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The State of AI Ethics Report (June 2020)

7 de July de 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.

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OpenMined: open source to make privacy-preserving of AI technologies

5 de July de 2020

With OpenMined, an AI model can be governed by multiple owners and trained securely on an unseen, distributed dataset.The mission of the OpenMined community is to create an accessible ecosystem of tools for private, secure, multi-owner governed AI

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Privacy Preserving AI – Andrew Trask, OpenMined

28 de June de 2020

Learn the basics of secure and private AI techniques, including federated learning and secure multi-party computation. In this talk, Andrew Trask of OpenMined highlights the importance of privacy preserving machine learning, and how to use privacy-focused tools like PySyft.

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Covid-19 and contact tracing apps

15 de June de 2020

Multi-presenter format with exciting Speakers from the current European ICT research projects AI4EU (www.ai4eu.eu) and Helios (helios-social.com/) as well as Guest Speakers.

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Trainings for Cybersecurity Specialists

12 de June de 2020

“ENISA CSIRT training material was introduced in 2008. In 2012, 2013 and 2014 it was complemented with new exercise scenarios containing essential material for success in the CSIRT community and in the field of information security. In these pages you will find the ENISA CSIRT training material, containing Handbooks for teachers, Toolsets for students and Virtual Images to support hands on training sessions. ” The materials continue to be updated in 2020 and are appropriate for use by cybersecurity specialists and decision-makers.

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Machine Learning for Medical Imaging Analysis Demystified

10 de November de 2020

This lecture will outline the fundamental ML processes involved in medical image analysis. Achieving prediction and classification for CAD applications will also be discussed. Some preliminary ideas of 3D reconstruction and viewing as applied in medical image analysis will also be presented.

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Guideline for AI for medical products

3 de June de 2020

The objective of this guideline is to provide medical device manufacturers and notified bodies instructions and to provide them with a concrete checklist to understand what the expectations of the notified bodies are, to promote step-by-step implementation of safety of medical devices, that implement artificial intelligence methods, in particular machine learning, to compensate for the lack of a harmonized standard (in the interim) to the greatest extent possible.

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