Language: A Cluster Analysis of Reality

Cluster analysis is the process of quantitatively grouping data in such a way that observations in the same group are more similar to each other than to those in other groups. This image should clear it up.

Whenever you do a cluster analysis, you do it on a specific set of variables: for example, I could cluster a set of customers against the two variables of satisfaction and brand loyalty. In that analysis, I might identify four clusters: (loyalty:high, satisfaction:low), (loyalty:low, satisfaction:low), (loyalty:high, satisfaction:high), and (loyalty:low, satisfaction:high). I might then label these four clusters to identify their characteristics for easy reference: “supporters”, “alienated”, “fans” and “roamers”, respectively.

What does that have to do with language?

Let’s take a word, “human”. If I define “human” as “featherless biped”, I’m effectively doing three things. One, I’m clustering an n-dimensional “reality-space”, which contains all the things in the universe graphed according to their properties, against the two variables ‘feathered’ and ‘bipedal’. Two, I’m pointing to the cluster of things which are (feathered:false, bipedal:true). Three, I’m labeling that cluster “human”.

This, the Aristotelian definition of “human”, isn’t very specific. It’s only clustering reality-space on two variables, so it ends up including some things that shouldn’t actually belong in the cluster, like apes and plucked chickens. Still, it’s good enough for most practical purposes, and assuming there aren’t any apes or plucked chickens around, it’ll help you to identify humans as separate from other things, like houses, vases, sandwiches, cats, colors, and mathematical theorems.

If we wanted to be more specific with our “human” definition, we could add a few more dimensions to our cluster analysis—add a few more attributes to our definition—and remove those outliers. For example, we might define “human” as “featherless bipedal mammals with red blood and 23 pairs of chromosomes, who reproduce sexually and use syntactical combinatorial language”. Now, we’re clustering reality-space against seven dimensions, instead of just two, and we get a more accurate analysis.

Despite this, we really can’t create a complete list of all the things that most real categories have in common. Our generalizations are leaky in some way, around the edges: our analyses aren’t perfect. (This is absolutely the case with every other cluster analysis, too.) There are always observations at the edges that might be in any number of clusters. Take a look at the graph above in this post. Those blue points at the top left edge, should they really be blue, or red or green instead? Are there really three clusters, or would it be more useful to say there are two, or four, or seven?

We make these decisions when we define words, too. Deciding which cluster to place an observation happens all the time with colors: is it red or orange, blue or green? Splitting one cluster into many happens when we need to split a word in order to convey more specific meaning: for example, “person” trisects into “human”, “alien”, and “AI”. Maybe you could split the “person” cluster even further than that. On the other end, you combine two categories into one when sub-cluster distinctions don’t matter for a certain purpose. The base-level category “table” substitutes more specific terms like “dining table” and “kotatsu” when the specifics don’t matter.

You can do a cluster analysis objectively wrong. There is math, and if the math says you’re wrong, you’re wrong. If your WCSS is so high that you have a cluster that you can’t label more distinctly than “everything else”, or if it’s so low you’ve segregated your clusters beyond the point of usefulness, then you’ve done it wrong.

Many people think “you can define a word any way you like”, but this doesn’t make sense. Words are cluster analyses of reality-space, and if cluster analyses can be wrong, words can also be wrong.


This post is a summary of / is based on Eliezer Yudkowsky’s essay sequence, “A Human’s Guide to Words“.

I Want To Cure Mortality.

Do you want to live forever?

No? Okay, let me phrase it another way. Do you want to live tomorrow?

Most people answer yes to this second question, even if they said no to the first. (If you didn’t say yes to the second, that’s typically called suicidal ideation, and there are hotlines for that.)

This doesn’t quite make sense to me. If I came to you tomorrow, and I asked the same question, “Do you want to live tomorrow?”, you’d probably still say yes; likewise with the day after that, and the day after that, and the day after that. Under normal circumstances, you’ll probably keep saying yes to that question forever. So why don’t you want to live forever?

