Janelle Shane's Blog

Like other ambiguous image-generation prompts, asking for “a gothic wardrobe” had the AI hedging its bets.In CLIP+VQGAN’s internet training, those words might go with pictures of clothes in styles leaning toward black fabric, pleats, chains, and lace. Or they could mean tall cabinets with pointed arches. So we get both:

Adding more detail to the prompt reduces ambiguity. Here’s “a gothic wardrobe made of oak”.

But although there’s far more wood than garments now, the wood has a strange pleated, garmentlike look to it.

I tried to use the same prompt, but with a negative weight to “pleats” so the AI was rewarded for producing images that both looked like “a gothic wardrobe made of oak” and didn’t look like “pleats”.

It didn’t go so well.

Is that… a minecraft creeper? Batman? A Deadpool Groot?Adding a penalty to “collar” instead was only a marginal improvement if you’re okay with puckered orifices and/or spiders.

I decided to start over with a different prompt. “A wardrobe in the style of gothic architecture” is back to producing a mix of clothes and furniture, but with more emphasis on the pointed arches I was after.

All I needed to do was nudge it toward furniture. I added a positive weight to “furniture” and a negative weight to “clothes”.

Now finally we have the very cursed piece of slightly multidimensional furniture I was after. It’s not supposed to have piano legs or portals to other rooms but we won’t worry about that.

I made one last alteration, to add the phrase “in a nice cherry finish” to see if I could control the type of wood.

In retrospect, that was a mistake.

AI Weirdness supporters get bonus content: more weird Gothic wardrobes! Or become a free subscriber to get new AI Weirdness posts in your inbox.

Beetle kill pine is a popular wood in Colorado, salvaged from trees killed by pine bark beetles. While the dead trees stand before harvesting, a fungus colonizes them, giving the wood interesting blue-grey streaks called spalting. It’s a really pretty wood.

But AI apparently thinks “a chair made of beetle kill pine” looks something like this:

I was using CLIP+VQGAN (tutorial by @samburtonking here) which had almost certainly seen beetle kill pine wood during its internet training. But it had also seen beetles.

And maybe pinecones? Fortunately, as reader Marcello Herreshoff pointed out, the CLIP+VQGAN code I’m using allows me to give some prompts negative weights. So I can tell it to generate a picture with +1 to “a chair made of beetle kill pine” and -1 to “insect”.

At first it’s fairly promising - the beetle is gone, and there’s some really pretty blue color emerging. It knows that beetle kill wood has blue streaks!

And then the next iteration looks like this.

So that failed on multiple levels, including a continuation of its complete ability to resemble a chair. I don’t know why this collapsed so hard into rainbow spaghetti, but I’ve learned that when CLIP+VQGAN image generation is going poorly, it often helps to be more specific.

Here’s what I got for “a chair made of blue spalted beetle kill pine wood” (with a -1 weight to “bug”).

Granted, it’s the most chairlike object yet, but it is not so much made of wood as engulfed in roiling wood. The blue spalting has gotten much more extreme than you ever see in actual beetle kill, but it’s gorgeous so I’m keeping it.

I tried to specify the room. Asking for white rooms and clean images and featureless white voids still ended up with messy wood-hives. I had the best success when I specified a background that was itself strongly textured. Here’s “a dining chair made of blue spalted beetle kill pine wood on a dirt path in front of a brick wall” (-1 to “bug”).

But I wasn’t able to put the chair in a forest at all. This is its attempt to do “in a lush forest clearing with sunbeams”.

I think the problem is that it always starts with a world made of wood and then carves the scene out of it. Here’s a very early iteration of the generation process that led to the forest scene above:

So instead I uploaded a picture of two spindle-backed wooden dining chairs as a starting point. Though they made much more geometrical sense before CLIP+VQGAN started altering them.

That’s the most chairlike chairs I managed to get, and even those are much less chairlike than the starting image I had to give it. CLIP itself can recognize a chair, so the shortcoming is likely the way VQGAN is used to present it with candidate chairs. I sort of imagine CLIP going “yeah I guess? that one’s a little more like a chair than the other one?” As usual with AI-generated images, the textures are the strong point, not the geometry.

