Learning in Progress: Equality Has Many Definitions

This is a Learning in Progress post. Contents are brief thoughts based on few sources, and have not been checked for accuracy or usefulness.

These notes are based on a section of Equality by Darrin M. McMahon. I haven’t finished reading it, and a bug deleted most of my notes from the first ~200 pages, so it is even less complete than it might otherwise be.

People are different, and this makes them inherently unequal. This has been used to justify bigotry on arbitrary differences throughout history, but declaring equality of all doesn’t make people equal either. Everyone has needs and capabilities, and the only path to equality is to have all people use their capabilities collectively to fulfill their collective needs.

Stalinism took “From each according to their ability, to each according to their need.” and replaced the word “need” with “work”. By including this seed of meritocracy, anyone injured, disabled, or elderly is excluded from equality. (I think every person has a phase where they see meritocracy as ideal. Fortunately, most people grow out of this phase.)

Nazis promoted equality of a few at the expense of everyone else. (How equality has been used throughout history changes. It is important to recognize that it means different things to different people.) Fascism creates a meritocracy exclusive to one class, relying on the existence of outsiders (who must be murdered1). In this way, fascism must shrink the accepted class to have more outsiders, and eats itself.

We claim all nations are equal, while propping up some, sabotaging others, and we can all see that nations are not equal. WWII’s devastation increased equality (see “four horseman of leveling” in Quotes). Post-WWII, economists claimed that industrialization forms a natural progression of brief extreme inequality that quickly brings in equality. (This is an obvious lie.) At the same time, economists claimed that it was better to make a nation wealthy than to fix its inequality, and that commerce is a leveling force. “When a rich man sells to the poor, they become equal.” cannot be true, and yet it was the predominant claim.

Quotes

  • “self-love is the great barrier to full human equality” I see in many people, especially myself, a critical lack of self-love, so this stood out to me as worth investigating further. It may not be true, or it may be more true than I am capable of recognizing right now.
  • “Christianity is Communism” If you research when and where Christianity was formed, the people were living under a form of communism.2 The ideals of Christianity are communist ideals, but have been changed and replaced by centuries of adaptation and interpretation.
  • “iron law of oligarchy” In every government, an elite few control all. There are many systems to stop this, but they have all failed so far.
  • “four horseman of leveling” – war, revolution, state failure, disease. These are all common things that have caused increases in equality by hurting everyone.

Questions

  • Does communism only work at small scales? It is implied to have only worked when implemented by communities instead of countries.
  • Does Marxism rely on individualism? The more I learn, the more I see that individualism is the biggest threat to progress. (Ever heard “divide and conquer”? Individualism IS self-division – a destruction of community. It makes us weak.)
  • What makes immigration “good”?3 From my education, I “know” that immigration has always had benefits, but what are those benefits? Why do we call them beneficial? As far as I know, the benefit has always been cheap labor (exploitation of immigrants). I want to challenge my education, and learn more about the complexities of immigration. (There is never a valid reason to stop immigration.)
  • Should we not want greatness? What IS greatness? Nietzsche argued for a constant personal struggle to achieve greatness, and against many institutions that improve equality. If seeking greatness requires sacrificing others, should we ever want it?
  • What was good/bad about the “New Deals”? They compensated for a destroyed economy, and produced infrastructure still used today, but what were the exact short-term and long-term effects?

Further Reading

  • Capital: A Critique of Political Economy by Karl Marx

Footnotes

  1. Fascism relies on exploitation of the unaccepted classes, which often literally involves mass murder, but also makes the unaccepted people leave. This is why fascists inevitably shrink their accepted class.
  2. Romans were the capitalists of their day, exploiting the people that became the first Christians. Communism is a broad and complex subject. In this context, communism is being used unrelated to the way it is used as a classification for modern countries.
  3. A partner reminds me that diversity is an inherent good, and that immigration increases diversity. (At minimum, diversity brings new ideas and perspectives into focus, and increases resiliency.)

(It’s kind of difficult to keep motivation when hard work is unceremoniously destroyed by a glitch..)

Learning in Progress

Learning is a long and complex process, and usually involves asking far more questions than getting answers. Sometimes, having notes can shortcut the most difficult parts of learning (like reading a thick and detailed book). Of course, shortcuts come with downsides, like inaccuracy.

I’ve been publishing some of my notes in their raw form instead of trying to make a “perfect product” out of them, but these are not easy to find due to how they’ve been published, and I am disorganized.

I’ve thought of this blog as a place for well-thought-out posts only, but that basically means I don’t publish anything, and the exceptions rarely meet my own required quality. I think I can solve a lot of my issues here by being willing to post thoughts that haven’t been fully designed, like this mess (but legible and formatted based on the source of each thought):

Illegible notes based on Equality by Darrin M. McMahon.