Maybe, you think that the question “do you want to live forever” implies “do you want to be completely incapable of dying, and also, do you want to be the only immortal person around”. Not being able to die, ever, could be kind of sucky, especially if you continued to age. (There was a Greek myth about that.) Further, being the only person among those you care about who can’t die would also suck, since you’d witness the inevitable end of every meaningful relationship you had.

But these sorts of arbitrary constraints are the realm of fiction. First, if a scientist invented immortality, there would be no justifiable reason that it wouldn’t be as available to those you care about as it would be to you. Second, it’s a heck of a lot easier to just stop people from aging than it is to altogether make a human completely impervious to anything which might be lethal.

Even after I’ve made sure we’re on the same page as to what exactly real immortality might look like, some people still aren’t convinced it would be a good idea. A decent amount of the arguments are some variant on “death gives meaning to life”.

To this, I’ll borrow Eliezer Yudkowsky’s allegory: if everybody got hit on the head with a truncheon once a week, soon enough people would start coming up with all sorts of benefits associated with it, like, it makes your head stronger, or it makes you appreciate the days you’re not getting hit with a truncheon. But if I took a given person who was not being hit on the head with a truncheon every week, and asked them if they’d like to start, for all these amazing benefits, I think they’d say no.

People make a virtue of necessity. They’d accept getting hit on the head with a truncheon once a week, just as they now accept the gradual process of becoming more and more unable to do things they enjoy, being in pain more often than not, and eventually ceasing to exist entirely. That doesn’t make it a good thing, it just demonstrates peoples’ capacity for cognitive dissonance.

These are the reasons I’ve made it my goal to cure mortality. The motivation is extremely similar to anyone’s motivation to cure any deadly disease. Senescence is a terminal illness, which I would like to cure.

It disrupts the natural order, but so does curing any other disease. Cholera was the natural order for thousands of years, but we’ve since decided it’s bad and nowadays nobody is considering the idea of mixing sewage with drinking water to bring it back. There were tons of diseases that were part of the natural order right up until we eradicated them. We don’t seem to have any trouble, as a society, deciding that cancer is bad. But death itself—the very thing we’re trying to prevent by curing all these diseases—is somehow not okay to attack directly.

Here’s the bottom line. I know for a fact I’m not the only one with this goal. Some of the people at MIRI come to mind, as well as João Pedro de Magalhães. I’d personally love to contribute to any of these causes. If you know someone, or are someone, who’s working towards this goal, I’d love to join you.

Intuition Is Adaptable

Or, why “X is counterintuitive” is just a way of saying “I haven’t seen any sufficiently intuitive explanations of X”.


As a kid, I was a huge science nerd. In particular, I loved Stephen Hawking’s work. He had a TV show at some point, which I watched whenever I got the chance. One of the first books I remember reading was “A Brief History of Time”.

The main reason I loved reading his writing is that it didn’t seem very complicated to me. Not in the way Richard Feynman’s work seems un-complicated—Feynman just seems like he’s dicking around all the time and happens to love dicking around in physics specifically, so much so that he got a Nobel Prize in it. (I’m aware this isn’t remotely what happened, but that’s the sense you get from reading his writing.) Instead, I found Stephen Hawking’s writing to be un-complicated in the way that I later found Eliezer Yudkowsky’s and Daniel Kahneman’s writings to be un-complicated: it just makes sense.

The best physical example of Stephen Hawking’s influence on kid-me is a sheet of sticky paper that’s still stuck to the wall of the library in my parents’ house, on which I wrote an explanation of the Many Worlds interpretation of quantum mechanics.

You’d think I was joking, but…

The easy way to explain this is just to say that I was a genius, or if you don’t feel like giving me that much credit, you can say that I was able to do a lot of book-learning because I had no social life. (This assessment isn’t wrong, by the way.)

But I’m actually going to give myself even less credit than that. I don’t think I’m a genius, and I also don’t think that the extra time I gained by skipping recess to read the encyclopedia (I already told you I was a nerd, get off my back) actually contributed in any meaningful way to my comprehension of “A Brief History of Time”. I don’t think it had anything to do with me at all; rather, it was almost entirely a property of the authors.