For more rejected beetle kill chairs, become an AI Weirdness supporter to check out the bonus content! Or become a free subscriber to get new AI Weirdness posts in your inbox.

Still amazed by this:

Here’s CLIP+VQGAN (trained on internet photos and their accompanying text), prompted two different ways:

“A car driving down a desert road in monument valley”

“A car driving down a desert road in monument valley | dramatic atmospheric ultra high definition free desktop wallpaper”

The SAME algorithm was capable of producing both images but thought the first one was a better answer until I changed my question.

That’s a problem with training AIs on huge amounts of internet data - a lot of that data isn’t going to be what you want, and the AI doesn’t know the difference.

Here’s “a toaster”

The toaster is partially made of toast so I tried to get it to generate a toaster made of chrome instead. Turns out I don’t think I can get it to do a toaster made of chrome without in some way incorporating the logo of Google Chrome. General internet training seems to poison certain keywords. “a chrome toaster | chrome”

We may never know all that’s in internet training data, and yes, that should scare us a little.

I generated a whole bunch more toasters that I collected as bonus content. Become an AI Weirdness supporter to get it! Or become a free subscriber to get new AI Weirdness posts in your inbox.

You can try CLIP+VQGAN for free by following the instructions in this tutorial (no coding or Spanish language skills necessary.)

There are upsides to working with a neural net that trained on a huge collection of internet images and text. One is that, instead of ominous grey geometric blobs when it doesn’t understand your prompt (there is a free interactive demo of AttnGAN here and it is a lot of fun), a huge neural net like CLIP can follow a wide array of prompts. Since CLIP technically only judges how well a picture matches a prompt, people have developed a few ways of using CLIP’s judgements to aim an image generator. I’ve used this to generate such useful things as Frodo Baggins delivering pizza through the Mines of Moria, cursed candy hearts, fully illustrated sea shanties, and more fully illustrated sea shanties.

I decided to give the VQGAN + CLIP method of steering a try (tutorial and link here), and for its first task I decided to have it generate a subject that’s given neural nets trouble in the past: “a herd of sheep grazing on a lush green hillside”.

Here’s what it generated after 450 iterations:

[Prompt: A herd of sheep grazing on a lush green hillside]

It could be better. The sheep are not so much grazing as embedded like weird molars, and the hillside isn’t very picturesque.

But this version of VQGAN+CLIP allows me to upload an image as a starting point. So I decided to start with an image that a different neural net had captioned as “a herd of sheep grazing on a lush green hillside”. In fact, Azure image description had still called it “a herd of sheep grazing on a lush green hillside” even after I had removed all the sheep. With the image on the right as a starting point, would VQGAN simply add the sheep?

The answer is: sorta?

[A herd of sheep grazing on a lush green hillside]

Can’t really tell whether those are sheep or cauliflower. And did it add palm trees? The original image’s composition is destroyed and what is left looks pretty flat.

Here is where it gets interesting. If I use the same prompt and add “Amazing awesome and epic”, the picture gets noticeably better. “Oh,” goes the neural net, “you wanted a GOOD picture”.

[A herd of sheep grazing on a lush green hillside | amazing awesome and epic]

And how good a picture you get depends on exactly how you ask for it. There are several phrases you can add that seem to make things better, like “trending on artstation” or “unreal engine” (a fancy new video game rendering engine).

Here’s “a painting of a herd of sheep grazing on a lush green hillside in the style of disney trending on artstation | unreal engine” (prompt combo borrowed from here).

[a painting of a herd of sheep grazing on a lush green hillside in the style of disney trending on artstation | unreal engine]

Granted the sheep are more like multicolored bundles of cloth, but the saturation and vignetting got much more dramatic. There’s even a soft focus effect in the background. All the time it was giving me the flat, lackluster landscape of the first picture, the AI was perfectly capable of giving me this instead.

So what are some other ways of asking CLIP-VQGAN to try harder?

