|
|
|
|
|
|
|
|
THE ML ENGINEER 🤖
Issue #45
|
|
|
|
|
|
|
|
| |
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!
|
|
|
| |
|
|
|
|
| |
Lately the topic of deep fakes has been raising more concerns due to an inceasing number of high profile stories. The use of deep fakes for malicious use-cases seems to have really huge potential to cause negative damage in society. A very interesting new initiative has been launched by the Partnership in AI, Microsoft, AWS and Facebook. This initiative invites researchers and practitioners to participate in a competition to detect deep fakes. The dataset is now released, and the competition starts in December.
|
|
|
| |
|
|
| |
|
In reinforcement learning, often agents can be trained efficiently by running them with other agents throughout a significant number of iterations. Although this may be quite efficielt, and may lead to hyper-optimised results, these may not be optimal when the agents have to interact with humans. This Berkeley project has set out to explore this topic in more detail by looking at how agents can be improved when trained with human interaction.
|
|
|
| |
|
|
| |
|
A very interesting project called GNES is tackling semantic search across image, text and beyond. As its popularity and use has been increasing there has been new challenges that the open source team have to dealth with. One of these key challenges has been data flow across complex jobs. This is why the GNES team released GNES flow, a framework that brings DAG-based data flow to GNES.
|
|
|
| |
|
|
| |
|
An incredible resource on all things Causal inference, which aims to support researchers, practitioners from various backgrounds, including epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists and beyond. The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data.
|
|
|
| |
|
|
| |
In previous editions of the MLE Newsletter we have covered how Netflix has built advanced infrastructure and introduced processes which has allowed for production experimentation at scale. It is great to see that Netflix is now open sourcing parts of their internal infastructure, starting with Polynote - an experimental polyglot notebook environment which supports Scala and Python (with or without Spark), SQL, and Vega. If you are interested in other open source data science notebooks we have an entire section in our Production ML list which would be worth for you to check out.
|
|
|
| |
|
|
|
|
|
|
| |
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.
|
|
|
| |
|
|
| |
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:
|
|
|
| |
|
|
|
|
| |
|
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.
|
|
|
| |
|
|
|
|
|
|
|
|
|
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.
|
| |
|
|
|
|
|
|
|
|
|
|
|
|
| |
|
© 2018 The Institute for Ethical AI & Machine Learning
|
| |
|
|
|
The Institute for Ethical AI & Machine Learning