Which authors are particularly poignant to you has a decent amount to do with you: I know a girl who thinks that Jen Sincero’s book You Are A Badass is the best book on the planet; I read the first paragraph and immediately put it back down again. But all other things being equal, a human brain is a human brain, and an intuitive explanation is an intuitive explanation.

Assuming you’ve got some very basic algebra, Eliezer Yudkowsky’s An Intuitive Explanation of Bayes’s Theorem will almost certainly make sense to you. (Whether or not you care is a function of your preferences in reading material, separate from whether or not you could understand it if you did care.) Even if you suck at math. I know, because I suck at math. There are books in every discipline that make things make sense to people, that clarify cloudy issues, that provide intuitive explanations.

This is very good news for those of us who think we are just bad at something, and we have no way to get better. I grew up thinking I was bad at math, since I hate algebra and I hate trig and I’d always give up before I finished a problem and it was generally just the dullest drek on the planet. And yet, I have no difficulty calculating conditional probabilities with Bayes Theorem. All I had to do was read a good enough explanation. If you think you’re irreparably bad at something, don’t give up on it, just keep reading.

This is moderately poor news for those of us who are in the habit of writing explanations, though, because we can’t blame our readers’ lack of comprehension on the difficulty of the subject matter. It may partially be about the subject matter—neither all subjects nor all readers are created equal—but there is always some way that we could be better writers, better explainers, and thus have our explanations make more sense.

Personally, I choose to take this as a challenge. If no subject is imperceptibly counterintuitive, no subject is outside my domain, if I’m good enough. I just need to get stronger.

PDP 3

This week, I went even further in depth into doing statistical analyses in Python. I learned how to do logistic regressions and cluster analyses using k-means. I got a refresher on linear algebra, then used it to learn about the NumPy data type “ndarray”.

Logistic regressions are a bit complicated. The course I used explains it in a kind of strange way, which probably didn’t help. Fortunately, my mom knows a decent amount about statistical analyses (she used to be a researcher), so she was able to clear things up for me.

You do a logistic regression on a binary dependent variable. It ends up looking like a stretched-out S, either forwards or backwards. Data points are graphed on one of two lines, either y=0 or y=1. The regression line basically demonstrates a probability: how likely is it that you’ll pass an exam, given a certain number of study hours? How likely is it that you’ll get admitted to a college, given a certain SAT score? Practically, we care most about the tipping point, 50% probability, or y=0.5, and what values fall above and below that tipping point.

This can be slightly confusing since regression lines (or curves, for nonlinear regressions) usually predict values, but since there are only two possible values for a binary variable, the logistic regression line predicts a probability that a certain value will occur.

After I finished that, I moved on to K-means clustering, which is actually surprisingly easy. You randomly generate a number of centroids (generic term for the center of something, be it a line, polygon, cluster, etc.) corresponding to the number of clusters you want, and you assign points to centroids based on least Euclidean distance, move the centroids to the center of those new clusters, then assign the points to the centroids a second time.

Linear algebra is a little harder to understand, especially if your intuition isn’t visual like mine is. In essence, the basic object of linear algebra is a “tensor”, of which all other objects are types. A “scalar” is just an ordinary integer; a “vector” is a one-dimensional list of integers, and a “matrix” is a two-dimensional plane of integers, or a list of lists. These are tensors of type 0, 1, and 2, respectively. There are also tensors of type 3, which have no special name, as well as higher-order types.

I learned some basic linear algebra in school, but I figured it was a bit pointless. As it turns out though, linear algebra is incredibly useful for creating fast algorithms for multivariate algorithms, with many variables, many weights, and many constants. If you use standard integers (scalars) only, you’d need a formula like:
y1 + y2 + … + yk = (w1x1 + w2x2 + … + wkxk) + (b1 + b2 + … + bk).
But if you let all relevant variables be tensors, you can simplify that formula to:
y = wx + b

There are a handful of other awesome, useful ways to implement tensors. For example, image recognition. In order to represent an image as something the computer can do stuff with, we have to turn it into numbers. A type 3 tensor of the form 3xAxB, where AxB is the pixel dimension of the image in question, works perfectly. (Why use a third dimension of 3? Because images are commonly represented using the RGB, or Red/Green/Blue, color schema. In this, every color is represented with different values of R/G/B, between 0 and 255.)