Each of these posts will be prefixed by a quote to indicate that their contents aren’t made of fleshed out thoughts:

This is a Learning in Progress post. Contents are brief thoughts based on few sources, and have not been checked for accuracy or usefulness.

(Previously, this post was titled “Reading, Absorbing Ideas, Distillation” because I was trying to be clever with an acronym for these posts. That was stupid and confusing, which invalidates the intent of this. That’s also why the URL for this post is stupid and doesn’t match the title anymore. Cool URLs don’t change.)

Notetaking in Public

I’m stealing Nicole van der Hoeven’s idea: Post your messy in-progress notes in public.

I Spent Two Hours Learning How to Take a Break Instead of Taking a Break

And so you hopefully don’t waste the same amount of time, here are just the conclusions:

Take breaks every 20 to 70 minutes. Finding the right frequency for you may take trial and error. Multiple sources agree that something near 50 minutes of work with 10 minutes of break works well. The Pomodoro technique combines more frequent shorter breaks with infrequent longer breaks, and is commonly used. The longer you go between breaks, the longer your breaks should be.

A break is only effective when you do something different from what you’re doing now. The primary differences that matter are using different areas of the brain (or not trying to utilize your brain during a break), a difference in eye-focus (stop looking at screens if you were, look far away if you were focused on something right in front of you), and a difference in physical activity (move more if you weren’t moving, or stop for a bit if you were).

(Vacations have beneficial effects, but these seem to be limited in scope and duration. I believe this take is missing important nuance.)


If you wish to read all my notes on this topic, they are publicly available here. (Note: In case that website goes down, I do regularly back up my notes (and blog posts) using the services linked to on Archives & Sources.)

How to Use ChatGPT

Note: Since the release of GPT-4o, ChatGPT has decreased remarkably in functionality, accuracy, and usability. This was written when GPT-3.5 was the standard. Unfortunately, it is no longer accessible.

I’m late to the party, but maybe that’s better. I’ve forgotten some of the hype around AI, and the pace of innovation has settled down a little. Think of ChatGPT as a thinking tool with access to an internet-sized – but imprecise – database. That database was last updated in September 2021, and is imprecise because due to how neural networks work. The thinking part of this tool is rudimentary, but powerful. It does many things well with the correct input, but also fails spectacularly with the “wrong” input.

I separate the idea of what ChatGPT is from how it functions and where its knowledge comes from, because it helps me think of uses while remembering its limits. For example, I used it to help me journal more effectively, but when I tried to probe its knowledge of Havana Syndrome – a conspiracy theory commonly presented as fact by USA officials, it expressed useless information, because it has no conception of how it knows anything, or where its knowledge comes from.

Things ChatGPT is Good At

This list is presented in no particular order, but it is important to stress that AI often lie and hallucinate. It is important to always verify information received from AI. This list is based on my experiences over the past month, and will be updated as I use ChatGPT more. It is not comprehensive, but is intended to be what I find most useful.

  • Socratic method tutoring: The Socratic method is essentially “Asking questions helps you learn.” ChatGPT is very good at explaining topics, just make sure you verify its explanations are factual. (Questions I asked: Why are smooth-bore tanks considered more advanced while rifling in guns was an important innovation? Why do companies decrease the quality of tools over time?)
  • Writing: ChatGPT tends to be too verbose, but you can make it simplify and rewrite statements, and it can help you find better ways to write. (I asked it to explain the Socratic method a few times, then wrote my own version.)
  • Scripting: I created a utility script for file statistics in 2-3 hours by refining output from ChatGPT. The end result is more reusable, better written, and more functional than it would’ve been if I had worked on it alone. And that’s ignoring the fact that I got something I liked far faster than I would’ve on my own. (Just.. you need a programmer still. It can do some pretty cool things on its own, but also forgets how to count often.)
  • Planning: This is a todo item for me. I haven’t successfully used it for planning yet, but I intend to, and have heard of good results from others.

Things ChatGPT is Bad At

  • Facts & math: AI hallucinate. Check everything they teach you.
  • Finding sources: ChatGPT’s knowledge is formed by stripping the least useful data out of most of the internet, and who said what is far less important than specific pieces of knowledge – like how do you make a heading in HTML?
  • An unbiased viewpoint: While ChatGPT is fairly good at avoiding most bias, everything is biased. Removing bias completely is impossible. Discussing anything where there is strong motive to present a specific viewpoint will lead to that viewpoint being presented more often than an unbiased viewpoint.
  • Violent, illegal, and sexual content: While it is possible to bypass OpenAI’s strict handling of content, it is difficult, inconsistent, and can lead to having access revoked. Sadly, this prevents many ethical use cases due to a heavy-handed approach, and embeds the bias of OpenAI’s team into the model directly. There are ways around this with non-ChatGPT models.
  • What to do in Minecraft: I tried so many TIMES to get interesting ideas. It just can’t do it.

