[ 02.05.16 • 4/100 DAYS OF PRODUCTIVITY ] 4 Days Till My Exams Yikes D: My First Exam Isn’t Even

A Note on “Weeder Courses”

Is your first year gen-ed (general education) course really hard for absolutely no reason? Or perhaps it’s an early course in your major that’s required for the rest of the degree. Maybe the homework is really hard to get through or the exams are just brutal. You might be in a “weeder course.”

Generally weeder courses are introductory level; the STEM field gen-eds are notorious for this. The thought process from an administrative level is to make these courses very difficult and challenging to vet out students who can’t hack it. They do it with the intro level courses to serve as a warning for students who might want to major in something, but aren’t ready for how rigorous the degree actually is. 

Now I have my own thoughts on that mindset but what I want to stress that these courses are designed to be difficult. You’re not making it up in your mind; they are designed to feel like hell. 

Personal anecdote: I got my undergrad degree in literal rocket science from a “name” university. In my first year I failed physics I, the very course that is the basis for the rest of your physics education. I nearly failed it again the second time I took it, passing by the skin of my teeth. Despite the material being more difficult, I found my calculus 4 course easier than my calculus 1 course.

And that was because, as I found out from an upperclassman years later, those intro courses were designed as weeder courses. They taught the material yes, but their primary function was to act as a buffer to students who the administration see as lacking the discipline to follow through on a major in that field.

My advice? If it is a field or major you love, do not let your performance in these classes stop you.

I cannot stress this enough: if you love the field and the major and the subject, don’t let terribly designed classes stop you. I worked as a peer advisor my senior year and I had these brilliant first and second year students come up to me and tell me that they were struggling in an intro level course, wondering if they should drop out of a major they genuinely loved because they felt like they weren’t smart enough. Every single one of them was smart enough. 

You are smart enough. You can and will get through it. 

Some advice of a more practical nature under the cut:

Keep reading

A User’s Guide To The Brain

More about the human brain and behaviour on @tobeagenius

yesterday I realised that I barely know anything in maths so I’m having to sort myself out - trying to go over 2+ topics a day, making what I like to call “emergency notes”, so far so good but #prayforzoë

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!

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

Read JY Yang’s “Waiting on a Bright Moon”, a story of rebellion among far-flung colonies united by song magic.

Xin is an ansible, using her song magic to connect the originworld of the Imperial Authority and its far-flung colonies— a role that is forced upon magically-gifted women “of a certain closeness”. When a dead body comes through her portal at a time of growing rebellion, Xin is drawn deep into a station-wide conspiracy along with Ouyang Suqing, one of the station’s mysterious, high-ranking starmages.

i was terrified of doing this in undergrad, and now that i’m asked to write them fairly often, i am fondly exasperated when my students don’t know how to ask for them. obviously there’s no single way, but here’s the way i usually do it.

(obviously ask in person if you can! but email is also fine.)

THE FIRST EMAIL

should be short & should mainly be asking whether they’re willing to write you the letter

should provide only the basics - what the professor absolutely needs to know.

the position you’re applying for

when the letter would be due

optional: if you’re afraid they won’t remember you, a quick line identifying yourself & your relation to them

i like to provide an “out,” in case they don’t want to or are unable to write the letter

SAMPLE Dear Professor X, I’m applying for a job as an English tutor at the University Student Resource Center, and was wondering if you’d be willing to write me a letter of recommendation for the position. [optional identification: I really enjoyed taking English 300 with you in Winter 2016, and I’m hoping to develop and pass on those skills to other students through this job.] The letter would be due by September 1st - I know you’re very busy, so I completely understand if you’re not able to write one.  All best, Your Name

THE SECOND EMAIL

they said yes!! amazing.

this one can provide a little more information – a link to the job posting, if there is one, or you can write a quick summary of the position, plus a sentence or two about why you’re excited/interested in the job.

also tell them where to send the letter!! 

directly to the recruiter for the job

to you, to add to your application packet

upload to an online LoR service or to an application website

99% of the time folks are fine with receiving electronic copies, but if they need to mail a hard copy, let them know up front.

SAMPLE: Dear Professor X, Thank you so much! I really appreciate it. Here’s the link to the job listing; the letter should be sent as a .pdf file to the email address at the bottom of the page, anytime before 9/1. Thanks again – I’m hoping that this job will provide me with some teaching experience and the opportunity to work on my own writing. Please let me know if you need any more information! Best,  Your Name

WHEN TO SEND A FOLLOW-UP

these stress me out real bad but here’s the deal: most professors have a very shaky relationship to deadlines (especially when they have half a dozen more important ones than your piddly LoR). 

the upshot: do not be afraid to nudge them. 

often they need the nudge and are appreciative of it.

when that nudge happens is up to you and how much room you’ve given them before the deadline, and it’ll look different depending on your relationship with that professor.