Tensors, in the context of NumPy, which has a specific object type which is designed to handle them, are implemented using “ndarray”, or n-dimensional array. They’re not difficult to implement, and the notation is for once pretty straightforward. (It’s square brackets, similar to the mathematical notation.)

This should teach me to think of mathematical concepts as “pointless”. Computers think in math, so no matter how esoteric or silly the math seems, it’s part of how the computer thinks and I should probably learn it, for the same reasons I’ve devoted a lot of time to learning about all humans’ miscellaneous cognitive biases.

I’ve asked a handful of the statisticians I know if they wouldn’t mind providing some data for me to do some analyses of, since that would be a neat thing to do. But if I don’t do that, this coming week I’ll be learning in depth about AI, which my brain is already teeming with ideas for projects on. I’ve loved AI for a long time, and I’ve known how it works in theory for ages, but now I get to actually make one myself! I’m excited!

The Last Enemy That Shall Be Destroyed Is Death

His wand rose into the starting position for the Patronus Charm.
Harry thought of the stars, the image that had almost held off the Dementor even without a Patronus. Only this time, Harry added the missing ingredient, he’d never truly seen it but he’d seen the pictures and the video. The Earth, blazing blue and white with reflected sunlight as it hung in space, amid the black void and the brilliant points of light. It belonged there, within that image, because it was what gave everything else its meaning. The Earth was what made the stars significant, made them more than uncontrolled fusion reactions, because it was Earth that would someday colonize the galaxy, and fulfill the promise of the night sky.

Would they still be plagued by Dementors, the children’s children’s
children, the distant descendants of humankind as they strode from star to star? No. Of course not. The Dementors were only little nuisances, paling into nothingness in the light of that promise; not unkillable, not invincible, not even close. You had to put up with little nuisances, if you were one of the lucky and unlucky few to be born on Earth; on Ancient Earth, as it would be remembered someday. That too was part of what it meant to be alive, if you were one of the tiny handful of sentient beings born into the beginning of all things, before intelligent life had come fully into its power. That the much vaster future depended on what you did here, now, in the earliest days of dawn, when there was still so much darkness to be fought, and temporary nuisances like Dementors.

On the wand, Harry’s fingers moved into their starting positions; he
was ready, now, to think the right sort of warm and happy thought. And Harry’s eyes stared directly at that which lay beneath the tattered cloak, looked straight at that which had been named Dementor. The void, the emptiness, the hole in the universe, the absence of color and space, the open drain through which warmth poured out of the world. The fear it exuded stole away all happy thoughts, its closeness drained your power and strength, its kiss would destroy everything that you were.

I know you now, Harry thought as his wand twitched once, twice, thrice and four times, as his fingers slid exactly the right distances, I comprehend your nature, you symbolize Death, through some law of magic you are a shadow that Death casts into the world.
And Death is not something I will ever embrace.
It is only a childish thing, that the human species has not yet outgrown.

– Eliezer Yudkowsky, “Harry Potter and the Methods of Rationality“; Chapter 45, “Humanism, Part III”


I already talked about why HPMOR is my favorite book on the planet. (Which is why I tried very hard not to spoil anything terribly important with the above excerpt, while still having it convey the intended meaning. I would love it if you’d read it.) Now, here’s an oil painting inspired by it. The title of this post, and of the painting itself, are inspired by a thematically similar section later in the book.

I had a bit of trouble finding decent reference pictures for ‘earth from space’, funnily enough. It’s difficult to distinguish high-quality photographs from digital art. The references (yes, plural) I ended up settling on were taken from NASA and the ISS. Even so, maybe it doesn’t matter, since I ended up using a pretty impressionistic style anyway.

