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Summer in the office

I know, I know, I know, I know better than to think at all

A tough-love motherfucker who was born a clown

"It is not bias or bigotry, the researchers say, that makes it difficult for people to distinguish between people of another race. It is the lack of early and meaningful exposure to other groups that often makes it easier for us to quickly identify and remember people of our own ethnicity or race while we often struggle to do the same for others."

#race

I don't have a TV, but I want this

#gribu #tv

aiweirdness

Welcome to Latent Space

I’ve written before about BigGAN, an image-generating neural net that Google trained recently. It generates its best images for each of the 1,000 different categories in the standard ImageNet dataset, from goldfish to planetarium to toilet tissue. And the images it produces are both beautifully textured and deeply weird. Some of the categories - scabbard, rocking chair, stopwatch - are delightfully aesthetic.

[scabbard, rocking chair, stopwatch]

Google has made the trained BigGAN model available to the research/art community, which is nice, since people have estimated that today it would take around $60k in cloud computing time to train one’s own.

But there’s more lurking in the BigGAN model besides the 1,000 ImageNet categories. The model thinks of each category as a big set of numbers that describes exactly how to smoosh and stretch and color random noise. Following one set of numbers will transform noise into a flower, while following another set will turn that same noise into a dog instead. But another thing a set of number is, is a position in space: latitude and longitude for example, or x,y,z coordinates - in math terms, we call the set of numbers a vector. And in machine learning, all the positions in space (granted, an approximately 100-dimensional space) that a model’s vectors can point to is called vector space.

So one set of numbers - the flower vector - points you to some location in vector space, and another set of numbers - the dog vector - points you to a different location.

[daisy, saluki dog]

But here is where it gets fun. The vectors are just numbers, which means you could, in theory, average them. What happens when you average together “saluki dog” and “daisy”? There’s no ImageNet category there, so what’s lurking in that spot in vector space, halfway between the two? Delightfully, dogflowers.

This, it turns out, is so cool. Joel Simon has put together an app called ganbreeder.app that lets you mix and match categories.

So, this is what you get when you travel to the point in vector space midway between bedlington terrier and geyser, with a little dingo thrown in.

And this spot in latent space is somewhere between Pembroke Terrier and espresso.

This aesthetic delight is bookshop + radio telescope, with a teensy bit of boston bull. (It turns out that since the ImageNet dataset is full of dogs, vector space is too)

Want to make something adorably small? Add a bit of thimble. (This is the bit of latent space midway between thimble + zucchini)

Want to make it really ornate and fancy? Throw in some church organ, or perhaps some saxophone. This, for the record, is conch + organ + sax + scabbard + book jacket.

This spot around electric locomotive + greenhouse + prison + vault + rocking chair + shoji is very beautiful.

I’m also fond of trilobite + carpenter’s kit + french horn + ladle + streetcar.

While the less said about the bit of latent space midway between bathtub + butcher shop, the better.

Go explore ganbreeder.app, which is free and so so fascinating!

And check out a few more of my favorite spots in latent space here in the bonus material!

2019.ais sākas apmēram šādi

Visited 28 UN countries (14.5%) out of 193. Make your own visited countries map.

#travel

#gribu #flip flops

aiweirdness

Try these neural network-generated recipes at your own risk.

Stuck in a rut in the kitchen? Tired of preparing sandwiches the same old way?

Machine learning can help!

I trained a neural network on over 30,000 examples of cookbook recipes, and it learned to produce new recipes of its own. You can learn more about the training process, and watch it learn to generate new recipes here.

They aren’t good recipes, though. In fact, almost all of them are terrible. I made one of them once, and now I still cringe at the faintest whiff of horseradish. SuperDeluxe made another of them, but at least they are professionals and were wise enough not to eat any.

Here for your entertainment I give you several more recipes the neural network has generated, with the caveat that if you should try to prepare or, god forbid, actually consume one of these, I am absolutely not responsible for the consequences.

Small Sandwiches

dish, chili, lemon, salads, seafood

½ cup shortening 1 cup snow peas and cut into ¼ inch cubes 1 1 inch 15 oz peach halves,remaining posting 1 salad dressing ½ cup barley 2 large bones sliced chicken salmon: 1 cup cheddar cheese; grated 4 each onions and cut into 8 servings 2 cup chicken stock or mayonnaise 2 tablespoon brown sugar ½ cup cream cheese; softened ¼ cup grated cheddar cheese 8 oz mashed potato fillets 3 tablespoon coarsely chopped green onion 1 minced fresh sage leaves 1 drained bean sprouts

—-LAKE CHOICE—-

1 pinch salt 3 bay leaves 2 garlic cloves 1 dash black pepper 6 large garlic cloves ¼ cup along with ½ teaspoon vegetable oil ¼ teaspoon salt, optional ¼ teaspoon white pepper 4 teaspoon sugar 3 tablespoon peanut butter ½ cup coconut

—-FILLING—-

1 cup minced season tomatoes 2 cup hot water ¼ cup vegetable oil 2 cup all-purpose flour 2 tablespoon the seasoned salt 1 cup margarine, melted 1 lb jumbo shrimp 1 or freshly ground black pepper 1 up 1 thai shrimp; finely chopped 1 garlic clove, minced

Mix all ingredients except cheese and process 1 hour. Pour over ribs.

