Lots And Lots Of Econ Notes. This Class Is Making Me Reflect A Lot On Political Policies And My Own Leanings…

Hello, lovelies! This week, I talk about how I got a 2300+ on the SAT without any outside tutoring or prep classes. Yes, it’s possible, and I tell you how to do it in the video.

I also put together a masterpost of resources below. Even if you aren’t self-studying, a lot of these things might be helpful:

PREP BOOKS

Official College Board SAT Study Guide (The Blue Book)

Direct Hits Vocabulary (Volume 1) // Direct Hits Vocabulary (Volume 2) — What makes these books stand out from other SAT vocab books is the use of pop culture references to explain definitions. For example, the first word in Volume 1, ambivalent, is given the sentence: “In The Avengers, Tony Stark, Steve Rogers, Bruce Banner, and Thor are initially ambivalent about joining S.H.I.E.L.D.’s Avengers Initiative.”

Barrons SAT 2400 — Fabulous book, helpful strategies. I didn’t read the whole thing or do all the practice problems; I only used it for extra help on the sections I struggled with.

Grubers SAT 2400 — Didn’t personally use it myself, but it was recommended by a lot of my friends.

CRITICAL READING

→ Non-SAT Critical Reading Advice

→ My favorite reading sources:

The Atlantic — mix of interesting articles

Variety — pop culture focus, but with more cultured language

New Yorker — very cultured, good place to pick up vocabulary

New York Times — classic SAT reading material

Boston Globe — I have a soft spot in my heart for their entertainment and style sections

National Geographic — exactly the sort of passages you’ll find on the SAT

→ Vocab Flashcards (mentioned in video)

WRITING

→ Top Writing Errors

→ Top Grammar Rules

MATHEMATICS

→ Khan Academy

heLLO!! this post includes really cool stuff just like u (;

*beauty guru voice* “lets just dive right in” 

time management

Hex Clock: This is literally the COOLEST THING EVER. im not good at describing things either but giving this one a shot. so basically its 21.56PM right now. its shows #215602 (hour, minute, second) and the background is that hex codes coLOR. cool am i right ladies ;)

Timer-Tab: This is extremely useful! It has a countdown thingy and when it ends it pops up a video from YouTube, guess what it is. Guess it. ITS A ANTIQUE CLOCK ALARM!! You can change it too! And the background! Aaand it has a stopwatch too! Quick hack: put fullcreen after the hashtag on the url. ;)

note taking

ZenPen: It gives you two background options and unlimited options to edit your text.

Escriba: Super simple and super easy to use.

educational

UReddit: UReddit hosts courses and lessons created by the public and can help users to learn languages, scientific principles or even PHP programming.

edX: “Best Courses. Top Institutions. Learn anytime, anywhere.” You have to check this site out.

money saving

Mint: Free to use, Mint can help you organize your finances and track your spending

Wise Bread: WiseBread is dedicated to living well on a tight budget – whether you’re a student or just trying to get more for your money. It offers advice on everything from debt management to growing your own fruit and vegetables.

RetailMeNot(us only): I feel your pain if you’re not in the USA.But I don’t if you’re in the UK because there’s a UK version of it;

MyVouchers(uk only): They both offer discounts for retail stores & restaurants.

random

Sleepyti.me: This site tells you the best times to go to bed if you have to be up at a certain hour

KeepMeOut!: Gives you warnings when you’re on a website(social media) when you’re meant to be studying.

MentalFloss: Hits you with that random fact. Did you know?  Michael Jackson wanted to do a Harry Potter musical. J.K. Rowling said no. BOOM!

ToDoist: Your classic to do list. But more clean and on a screen.

other masterposts:

youtube channels about science

online stationery shops

resources: x,x

hi chelsea!! i'm an undergrad student right now, considering a career in academia. my adviser and all my professors tell me i have a lot of potential and i love the idea of spending my life teaching and doing research, but i've read so many horror stories about people trying to find & keep jobs and eventually leaving academia. i was wondering if you had any insight or advice, maybe even places where i could find a more positive & encouraging (but still realistic) perspective. thank u!!

i’m not sure i’m in a great place to answer this for you! the realistic picture is just… not encouraging. finding someone who’ll say “of course you’re going to get a job!” might make you feel better temporarily, but that person is lying to you, and the lie is going to come back around and hurt worse in five years or so. 

