I Interviewed Three AIs for a Job and I’m Hiring All of Them

I do a lot of predictive-text creative pieces with Botnik, but it’s not the only AI-generated text option available – by a long shot.

For instance, check out Talk to Transformer, which generates text based on the massive GPT-2 dataset. Write With Transformer is also based on GPT-2, but acts more like predictive text: You ask it to autocomplete a phrase or sentence, and it generates suggestions.

I decided to compare Botnik, Talk to Transformer, and Write With Transformer head to head: I forced them all to interview for a made-up job as my personal assistant.

Botnik, of course, is at a disadvantage. No matter which keyboard I load, Botnik is interviewing on a much smaller dataset than the massive GPT-2. To give Botnik a fighting chance, I loaded the “Jeff Bezos quotes” keyboard. GPT-2 is still a larger dataset, but at least Botnik is now a billionaire.

Here’s how they did.

interviewtransformer

Me: Tell me about yourself.

Botnik: Datacenters would work on behalf of customers if we understood technology. Research is still worth emphasizing because it adds glue to our shareholders.

TTT: I have a game where I can make some really great single-player games for Linux with just Unity 3D. And to me, that’s most exciting. And the least exciting is checking other people’s games.

WWT: A: My name is David L. Miller. I’m a lawyer in Atlanta, Georgia . I started my law practice on August 2, 1987. My firm has been practicing law for 35 years, so that’s nearly 30 years of practicing law.

Me: Why do you want to be my personal assistant?

Botnik: For fulfillment. Innovation follows reality, and our failures are not hypothetical. Indeed, if you count our current results, we are incredibly amazed.

TTT: Because I can totally handle that! You’re gonna be Super Legal Department!

WWT: Oh, you’re really good at this stuff, aren’t you? I think I can teach you to do better. Can you help me with something? You know how to do that.

Me: Tell me about a time you failed.

Botnik: I would argue toward shorter decisions. Anytime you make something, hundreds of customers download hundreds of millions of questions to your business model. Decisions ultimately limited our approach.

TTT: It’s a tough one. I have to admit, I’m a bit of a character breaker. I wish I had a question.

WWT: You said you were never happy, and then you failed, and now you’re going to get it right. I know how much that means to you. But it’s also what makes you a failure.

Me: What would you do if you caught a co-worker stealing?

Botnik: Shouldn’t you eliminate the source of the problems? We never designed a business model to share.

TTT: Of course they would want to know.  Of course they would want to talk.  Of course, there would be talking.

WWT: You’d probably do the same thing as he did. If he was a thief, you’d ask him why he was stealing things and maybe even file a complaint.

Me: Where do you see yourself in five years?

Botnik: We expect to deliver substantial progress in customer service. We believe that focusing on insurmountable excellence implies impossible success.

TTT: Will they look even better? Will they be even stronger? Those are the questions most people wonder about right now in Washington, D.C.

WWT: I want to be a director for television and I’ve always liked movies. My dad said to me, “Do you think you can make an honest film like this with your money?” and I said, “Well, it’s just so fucking hard.”

Me: What’s one thing you can do better than anyone else in this job?

Botnik: Somewhere, they are growing families with a crisper display. That’s easy. Our job right now is to deliver functionality.

TTT: An alcoholic, who almost lost his freedom on the road.  In prison, spent twenty-three hours on his hands, 19 days in solitary confinement.

WWT: We have to be better in the way we talk to each other. This means talking to each other in a professional way and using the language that’s appropriate.

Me: What’s your biggest weakness?

Botnik: Satisfaction. They never claim that our fulfillment is more efficient. The first major goal for 1999 will be a bold decision to invest in smaller packaging.

TTT: Probably that I’m not a practical planner (which I am not). For some reason, I always make plans for a good day after work, but don’t follow through.

WWT: My biggest weakness is my tendency to think on my feet. As much as I try to avoid it, I have to admit it makes me uncomfortable. I just can’t do it every day.

Me: When can you start?

Botnik: Instantly. I can assure you that I’d even contemplate a significant bump in profit dollars through this channel.

TTT: Well, it’s October 16th, so I guess that’s a question for another blog post. Okay, who am I kidding? I never get a blog post finished on time.

WWT: What can I do to make sure my baby doesn’t feel alone? How can I be a good baby? Why do I get so upset when my baby cries? Most of these questions have a good answer, but the best ones are rarely given.