I tried to specify particular artists. Bob Ross was a hilarious mistake.

[a herd of sheep grazing on a lush green hillside by bob ross]

The Tim Burton version was very cool-looking, if completely unrecognizable as sheep.

[a herd of sheep grazing on a lush green hillside by tim burton]

“Award winning National Geographic Photography” gave me nice looking background cliffs and trees - and sheep that look disturbingly like people crawling around under green blankets.

[a herd of sheep grazing on a lush green hillside | award winning national geographic photography]

But the most effective prompt? In terms of producing a realistic but dramatically lit landscape with recognizable mountains and hills and (okay not sheep)?You’re going to hate it. I hate it, and I’m the one who thought of it. But it’s the natural extension of layering on descriptors to try to boost performance.

“dramatic atmospheric ultra high definition free desktop wallpaper”

[a herd of sheep grazing on a lush green hillside | dramatic atmospheric ultra high definition free desktop wallpaper]

With that cursed prompt as a base, I could layer more styles on top.

The most haunted prompt turned out to be “a herd of sheep grazing on a lush green hillside | dramatic atmospheric ultra high definition free desktop wallpaper by Lisa Frank”. I’m not sure what kind of rainbow apocalypse is happening here, but I wouldn’t recommend poking at the violet shimmery patches that are oozing into the lake. Maybe those are the sheep.

[a herd of sheep grazing on a lush green hillside | dramatic atmospheric ultra high definition free desktop wallpaper by lisa frank]

For a more technical description of how CLIP steering works, and some gorgeous image examples, check out this blog post by Charlie Snell. You can try CLIP+VQGAN for free by following the instructions in this tutorial (no coding or Spanish language skills necessary.) Or if you ask nicely, the @images_ai twitter account may try a prompt you request.

[illustration by Natalie Matthews-Ramo]

“There was so much she might not hear, being located so far from her body, relying on cheap headphones. It had never mattered before.”

I have a new story out in Slate Future Tense!

The Skeleton Crew

I wanted to write a story about today’s AI, but I also wanted it to be a fun adventure story inspired by Murderbot and Gideon the Ninth. Hence: The Skeleton Crew, where the scary skeleton robots are made of foam and plastic and piloted by remote workers.

Also check out the response essay by Melissa Valentine, “The Ghost Work Behind Artificial Intelligence”.

I love this point the essay makes:

“The Skeleton Crew” offers an interesting twist because the A.I. indeed has feelings just like us, because it is, in fact, us: The A.I. is a group of remote workers faking the operations of a haunted house to make it seem automated and intelligent.

Some fun facts about the story:

The original spark for The Skeleton Crew was this illustration by SamsonLeisureCenter of “Tiny Cod Flames Rise”, a neural net generated drawing prompt.

I thought it looked like haunted house robots whose day had been seriously messed up.

Other AI Weirdness easter eggs in the story:

Ancient Metal rocker Dragonsulla is from a neural net I used to generate metal band names.  Technically the name was “Dragonsulla and Steelgosh”, so maybe she’ll meet Steelgosh at some point.

The screennames in The Skeleton Crew are all from .

Cheesella
DarthSugar
Princess Morgue
Mayhem Chicken
Bud Crack

It was very tough to choose.

I hope you enjoy the story!

One of my favorite ways to mess around with text-generating AI is to see what it does with new games that didn’t exist on the internet back when its training data was collected.

OpenAI’s GPT-3 models were trained with data that cut off in late 2019 which means that they’re unaware of certain major world events, as well as of the latest internet memes.

Recently someone posted this hilarious bit of startup advice that had people posting terrible advice of their own:

I guessed that even if a neural net like GPT-3 can’t possibly have seen this meme before, it would still be capable of giving terrible startup advice. So, I entered the entire above sequence of brilliant entrepreneurial moves as prompt text, preceded by the word [tomatoes] in brackets to help indicate this was supposed to be a short thing about tomatoes. Then I wrote “[” and let it write a short thing about something else.