in other words, the horror stories are ubiquitous because the experience is ubiquitous (and tbh, though quit lit has def blossomed over the last few years, there’s still a far greater number of people leaving academia than are represented in those accounts–many people see leaving as “washing out” or admitting defeat, and don’t talk about their decisions to go). 

this is not to say that you shouldn’t go into academia. but as i tell my own mentees, you should not go into academia with the expectation of getting a job at the end. i went into grad school because i liked doing the work, and because i did some self-assessment and concluded that even if i didn’t leave six years later with a tenure-track teaching job, i wouldn’t regret taking the time & doing the work to get the doctorate. then i sat down and had a little chat with myself two-ish years ago and decided that yes, i was going to Go For It, which has meant avoiding quit lit for my own mental health and focusing on the positives (your professors! those are the extant examples of people who got the kind of research & teaching job you want!). asking your advisors about this directly is a great place to start; you can also read karen kelsky’s the professor is in, which i’ve mentioned here a few times, for a fairly realistic description of what the market is like at the moment. 

2 am study sessions that become 1 am study sessions >>>>

Where do you recommend getting textbooks from? (renting, buying, online etc)

well textbook companies are evil and sometimes you have no choice but to buy a textbook new, but for other times where that’s not the case i’d recommend you check out slugbooks! ^_^ they compare a bunch of different sites selling the textbook you need so you can find it at the cheapest price :) it’s like the kayak of academia lol!

When algorithms surprise us

Machine learning algorithms are not like other computer programs. In the usual sort of programming, a human programmer tells the computer exactly what to do. In machine learning, the human programmer merely gives the algorithm the problem to be solved, and through trial-and-error the algorithm has to figure out how to solve it.

This often works really well - machine learning algorithms are widely used for facial recognition, language translation, financial modeling, image recognition, and ad delivery. If you’ve been online today, you’ve probably interacted with a machine learning algorithm.

But it doesn’t always work well. Sometimes the programmer will think the algorithm is doing really well, only to look closer and discover it’s solved an entirely different problem from the one the programmer intended. For example, I looked earlier at an image recognition algorithm that was supposed to recognize sheep but learned to recognize grass instead, and kept labeling empty green fields as containing sheep.

When machine learning algorithms solve problems in unexpected ways, programmers find them, okay yes, annoying sometimes, but often purely delightful.

So delightful, in fact, that in 2018 a group of researchers wrote a fascinating paper that collected dozens of anecdotes that “elicited surprise and wonder from the researchers studying them”. The paper is well worth reading, as are the original references, but here are several of my favorite examples.

Bending the rules to win

First, there’s a long tradition of using simulated creatures to study how different forms of locomotion might have evolved, or to come up with new ways for robots to walk.

Why walk when you can flop? In one example, a simulated robot was supposed to evolve to travel as quickly as possible. But rather than evolve legs, it simply assembled itself into a tall tower, then fell over. Some of these robots even learned to turn their falling motion into a somersault, adding extra distance.

[Image: Robot is simply a tower that falls over.]

Why jump when you can can-can? Another set of simulated robots were supposed to evolve into a form that could jump. But the programmer had originally defined jumping height as the height of the tallest block so - once again - the robots evolved to be very tall. The programmer tried to solve this by defining jumping height as the height of the block that was originally the *lowest*. In response, the robot developed a long skinny leg that it could kick high into the air in a sort of robot can-can. 

[Image: Tall robot flinging a leg into the air instead of jumping]

Hacking the Matrix for superpowers

Potential energy is not the only energy source these simulated robots learned to exploit. It turns out that, like in real life, if an energy source is available, something will evolve to use it.

Floating-point rounding errors as an energy source: In one simulation, robots learned that small rounding errors in the math that calculated forces meant that they got a tiny bit of extra energy with motion. They learned to twitch rapidly, generating lots of free energy that they could harness. The programmer noticed the problem when the robots started swimming extraordinarily fast.

Harvesting energy from crashing into the floor: Another simulation had some problems with its collision detection math that robots learned to use. If they managed to glitch themselves into the floor (they first learned to manipulate time to make this possible), the collision detection would realize they weren’t supposed to be in the floor and would shoot them upward. The robots learned to vibrate rapidly against the floor, colliding repeatedly with it to generate extra energy.

