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International scientific and technical publication on Artificial Intelligence | ISSN 2695-6411 | Officially founded in September, 2019

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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.

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Undergraduate Diagnostic Imaging Fundamentals

The structure and content of this work has been guided by the curricula developed by the European Society of Radiology, the Royal College of Radiologists, the Alliance of Medical Student Educators in Radiology, with guidance and input from Canadian Radiology Undergraduate Education Coordinators, and the Canadian Heads of Academic Radiology (CHAR).

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Medical Open Network for AI (MONAI), AI Toolkit for Healthcare Imaging

The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

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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

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Privacy Preserving AI – Andrew Trask, OpenMined

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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Montreal AI Ethics Institute: Response to the European Commission’s white paper on AI

In February 2020, the European Commission (EC) published a white paper entitled, On Artificial Intelligence – A European approach to excellence and trust. This paper outlines the EC’s policy options for the promotion and adoption of artificial intelligence (AI) in the European Union. We reviewed this paper and published a response addressing the EC’s plans to build an “ecosystem of excellence” and an “ecosystem of trust,” as well as the safety and liability implications of AI, the internet of things (IoT), and robotics.

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A Rigorous Analysis of Self‐Adaptation in Discrete Evolutionary Algorithms

A key challenge to making effective use of evolutionary algorithms (EAs) is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimization problem, which is often unknown to the user. Non‐deterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process.

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Interpretable Machine Learning (A Guide for Making Black Box Models Explainable)

The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.

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Explaining Autonomous Driving by Learning End-to-End Visual Attention

In this work we propose to train an imitation learning based agent equipped with an attention model. The attention model allows us to understand what part of the image has been deemed most important. Interestingly, the use of attention also leads to superior performance in a standard benchmark using the CARLA driving simulator.

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Unconventional Computer Arithmetic for Emerging Applications and Technologies

Arithmetic plays a major role in computing performance and efficiency. It is challenging to build platforms, ranging from embedded devices to high performance computers, supported on traditional binary arithmetic and silicon-based technologies that meet the requirements of today’s applications. In this talk, the state-of-the-art of non-conventional computer arithmetic is presented, considering alternative computing models and emerging technologies.

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Digital Health And The Fight Against The COVID-19 Pandemic

You will find up-to-date, reliable information about the latest innovations, technologies, and trends in the context of COVID-19, and the best examples of 14 digital health technologies already sent to the battle successfully

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Dive into Deep Learning

“We set out to create a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; and (v) be complemented by a forum for interactive discussion of technical details and to answer questions”.

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Covid-19 and contact tracing apps

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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Will the fashion industry survive without AI?

Yooneeque has made digitalisation its motto. An artificial intelligence called YOONA is the fashion designer here. This time again during the Berlin Fashion Week the latest outputs of the software were presented

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The Art of Machine Learning (Algorithms + Data + R)

I wrote this book because: • ML is not a recipe. It is not a matter of knowing the syntax and mechanics of various software packages.• ML is an art, not a science. (Hence the title of this book). • One does not have to be a math whiz or know advanced math in orer to use ML effectively, but one does need to understand the concepts well — the Why? and How? of ML methods

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Microsoft NLP Best Practices

This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language

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The Super Duper NLP Repo & The Big Bad NLP Database

A database housing more than 100 Colab notebooks running ML code for various NLP tasks. Colab is an excellent destination to experiment with the latest models as it comes with a free GPU/TPU housed in Google’s back-end servers… And a collection of more than 400 NLP datasets that it include papers.

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Trainings for Cybersecurity Specialists

“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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Best Practices in Dataviz: An R Perspective

By the end of this you will have had a whirlwind tour of the very tip of the data visualization best-practices iceberg. We will go over a broad range of topics generally applicable to data science usecases but not dive too deep into any single one. One thing to keep in mind the whole time is none of this is absolutely set in stone, most often in the real world you have to bend or break some of these rules to do what you want.

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Machine Learning for Medical Imaging Analysis Demystified

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

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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Machine Learning From Scratch

An extensive list of fundamental machine learning models and algorithms from scratch in vanilla Python.

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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.

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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.

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A socio-technical framework for digital contact tracing

In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applications differ in technology and methods, there is an increasing concern about their implications for privacy and human rights. Here we propose a framework to evaluate their suitability in terms of impact on the users, employed technology and governance methods.

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