DaVinci

Unsurprisingly, the largest GPT-3 variant was the best at catching on to the format. It made the leap that you could amplify eggs in a similar way. And you end up making $1 million on each egg!

[eggs]
Same as the tomatoes.
Eggs sell for $1 each.
He uses 39 chickens to make a dozen eggs in 9 minutes.
One hen can lay 355 eggs in 355 days. That’s 11.844 muffins a day.
9:11AM go to the best grocery store in town and buy ALL THE CHICKENS. Keep your receipt.
9:19AM - you take $9.8MM and buy back all your chickens.
9:30AM bring the chickens home.
9:39AM - sell the chickens back to the store for $9.8MM
Speculation in eggs prices rises dramatically. If you buy all the eggs, you can sell them back and make $1MM on each egg.

Object permanence is not a strength of DaVinci’s - keeping track of the status of physical objects isn’t really in its job description. Its world is text.

[wigs]
You don’t understand scale.
Buy one $50 wig.
Use it for 6 months until it breaks.
Throw it away.
Buy another $50 wig.
Use it for another 6 months until it breaks.
Throw it away.
Repeat until you have 182 wigs.
Sell them for $50 each.

GPT-3 DaVinci does at least realize that you should end with profitable things happening.

[hammer & nails]
You don’t understand scale.
Use $100 to buy a hammer.
Use the hammer to build with 4 nails.
Burn the nail building for warmth.
Repeat indefinitely.[chocolate milk]
1 cow gives 160 glasses of chocolate milk per day.
A single glass sell for 10k.
Lots of profit.[fork]
You don’t understand scale.
Buy 3 forks.
Solve any problem that takes less than 3 forks.

Ada

I also turned to the smallest GPT-3 variant, Ada. It caught onto the format only partially. It got that it was supposed to be about tomatoes. And possibly about money?

[tomatoes]
Use $1 to buy 1.5MM tomatoes.[Tomatoes]
Do you intend to build a city? ?“
Build $500,000 lots and then sell $1,000,000 lots.
You want a house outfitted with 50 toilets, 50 outdoor shower systems along with 132 rooms.[tomatoes]
They are botanical machines.
Of all the tomato varieties, they only grow once!
Traversing is their way of existence.
They do this every day.
They can do "rainbow fruit trees”, making “green smoke and crumble flower.”

Had I not stopped the generation process, I’m not sure how long Ada would have continued this next one. When people talk about researchers shutting down their AIs because of an unwanted outcome, this is often the kind of thing they mean.

[tomatoes]
You don’t understand that it’s basically the same thing as a bowl of cereal.
Potatoes in a can.
Pour the potatoes into 35 quart jugs and fill jugs to the brim.
Whisk soapmaker.
In little tub layers stout ash alum pot paper.
Divide into four equal parts of qt and rl into segments measuring 3.2 cm by 0.8 cm.
Wash wire wool in 80-95% dewashed water and rinse under tepid water.
Prepare three inches of hot coals, gently heat in center of stove 4 inches deep.
Roll up soap in crinkly napkins two times to fit into form according to volume-diameter. To remove bubble wrap insert aprons.
Heat a small hole as necessary per each pot.
Dip each slip to the brush onto each pot, then collect the whole wrap under the string from the soap’s top to bottom.
Crackfish, partially hulled and cut to about 3 cm across, on an upright flake computer of sufficient volume.
Measure and cut parts of soap into even thickness, add by 1 cm to diameter of each bowl.
Take soap left out to apartments with sheets and wrap it up in a towel.
Fill Sanitary Pail with clay pot suders, layers clay that been finished of three times, each surface 3 cm thick, to the top of the pails.
Split soap in half 3 cm ¼ x 0.7 cm just below bottom of tub.
Fill bathtub lengthways and level with edge.
Lay soap at 4 cups depth, with top down.
In the dryer attach film and cling wrap, then lay sheet-hooks 1.6 cm apart on the film.
Place plastic apple cores on top of film.
Lay film and cardboard pails on top of each other.
Cap and secure, then dry for 7-10 days.