Things ChatGPT is Okay At

It’s important to know where AI can be a useful tool, but must be used carefully due to mixed results, so I am also including a list of things that work sometimes.

  • Advice: Similar to the Socratic method, a back and forth conversation can help you with your thoughts. Just be aware that ChatGPT can give some really bad advice too. For example, I wanted to see what it had to say on turning hobbies into jobs, and it covered none of the downsides, only talking about it as a purely positive experience.
  • Game design: I have spent too much time telling ChatGPT to design games for me. It will generate an infinite rabbit-hole of buzzwords and feature ideas, but cannot understand the concepts of limited time or scope. If you try to follow its designs, you will never complete anything.
  • Summarizing: If given text as input directly, when it is short enough, a summary can be reliably generated. If asked to summarize something extremely popular before its data cut-off, the summary can be okay. The drop-off on this is insane. Try asking it about Animorphs for example, something talked about occasionally, and certainly known about, but not something it can summarize.

This draft sat around for about 2/3rd of a month nearly complete. I would like it to have even more information, but I would like it more for it to be public. Apologies if it was a little short for you, but hopefully someday I’ll make a better version.

Semi-Empirical Stellar Equations

I’ve spent a lot of time trying to answer certain questions in astronomy, where I just want a rough approximation for the purpose of a simulation, and don’t need an exact answer. These are some of the equations I’ve come up with.

Yerkes Classes’ T/Mv Relations

These equations are shown as functions where x is temperature in Kelvin, and f(x) is absolute magnitude (visual). Most are valid from 2400 K to a little bit past 30000 K, the exceptions are noted. For the hypergiants and white dwarfs, the range is within an elliptical region, of which these functions define the major axis; for all others, they are a trend line along a Yerkes classification.

  • Hypergiants (0):
    f(x) = -8.9
  • Supergiants (Ia):
    f(x) = -0.00135x3 + 0.0233x2 + 0.0187x – 7.349
  • Supergiants (Ib):
    f(x) = 0.00329x3 – 0.0962x2 + 0.829x – 7.209
  • Bright Giants (II):
    f(x) = 0.00557x3 – 0.166x2 + 1.505x – 6.816
  • Giants (III):
    f(x) = 0.0135x3 – 0.373x2 + 3.019x – 7.233
  • Subgiants (IV):
    f(x) = -0.151x2 + 2.216x – 5.128 (4450-100000 K only)
  • Dwarfs (V):
    f(x) = 0.00193x5 – 0.0615x4 + 0.742x3 – 4.257x2 + 12.439x – 12.996 (note: this one is the least accurate)
  • Subdwarfs (VI):
    f(x) = 0.131x3 – 3.275x2 + 27.576x – 71.7 (3050-6000 K only)
  • White Dwarfs (VII):
    f(x) = 0.489x + 10.01 (4450-30000 K only)

Simple Conversions

  • Absolute Magnitude (visual) → Luminosity (solar luminosities):
    Lsun = 100 * exp(-0.944 * Mv)
    Accurate between 0-10 Mv. Probably continues accuracy relatively well.
  • Main Sequence Luminosity / Mass Relation:
    Lstar / Lsun = (Mstar / Msun)3.5
    Lstar = ( 3.68 * ln(Mstar) – 0.244 )e
    (The second equation applies to all stars.)
  • Mass / Lifetime Relation:
    Tyears = ( 23.4 – 2.68 * ln(Mstar) )e
    (This applies to all stars.)

Spectral filters are used to help classify stars. Ultraviolet, Blue, Visual, Red, and Infrared. Objects are listed in different indexes:

  • UV for the hottest objects (stellar remnants, galaxies)
  • BV (the majority of stars)
  • RI for the coolest (LTY “stars” and below)

Equations I no longer recommend

Provided for completeness, in case they are found to be useful.

  • Temperature (K) → Absolute Magnitude (visual):
    Mv = 35.463 * exp(-0.000353 * T)
    Roughly accurate between 2000-50000 Kelvin. Probably doesn’t continue accuracy at hotter temperatures. Previously this was at the top of this post, now I am not sure why (perhaps the simplicity). The Yerkes’ classifications are a better system.
  • B-V index (x) → Temperature (K):
    T = -772.2x3 + 3152x2 – 6893x + 9500
    Sorta accurate between 0-2. (This equation I am least comfortable with, and don’t plan to use.)