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Issue #67
This week in Issue #67:
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There has been great momentum from the machine learning community to extract insights from the increasingly growing COVID-19 Datasets, such as the Allen Institute for AI Open Research Dataset as well as the data repository by Johns Hopkins CSSE - the best insights have come out of cross-functional collaborations across ML practitioners and relevant domain experts such as infectious disease experts. Chief Scientist at the Institute for Ethical AI Alejandro Saucedo has put together a brief hands on tutorial to showcase how to deploy COVID-19 AI Solutions at scale, encouraging cross functional collaboration across domain experts such as data scientists, software engineers and even epidemiologists & healthcare professionals.
An incredibly fascinating tutorial that showcases yet further improvement and simplification into the creation of deep fakes, making it even easier for researchers and practitioners to create deep fakes. The rate of improvement of the quality and simplicity around creation of deep fakes is improving with break-neck speed, and with that a lot of very interesting questions arise around the ethical, privacy- and security-related concerns, across others. Try out the hands on collab notebook to try it yourself. You can also check out the video that covers the paper and implementation into further detail.
The Data Exchange comes back this week with a fantastic podcast in converastion with Shopify VP and Head of Data Science and Data Platform Engineering Solmaz Shahalizadeh. In this podcast they dive into building and scaling machine learning data products, building and scaling data teams, and data informed product building.
Chief Scientist Ben Lorica has put together a great article that covers a high level overview of enterprise applications of reinforcement learning. The post covers applications of reinforcement learning in recommenders systems, simulation modelling & opimisation, and dives into some of the tools that power some of those solutions, together with an insight on some of the biggest challenges currently in this space.
A fantastic presentation that covers a very comprehensive overview and deep dive on all-things-transfer-learning in NLP. The presentation covers the motivations, open problems, definitions/terminology, as well as some of the current work in the research and practitioner communities. The slides are available, as well as a version of that presentation in video format.
The topic for this week's featured production machine learning libraries is Industrial Strength NLP. 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:
  • 🤗 Transformers - Huggingface's library of state-of-the-art pretrained models for Natural Language Processing (NLP).
  • Grover - Grover is a model for Neural Fake News -- both generation and detection. However, it probably can also be used for other generation tasks.
  • Kashgari - Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.
  • Github's Semantic - Github's text library for parsing, analyzing, and comparing source code across many languages .
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
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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