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justjukka:

irrevocably-delicious:

So this official poster has been released for How To Train Your Dragon 3 and it has left me with… opinions. 

My first initial reaction was excitement! Oh hell yeah HTTYD 3 is coming out! I adored the first two! But then i saw…

SIIIIIGGGGGGGHHHHHHH I knew immediately that this was most likely a female night fury and fuck yeah shit fuck it is which is so disappointing. I could write a huge essay on how female characters are portrayed in media. I could write a massive blog about smurfette syndrome and how female characters are always just a pink, soft version of their male counterparts, or how female animal or anthro characters still have to fall into society’s beauty standards so we do crazy things like give ducks tits or large eyelashes. 

I COULD talk about why these things occur, and how this is a worrying reflection of how society views human females, that males are the default and females are the other… but I’m not going to do that TODAY.

Hi my name is India and not only do I have an animation degree, but I also have a degree in animal and veterinary science.

This design doesn’t just insult me as an animator. This design insults me as a scientist. 

Let’s begin. 

Keep reading

An Alolan night fury!

lewisandquark:

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:

1. The neural network really likes brown, beige, and grey.
2. The neural network has really really bad ideas for paint names.

Important advancements in computing

(via lewisandquark)