What I’ve been up to

What I’ve been reading

This is a short selection of things I’ve been reading, watching, or listening to.

  • Visualizing transformers and attention | Talk for TNG Big Tech Day '24
    Authors: Grant Sanderson
    Date: 2024-01-01 | Date read: 2025-01-14
    Summary
    Based on the 3blue1brown deep learning series:    • Neural networks
  • Why it is crucial for scientists to start heeding the lessons of Thomas Bayes
    Authors: David Papineau
    Date: Unknown | Date read: 2025-01-14
    | Archive link: https://archive.is/d2afM
    Summary
    Thomas Bayes | Philosophy Essay | David Papineau argues that it is crucial for scientists to start heeding the lessons of Thomas Bayes
  • How to explore your scientific values and develop a vision for your field
    Authors: Grace Lindsay
    Date: 2024-01-01 | Date read: 2025-01-14
    Summary
    As a new professor, I was caught off guard by one part of the job: my role as an evaluator.
  • The World John von Neumann Built
    Authors: David Nirenberg
    Date: 2022-01-01 | Date read: 2025-01-14
    Summary
    Game theory, computers, the atom bomb—these are just a few of things von Neumann played a role in developing, changing the 20th century for better and worse.
  • What Is Entropy? A Measure of Just How Little We Really Know.
    Authors: Zack Savitsky
    Date: 2024-01-01 | Date read: 2025-01-14
    Summary
    Exactly 200 years ago, a French engineer introduced an idea that would quantify the universe’s inexorable slide into decay. But entropy, as it’s currently understood, is less a fact about the world than a reflection of our growing ignorance. Embracing that truth is leading to a rethink of everything from rational decision-making to the limits of machines.
  • The Depths of Wikipedians
    Authors: Anne Rauwerda
    Date: 2024-01-01 | Date read: 2025-01-07
    Summary
    A conversation about yogurt wars, German hymns, tropical cyclones, and the people who make Wikipedia function.
  • How to Interpret Statistical Models Using marginaleffects for R and Python
    Authors: Vincent Arel-Bundock, Noah Greifer, & Andrew Heiss
    Date: 2024-01-01 | Date read: 2024-12-02
    Summary
    The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes nonlinear components, interactions, or transformations. Analysts who fit such complex models often seek to transform raw parameter estimates into quantities that are easier for domain experts and stakeholders to understand. This article presents a simple conceptual framework to describe a vast array of such quantities of interest, which are reported under imprecise and inconsistent terminology across disciplines: predictions, marginal predictions, marginal means, marginal effects, conditional effects, slopes, contrasts, risk ratios, etc. We introduce marginaleffects, a package for R and Python which offers a simple and powerful interface to compute all of those quantities, and to conduct (non-)linear hypothesis and equivalence tests on them. marginaleffects is lightweight; extensible; it works well in combination with other R and Python packages; and it supports over 100 classes of models, including linear, generalized linear, generalized additive, mixed effects, Bayesian, and several machine learning models.
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