The MachineLearning EngineerIssue #70

This week in Issue #70:

• [Updated] AI Conferences Gone Virtual in 2020
• Privacy Preserving AI Lecture
• Backpropagation 101 from Thinc
• Harvard Offering Free Courses
• GPT2 AI Dungeon Game Update
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

AI Conferences Gone Virtual 2020

[Updated List 19/04/2020] Due to the current global situation, a large number of conferences have had to face hard choices, several which decided going fully virtual. This hard choice has now open the doors to people from around the world to gain access to the great online content generated by expert speakers and contributors. We wanted to highlight some of these key conferences so they are not missed - these include:

• Open Data Science Conference - April 14th
• Tensorflow London Online - April 15th
• Future Healthcare - AI and Ethics Online - April 17th
• TeensInAI COVID-19 Hackathon - April 25th
• ICLR Online - April 26th
• Deep Learning World - May 11th
• Michael Jordan on the Future of ML and AI, May 13th
• Applied AI Virtual Summit - May 21st
• Virtual Federated & Distributed ML Conf - June 18th
• Databricks Applied AI summit - June 22nd
• ICML Online - July 12th

Did we miss any? Please let us know by replying to the newsletter email or by simply emailing us at a@ethical.institute

Privacy Preserving AI Lecture

Andrew Trask joins Lex Fridman to deliver a full-length lecture on privacy preserving AI. This excellent resource covers a broad set of areas within the space of privacy preserving AI, including the premise of the problem it aims to solve, the different tools available at our disposal, terminoogy and other key concepts in this area.

Backpropagation 101 from Thinc

SpaCy Cofounder Matt Honibal has put together a fantastic resource that dives into a step by step intuitive overview of the backpropagation algorithm and it’s implementation in deep learning, and breaks in down in its constituent terms with hands on practical examples.

Harvard Offering Free Courses

Harvard is offering free online courses for anyone that wants to expand their knowledge boundaries, which is fantastic as they are accessible for free. These courses cover a broad range of topics in computer science, including their AI with Python course. For anyone interested, they also have made available over 50 courses across various other academic fields that are also available for free.

GPT2 AI Dungeon Game Update

AI Dungeons is a narrative based game built on top of the natural language generation model GPT-2, which allows for a fully unique and pseudo-personalised gaming experience. They have released new functionality which allows players to try it out without any code, and even being able to play in multi-player model

OSS: Data Visualisation

The topic for this week’s featured production machine learning libraries is Data Visualisation. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:

• Redash - Redash is anopen source visualisation framework that is built to allow easy access to big datasets leveraging multiple backends.
• Plotly Dash - Dash is a Python framework for building analytical web applications without the need to write javascript.
• Streamlit - Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file
• PDPBox - This repository is inspired by ICEbox. The goal is to visualize the impact of certain features towards model prediction for any supervised learning algorithm. (now support all scikit-learn algorithms)

If you know of any libraries that are not in the “Awesome MLOps” list, please do give us a heads up or feel free to add a pull request!

OSS: Awesome AI Guidelines

As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle thiese challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. We will be showcasingitg three resources from our list so we can check them out every week. This week’s resources are:

• AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems

• An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles

• ACM’s Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018

• From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices

If you know of any guidelines that are not in the “Awesome AI Guidelines” list, please do give us a heads up or feel free to add a pull request!

About us

The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning systems.

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