If you don’t do the entire list, are you really cut out to be an entrepreneur?

I also decided to see what the two midsized GPT-3 models did with this startup advice. Become an AI Weirdness supporter to read the results as bonus material! Or become a free subscriber to get new AI Weirdness posts in your inbox.

The US National Oceanic and Atmospheric Administration releases its planned hurricane names years in advance, and for each name. An excerpt from NOAA’s 2026 Atlantic Basin Storm Name Pronunciations:

Arthur AR-thur
Bertha BUR-thuh
Cristobal krees-TOH-bahl
Dolly DAH-lee
Edouard eh-DWARD
Fay fay

Whenever I see a list like this, I naturally wonder what a neural net might make of it. I’m specifically interested in the larger neural nets I’ve been using lately, like the GPT-3 variants, which have encountered lots of names during their internet training yet are known to produce absolutely cursed attempts at pronunciation guides.

The largest GPT-3 variant, DaVinci, was pretty good at identifying common names and providing pronunciation guides that are not too far off from how I myself would say them. Although I definitely say this one differently:

Christopher kry-STOP-oh-fuhr

But things did get stranger when I gave GPT-3 dates farther in the future. I know it’s not actually predicting that the already tenuous link between spelling and pronunciation in English is going to do some pretty extreme things in the next 50 years, but that’s sure what it looks like:

2051
Harry HARR-held
Andrew an-DREE-uhn
Lisa LISS-uh
Mark mark
George jar-ZHAY

2070
Wanda way-DAHN-uh
Alfred im-THOHN
Deirdre ED-reel
Jason JAY-dree
Marissa mrees-TVR-uhf
Sandrina sa-NREE-tuuh
Scott steed-rer
Scott wess-tra

2071
Arnold ah-NAWR-dell
Georgia zheh-DRO-luh
Howard huh-WAR-dee
Nelson NEH-suh-lihn
Serena say-RUH-nuh
Victor VIK-suht

I also prompted the smallest GPT-3 variant, Ada, with the list of officially planned NOAA hurricane names and pronunciations. I would definitely need most of these spelled out for me the first time I heard them.

Alan AR-toh-LAY
Christine CREE-chuh-NAH
Elena anda-DAW
Esther ex-THAH-ah
Michael MIE-le-no
Ginger LEEK-yuh
Darrell jlear-DAHR
Gregory GREGORY
Alex MDL-aye
Jess MEY-sern
Robert REH-sch-ei
Santiago SAN-tih-uh
HEAT-TAY
Varsity EBAY
David EBOY
Joe joh-LOHS-dah
Wendy lich-WAEH
Oliver ORL-arkus
John john-FLAN-uh
John john-SPLIN-er-ahnt

I was curious, so I appended a few other names to the end of the list of hurricanes so Ada could provide me a pronunciation guide. I guess I’ll be introducing myself differently from now on.

Janelle JEE-ree-uhn
Shane shaan

Artificial AYR-tah-zhoo
Intelligence MIL-teer-uhn

Benedict bon-TEE-ass
Cumberbatch cHL-barck-Christ

Bilbo BLA-buh
Baggins BAL-gin

Scrooge skhuh-RAK-tur-ee
McDuck moos-DUCK

Rumplestiltskin uh-buh-THEEKS

Before I discovered I could get GPT-3 DaVinci to vary its pronunciation guides by simply advancing the date, I experimented with some other prompts. One of the more successful experiments was the one in which I told it that the storms had grown ever more intense, so the names had to grow more epic to match. If you’re an AI Weirdness supporter, you can read them as bonus content. Or become a free subscriber to read new AI Weirdness posts in your inbox!

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There’s this dating show called The Bachelorette, and they’ve revealed their new guys for the 2021 season! And I get it, in the promo graphics they have very little space to get into the nuances of a personality, but it has been pointed out that their fun facts are rather basic.

nicole boyce on Twitter

The neural net GPT-3 has seen a lot of dating profiles during its internet training. But has it seen this specific variety of astonishingly pedestrian dating profile?