I’ve actually never drawn space or planets before. I would absolutely not trust myself to be non-detail-focused enough to do this in markers, hence the painting. (Also, since this is my warm happy thought as well as Harry’s, I want to hang this on my wall, and oil paintings are better for that.) It was an interesting experiment to try and loosen up enough to draw something from such a high level, especially when my brain was busy making me think thoughts like “okay just remember that if you move your brush in slightly the wrong way you’ve erased the entire state of Texas”. As you zoom out more and more, you have to suggest more and more stuff with subtle brush techniques, and when the things you’re suggesting are on the order of entire states or countries… it gets moderately stressful.

Still, I think it came out alright. I’m pretty happy with the color of the ocean, and the general texture of the clouds. The space was both the easiest and the most fun part, starting with a black gesso and painting over it with blues and purples. I may touch this up later, but it’s good for now.

As a final note: Unlike the rest of my paintings on this blog, this one is not for sale. I’m happy to make a copy if you’d like one (which includes making modified versions, ex., with the U.S.S. Enterprise in the foreground); for details on making commissions, visit my Commission Me page.

Book Review: Methods of Rationality

It’s high time I did a real review of my favorite book in the universe. I read it for the first time at the age of 13, and it triggered an utter obsession with cognitive science, rationality, and artificial intelligence that has not disappeared to this day. (It has, however, become more mature: I no longer write shitty romantic poetry about cognitive science.)

I will attempt to describe this masterpiece of literature; more than once since I will absolutely fail several times.

Harry Potter and the Methods of Rationality is a 122-chapter parallel-universe Harry Potter fanfic in which Lily Evans married an Oxford professor, Michael Verres, and Harry was adopted and raised in a loving home filled to the brim with books. It is written by one Eliezer Yudkowsky, co-founder of the Machine Intelligence Research Institute, who writes frequently for the blog Less Wrong, which I’ve cited here before, and is best known for popularizing the idea of Friendly AI.

Harry Potter and the Methods of Rationality is a book about an eleven-year-old who knows both magic and calculus and wants to take over the world using Science so he can get more books.

Harry Potter and the Methods of Rationality is a book that successfully taught a 13-year-old girl—who wasn’t and still isn’t a genius—the underlying fundamentals of cognitive psychology, quantum physics, artificial intelligence, and Bayesian probability theory. If you read it, you will also learn these things, without ever realizing you have learned them. It will simply make sense, in a way that makes you wonder how you ever didn’t understand it.

While reading Harry Potter and the Methods of Rationality, you will frequently have absolutely no idea whether Harry is the villain or the hero. You will frequently have absolutely no idea whether Draco Malfoy is the villain or the hero, either. This goes for most of the characters, with the exception of Hermione and McGonagall. It does not exclude Voldemort.

This book will make you laugh, cry, learn, and question human existence. It will make you very aware of the sound of snapping fingers, and the shape of the night sky. It will show you the best and worst of humanity, and make both understandable. If you let it, it will teach you some of the most valuable life lessons you might ever learn.

Find the completed book at hpmor.com. You can read it in however much time you like, but given the length, it takes a fast reader about three or four days to binge straight through, so you probably can’t read it any faster than that. In any case, when you do finish it, please leave a comment telling me what you thought! And of course, give the author some feedback and leave reviews on however many chapters you like.

As an end note, in case you might not have believed me, here is only one of the shitty romantic poems I wrote about rationality. Please be nice to the author, she was a little girl who fell in love with science, not a poet, and she was doing her best.

Be skeptical, not cynical;
be open, but not gullible.
Be curious, not clever;
no rationalization, ever.

Accept the truth for what it is;
and look for contradictions
in all arguments, yours included;
you’re more confused by fiction.

A word is just a label
before you know the referent;
a lie gets told a long time,
if someone’s to protect it.

Certain kinds of people
truth they wrongly construe,
but they’ll do it in the name of
who they think is watching you.

Humans tend to think
they could predict things in advance
but that’s some hindsight bias
when really there’s low chance.

Don’t explain all this all at once,
mind inferential distance,
plus the illusion of transparency,
and all peoples’ heuristics.

People don’t like weird ideas,
or saying they don’t know;
but even with our biases,
There’s a long way we shall go.