GRAD SCHOOL LETTERS

i offered to send my professors essays that i had written for their classes, especially if i had taken those classes more than a year before asking them to write the letter, just so they could refamiliarize themselves with my work. you can also offer to send them your writing sample, if you haven’t already asked them to look it over for you.

honestly i’d recommend asking for these in person bc it’ll give you a chance to talk to them about their grad school experience and your own hopes & aspirations, which will help them write a more personal, fleshed-out letter.

one important note: if this letter is intended for use in grad school applications, do not stress out if it’s a little late. most programs do not care, and pretty much all of them accept late letters without a problem. your professor’s ability to meet deadlines does not reflect on you, and professors are intimately familiar with running late on LoRs. they really honestly don’t care. as long as it gets there before too long, you’ll be fine.

thank-yous are up to you! keep in mind that many departments have policies about gift-giving. i did give thank-yous to my three major letter writers, but they were handwritten cards & homemade cookies, nothing store-bought or expensive.

study less study smart

I decided to share my notes taken from this amazing 1 hour Youtube lecture by Marty Lobdell. i really liked him and his tips, i think they are super effective and cover a lot of situations! i highly recommend it!!!!! wow

but here are the tips and examples Marty mentions, so if you don’t have the time to go through the full hour, you can just scroll down. hope this helps somebody!

Break your study time in chunks with breaks

most students lose focus at 25 minutes

it’s a mistake to keep going once you do, since you won’t actually learn anything and you’ll hate every minute of it

so when you start losing focus take a 5 minute break

do something nice like talking to someone or listening to music

it’s something you practice so with time you’ll be able to work for more time without losing focus

in the end of the study session have a big reward you look forward to

Create a study area

environment highly affects the way you act. Bedrooms are for sleeping, kitchens for eating: you’ll feel sleepy in your bedroom and hungry in the kitchen. So if you have a study area, it’ll be easier to start studying and staying focused.

study in a specific room like an office or school library if you can

if you have to study in your bedroom use a specific object you only use for studying: a lamp/desk. Make it a no-distraction, away from your bed, blank walls area.

 The more active the learning, the better

80% active learning 20% passive

ask yourself: is it a concept or a fact?

learning a concept: understand/grasp/know it

put it into your own words

really think about the meaning of it

relate it with something you already know

teach somebody else. Recapitulate what you’ve learned. Talk out loud even if you don’t have anyone to teach - talk alone. Or at anyone that listens.

learning a fact: memorize it

use mnemonics

Acronyms (e.g. colours of the rainbow RoyGBiv – red orange yellow green blue indigo violet)

 Coined sayings - anything popular or sayings you’ve heard since you were a child.

Interacting images – work even better if they’re weird. Creative associations make you never forget specific details. (e.g. 1 gram of fat has 9 calories: picture a fat cat – each cat has 9 lives)

any time 2 things are highly similar but not the same you will get maximal interference!! USE mnemonics!! (e.g. afferent vs efferent neurons: SAME - Sensory Afferent Motor Efferent)

Be a part of  study groups

others can help you in ways you never thought before

Recognizing VS remembering

never confuse the two

while reviewing a chapter you may recognize concepts but not actually know them

and when you get to the test you won’t be able to remember any of it

so quiz yourself without looking at it

or stop in a page of your notes/textbook and ask yourself what is the concept immediately after and before it

Get your REM Sleep 

get ~8 hours so you don’t undo your studying

this is how your brain stores permanent memories

without it you’re ability to remember seriously decreases

most people don’t even begin to take the advice but it’s simple: sleep better. Do better.

There’s 162 hours in a week. There is time.

reflect on what you are doing with your time and what activities you have to prioritize to succed as a student

Taking notes is vital.

right after the class take 5 minutes to expand everything you’ve jotted down, give it depth.

NOT hours later. You won’t remember half the things you wrote down.

Ask your questions to class mates and teachers.

teachers want you to succeed and it’s more than ok to ask your question in the teacher’s office or in the next class

How to use a textbook: SQ3R technique

Survey Question Read Recite Review

Survey: skim through the entire chapter in a couple of minutes.

Raise questions: e.g. what is osmosis? What is this graphic about? What is a prototype?

it causes you to look for answers and you’ll find the information better once you actually study it after. If you intend to find something you learn it better.

Start studying for tests early.

don’t undo yourself. You should only be reviewing the days before the test. don’t leave it till the last minute!

(don’t just scroll through this!!!! really think about these methods and how you can actually implement them so you can benefit from them!!! these actually work but only if you put them into practice boo good luck!)

I kind of wish that the idea that you can just be was a little more mainstream.

Like, having drive and ambition is great. But it gets drilled in kids’ heads that there is some pressure to constantly be looking for the next move up, to be bigger than life. It wears you down to never be satisfied.

Not everyone is destined for greatness. It just doesn’t suit some people.

There’s nothing wrong with having a quiet life, making enough to get by, having a small apartment where you’re comfortable, and just living. You don’t have to constantly be looking to go onward and upwards. Sometimes the best thing you can do for yourself is to just be.

This is a summary of college only using two pictures; expensive as hell.

That’s my Sociology “book”. In fact what it is is a piece of paper with codes written on it to allow me to access an electronic version of a book. I was told by my professor that I could not buy any other paperback version, or use another code, so I was left with no option other than buying a piece of paper for over $200. Best part about all this is my professor wrote the books; there’s something hilariously sadistic about that. So I pretty much doled out $200 for a current edition of an online textbook that is no different than an older, paperback edition of the same book for $5; yeah, I checked. My mistake for listening to my professor.

This is why we download. 

 Alternatives to buying overpriced textbooks

Textbooknova 

Reddit

Bookboon 

Textbookrevolution 

GaTech Math Textbooks

Ebookee 

Freebookspot 

Free-ebooks

Getfreeebooks 

BookFinder

Oerconsortium 

Project Gutenberg