“Spaced repetition is a centuries-old psychological technique for efficient memorization & practice of skills where instead of attempting to memorize by ‘cramming’, memorization can be done far more efficiently by instead spacing out each review, with increasing durations as one learns the item, with the scheduling done by software. Because of the greater efficiency of its slow but steady approach, spaced repetition can scale to memorizing hundreds of thousands of items (while crammed items are almost immediately forgotten) and is especially useful for foreign languages & medical studies.
I review what this technique is useful for, some of the large research literature on it and the testing effect (up to ~2013, primarily), the available software tools and use patterns, and miscellaneous ideas & observations on it.”-Gwern Branwen, “Spaced Repetition for Efficient Learning.” Gwern.net. March 11, 2009.
The obvious application for spaced repetition is learning vocabulary.
Globalisation/migration (domestic vs external inequality) and educational expansion (education vs property inequality) have created new multi-dimensional conflicts about inequality, leading to the collapse of the postwar left-vs-right party system.
Why didn’t democracy reduce inequality?
Because multi-dimensional coalitions are complicated.
Without a strong egalitarian-internationalist platform, it’s difficult to have the low-education, low-income voters from all origins vote for the same party.
Racism/nativism & higher education = powerful forces dividing the poor if there’s no strong uniting platform.-Thomas Piketty, “Brahmin Left vs. Merchant Right: Rising Inequality and the Changing Structure of Political Conflict.” EHESS & Paris School of Economics Sciences Po, January 24 2019
h/t Boing Boing, which provides a nice overview. Open Question: Am I a member of the Brahmin Left?
“In a psychic context, racial reparations may be less obviously needed, but needed nonetheless, for those of us from white families and communities that practice racial thoughtlessness. We, too, need to study race and racism in order to understand ourselves, because the implicit attitudes about race that we have consciously and unconsciously created and accepted are poisoning us and have imprisoned our minds. We are literally dying from our inability to break free of our prejudice, our unexamined opinions. There are direct correlations to be made between white attitudes about race, lack and fear of education, hopelessness and despair, and addiction. It is just not possible to think freely when you are also desperate to cling to your biases.”
—Leslie W. Lewis, “Liberatory Education.” Hedgehog Review. 21.2 (Summer 2019).
Read this essay. It is absolute fire, and equally applicable to gender, class, sexual orientation, disability and other prejudices.
“A new interactive visualization from Open Syllabus turns this trove of data into a color-coded stippling of different-sized dots, each one representing a particular text. Float over each dot and a box appears in the corner of the screen, showing the number of syllabi that have assigned the text, and a link to a profile page with more detailed analysis. Called the “Co-Assignment Galaxy,” the infographic does what a list cannot: draws connections between all these works and their respective fields of study.-Josh Jones, “A New Interactive Visualization of the 165,000 Most-Frequently Assigned Texts in College Courses.” openculture.com. July 22, 2019.
A list of the top 50 works is available from the Open Syllabus Project website.
“AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take. In this course, you will learn: – The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science – What AI realistically can–and cannot–do – How to spot opportunities to apply AI to problems in your own organization – What it feels like to build machine learning and data science projects – How to work with an AI team and build an AI strategy in your company – How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.”
—A.I. for Everyone.
Audit the course for free. Takes 6-8 hours to complete.
h/t, Open Culture.