PDP 2

This week, I rehashed all the basics of Python. Since I haven’t studied it at all in ten years, this was a very useful refresher. (Basically, it seems to me that Python is essentially Java structure with something like JavaScript syntax. This is a huge oversimplification, but hey, it’s an extremely high-level language that I’m using it in an object-oriented way for this purpose. There are demonstrable similarities.)

The course I’m currently using doesn’t go over Python in any great detail, so if you’d like to supplement the Python they teach you, or you’d like to add to your knowledge of the language (since this course teaches only a very limited scope of Python), I highly recommend Learn Python The Hard Way. Python was my first programming language ever, and this was the course I used. It gives you a solid grasp of not just Python but how programming works in general.

In addition to the general Python refresher, I learned about all the libraries that I’ll need to use it to do data science: namely, NumPy, Pandas, SciPy, StatsModels API, MatPlotLib, Seaborn, and SciKitLearn. In combination, these libraries add methods that can import data from a variety of sources including Excel spreadsheets, conveniently calculate and tabulate relevant statistical data, do a variety of regressions and cluster analyses, and display elegant and understandable graphs.

This week, I learned how to do a simple linear regression (least squares). Next week, I’ll learn how to do multiple regressions and cluster analyses! And after that, the real fun begins with deep learning and AI. I’m looking forward to it!

In the future, expect me to start creating some little projects. I can’t do much with what I’ve learned this week, but by next week, I’ll absolutely have something at least moderately interesting, and I’ll absolutely do a nice write-up for it.

Japanese Journal 5: Learning Kana

Japanese writing is a little bit intimidating. Even if we don’t consider kanji, there are two other writing systems to worry about, each with around fifty letters each. While these systems are phonetic (letters correspond directly to sounds), that’s still a hundred letters! (Well, 92.) How do you memorize them all?

When I first started learning Japanese, this was the bane of my existence. I knew I couldn’t do anything if I didn’t learn the alphabet, but unlike the Greek alphabet (which I’d learned before), Japanese “kana” come in blocks of two sounds, a consonant and a vowel. Further, though this is nice for reading comprehension after you already know the kana, the set of kana that begin or end with the same sound don’t similar at all (sa looks like this さ, se looks like this せ, and so looks like this そ). I had a huge problem learning nearly a hundred completely unrelated characters that had no direct connection to the individual phonemes.

One of the things that I tried when I was first getting started with learning the alphabets was cutting a hundred index cards in half and put a letter on each. Yet, a hundred Japanese-letter-to-romaji-translation cards later, I hadn’t really learned anything, and reviewing those cards just frustrated me.

It turns out, though, that the answer was incredibly simple, if perhaps slightly daunting. Here it is. Read. Even if you can’t understand anything. Read the Japanese version of the “this plastic bag is a choking hazard” warning on some packaging. Don’t worry about the kanji, just read the kana. Randomly use Japanese Wikipedia (fyi, the Japanese word for Japanese is 日本語, in case you’re wondering which language option to choose). When you look up kanji, look them up using a dictionary like Rikai, which gives you the pronunciation using kana. The optimal way to memorize anything is to use it.

When you first start, keep a kana chart (like the one at the beginning of this article) on hand and reference it for kana you don’t know yet. After a while, you’ll naturally be able to put the chart away and just read stuff. Even after you’re competent enough to not need the chart, keep reading: you’ll become so fluent with the kana that it will be easier to read them than to read romaji. (I have!)

I think the reason for this was that when I reviewed flashcards, every letter I didn’t know was a failure. However, when learning by reading, every letter I did know was a success. For me at least, it was a mindset shift. (Note: of necessity, I write from my own experience, and in my experience, reading works better than flashcards. However, if you do learn well with flashcards, I recommend using them… in addition to reading. You’ll want to eventually be able to read fluently anyways, right?)

All of that being said, here’s the most surefire way to make sure you don’t memorize kana: don’t use them. Learn Japanese using romaji. (Not only will this mess up your ability to learn kana, it will mess up your pronunciation.) Focus on grammar and vocabulary before learning the alphabet. Never be able to read real Japanese. There are many methods of learning Japanese, but I can inform you that this is the worst one.