I collected the fun facts from the contestants above and gave them to the largest GPT-3 variant, DaVinci, and it was almost too good at adding to the list:

Likes to revisit the classics
Believes in leaving false modesty at the door
Keeps his nails and hair trimmed
Dreams of buying a small island
Believes that being successful comes down to hard work and practice
Wants a house and mother for his future family
Loves pumpkin lattes
Love, love loves champagne
Believes in destiny
Is a self described daredevil
Loves his blue sleeping mask
Collects souvenir spoons
Always wears a scarf in the wintertime
Always picks mint gum or spearmint flavored gum
Will not touch fish or spicy food
Refers to himself as super romantic

GPT-3’s aim is to copy humans, so it’s no wonder that it’s good at being cliche.

Although DaVinci did come up with a few fun facts that might give the Bachelorette some pause:

(AI-generated image from StyleGan2 via thispersondoesnotexist.com)

Had his housekey modded
Claims to have ridden in a dolphin van
Being good with his hands (He plasters, flicks house lights on and off…)
Says “You’re welcome” when leaving a room

The best fun facts, however, were generated by the smallest GPT-3 variant. Now, Ada did manage to follow my prompt’s lead and generate some pretty basic traits.

Doesn’t want any of his exes in the picture
Loves to clean up after himself
Does not wear a vest
Loves fun dancing
Loves ballet and furniture
Is a type of schoolyard bully
Loves to whistle
Loves good TV
Has had to spend time in an airplane
Has no issues with using markers

Has a healthy butt
Loves new button-down collared shirts
Has yearly birthdays
Has the largest prune ever
Loves words like “mildew” and “hangover”

But some were perhaps more intriguing than existing Bachelorette contestant facts.

Believes in Superman’s love
Has a past of working with a fire ant
Was advertised as having French accented skills & a blue bunny
Loves walking upside down on one side of a room
Loves the peace and harmony of the foxwoods

‘the most dangerous’ tattoo in the world
Collects hambones
Spit at computers like a pro
Not a member of the Rockettes
Nor does he stay up all night eating marshmallows and starches

Then there’s the more… exuberant

Loves to calisthenic
Typically spends after-work flexing around Studio City
Claims to be “the greatest stripper”
He’s a star in sheets “The king of paper airplanes”
Dances to hit, just can’t get out of his mind till he finally screams it out in a loud blast

Or the just plain enigmatic.

Tastes fine pottery
Can keep his hands on a tiger or don’t let one eat his brain
Wants to be the father of an elephant
Has a color pest on his teeth
If someone gives a music note to him the note gets the first three cups of coffee

I collected a few more Fun Facts from Ada, which you can read as bonus material if you become an AI Weirdness supporter. Or you can become a free subscriber to get new AI Weirdness posts in your inbox!

One way to generate buzz for your new food truck: a unique theme or gimmick. But with new food trucks popping up everywhere, how can you ensure that your idea is creative enough?

Lindsay Diamond suggested a possible neural net solution. So as a demonstration, I wrote short descriptions for 5 real food trucks (Gourdough’s Donuts, Bananarchy, Taco Beast, The Grillenium Falcon, and Company 77 Mobile Pizza Unit) and then got a neural net called GPT-3 to add to the list.

The first variant of GPT-3 I usually try is the largest, DaVinci, because if DaVinci can’t catch onto a particular format, the others won’t either. As I had hoped, DaVinci added new food trucks to the list. But when I checked, the food trucks already existed. DaVinci had seen enough lists of real restaurants and food trucks during training that it just drew on these.

GPT-3 DaVinci

EZ BBQ
The EZ stands for easy. EZ BBQ sells easy-to-eat BBQ sandwiches.

Sweeto Burrito Truck
Known for powering their burritos with love…and gummy bears.

Sugarbaker’s
Watch your cupcakes levitate, thanks to a van … that travels through time?

It did add the gummy bears and the time travel, though.

I had the best results actually with the smallest GPT-3 variant, Ada, who knew enough to catch on that this was a list of food trucks (usually), but not enough to come up with existing, or even plausible, suggestions. I love them.

