Machine Learning & Data Science
Our world is full of Machine Learning and Data Science, so it's best to stay informed and use it for good. I've encountered a ton of nice learning resources, articles or simply fascinating inventions directly or indirectly related to ML & DS, that are listed below. Keep in mind, ML is at the intersection of Linear Algebra, Statistics, and Computer Science.
- An article by Ben Evans about what ML is and is not (at this point).
- The Elements of Statistical Learning
- Neural Networks and Deep Learning
- Machine Learning
- TensorFlow World Resources
- Jay Alammar's website contains lots of useful resources, e.g., an explanation of transformers among many others!
Responsible AI
To me, Responsible AI (RAI) is one of the defining (research) areas of our time with the potential to have a positive impact on people's lives. This is includes topics like fairness, interpretability & explainability, differential privacy, and several related ones. Below are some of the best resources I've come across:
- Partnership on AI is maintaining a database of AI incidents. The incidents are crowd-sourced and I have contributed a few myself. It's super useful if you're exploring the harms caused by AI systems in the past to avoid them in the future.
- Fairness and Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan is currently still a work in progress, but what I've seen from it seemed extremely promising.
- Algorithmic Fairness is a blog with lots of articles about the topic by Suresh Venkatasubramanian.
- Moritz Hardt's class website for Fairness in Machine Learning has a great collection of material.
- In Black Mirror, Light Mirror: Teaching Technology Ethics Through Speculation, Casey Fiesler walks through some thought-provoking ways of thinking about tech ethics including the "Black Mirror Writers' Room".
- The AI Ethics Weekly newsletter curated by Lighthouse3 keeps you up-to-date on the latest RAI jobs, news, and articles by RAI experts.
- The Montreal AI Ethics Institute is building a community of people in AI Ethics through events, articles, and research. For a good overview, I highly recommend their newsletter.
ML applied to Natural Language Processing
Machine Learning in the browser
Finding Research Papers
Whether you're at university or just generally interested in research, in most cases you won't know exactly what you're looking for. Two great websites to get started are Google Scholar and Semantic Scholar. They essentially built search for research papers/journals/books/etc. Semantic Scholar offers a few features Google Scholar doesn't have, while Google Scholar seems to have more data overall at least in my experience.
- ArXiv is an archive for articles, usually used for sharing ideas that will later get submitted as papers to conferences. With ArXiv Vanity, you can read the papers rendered in the browser as opposed to the PDF format.
- Distill is aiming at making research "clear, dynamic and vivid". It is a super-interesting effort that shows that research does not have to be constrained by a printable A4 sized PDF format. Maybe all research can be published like this someday.