(I wouldn’t say this so confidently if I were the only one who believed it. Here’s an article from a Japanese learning website, and a source from reddit, and one that tells you how to use romaji as a learning tool before dropping it.)

Learning three alphabets can be pretty intimidating, but with a bit of time and dedication, and a lot of making sure you don’t beat yourself up when you don’t know stuff, you can get there.

How To Bake Industrially

Got a big baking spree coming up? Be it a Christmas dinner, a local bake sale, or anything else, if you need to do a lot of baking in a short amount of time, this post will tell you how to do it. Even if you have a more moderate amount of baking to do, following these tips will make the entire process that much more effortless, so you can make perfect, delicious cookies every single time.

Here are my baking credentials. First, I worked in a restaurant for three years, and during that time, I baked more pies, cakes, and cookies than most people will probably ever bake in their lives. Furthermore, every year, my family bakes an absolutely absurd number of cookies for Christmas. I’m taking 12+ batches, each of which makes multiple dozen cookies. We give bags of assorted cookies to coaches, teachers, and instructors of all varieties, then have enough left over to feed our household of seven for over a week.

To start you off, here’s your minimally adequate amount of equipment for any industrial baking spree. You can always have more than this, but here’s what you need to get started.

  • Electric mixer
  • Two sets of beaters for it, optionally also a whisk but you can whisk almost anything except meringue by hand without much difficulty
  • Four cookie sheets: at any given time, there should be two in the oven and two out of the oven being prepped with more cookies
  • At least two of each measuring implement (cups, spoons, etc.)
  • Sifter
  • Large bowls, a few of which are microwaveable
  • Other miscellaneous kitchen necessities: plastic and rubber spatulas, wooden spoons, oven mitts, cookie sheets, etc etc.

For any large baking spree, preparation is of the utmost importance. You need to make sure you have enough of all the ingredients, preferably on only one grocery store run. In order to do this preparation efficiently, run through every recipe you’re making (being sure to double, triple, quadruple, etc. the recipe as you’re planning on making it), note down every ingredient in its correct amount, and create a comprehensive tally. Then, take that list and check it against what you have in your house. Making conservative estimates, subtract the amount you have from the amount you need, and note down the delta. Create a shopping list from all those deltas for the ingredients, then shop from that list.

Great! You’ve prepared your ingredients, now prepare yourself.

First, make sure you have the right attire. You’ll want a short-sleeved shirt, a decently sized apron, and close-toed shoes. Here’s why, in order. You don’t want batter on your sleeves and you don’t want sleeves in your batter. Flour always makes a gigantic mess and there’s nothing you can do about that, also, it’s more convenient to have a place to dust off your hands. You will absolutely spill something or other on the floor and you don’t want to have the impulse to wipe off your feet, thus dirtying your hands.

After you’re wearing the right stuff and you’ve washed your hands, consider putting on some kitchen gloves. If you’re making multiple hands-on recipes (that’s any recipe that requires you mould dough with your hands), it’s way easier to change pairs of gloves than to wash your hands thoroughly.

Finally, set out all the ingredients for your first recipe. Organize primarily by the order the ingredients are used in the recipe and by what tools are required to complete that portion of the recipe. For example, all the ingredients which need to be sifted together should sit together next to the sifter itself; all the ingredients which need to be directly mixed together using the electric mixer should sit next to the mixer and the outlet it plugs into, and all the ingredients for the icing should sit off to the side with the piping bags.

I’m not being so anal about all of this for no reason. You’re going to run out of both time and counter space really fast, so it’s important to be hyper-efficient with both while you still have the mental bandwidth.

We’re ready to start baking now! Here are a few tips for preparing your recipe, before it goes in the oven.

When I worked as a prep cook in a restaurant, I had a tiny room—about the size of a home kitchen—to prepare nearly every dish that went through the restaurant. This is what I had. A counter along two walls with a sink and a gigantic electric mixer, a shelf containing dishes and measuring implements, and two 1.5*2.5 foot tables. I got really good at space efficiency. The biggest thing I learned, in addition to what I said earlier about grouping ingredients together, was that no matter how many recipes you have going at the same time, whether it’s one or ten, organization matters. If you’re not using an ingredient, put it away. I don’t care if you’re getting it right back out in an hour for your next set of recipes. Put it away.

