communications Networks, Content and Technology (DG CNECT), and the German Institute of Standardisation (DIN), organised in Brussels on 28-29 March 2019 the Putting-Science-Into-Standards (PSIS) workshop on Quantum Technologies.

https://editorialia.com/wp-content/uploads/2020/05/european-data-supervisor-2019-1.jpg

The European Data Protection Supervisor, 2019 Annual Report a year of transition

With new legislation on data protection in the EU now in place, our greatest challenge moving into 2020 is to ensure that this legislation produces the promised results. This includes ensuring that new rules on ePrivacy remain firmly on the EU agenda. Awareness of the issues surrounding data protection and privacy and the importance of rotecting these fundamental rights is at an all time high and we cannot allow this momentum to decline.

https://editorialia.com/wp-content/uploads/2020/05/google_engineering_practices_documentation-1.jpg

Google Engineering Practices Documentation

Google has many generalized engineering practices that cover all languages and all projects. These documents represent their collective experience of various best practices that they have developed over time. It is possible that open source projects or other organizations would benefit from this knowledge.

https://editorialia.com/wp-content/uploads/2020/04/cmglee_cambridge_science_festival_2015_da_vinci.jpg

The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition