Romero recently released a book, _Doom Guy: Life in First Person_, if you'd like a long-form answer to that question.
Other commenters give more substantial answers, but I can vouch for the book as having a lot of history, clearly being written by Romero himself, and having a lot of self-reflection on the questions you're asking.
I'm going to give the advice here that saved my sanity in an open office: use headphones with "passive" noise canceling. I bought a pair of 3M WorkTunes with Bluetooth. They're just passive, sound deadening, hearing protection ear muffs like you would wear on a factory floor or in a construction workplace, with Bluetooth built. It blocks out all noise, not just white noise, and they're much cheaper than ANC head phones.
I wear the WorkTunes, plus ear plugs, plus I play cafe or brown noise through the headphones, plus I sometimes put on music. If that hadn't worked, I was going to go insane from the sound of the keyboard next to me.
My comment was a rebuttal to the "single income earner" argument which suggests that in the post-war era we labored half as much as we do today.
I've got no other points to defend. But I'm happy to look at any of your points, and I understand and accept the argument that hours of leisure time is not a direct measurement of well-being. (:
There are fewer hours of work done at home now. The increase in leisure time reflects a decrease in total labor: income-earning labor, labor around the house, etc. Figures in [1] break it out a bit, although the years-reflected are a bit different than I used above. Search for "60.9" to find the relevant table.
That's probably one of the caveats and addenda. So, my counterargument doesn't prove that life has gotten better--maybe an additional kid is "better" than six hours of leisure time by some calculuses. But it does rebut any argument that we used to get by one 40 hours per week per household of labor but now we need 80.
Edit: Of course, as parent notes the drop in number of children represents at most _part_ of the increase in leisure hours. We can definitely attribute part of it to things that are definitely modern advantages, like labor-saving devices such as dish-washing machines. The year 1965 is kind of a poor year for my argument, in that by that point most households had the most labor-saving devices: refrigerators that allow you to shop and cook days at a time, washing machines that saved many hours of labor washing clothes, etc.
You make a few substantial points, but any use of the "single income earner" argument is incomplete if it doesn't acknowledge that _women were also working at home_ while their husbands were doing income-earning work.
It's not only income-earning work that counts against prosperity: it's all work. Studies indicate that men and women gained 6-8 hours of leisure from 1965-2003. There are probably caveats and addenda to that, and you make other good points, but please also consider non-income work in your arguments.
I've found that an emphasis on a clear spec, decoupling, testing, and producing quality code significantly reduce the "spin up" time that gets ever more daunting in an infrequently-visited project.
A clear spec means that I know what all has to be programmed. Decoupling my components means that I can make changes to my business-logic/back-end without having to make changes to my display/front-end, as long as the interface remains the same. Testing means that I can make changes without fear that I'm going to unknowingly break existing features. Quality code means that I can more easily understand the code that I've written after an absence. The upshot is that I always feel comfortable making a few quick changes, pushing commits, even after being away from the code for a few days.
A few concrete tools for writing quality code: write it to be open sourced, write it to be viewed and collaborated upon; use code quality tests like pylint, jshint, code-climate, whatever is appropriate for your language; display your code quality metric badges in your repo, badges for coverage, built-status, etc.
In general, "machine learning" means that an algorithm did it, and that the algorithm engages in some analysis of the problem space or the solution space. Usually that analysis involves an iterative or repetitive element: making several tries and modeling what makes a try good or bad, making a try and then changing the solution tiny bits to find a try that's slightly better, etc. And "good" or "bad" is determined according to a human-provided rubric (eg: +1000 points for every sight seen, -1 point for every mile driven, -10 points for every day taken, etc.)
Unless the problem is very constrained, there usually is no guarantee of optimality; here there is probably no guarantee of optimality. It might be "locally" optimal, in that there might be no better trip that differs from this one by only a tiny bit.
And, no, "machine learning" here doesn't mean the enterprise and establishment of "Machine Learning", just some algorithm that the author used.
Respectfully, I make no conjectures. When I say that psychedelics can cause us harm, I mean that psychedelics can cause the individual harm, and I generalize from my own (extremely mild) experience.
> They open you up to the possibility that everything you know is wrong.
In mathematical modeling, they say that every model is wrong, but some are useful.
Insofar as psychedelics are dissociatives, they can cause harm by convincing us to discard our wrong but useful models of the world. And I have experienced that when no associations seem significant, all associations gain significance, independent of usefulness.
Our brain works pretty well with our incomplete-but-useful heuristics, and the ability to discover these heuristics and transmit them culturally is one of our central powers as a species. I don't have the perspective of a state to say why exactly they have been banned, and I don't have the perspective of a god to say whether the lubrication of psychedelics might slip us into a greater local optimum. But I do have enough personal experience to say that bad things can happen to an unmoored mind.
It's nice in that it might let you get away with less oil day-to-day. My best answer is that you can get away with skipping oven-seasoning if you follow the daily practice, but not vice-versa.
Yeah, they've definitely started out closer to smooth than ours. As you say, I don't know if mirror smoothness is terribly important, but it might let you get away with a bit less oil.
When I first got into cast iron, I spent a lot of time on oven-seasoning. It turns out that your daily practice is much more important than that oven seasoning, and the two important steps are:
1. Get the oil hot before you add any foods.
2. Use a sheet-metal spatula/flipper
Humanity has known for a long time that when you get your cooking oil hot, it repels food instead of binding it to the cooking surface; but the amateur cook has forgotten because of a reliance on non-stick surfaces.
The sheet-metal spatula/flipper lets you clean the cast iron with each pass of the tool. A rubber/wooden tool will leave small, burnt on bits of food, which accumulate more bits of food; a sheet-metal tool with scrape those off before they become a problem. Also, a sheet-metal spatula will scrape the roughness of the cast-iron from the bottom of your pan over the course of decades, moving you towards that inky-black mirror of grandma's old pans.
I can cook anything on my cast iron, just by following those steps, even fried eggs: the surface is totally non-stick. And cleaning is simple too: sometimes I'll make a few passes with the spatula to scrape off any food that has dried on, but that's all I ever do.
With a metal spatula, you're machining the pan smooth over the course of decades, and also constantly scraping off any good bits that are getting stuck.
It would be excellent to have some better verbage for what the authors are trying to express. There are some components of white privilege that apply in this case, and some that don't. Obviously, Asian and Indian Americans don't get the benefit of "you're white and I'm white, so I like you," but they do get the benefit of some kind of "expectation of quality." And there's probably a third component, which may be closer to the authors' original intent: "you have similar cultural values to me, so I like you."
I think it's useful to consider those components separately. They suggest different vectors of attack against these endemic problems.
My thinking here is framed by John Ogbu's concept of the involuntary minority: all the minorities that became minorities in America by choice are doing pretty well; all of the minorities that were made minorities in America by force (either conquered in situ or brought by force) are doing pretty poorly. It takes a shoe-horn to fit native-blooded Hispanics into that dichotomy, but it helps there too.
I had similar questions. You're saying that if, for example, the quality among the pool of women applicants were higher than the quality among the pool of men applicants, then you would expect women to have greater representation among successful applicants than among applicants in general, and that if the female applicant pool were of higher quality and yet female applicants were accepted in the same percentage at which they applied then this would be evidence of an anti-female bias at Hacker School.
In other words, you argue that the genders are accepted at the same rate at which they apply is evidence that either (A) the quality between the two pools is identical and Hacker School is not biased, or (B) the quality between the two pools is not identical and Hacker School is biased. And that from the evidence there is no way to distinguish from the evidence between the two scenarios.
You might be able to distinguish between the two scenarios given the rate at which anonymized applications are accepted for further review. That would be your "metric that is blind to X".
I guess that the fact that for (B) to be true, there would have to be some coincidental match between quality differential and reviewer bias argues against the case of bias. And also that each stage approves the same proportion of genders is an argument against some kinds of bias (e.g. reviewers who, at each stage, sheer off a different proportion of each gender's applicants).
So as you point out, Hacker School's analysis requires some model assumptions. I think that the conclusion as written might have been too strong. I hope that any organization that might attempt to rigorously address the endemic issues of gender and race in our vocation will take exactly the steps you recommend regarding assessment of applicant quality, and if we find that males and females are entering into applications with different levels of quality then we will have another vector by which to address those issues.
I took this post as a kind of informal report about steps that Hacker School has taken to lessen bias in their application process and the industry in general. And given what I've written above, I take their statements as evidence--not proof, but evidence--of good measures against bias.
Another solution would be to allow those interested in PhDs an informed decision as to whether or not to pursue the degree. Major media could make occasional reports on the status of the post-doctoral job market available to all interested parties; those parties could then be allowed to balance personal cost against expected outcome.
The central limit theorem shows us that unimodal data with lots of independent sources of error tends towards a normal distribution. That description is a good first-pass, descriptive model for lots and lots of contexts, and standard deviation speaks well to normally distributed data.
Squaring error isn't just a convenient way to remove sign, it's driven by a lot of data-sets' conformance to the central limit theorem.
Other commenters give more substantial answers, but I can vouch for the book as having a lot of history, clearly being written by Romero himself, and having a lot of self-reflection on the questions you're asking.
Here's some additional HN discussion: https://news.ycombinator.com/item?id=37649594