Cover and bake for 30-35 minutes. Serve with warm milk and marinade distributed; prepare the bottoms.

Watch the end of the fillets to the heat and set in a bowl and heat at this low for 5 minutes, until softened. Top with a little the next 2 ingredients; spoon the one day, 1 ½ hours. Take an and inverting it and turn the center. Let cool in the pan on wire rack. To serve cooking time: It is been ribsotro. while the serving is alternatively rich will puree in the miquinally preparing gravy. They should seal.

Yield: 4 servings

Beothurtreed Tuna Pie

pastries, fruits, pork

1 hard cooked apple mayonnaise 1 onion 3 tablespoon butter 5 cup lumps; thinly sliced ½ cup chicken broth 1 carrot, spinach (vanilla estach w/pecans) 1 freshly ground black pepper - optional

Surround with 1 ½ dozen heavy water by high, and drain & cut into ¼ in. remaining the skillet.

Pour liquid into thin baking pan.

Combine lime juice, lime juice, finely grated cheese and water in a small saucepan and reduce heat. Cover and simmer about 20 minutes at medium-high speed until thickened.

Yield: 4 servings

Tart Cover Shrimp Butter Wol

seafood

½ cup catsup 1 teaspoon cornstarch ½ cup lemon juice 1 teaspoon cumin seeds 1 can fried pale fruit to cover that drain ¼ lemon 1 fresh parsley for garnish

WINE-POCKED COST WITH PUDDING. KEEP WARM. Heat oil in large skillet over medium-high heat, add green meat. Season with salt and pepper boiled coated mixture. Sprinkle remaining ground beef in greased 9-inch pie plate. Lay border fillet on layer of custard.

Topping: In small skillet, cook onions and celery until potatoes are done, about 3 minutes. Serve hot.

Yield: 1 servings

Good Ponesed Dressing

deserts

—-TOPPING—-

4 cup cold water or yeast meat ½ cup butter ¼ teaspoon cloves ½ cup vegetable oil 1 cup grated white rice 1 parsley sprigs

Cook the onions in oil, flour, dates and salt together through both plates.

Put the sauce to each prepared Broiler coated (2 10" side up) to lower the fat and add the cornstarch with a wooden toothpick hot so would be below, melt chicken. Garnish with coconut and shredded cheese.

Source: IObass Cindypissong (in Whett Quesssie. Etracklitts 6) Dallas Viewnard, Brick-Nut Markets, Fat. submitted by Fluffiting/sizevory, 1906. ISBN 0-952716-0-3015 NUBTET 10 , 1972mcTbofd-in hands, christmas charcoals Helb & Mochia Grunnignias: Stanter Becaused Off Matter, Dianonarddit Hht

5.1.85 calories CaluAmis

Source: Chocolate Pie Jan 584

Good Wine Drained Chili

meats, chicken, low cal

4 quart milk 3 cloves garlic, finely chopped 2/3 cup chocolate chips 1 teaspoon cornstarch 1 wine, mashed 12 oz kettle (garnished" powder, cinnamon 3 tablespoon paprika

Heat broiler in warm place for 15 minutes.

From: Martha Stewart Living/Marketing Orden GRande N… Sc. (115) on serving and produce id the Backdacake: Typed for you by Schan Scandet Refrigerate by Mary Herther, Amarant, Miry’s to the Markning Cinnamon Marthan Sear Foods county Typis by Matiersr : Turtlean Wo Genie Alse

From: Quick Times with frosting.

! AUTRIMANONISG ROBLIME Tomethe Aspic ead Savor Kr Confit. - Vegetarian Brunding

Yield: 6 servings

Want more? You can find all my other neural network recipe experiments here.

Want more than that? I’ve got a bunch more recipes that I couldn’t fit in this post. Enter your email here and I’ll send you 38 more selected recipes.

Want even more? Check out Tom Brewe’s list, which inspired my own neural network recipe experiments. May I direct you in particular to the shredded bourbon?

#recipes #machine learning #lis