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Issue #75
This week in Issue #75:
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Microsoft and OpenAI shared yet another interesting use-case of GPT-2 text generation - this time its function is to generate Python code. They showcased it during the Microsoft Build 2020 last week, which although was only a prepared demonstration, does seem to have some really interesting insights showing how to generate suggested code based on an initial input.
Arize AI Co-Founder Aparna Dhinakaran has put togher an overview on the tools available to address the challenges present in the end to end machine learning lifecycle. This article proposes over a dozen different t hemes to classify the various different technologies available, which shows the complexity that the end to end ML challenge encompasses, and provides a brief intuitive overview of each
Thanks to advances in the development of data-driven technologies, we now have unprecedented opportunities to unlock the social value of data. Data could now truly function as a common resource and a public good with transformative power for communities and society. This Friday we are organising an online session where Head of Public Engagement at the UK Ada Lovelace Institute, Reema Patel, will be share insights on the ethical challenges about data use and we will dive into how we can best ensure data is used in the interests of society.
Last week we shared the Advanced NLP Course that dives into how to use SpaCy to tackle intermediate and advanced NLP real-life challenges. This week the SpaCy team has shared a video series they have created which covers the NLP Course end to end. This is a fantastic resource which has now (and still is) been translated into a large range of different languages (with Humans help not NLP in this case, we're fully not there just yet).
O'Reilly has put together a great piece that emphasises the implications AI use-cases have when they go wrong, namely it introduces the motivation of such reflection quoting several relatively recent high profile incidents that showcase their impact. In this post they outline how these AI cases are different, as well as terminology around "AI incidents", and some of the best practices and approaches available to mitigate these scenarios.
[Updated List 24/05/2020] 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:
Did we miss any? Please let us know by replying to the newsletter email or by simply emailing us at
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:
  • XAI - eXplainableAI - An eXplainability toolbox for machine learning.
  • Microsoft InterpretML - InterpretML is an open-source package for training interpretable models and explaining blackbox systems.
  • SHAP - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model.
  • ELI5 - "Explain Like I'm 5" is a Python package which helps to debug machine learning classifiers and explain their predictions.
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
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:
  • 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
© 2018 The Institute for Ethical AI & Machine Learning