Back In The 1960s, The U.S. Started Vaccinating Kids For Measles. As Expected, Children Stopped Getting

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It’s way too late for this, but it’s important to note that NASA didn’t discover the new earth-like planets. It was a group of astronomers lead by a dude name Michaël Gillon from the University of Liège in Belgium. Giving NASA credit for this gives the United States credit for something they didn’t do, and we already have a problem with making things about ourselves so. just like…be mindful. I’d be pissed if I discovered a small solar system and credit was wrongfully given to someone else.

A guy asks an engineer “hey what 2 + 2?”

 Engineer responds “4. No wait make 5 just to be on the safe side.”

cyanobacterium: i have made Oxygen

chemotrophs: you fucked up a perfectly good planet is what you did. look at it. it’s all rusty

Beyond "Mama" and "Dada": Why Babies Learn Certain Words

Babies learn different types of words first depending on their native language

English-speaking parents tend to use vague, one-size-fits-all verbs as they emphasize nouns: cars, trucks, buses, bicycles and scooters all simply “go.” Mandarin speakers do the opposite: they use catchall nouns such as “vehicle” but describe action—driving, riding, sitting on, pushing—with very specific verbs. “As a native English speaker, my first instinct when a baby points is to label,” Tardif says. Her babysitter, on the other hand, was a native Mandarin speaker, whose instinct was to name the action she thought the child was trying to achieve.

via Twitter

One of the best ways to get people to do something is to make it fun. A marketing campaign called ‘The Fun Theory’ found that when stairs next to an escalator were turned into giant, functional piano keys, the amount of people who took the stairs increased 66%, and when a trash can had sound effects that made it seem infinitely deep, people started picking up and throwing away litter just to hear it. Source

When engineers are bored.

Paint colors designed by neural network, Part 2

So it turns out you can train a neural network to generate paint colors if you give it a list of 7,700 Sherwin-Williams paint colors as input. How a neural network basically works is it looks at a set of data - in this case, a long list of Sherwin-Williams paint color names and RGB (red, green, blue) numbers that represent the color - and it tries to form its own rules about how to generate more data like it. 

Last time I reported results that were, well… mixed. The neural network produced colors, all right, but it hadn’t gotten the hang of producing appealing names to go with them - instead producing names like Rose Hork, Stanky Bean, and Turdly. It also had trouble matching names to colors, and would often produce an “Ice Gray” that was a mustard yellow, for example, or a “Ferry Purple” that was decidedly brown.  

These were not great names.

There are lots of things that affect how well the algorithm does, however.

One simple change turns out to be the “temperature” (think: creativity) variable, which adjusts whether the neural network always picks the most likely next character as it’s generating text, or whether it will go with something farther down the list. I had the temperature originally set pretty high, but it turns out that when I turn it down ever so slightly, the algorithm does a lot better. Not only do the names better match the colors, but it begins to reproduce color gradients that must have been in the original dataset all along. Colors tend to be grouped together in these gradients, so it shifts gradually from greens to browns to blues to yellows, etc. and does eventually cover the rainbow, not just beige.

Apparently it was trying to give me better results, but I kept screwing it up.

Raw output from RGB neural net, now less-annoyed by my temperature setting

People also sent in suggestions on how to improve the algorithm. One of the most-frequent was to try a different way of representing color - it turns out that RGB (with a single color represented by the amount of Red, Green, and Blue in it) isn’t very well matched to the way human eyes perceive color.

These are some results from a different color representation, known as HSV. In HSV representation, a single color is represented by three numbers like in RGB, but this time they stand for Hue, Saturation, and Value. You can think of the Hue number as representing the color, Saturation as representing how intense (vs gray) the color is, and Value as representing the brightness. Other than the way of representing the color, everything else about the dataset and the neural network are the same. (char-rnn, 512 neurons and 2 layers, dropout 0.8, 50 epochs)

Raw output from HSV neural net:

And here are some results from a third color representation, known as LAB. In this color space, the first number stands for lightness, the second number stands for the amount of green vs red, and the third number stands for the the amount of blue vs yellow.

Raw output from LAB neural net:

It turns out that the color representation doesn’t make a very big difference in how good the results are (at least as far as I can tell with my very simple experiment). RGB seems to be surprisingly the best able to reproduce the gradients from the original dataset - maybe it’s more resistant to disruption when the temperature setting introduces randomness.

And the color names are pretty bad, no matter how the colors themselves are represented.

However, a blog reader compiled this dataset, which has paint colors from other companies such as Behr and Benjamin Moore, as well as a bunch of user-submitted colors from a big XKCD survey. He also changed all the names to lowercase, so the neural network wouldn’t have to learn two versions of each letter.

And the results were… surprisingly good. Pretty much every name was a plausible match to its color (even if it wasn’t a plausible color you’d find in the paint store). The answer seems to be, as it often is for neural networks: more data.

Raw output using The Big RGB Dataset:

I leave you with the Hall of Fame:

RGB:

HSV:

LAB:

Big RGB dataset:

Now we know the (proposed) names of the four new elements, here’s an updated graphic with more information on each! High-res image/PDF: http://wp.me/p4aPLT-1Eg

THE 👏🏾 ALIENS 👏🏾 ARE 👏🏾 COMING 👏🏾 BINCH