[Image: robot moving by vibrating into the floor]

Clap to fly: In another simulation, jumping bots learned to harness a different collision-detection bug that would propel them high into the air every time they crashed two of their own body parts together. Commercial flight would look a lot different if this worked in real life.

Discovering secret moves: Computer game-playing algorithms are really good at discovering the kind of Matrix glitches that humans usually learn to exploit for speed-running. An algorithm playing the old Atari game Q*bert discovered a previously-unknown bug where it could perform a very specific series of moves at the end of one level and instead of moving to the next level, all the platforms would begin blinking rapidly and the player would start accumulating huge numbers of points. 

A Doom-playing algorithm also figured out a special combination of movements that would stop enemies from firing fireballs - but it only works in the algorithm’s hallucinated dream-version of Doom. Delightfully, you can play the dream-version here

[Image: Q*bert player is accumulating a suspicious number of points, considering that it’s not doing much of anything]

Shooting the moon: In one of the more chilling examples, there was an algorithm that was supposed to figure out how to apply a minimum force to a plane landing on an aircraft carrier. Instead, it discovered that if it applied a *huge* force, it would overflow the program’s memory and would register instead as a very *small* force. The pilot would die but, hey, perfect score.

Destructive problem-solving

Something as apparently benign as a list-sorting algorithm could also solve problems in rather innocently sinister ways.

Well, it’s not unsorted: For example, there was an algorithm that was supposed to sort a list of numbers. Instead, it learned to delete the list, so that it was no longer technically unsorted.

Solving the Kobayashi Maru test: Another algorithm was supposed to minimize the difference between its own answers and the correct answers. It found where the answers were stored and deleted them, so it would get a perfect score.

How to win at tic-tac-toe: In another beautiful example, in 1997 some programmers built algorithms that could play tic-tac-toe remotely against each other on an infinitely large board. One programmer, rather than designing their algorithm’s strategy, let it evolve its own approach. Surprisingly, the algorithm suddenly began winning all its games. It turned out that the algorithm’s strategy was to place its move very, very far away, so that when its opponent’s computer tried to simulate the new greatly-expanded board, the huge gameboard would cause it to run out of memory and crash, forfeiting the game.

In conclusion

When machine learning solves problems, it can come up with solutions that range from clever to downright uncanny. 

Biological evolution works this way, too - as any biologist will tell you, living organisms find the strangest solutions to problems, and the strangest energy sources to exploit. Sometimes I think the surest sign that we’re not living in a computer simulation is that if we were, some microbe would have learned to exploit its flaws.

So as programmers we have to be very very careful that our algorithms are solving the problems that we meant for them to solve, not exploiting shortcuts. If there’s another, easier route toward solving a given problem, machine learning will likely find it. 

Fortunately for us, “kill all humans” is really really hard. If “bake an unbelievably delicious cake” also solves the problem and is easier than “kill all humans”, then machine learning will go with cake.

Mailing list plug

If you enter your email, there will be cake!

my july spread! 7 days in, but still good :) 

Rainy days and lists.

the thesis progress journal

it’s all based on louise desalvo’s concept of a process journal for writers, from her book ‘the art of slow writing’ which i read way back in 2014 but has stayed with me all this time. she based that concept on sue grafton’s journal, which “stands as a record of the conversation she has with herself about the work in progress.” desalvo talks about her own process journal : “to plan a project, list books i want to read, list subjects i want to write about, capture insight about my work in progress, discuss my relationship to my work (what’s working and what’s not, whether i need to make changes to my writing schedule, how i’m feeling about the work)” 

her view of the concept is so interesting and can easily be applied to grad school : “keeping a process journal helps us understand that our writing is important work. we value it enough to plan, reflect, and evaluate our work. a process journal is an invaluable record of our work patterns, our feelings about our work, our responses to ourselves as writers, and our strategies for dealing with difficulties and challenges.”

she says, and i quote : “our progress journals are where we engage in the nonjudgmental, reflective witnessing of our work. here, we work at defining ourselves as active, engaged, responsible, patient writers.” and like ???? yes, go off louise!

