Discover more from Unruly Sun
"To succeed in the world, it is not enough to be stupid, you must also be well-mannered."
The last two weeks were bustling. A group of friends associated with a new research group were in town, and we talked until my jaw grew tired. From the metaphysics of mathematics to the moral character of cryptographic work, we followed strands of heady topics that I hadn’t touched in months. And I loved it.
There’s a hardening that I’ve noticed among people as they age; highly abstract questions like ‘what makes life meaningful?’ and ‘what is the nature of good?’ at some point become uncool. It feels like these questions were once considered and deemed unanswerable, and so discarded. If touched upon at all, they’re regarded with irony as sophomoric dead-ends.
Yet a part of me was lit aflame when I was prompted to think like this again. I’m moved to recall the aphorism that goes something like “wisdom is borne from a lifetime of asking… it’s not the answer that enlightens, but the question.”
(Another update: I think I’ve hit upon my overarching research question)
Most of my writing attention was focused on articulating the value of OSML, and about analyzing the constitutions of DAOs (more on this soon). At some point I still plan to write the post that introduces Ostrom’s Institutional Analysis and Development for the layperson.
People are often driven by patterns of thought formed during childhood. This especially true when there was a traumatic experience involved (hence the fairly well established pattern of victims of abuse continuing to seek abusive partners). Writers, for instance, almost inevitably in fiction the traumas they’ve encountered early on. I’ve noticed certain types of researchers share oddly specific patterns of thinking, and I can’t help but think many research programs are formed by traumas, unconsciously reenacted.
Will Buckingham suggests that stories are the most potent medium for ethics. What if, instead of writing constitutions with explicit values, we told stories as a group? And the process of incorporating new members isn’t done by signing a constitution, but by retelling a series of stories?
What are the limits of the cathedral (top-down) vs the bazaar (bottom-up) model of project development? What types of ‘top’ and ‘bottom’ are there? Polycentric governance may give a clue.
Two Ethical Moments in Debian, from Gabriella Coleman. Superb example of an ethnography of a technical community with a normative (but nonjudgmental) lens.
Geeks, MOPs and sociopaths in subculture evolution. From Chapman. “The muggles who invade and ruin subcultures come in two distinct flavors, mops and sociopaths, playing very different roles.”[sic]
A friendly introduction to machine learning optimizers and compilers. Self explanatory. A surprisingly pleasant read.
The steep cost of capture. About the industry capture of AI. Acerbic yet recommended.
The year 2012 showed the commercial potential of supervised machine learning, and the power of the term AI as a marketing hook. Tech companies quickly (re)branded machine learning and other data-dependent approaches as AI, framing them as the product of breakthrough scientific innovation. Companies acquired labs and start-ups, and worked to pitch AI as a multitool of efficiency and precision, suitable for nearly any purpose across countless domains. When we say AI is everywhere, this is why.
The Portable Hannah Arendt. The opening of ‘Truth and Politics':
The subject of these reflections is a commonplace. No one has ever doubted that truth and politics are on rather bad terms with each other, and no one, as far as I know, has ever counted truthfulness among the political virtues. Lies have always been regarded as necessary and justifiable tools not only of the politician’s or the demagogue’s but also of the statesman’s trade.
Assessment of fraction of ML research papers that use open source software (not done)
‘Towards Assessing Value in OSML’ skeleton (done)
Update proposal and send to DW + DM (done)
Read about Chinese OS strategy (kind of done)
Notes on OS business strategies (kind of done but want to do more)
Next Two Weeks
Articulate AI as Institution with example to DH
Determine methodology for ‘Assessing Value in OSML’ and articulate different types of value
Collect potential methodologies for ‘how do institutions shape ML research through OSML’