I'll procrastinate later.

Summer is time to relax but we should take the time to think. Here are my favorites ted talk videos that I think everyone should watch:

The virginity fraud - Nina Dølvik Brochmann and Ellen Støkken Dahl

This is what it’s like to go undercover in North Korea - Suki Kim

My escape from North Korea -  Hyeonseo Lee

The Muslim on the airplane - Amal Kassir

The Secret of Becoming Mentally Strong - Amy Morin

The skill of self confidence - Dr. Ivan Joseph

How to stop screwing yourself over - Mel Robbins

The Magic of Not Giving a F*** - Sarah Knight

Why the universe seems so strange - Richard Dawkins

The pattern behind self-deception - Michael Shermer

Militant atheism - Richard Dawkins

Why domestic violence victims don’t leave - Leslie Morgan Steiner

Lessons from death row inmates - David R. Dow

Lessons from the Mental Hospital - Glennon Doyle Melton

Why we choose suicide - Mark Henick

What’s Wrong with Dying? - Lesley Hazleton

I’m Taking My Body Back - Rupi Kaur

My philosophy for a happy life - Sam Berns

The surprising habits of original thinkers - Adam Grant

The surprising secret to speaking with confidence - Caroline Goyder

Want to sound like a leader? Start by saying your name right - Laura Sicola

Programming your mind for success - Carrie Green

So if you’ve ever picked out paint, you know that every infinitesimally different shade of blue, beige, and gray has its own descriptive, attractive name. Tuscan sunrise, blushing pear, Tradewind, etc… There are in fact people who invent these names for a living. But given that the human eye can see millions of distinct colors, sooner or later we’re going to run out of good names. Can AI help?

For this experiment, I gave the neural network a list of about 7,700 Sherwin-Williams paint colors along with their RGB values. (RGB = red, green, and blue color values) Could the neural network learn to invent new paint colors and give them attractive names?

One way I have of checking on the neural network’s progress during training is to ask it to produce some output using the lowest-creativity setting. Then the neural network plays it safe, and we can get an idea of what it has learned for sure.

By the first checkpoint, the neural network has learned to produce valid RGB values - these are colors, all right, and you could technically paint your walls with them. It’s a little farther behind the curve on the names, although it does seem to be attempting a combination of the colors brown, blue, and gray.

By the second checkpoint, the neural network can properly spell green and gray. It doesn’t seem to actually know what color they are, however.

Let’s check in with what the more-creative setting is producing.

…oh, okay.

Later in the training process, the neural network is about as well-trained as it’s going to be (perhaps with different parameters, it could have done a bit better - a lot of neural network training involves choosing the right training parameters). By this point, it’s able to figure out some of the basic colors, like white, red, and grey:

Although not reliably.

In fact, looking at the neural network’s output as a whole, it is evident that:

The neural network really likes brown, beige, and grey.

The neural network has really really bad ideas for paint names.

As someone who doesn’t intend to take a physics class ever again, I was relieved when I walked out of my second semester physics final. That said, physics doesn’t have to suck or drag your average down. 

(1) How to enjoy physics: Adjust your attitude. Physics is so cool if you actually think about it. Your attitude will dictate your experience. (2) But physics is so hard: Change the way you study and don’t give up. I did better in university physics than in high school. The content was way more difficult but it was my studying methods that made the difference.

This post is split into 3 parts: Introductory physics (very basic physics, that unit of physics you had to do in a lower level science class), high school physics (physics from an algebra-based perspective), and university physics (calculus-based physics and labs). (Obviously these overlap a lot but I needed to organize this somehow)

INFO IS UNDER THE CUT B/C THIS POST IS RIDICULOUSLY LONG