every week i make an entry with my three to five priorities. since i currently still have seminars, my entire week cannot be dedicated to my thesis, so these priorities allow me to really focus on specific things. they can be bigger or smaller depending on the amount of time i have to work on my thesis.

every day i work on my thesis, i make an entry. i try to answer two questions : “what did i do that day to make progress on my thesis?” as well as “how am i feeling & what i can do to feel better?” i also choose two to five specific tasks to achieve that day and write about the progress. for example, if my task is reading an article, i’ll write it down, check the box once i do it and write a summary of the “experience” (how was the article, was it useful for my research, should i read more of that author’s work, etc.) that way, i can look back at previous tasks, know what happened and learn from it.

i also use the journal almost like a bullet journal (the OG kind) with ongoing lists of important things. of course, there are some to do lists here and there (even though i prefer having my comprehensive task list on todoist), but it’s mostly things like

names of people who have helped me so i can thank them in my thesis

call numbers of books to borrow or archives to consult

research hypotheses 

things to look for in the archives i consult

questions to ask my professor/advisor/archivist/etc.

issues that need to be fixed in my thesis

books/articles to read

additional things to research

i also use it as a regular notebooks for all things thesis. one of my seminars this semester is a methodology course, so i take notes in my journal as reference. i also sometimes will write some reading notes if i don’t have my computer on me, such as key quotes or arguments. also, all of my notes from meetings/calls/emails with my advisor are put in the journal, as well as a any pertinent meeting notes (with an archivist, fellow student, my mom, etc.) lastly, sometimes it just becomes a catch all for brainstorm sessions and random thoughts.

for me, this thesis progress journal is the best way to take a step back from the actual work and reflect on what i’m doing, good or bad, and what i can do to make things better, but most importantly, it allows me to understand my progress.

Introduction to LaTeX!

No, not the rubbery plasticy stuff

LaTeX (pronounced “lay-tech”) is a free document-formatting system commonly used in STEM fields. This post is going to explain why it is really useful and where you can start!

So if you’re a STEM student, you’ve probably had to write a whole lot of lab reports and know how annoying it is to either figure out Word’s equation editor or screenshot a picture of whatever equation you’re trying to include in your report. Not to mention how awful Word is at formatting pictures, holding them in place and keeping things consistent. LaTeX solves most of those problems for you.

It works with some simple commands and packages that allow you to create journal-style articles, lab reports, and all sorts of other document types. You can include all sorts of mathematical and scientific symbols and equations and LaTeX formats them correctly for you. It might seem daunting at first, but most of the commands are very intuitive and you have a lot of easy customisation and consistent formatting. It can even do referencing for you. It has a lot of other cool things like generating a table of contents, automatically numbering your tables and figures, that contribute to producing a professional-looking scientific document.

All in all, the learning curve is very shallow, and the skill payoff is worth the small time investment it takes to learn. LaTeX typesetting is a very valuable skill to have in STEM.

Where to start:

There are dozens of downloadable LaTeX text editors, but I prefer to use an online one called Overleaf. It has several templates available for you to start from, provides shareable links, renders your document as you work, and, because it’s online, you can upload all your files to the server and work from anywhere.

I found the first chapter of this guice very helpful when I began using LaTeX  [x].

Some random tips I picked up so far:

1) Most problems/errors are easy to solve with a quick google search. Because LaTeX is so widely used, there are a LOT of stack exchange solutions to small problems. 

2) Manually creating LaTeX tables is a nightmare. It’s usually easiest to make them first in Excel, then copy-and-paste into an online LaTeX table generator.

3) Lots of journal articles have a pre-formatted bibtex citation linked somewhere. Using that citation works really well.

4) I like to open a text file, copy-and-paste all my bibtex formatted citations into it and convert it into a .bib file by renaming.

Some other useful links:

~LaTeX table generator [x]

~LaTeX reference generator [x]

~Find the LaTeX command for any symbol that you draw [x]

~List of mathematical symbols for LaTeX [x]

~LaTeX Stack Exchange [x]

I hope you find this helpful! If you have any questions, don’t hesitate to send me an ask or a message. Check out my study instagram if you’re keen on seeing some of my studyspo. Happy studying!

xx Munira