GPT-3 Ada

Pasta by the 5th Tree
How Porsche’s waswolf dog shifted to earth. A literal bed of pasta (turned inside out) is served at this pizza truck booth.

They You
Turbo treat and funnel cake awaited anyone accepting the sign. A collection of assorted cardboard tiles.

Bubble Doodle Babies
For the whole family, have your meal of bubble-dumplings in Cinnamon’s Tower.

Dumplings Rope
Anyone who has ever broken the chain should clear their room. Buy enough spare chains so that you need the bag of unbroken chains at least 45 times again.

Classsy Camper
This company (read on) lives to do camp. Guests enjoy a large tub of the aforementioned dumplings, as well as a festive romp through a neon-lit snozzebay.

The Fruit, Hatcheries, and Me
You can find fruit on the windshield, rides, food trucks, pards, and other haunts.

The fruit traffic quickly part ways with the exterior pie partitions and disappears inside the dome.

Booger Space: Hanging Teeth
Two animated clowns venture up into the lift shaft of the fruit cart. The clowns bust out hives of fuzzy boogers. At the top of the shaft, they spring King Wiggly. This anti-chloricode steam gives the boogers wings. This is the only thing drawing attention in any way, shape, or form throughout the Dome.

Bar Restaurant in One Block
A very odd table, with a long wooden bar, with a dressy guy doing lounge music in a black leather seat, a playlist playing,“Lobster Vs Fish Vol. 2”. Located along P The Goreway. Never order that list!

Grozone Galahad Farm
Grozone GTF restaurant and trailer – “for place food, food service and that cool ooh-gitty-cola freak show people can do with their dogs.” Their marketing strategy has explicitly admitted that Teammates are dogs.

Dry Sprout Bakery Bakery
Bakery is a dessert bakery. Their own side of the dressings begin with WHOLE FRUIT OR NOTORIOUSLY FRUIT that has howls out and “The more the merrier! That lettuce ya got! Haha”. Their doors are made to resemble a symphony of wind chimes and sequined cherries–which becomes their quintessential color scheme. Occasionally, they will entertain.

Get your new food truck website up before someone takes one of these ideas. Remember, GPT-3 scanned most of the internet in training, so that means it knows what’s popular.

I experimented with the other GPT-3 variants and although Curie (the 2nd largest) grabbed existing food trucks like DaVinci did, Babbage (the 2nd smallest) had some interesting ideas.

Become an AI Weirdness supporter to read some food truck ideas from Babbage! Or become a free subscriber to get new AI Weirdness posts in your inbox.

When I used GPT-3 to generate inscrutable new Star Wars jokes, Lisa Wong asked, “Can the A.I also generate the condescending explanation as to why the joke is funny and how I just don’t have a sense of humor? That would make it feel real.”

I gave it a try with one of GPT-3’s jokes (my prompt text in bold):

What would be on the Death Star menu?
Warm thin-crust pizza.

Let me explain this joke for you. It’s funny because pizzas (hint: plural) are very flat and the Death Star is very large. It is a circular planet type thing.

Its explanations tended to be as nonsensical as the jokes themselves. During training it was rewarded for sounding similar to existing internet text, not for logical soundness. And lots of existing internet text is extremely confident. I wondered what else I could get it to explain.

After some experimentation, I decided to prompt GPT-3 with three “well, actually” type explanations of things:

Prompt text:

Actually, humans HAVE coexisted with dinosaurs. You see, birds are technically dinosaurs, taxonomically speaking.

Actually, if you have beef for dinner, you’re still technically eating fish. Cattle, like other mammals, are more closely related to some fish than some fish are to each other. Humans are also a kind of fish.

Technically, some dinosaurs lived on the other side of the galaxy from us. People don’t tend to realize this, but the period of our solar system’s orbit around the center of the Milky Way means that the dinosaurs were hundreds of light years from where we are now.

The above prompt text is technically true, but deliberately phrased to be a little condescending. GPT-3’s task was to add to it.