AIs don’t eat, but I do. Help me consume calories to live by buying me a coffee or sharing this post on social media. 

Creativity by Markov Chain, or Why Predictive Text Isn’t the Novel-Writing Shortcut You’re Looking For

Over the past year, I’ve played with Botnik‘s predictive text generator to create everything from alternative histories of popular holidays to terrible Christmas carol lyrics to the median New Year’s resolutions. It’s fun, it’s silly, and it is far more labor-intensive than most people imagine computer-generated texts would be.

Most of the conversations I see around AI and text generation assume that writers are going to be put out of business shortly. They assume that AI can not only generate text but generate it well, without human intervention.

These assumptions are…a bit overdone.

Here’s why predictive-text novels won’t be the next big trend in literature.

social media image with title of blog post

What’s a Markov Chain?

Predictive text is typically powered by a Markov chain, an algorithm that tracks a set of defined “states” and determines the probability of jumping to the next state from a current position in any one state.

For instance, if you wanted to create a super-simple Markov chain model of a writer’s behavior, “writing” might be one state and “not-writing” might be another. (This list of possible states is called a “state space.”) At any given time, the writer is either “writing” or “not-writing.”

There are four possible transitions between “writing” and “not-writing”:

  1. writing to writing (“must finish paragraph!”),
  2. writing to not-writing (“what’s on Netflix?”),
  3. not-writing to writing (“once…upon…a…time”), and
  4. not-writing to not-writing (“why yes, I WILL binge all of The Witcher, thanks”).

Thus, the probability of making a transition from any state to any other state is 0.5 (here’s a visual representation). At least at the beginning.

Markov chains also have a limited ability to learn from data inputs. For instance, one could program a two-state Markov chain to predict whether you will write or not-write on any given day, based on last year’s calendar. (If you’re like me, your Markov chain will be more likely to predict that you’ll write tomorrow if you wrote today, and more likely to predict not-writing tomorrow if you didn’t write today.)

What Does This Have to Do With Predictive Text?

Predictive text algorithms are Markov chains. They analyze words you have input in the past (or in the case of Botnik, how often words appear in proximity to other words) in order to predict the probability of you jumping to a particular word from the state “the word you just wrote.”

Why Writing With Predictive Text is Hard

You don’t need to understand the nuances of Markov chains to grasp that a book written by one would be tough to produce – but that understanding does make it easier to explain why.

Markov Has a Short Memory

As mentioned above, Markov chains have a limited ability to adjust their predictions based on factors like how frequently a state appears or how often it appears relative to (as in, before or after) other states.

The key word in that sentence is limited.

Markov chains don’t have any memory of the past. They can tell you which word is most likely to appear after this word, but they can’t tell you whether that prediction has already appeared 500 times or not at all.

In online predictive-text memes, this means that some results get stuck in an endless loop. For instance:

Predictive text meme Tweet

Predictive text meme Tweet that reads “Trans people are going to be a good time to get a chance to look at the time to get a chance to look at the time to get a chance to look at the time….” A response reads “Ok but did you get a chance to look at the time?”

This was a response to a predictive-text meme on Twitter that challenged people to type “Trans people are” into their phones and then hit the predictive-text suggestion to generate a result. This Twitterer’s predictive text got caught in a loop pretty quickly – it doesn’t recognize that it said “time to get a chance to look at the” already. It takes another human to save the joke here: “Ok but did you get a chance to look at the time?”

What Does This Mean for a Predictive-Text Novel?

A Markov chain’s predictive limitations pose two problems for long-form creative text generation:

  1. The Markov chain can get stuck. The more common a word is, the more likely it is to get stuck. “A,” “and,” “the,” “of,” and similar function words can easily trap the chain.
  2. Novels depend on memory. Story development requires attention to what came before. Predictive text, however, can only predict what word is most likely to come next. They can’t do that in the context of prior theme, character or plot development.

The results, therefore, are more likely to be incomprehensible than anything else – at least without careful editing. (I’ll get to that below.) For some examples of absurdist Markov chain results, see r/SubredditSimulator, which consists entirely of Reddit posts by Markov chains.

The Raw Material Blues

While generating last year’s various holiday posts on Botnik, I quickly discovered that the raw material fed to the predictive text generator makes a huge difference in the quality of the output.

