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THE ML ENGINEER 🤖
Issue #46
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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!
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Booking.com has put together 6 lessons learned from building 150 models that were successful in production inference. This has come from their currently massive user-base which consists of millions of accomodation providers and millions of guests. Most of their use-cases consits of advanced and specialised recommender systems, with a constraint of massive throughput in processing. Their main conclusion is that an iterative, hypothesis driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning.
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The first annual EurNLP Summit took place in London on October 11th. This was a great opportunity to foster discussion and collaboration between NLP researchers in academia and industry. The talks from this event were recorded and are all available at their Facebook page.
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A very interesting paper, which proposes that current metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. The paper proposes a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary. This proposed approach verifies three key things: 1) identify whether sentences remain factually consistent after transformation, 2) extract a span in the source documents to support the consistency prediction, 3) extract a span in the summary sentence that is inconsistent if one exists.
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LF AI is an umbrella foundation of the Linux Foundation that supports open source innovation in artificial intelligence, machine learning, and deep learning. To build trust in the adoption of AI, the Trusted AI Committee has been established as part of Linux Foundation AI. We are proud to be contributing to this foundation, which has the objectives of: 1) define policies, guidelines and tooling, 2) survey and contract current OSS projects to join LFAI, 3) create a badging or certification process for OSS projects, and 4) standardise taxonomy around trusted AI
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As AI becomes more prevalent in society, we face thougher challenges around privacy, security and trust of systems. These challenges often create scenarios that may raise ethical questions which practitioners and leaders will have to tackle. Because of this, learning and studying the underlying philosophical concepts that have been built throughout the millenia could provide incredibly positive results. We started a lunch group in London to dive into these topics once a month. Last session Dr. Ryan Dawson provided in introducion on Aristotle's Nichomachean Ethics, which followed by a discussion around their relevance in today's connected world. Next session's topic is "Whose Ethics?" where we'll be diving into the similarities and differences of Western and Eastern philosophy and its modern relevance into AI Ethics.
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The theme for this week's featured ML libraries is Data Science Notebooks, and we're happy to share brand new libraries into that section to showcase tools beyond the good old Jupyter Notebooks. The four featured libraries this week are:
- ML Workspace - All-in-one web IDE for machine learning and data science. Combines Jupyter, VS Code, Tensorflow, and many other tools/libraries into one Docker image.
- Polynote - Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.
- Stencila - Stencila is a platform for creating, collaborating on, and sharing data driven content. Content that is transparent and reproducible.
- RMarkdown - The rmarkdown package is a next generation implementation of R Markdown based on Pandoc.
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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 showcasing 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.
- Ethics Canvas - A resource inspired by the traditional business canvas, which provides an interactive way to brainstorm potential risks, opportunities and solutions to ethical challenges that may be faced in a project using post-it note-like approach.
- ISO/IEC's Standards for Artificial Intelligence - The ISO's initiative for Artificial Intelligence standards, which include a large set of subsequent standards ranging across Big Data, AI Terminology, Machine Learning frameworks, etc.
- GDPR.EU Guide - A project co-funded by the Horizon 2020 Framework programme of the EU which provides a resource for organisations and individuals researching GDPR, including a library of straightforward and up-to-date information to help organisations achieve GDPR compliance (Legal Text).
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We feature conferences that have core ML tracks (primarily in Europe for now) to help our community stay up to date with great events coming up.
Technical & Scientific Conferences
- Data Natives [21/11/2019] - Data conference in Berlin, Germany.
- ODSC Europe [19/11/2019] - The Open Data Science Conference in London, UK.
Business Conferences
- Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.
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About us |
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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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© 2018 The Institute for Ethical AI & Machine Learning
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