Am Neumarkt 😱

#DS#ML The “AI Expert Roadmap”. This can be used as a checklist of prelims for data people. https://i.am.ai/roadmap/#note

#statistics This is the original paper of Fraser information. Fisher information measures the second moment of the model sensitivity; Shannon information measures compressed information or variation of the information; Kullback (aka KL divergence) distinguishes two distributions. Instead of defining a measure of information for different conditions, Fraser tweaked the Shannon information slightly and made it more generic. The Fraser information can be reduced to Fisher information, Shannon information, and Kullback information under certain conditions. It is such a simple yet powerful idea. Fraser DAS. On Information in Statistics. aoms. 1965;36: 890–896. doi:10.1214/aoms/1177700061 https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-36/issue-3/On-Information-in-Statistics/10.1214/aoms/1177700061.full

#DS Wing JM. Ten research challenge areas in data science. Harvard Data Science Review. 2020;114: 1574–1596. doi:10.1162/99608f92.c6577b1f https://hdsr.mitpress.mit.edu/pub/d9j96ne4/release/2

#ML Voss, et al., "Branch Specialization", Distill, 2021. https://distill.pub/2020/circuits/branch-specialization/ TLDR; - Branch: neuron clusters that are roughly segregated locally, e.g., AlexNet branches by design. - Branch specialization: branches specialize in specific tasks, e.g., the two AlexNet branches specialize in different detectors (color detector or black-white filter). - Is it a coincidence? No. Branch specialization repeatedly occurs in different trainings and different models. - Do we find the same branch specializations in different models and tasks? Yes. - Why? The authors' proposal is that a positive feedback loop will be established between layers, and this loop enhances what the branch will do. - Our brains have specialized regions too. Are there any connections?

#ML Silla CN, Freitas AA. A survey of hierarchical classification across different application domains. Data Min Knowl Discov. 2011;22: 31–72. doi:10.1007/s10618-010-0175-9 A survey paper on hierarchical classification problems. It is a bit old as it didn’t consider the classifier chains, but this paper summarizes most of the ideas in hierarchical classification. The authors also proposed a framework for the categorization of such problems using two different dimensions (ranks).

AI researchers allege that machine learning is alchemy | Science | AAAS https://www.sciencemag.org/news/2018/05/ai-researchers-allege-machine-learning-alchemy

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#TIL How the pandemic changed the way people collaborate. 1. Siloing: From April 2019 to April 2020, modularity, a measure of workgroup siloing, rose around the world. https://www.microsoft.com/en-us/research/blog/advancing-organizational-science-using-network-machine-learning-to-measure-innovation-in-the-workplace/

#DataScience (Please refer to this post https://t.me/amneumarkt/199 for more background.) I read the book "everyday data science". I think it is not as good as I expected. The book doesn't explain things clearly at all. Besides, I was expecting something starting from everyday life and being extrapolate to something more scientific. I also mentioned previously that I would like to write a similar book. Attached is something I created recently that is quite close to the idea of my ideal book for everyday data science. Cross Referencing Post: https://t.me/amneumarkt/199

#fun https://press.princeton.edu/books/hardcover/9780691176901/music-by-the-numbers

https://pudding.cool/2021/03/wine-model/