bookmark | Chris Aldrich

🔖 The influence of collaboration networks on programming language acquisition by Sanjay Guruprasad | MIT

Bookmarked The influence of collaboration networks on programming language acquisition by Sanjay Guruprasad (Massachusetts Institute of Technology)

Many behaviors spread through social contact. However, different behaviors seem to require different degrees of social reinforcement to spread within a network. Some behaviors spread via simple contagion, where a single contact with an "activated node" is sufficient for transmission, while others require complex contagion, with reinforcement from multiple nodes to adopt the behavior. But why do some behaviors require more social reinforcement to spread than others? Here we hypothesize that learning more difficult behaviors requires more social reinforcement. We test this hypothesis by analyzing the programming language adoption of hundreds of thousands of programmers on the social coding platform Github. We show that adopting more difficult programming languages requires more reinforcement from the collaboration network. This research sheds light on the role of collaboration networks in programming language acquisition.

[Downloadable .pdf]
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 26-28).

Advisor: César Hidalgo.

URI: http://hdl.handle.net/1721.1/119085

I ran across this paper via the Human Current interview with Cesar Hidalgo. In general they studied GitHub as a learning community and the social support of people’s friends on the platform as they worked on learning new programming languages.

I think there might be some interesting takeaways for people looking at collective learning and online pedagogies as well as for communities like the IndieWeb which are trying to not only build new technologies, but help to get them into others’ hands by teaching and disseminating some generally tough technical knowledge. (In this respect, the referenced Human Current podcast episode may be a worthwhile overview.)

🔖 The New Testament: A Historical Introduction to the Early Christian Writings by Bart D. Ehrman

Bookmarked The New Testament: A Historical Introduction to the Early Christian Writings by Bart D. Ehrman (Oxford University Press; 6 edition)

Featuring vibrant full color throughout, the sixth edition of Bart D. Ehrman's highly successful introduction approaches the New Testament from a consistently historical and comparative perspective, emphasizing the rich diversity of the earliest Christian literature. Distinctive to this study is its unique focus on the historical, literary, and religious milieux of the Greco-Roman world, including early Judaism. As part of its historical orientation, the book also discusses other Christian writings that were roughly contemporary with the New Testament, such as the Gospel of Thomas, the Apocalypse of Peter, and the letters of Ignatius.

An interesting looking textbook from Ehrman.

This is a recommended text for Dale Martin’s course Introduction to the New Testament History and Literature.

🔖 Introduction to Renormalization | Simon DeDeo | Complexity Explorer

Bookmarked Introduction to Renormalization by Simon DeDeo (Complexity Explorer)

What does a JPEG have to do with economics and quantum gravity? All of them are about what happens when you simplify world-descriptions. A JPEG compresses an image by throwing out fine structure in ways a casual glance won't detect. Economists produce theories of human behavior that gloss over the details of individual psychology. Meanwhile, even our most sophisticated physics experiments can't show us the most fundamental building-blocks of matter, and so our theories have to make do with descriptions that blur out the smallest scales. The study of how theories change as we move to more or less detailed descriptions is known as renormalization.

This tutorial provides a modern introduction to renormalization from a complex systems point of view. Simon DeDeo will take students from basic concepts in information theory and image processing to some of the most important concepts in complexity, including emergence, coarse-graining, and effective theories. Only basic comfort with the use of probabilities is required for the majority of the material; some more advanced modules rely on more sophisticated algebra and basic calculus, but can be skipped. Solution sets include Python and Mathematica code to give more advanced learners hands-on experience with both mathematics and applications to data.

We'll introduce, in an elementary fashion, explicit examples of model-building including Markov Chains and Cellular Automata. We'll cover some new ideas for the description of complex systems including the Krohn-Rhodes theorem and State-Space Compression. And we'll show the connections between classic problems in physics, including the Ising model and plasma physics, and cutting-edge questions in machine learning and artificial intelligence.

🔖 The Deep Learning Revolution by Terrence J. Sejnowski | MIT Press

Bookmarked The Deep Learning Revolution by Terrence J. Sejnowski (MIT Press)

How deep learning―from Google Translate to driverless cars to personal cognitive assistants―is changing our lives and transforming every sector of the economy.

The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

🔖 The Formula: The Universal Laws of Success by Albert-László Barabási

Bookmarked The Formula: The Universal Laws of Success by Albert-László Barabási (Little, Brown and Company)

In the bestselling tradition of Malcom Gladwell, James Gleick, and Nate Silver, prominent professor László Barabási gives us a trailblazing book that promises to transform the very foundations of how our success-obsessed society approaches their professional careers, life pursuits and long-term goals.

Too often, accomplishment does not equal success. We did the work but didn't get the promotion; we played hard but weren't recognized; we had the idea but didn't get the credit. We convince ourselves that talent combined with a strong work ethic is the key to getting ahead, but also realize that combination often fails to yield results, without any deeper understanding as to why. Recognizing this striking disconnect, the author, along with a team of renowned researchers and some of the most advanced data-crunching systems on the planet, dedicated themselves to one goal: uncovering that ever-elusive link between performance and success.

Now, based on years of academic research, The Formula finally unveils the groundbreaking discoveries of their pioneering study, not only highlighting the scientific and mathematic principles that underpin success, but also revolutionizing our understanding of:
Why performance is necessary but not adequate
Why "Experts" are often wrong
How to assemble a creative team primed for success
How to most effectively engage our networks
And much more.

Caught an interesting reference to this in an episode of Human Current, but I’ve also recently finished his prior book Linked. I’ll likely read it, but I’ll probably wish I had read the relevant papers instead.