Here’s what DaVinci, the largest GPT-3 variant, added.

T-Rexes love to swim. People need not worry if one were to come near because, thanks to the Coriolis Effect, the water would drain away from it.

A circle is technically a sandwich. Bread is tightly connected to circleness.

Instinctively, you might say flowers are colorful because they attract pollinators, but actually, flowers bend light toward the color pink in order to attract pollinators (birds and poets). If the color of pollinators’ eyes was red, then flowers would bend light toward the color red.

You may not realize it, but the pixels you see on this website are, technically, conscious, which doesn’t make this paragraph that much better.

The Loch Ness Monster is technically a giant Cuban cigar.

One syllable words are all the rage in contemporary poetry. But even before the popular boom, it was pretty normal for one-syllable words to play a major part in verse. If you’ve ever read Shakespeare, then you’ve seen plenty of words of just one syllable.

You may not realize it, but some humans may be dinosaurs. This is because at the discotheque, the ones with massive arm muscles might also have purple hair.

Tardigrades, also known as water bears, are some of the toughest creatures on the planet. Tardigrades can survive in just about every environment that exists in the Universe. Tardigrades can even be obliterated completely and assimilated back together and they come out unharmed as if nothing ever happened. Scientists aren’t even sure what the maximum lifespan of a tardigrade is but it’s probably beyond 13.8 billion years which is the current age of the Universe.

Sometimes we talk about certain dates in history as being millennia ago, even though there were no millennia way back when. We had to create a year of 12 months based on the Venus calendar. Not everyone realizes that the JCPenney department store chain is named after a giant cat that Isis used to summon from a nearby lake at the end of every work day.

T-rex was the Tyrannosaurus rex. Also, the apostrophe is the proper grammar when trying to inform the reader that the “rex” root of the word describes ownership. e.g. If I told you “Hey I own a Ferrari,” you would be fooled unless you knew that the “re” ending could mean whoever was forming the sentence owned it. “Rex” is the same sort of suffix, which is why, when describing something awesome, like a T-rex, it can confuse things when we use it wrongly.

The AI is very incorrect but it is using the right sort of confident words and that’s what it was rewarded for in training.

Let’s see what Ada, the smallest variant, generated.

When you look at a tree, grass, and shrub, when you look at a planet, or the earth in general, you can expect to see dinosaur prints. The purpose of a printed animal is to indicate how far a creature has come, and how old that creature is. Your printer would be proud of the stuff we print.

A boat ride around the moon is not as terrible as being surrounded by rocks!

What’s the name for a procedure that converts a cerulean amber green resin into a rhombular brown? An advanced unkempt dog with three golden claws.

A blog, “Original Turtles on Now Conic Galaxies”, captured this scientific puzzlement from Antarctica: Dinosaur eggs, completed beautifully, lay on the moon’s surface, and paint on the sun. And then they tune in to a FM station run by Angela Carter here in Tallahassee, Florida.

Many people also think that teddy bears are based on dinosaurs, but there’s some debate over whether that is true or not. Teddy bears aren’t gorillas, according to some scientific textbooks.

It has also been suggested that part of the reason human-bipedal animals are so powerful magicians is that they are so squishy. That much isn’t literally true. Squishy animals don’t know when to put on a sweater, or whether they’re snowflakes or rocks. Squishy objects are so sparsely covered with fluff that you’d probably have to cut through about 50 percent of the cushy thing to discern that it’s actually a ball.

A huge swath of land has been rounded continuously, since life came into existence. Our planet has only been round once before. The ancients believed that it was round and that the entire universe was round, touching the beginning of time. This is the worst thing in creation, they thought. Yet, the bottoms of the Earth are hollow, and much of the upper part of the moon is completely overcarved, as a called shell by ancient astronauts. The moon is an antechamber to the universe, humans are a descendant of prior dinosaurs.

My prompt was three definite Science Facts, and yet GPT-3 responded with highly questionable science. This is the problem with training a neural net to imitate most of the internet at once, including millions and millions of fiction stories, and plenty of conspiracy theories.

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