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Q: Why Do Keynote Speakers Keep Suggesting That Improving Security Is Possible? A: Because Keynote Speakers Make Bad Life Decisions and Are Poor Role Models | USENIX
"Some people enter the technology industry to build newer, more exciting kinds of technology as quickly as possible. My keynote will savage these people and will burn important professional bridges, likely forcing me to join a monastery or another penance-focused organization. In my keynote, I will explain why the proliferation of ubiquitous technology is good in the same sense that ubiquitous Venus weather would be good, i.e., not good at all. Using case studies involving machine learning and other hastily-executed figments of Silicon Valley’s imagination, I will explain why computer security (and larger notions of ethical computing) are difficult to achieve if developers insist on literally not questioning anything that they do since even brief introspection would reduce the frequency of git commits. At some point, my microphone will be cut off, possibly by hotel management, but possibly by myself, because microphones are technology and we need to reclaim the stark purity that emerges from amplifying our voices using rams’ horns and sheets of papyrus rolled into cone shapes. I will explain why papyrus cones are not vulnerable to buffer overflow attacks, and then I will conclude by observing that my new start-up papyr.us is looking for talented full-stack developers who are comfortable executing computational tasks on an abacus or several nearby sticks."
lulz  stats:machine-learning  tech:security 
yesterday
Martin McKee's blog: E-cigarettes - more evidence of English exceptionalism
"Fortunately, other countries are not following England's direction, despite massive pressure from a very well funded Big Tobacco operation. Instead, they are waiting for the results on our giant experiment on our people."
public-health:tobacco-control  uk:health-policy 
2 days ago
Rust in R
"Example R packages wrapping Rust Cargo crates"
r 
2 days ago
David Runciman reviews ‘The World as It Is’ by Ben Rhodes · LRB 2 August 2018
"Rhodes is haunted throughout his time in the White House by two earlier catastrophes of American foreign policy, neither of which Obama had any responsibility for, and both of which therefore he might have been able to put right. One is the Iraq War, which Obama does what he can to get out from under, though it was always going to be too little, too late. The other is the Rwandan genocide."
usa:empire  usa:presidency 
5 days ago
TG Pro Tutorial | Tunabelly Software
"Peek inside your Mac with fan control for cooling, temperature monitoring & diagnostics."
mac:utilities 
8 days ago
Life Aboard the Rocket Ship: An Interview with an Anonymous [Software] Engineer
"That was disillusioning to me, because I had really bought into the ideology that building a business was the best way to make the world a better place. That was something that was drilled into my head at my elite college. I had completely bought into it."
web:economy  at_a_loss_for_tags 
13 days ago
Amazon's Hanging Cable Problem (Golden Gate Edition)
"In this post we use R's capabilities to solve nonlinear equation systems in order to answer an extension of the hanging cable problem to suspension bridges. We then use R and ggplot to overlay the solution to an image of the Golden Gate Bridge in order to bring together theory and practice."
r  mathematics 
13 days ago
The Great Regression. Machine Learning, Econometrics, and the Future of Quantitative Social Sciences
"What can machine learning do for (social) scientific analysis, and what can it do to it? A contribution to the emerging debate on the role of machine learning for the social sciences, this article offers an introduction to this class of statistical techniques. It details its premises, logic, and the challenges it faces. This is done by comparing machine learning to more classical approaches to quantification – most notably parametric regression– both at a general level and in practice. The article is thus an intervention in the contentious debates about the role and possible consequences of adopting statistical learning in science. We claim that the revolution announced by many and feared by others will not happen any time soon, at least not in the terms that both proponents and critics of the technique have spelled out. The growing use of machine learning is not so much ushering in a radically new quantitative era as it is fostering an increased competition between the newly termed classic method and the learning approach. This, in turn, results in more uncertainty with respect to quantified results. Surprisingly enough, this may be good news for knowledge overall."
stats:machine-learning  soc:quantification 
15 days ago
General Resampling Infrastructure • rsample
"rsample contains a set of functions that can create different types of resamples and corresponding classes for their analysis. The goal is to have a modular set of methods that can be used across different R packages for:
- traditional resampling techniques for estimating the sampling distribution of a statistic and
- estimating model performance using a holdout set"
r  stats:bootstrap 
16 days ago
What if a few grad programs were run for the benefit of the graduate students? | asecondmouse
"3. Real world problems solved using remote teaming

Toy problems and standardized data sets are fine for [some] instruction and [some] incremental journal publications, but if you want training applicable to the private sector, you need to be working with raw data that is [mostly] complete crap, digital offal requiring hours of tedious prep work before you can start applying glitzy new methods to it. Because that, buckeroos, is what data science in the private sector involves itself with, and that’s what pays the bills. Complete crap is, however, fairly difficult to simulate, so much better to find some real problems where you’ve got access to the raw data: associations with companies—the sorts of arrangements that are routine in engineering programs—will presumably help here, and as I’ve noted before, “data science” is really a form of engineering, not science."
academia:teaching  data:science 
28 days ago
A Global Guide to State-Sponsored Trolling
"'States are using the same tools they once perceived as a threat to deploy information technology as a means for power consolidation and social control, fueling disinformation operations and disseminating government propaganda at a greater scale than ever before.'"
polisci:power  web:trolling 
4 weeks ago
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