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These events give me hope for the future as I ignore the present
I was where you are a few months ago. I recommend playing around with Digital Ocean droplets to understand web servers, they make it very easy (and their documentation is excellent). With nginx you can have a static site up in <15 min.
I have a peripatetic posting style....Let me know if you have any questions about web frameworks, I had several conversations with friends that led to eureka moments.
- simply npm your webpack via grunt with vue babel or bower to react asdfjkl;lkdhgxdlciuhw
This is about the level of detail a lot of online guides/tutorials have for most of these packages/workflows/tools/watchamacallitnow. The next step is to read through the documentation, which is often verbose, not pedagogically structured, and sometimes non-existent. The method of last resort is to attack the source code: akin to reading someone's notebook written in a foreign language. You have to get used to their style and shorthand.
"Remember, we're just manipulating text" is what I repeat to keep me sane when troubleshooting tooling issues. It's a big time investment to understand the full-stack; a bigger one to keep on top of it. This issue will only grow exponentially as more code is written and more individuals become developers.
(If you want an example of atrocious documentation, check out the Galago Search Engine/Lemur Project. It's so opaque a research paper dedicated a section to how hard it is for phd's to understand the source code)
True. To be fair, the meat of the information is Gates's comment rather than any editorialization around that. I think he makes a sharp point by implying that the costs over time of sheltering further refugees will be greater than increasing foreign aid to the African continent to alleviate suffering. Especially considering the numbers of refugees and Europe's history in affected regions.
Link to Paper (ScienceMag Paywall): http://science.sciencemag.org/content/356/6334/133
Algorithm used for analysis: GloVe - Global Vectors for Word Representation
- GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.
- GloVe is essentially a log-bilinear model with a weighted least-squares objective. The main intuition underlying the model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning . . . The training objective of GloVe is to learn word vectors such that their dot product equals the logarithm of the words' probability of co-occurrence. Owing to the fact that the logarithm of a ratio equals the difference of logarithms, this objective associates (the logarithm of) ratios of co-occurrence probabilities with vector differences in the word vector space. Because these ratios can encode some form of meaning, this information gets encoded as vector differences as well. For this reason, the resulting word vectors perform very well on word analogy tasks, such as those examined in the word2vec package.
Perhaps more interestingly, Extended Reading:
- OMBJT characterized their average correlation finding for IAT measures (which they estimated as r .148, in the domain of
intergroup behavior) as indicating that the IAT was a “poor” predictor (pp. 171, 182, 183). This section’s analysis reaches a very different
conclusion by applying well-established statistical reasoning to understand the societal consequences of small-to-moderate correlational
effect sizes. The first step of this analysis shows that OMBJT’s and GPUB’s meta-analytic findings had very similar implications for the
average percentage of criterion-measure variance explained by IAT measures. The second step explains how statistically small effects can
have societally important effects under two conditions—if they apply to many people or if they apply repeatedly to the same person. In
combination, the two steps of this analysis indicate how conventionally small (and even subsmall) effect sizes can have substantial
societal significance . . .
- Small effect sizes comprise significant discrimination. For most of the time since the passage of the United States’ civil rights
laws in the 1960s, U.S. courts have used a statistical criterion of discrimination that translates to correlational effect sizes that are
often smaller than r .10. This criterion is the “four-fifths rule,” which tests whether a protected class (identified by race, color,
religion, national origin, gender, or disability status) has been treated in discriminatory fashion. A protected class’s members
receiving some favorable outcome less than 80% as often as a comparison class can be treated by courts as indicating an “adverse
impact” that merits consideration as illegal discrimination (U.S. Equal Employment Opportunity Commission, 1978, §1607.4.D).
Tsipras never wanted the referendum to succeed in the first place - Syriza mismanaged negotiations, the Germans have been ferociously harsh with the terms of the bailout - this crisis is everyone's fault (some more than others though)
the key here is that AIG has already been awarded monetary damages several years ago in the form of a bailout (although with harsher terms, hence the lawsuit). Why would you give them more? The judge is reprimanding both plaintiff and defendant in this ruling, which is appropriate given the situation
This is actually a good ruling as it limits the Fed's abilities to bailout large banks. The main crux of the argument is that the Fed treated AIG (insurance) in a tougher manner than Morgan Stanley (finance), etc. and that, with other things, is illegal (basically, the Fed unjustly favored the banks with the bailout. It demonstrates how the Fed is more beholden to Wall Street). This ruling makes it much riskier to depend on receiving a bailout from the Fed in order to publicly subsidize risky activities. Here's some brief analysis.
It's also pushing back on Wall Street's encroachment into monetary policy.
Not that I like bitcoin, but there are some interesting applications of the blockchain technology that can allow for autonomous systems to exist financially independent from human institutions. Here is a developer discussing what an autonomous taxi service - among others - with no middle-men would look like at the Turing Festival in 2013
It's an opinion on the WORD millennial with this implied, angsty attitude towards the perceived marginalization of his (and my) generation. He doesn't have a problem with the word, he has a problem with popular culture's perception of millennials.
Do you want to be called Baby Boomers? Generation X? These are meaningless tags so we don't have to say Born: 1920-1940, Born 1940-1960, etc. He may as well complain about why we name hurricanes in alphabetical order.
I just don't view this as productive: it's not a discussion of WHY we are perceived this way and what this informs us about generational differences - it's the author complaining about how people complain about him.