(1:20.00-1:40.00) What he describes is the following: Most of his notes originate from the digital using hypothes.is, where he reads material online and can annotate, highlight, and tag to help future him find the material by tag or bulk digital search. He calls his hypothes.is a commonplace book that is somewhat pre-organized.

Aldrich continues by explaining that in his commonplace hypothes.is his notes are not interlinked in a Luhmannian Zettelkasten sense, but he "sucks the data" right into Obsidian where he plays around with the content and does some of that interlinking and massage it.

Then, the best of the best material, or that which he is most interested in working with, writing about, etc., converted into a more Luhmannesque type Zettelkasten where it is much more densely interlinked. He emphasizes that his Luhmann zettelkasten is mostly consisting of his own thoughts and is very well-developed, to the point where he can "take a string of 20 cards and ostensibly it's its own essay and then publish it as a blog post or article."

The essence for this video is correct; active learning, progressive summarization, deep processing, relational analytical thinking, even evaluative.

Yet, the implementation is severely lacking; marginalia, text writing, etc.

Better would be the use of mindmaps or GRINDEmaps. I personally would combine it with the Antinet of course.

I do like this guy's teaching style though 😂

(13:36) Perhaps this is also why visual information is so useful for learning and cognition (see GRINDE)... Maybe the visual medium should be used more in instruction instead of primarily auditory lectures (do take into account redundancy and other medium effects from CLT though)

When it comes to thinking, the Zettelkasten solves an important issue which is the problem of scope, which is impossible at the current moment in mindmapping software such as Concepts.

Mainly, Zettelkasten allows you gain a birds-eye holistic view of a topic, branch, or line of thought, while allowing you to at the same time also gain a microscopic view of an "atomic" idea within that thought-stream, therefore creating virtually infinite zoom-in and zoom-out capability. This is very, very, beneficial to the process of deep thinking and intellectual work.

CIFAR-10 performance results are overestimates since some of the training data is essentially in the test set.

Kozlowski, Diego, Jennifer Dusdal, Jun Pang, and Andreas Zilian. ‘Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation’. ArXiv:2011.02887 [Physics], 5 November 2020. http://arxiv.org/abs/2011.02887.