Make sure you follow the recipe exactly. If it says to put the eggs in one at a time and mix well after each addition, you had better do that. The recipe isn’t telling you to do it for no reason. Note that I’m not trying to say you can’t experiment yourself and change the recipe—actually, you should absolutely do that, because what works for everyone else might not work for you, and further, the person who made the recipe might have some kind of an agenda (the recipe for chocolate chip cookies that you find on bags of Nestle chocolate chips requires far more chocolate chips than you should justifiably put in, because that’s what they’re trying to sell you). I’m trying to say that your reason for changing the recipe should be something better than “eh, it can’t be that important”. I make the best chocolate chip cookies anyone I know has ever had and the only reason is because I follow the damn recipe.

To conserve measuring implements, measure out dry ingredients before wet ones. Measure baking soda before vanilla extract, flour before milk, etc. As a special rule, if you’re measuring molasses for your recipe (ex. if you’re making ginger snaps), swish a bit of vegetable oil around in the cup measure before you put the molasses in. It will make the molasses stick to the cup measure less.

Here’s my final prep tip: take a sizable swath of counter space and lay out some parchment paper. If you’re like most normal people, you have nowhere near enough counter space for even a few dozen cookies on cookie sheets. Further, you probably don’t have enough available cookie sheets for that. However, if you snug the cookies up next to each other once they’re cool enough to scoop off the cookie sheet, you can fit a ton more in the same amount of space, and you don’t use up your cookie sheets.

While the first batch of cookies are in the oven, prep the next two sheets. I promise, the bake time is long enough for you to be able to do this. I’ve made two sheets of cookies in less than six minutes before. See, nobody cares what the cookies look like so long as they’re tasty, so you can be fast. Your literal only constraint is to make sure all the cookies in the oven at any given time are roughly uniform in size.

When you take a batch out of the oven, cool them on the racks for only a few minutes, then scoop them off and stick em on that parchment paper you laid out earlier. They’ll cool the rest of the way there, and you’ll have the trays freed back up to put more cookies on.

The baking is always the hectic part, since the perpetual cycle of bake-cool-transfer-prepare takes up every available moment. If you’ve done the previous organizational and preparation steps correctly, though, you can minimize the hecticness.

That’s it! My comprehensive list of steps for industrial baking. May your endeavors be successful, and your cookies be sweet. Good luck!

PDP 1

(Professional Development Project. Week 1: first update.)

Over the past year, as I finished up my AS in Computer Science, as I’ve been a participant in the Praxis program, I spent a good deal of time gaining entry-level skills in a variety of technological areas: SQL, systems analysis, web development skills including HTML, CSS, and JavaScript, etc. After all that, I wanted to pick something to focus on for continuing professional development.

As I considered what I should do next, I realized that while I have some decent skills in web development, I have a stronger aptitude in analytical areas. Further, I really enjoy solving problems and doing analyses, so I decided that I would go ahead and start doing that.

Ultimately, I decided on a data science course from Udemy, which I’ve now been working on for a week.

So far, I’ve completed about 130 out of 470 total segments (each includes a lecture and an accompanying quiz). This was basically the first two major sections: an overview including definitions of industry jargon, and an in-depth section on descriptive and inferential statistics. Given that I just finished a class in statistics as one of the final classes for my degree, I was able to skim through the second section.

Besides the refresher on statistics, what I basically learned this week was a lot of jargon and technical terms. I learned the distinctions between analysis and analytics; between business analysis, systems analysis, and data analysis; between neural networks and deep learning.

From here on out, you can expect weekly updates every Sunday, detailing what I’ve done that week. When it becomes applicable, I’ll be doing some coding projects as demonstrations of what I’ve learned by that point. I’ll post those here too, giving each project its own write-up, and I’ll then link back to those project posts at the end of each week in the wrap-up post.