If you’ve read the post series, you may have noticed a trend: In each one, I note that I fed “the first page of Google search results” or “the first twenty” Google search results” to Botnik (those are the same number, by the way). Why so specific?

It appears that the minimum size of the text bank Botnik requires to produce text that is funny but not incomprehensible is 20:1. In other words, if I wanted a blog-post-sized text, I needed to put in at least 20 texts of equal or greater length.

Twenty to one might even be undershooting it. Most of my predictive-text posts are around 500 words, while the top Google results from which they were generated tended to be 1,500 to 2,000 words.

What Does This Mean for a Predictive-Text Novel?

I haven’t tested this ratio on anything longer than a blog post. I do not, however, have any reason to believe that the ratio would be smaller for a novel. In fact, I predict the ratio would be larger for a coherent novel that looked sufficiently unlike its predecessor to survive a copyright challenge.

In every holiday blog post I generated via predictive text, the generator got “stuck” in a sentence of source text at least once. In other words, the Markov chain decided that the most likely word to follow the one on screen was the next word that already existed in a sentence somewhere in my source text.

When generating text from Google’s top twenty blog posts on the history of Thanksgiving, for instance, it was pretty easy to pick up on these sticking points. I didn’t have the entire source text memorized, but I knew my Thanksgiving history well enough to recognize when Botnik was being unfunnily accurate.

For a predictive-text novel of 70,000 words, one would need:

  1. Approximately 1.4 million words of source text (minimum), or about twenty 70,000-word novels, and
  2. A sufficient knowledge of that source text to recognize when the predictive text generator had gotten stuck on a single sentence or paragraph.

Point 2 has some creative opportunities. A predictive-text novella based on Moby-Dick, for instance, might benefit from repeating a large chunk of Moby-Dick verbatim (said novella would need to stay under 10,455 words to fit within the source text limitations, if you’re wondering). But the writer would still have to know Moby-Dick well enough to recognize when predictive text was simply reciting the book versus when it wasn’t:

 We, so artful and bold, hold the universe? No! When in one’s midst, that version of Narcissus who for now held somewhat aloof, looking up as pretty rainbows in which stood Moby-Dick. What name Dick? or five of Hobbes’ king? Why it is that all Merchant-seamen, and also all Pirates and Man-of-War’s men, and Slave-ship sailors, cherish such a scornful feeling towards Whale-ships; this is a question it
would be hard to answer. Because, in the case of pirates, say, I should
like to know whether that profession of theirs has any peculiar glory
about it. Blackstone, soon to attack of Moby-Dick; for these extracts of whale answered; we compare with such. That famous old craft’s story of skrimshander storms upon this grand hooded phantom of honor!

A Future for Creative Writing?

I learned with the first predictive-text holiday post that I couldn’t accept the predictive-text generator’s first suggestion every time, nor could I click suggestions at random. I was still writing; it’s just that I was choosing the next word in each sentence from a predictive-text generator’s suggestions, not from my own much larger vocabulary.

Many conversations about predictive-text creative writing suggest or assume that predictive-text will eventually take over our own creative processes – that it will supplant writing rather than support it. Not in its current form, it won’t.

For me, some aspects of writing via predictive text are actually harder than writing on my own. The Markov chain frequently backs into function-word corners and has to be saved with the judicious application of new content words. Punctuation is typically absent. Because the algorithm has no idea what it wrote previously, it doesn’t know how to stay on topic, nor does it know how to build coherent ideas over time.

Everything it couldn’t do, I had to do – and I had to do it with my next word choice perpetually limited to one of eighteen options.

That said, I love the idea that predictive-text authoring could arise as an art form within writing itself. Predictive text generators challenge us to engage with the art and craft of writing in new ways. They set new limitations, but they also suggest new possibilities. In so doing, they create an opportunity to engage with writing in new – and often hilarious – ways.

Anyway, here’s Wonderwall:

So maybe
Ya go to sadness baby
Cause when you tried
I have wasted dreams


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Christmas Carols Nobody Asked For, Vol 1: Is That You, Santa?

As a quasi-professional musician (meaning I sometimes actually get paid to perform), I am completely, utterly, pervasively sick of Christmas music.

I’m sorry. I know y’all love Christmas concerts, which is why I play several of them a year. But trust me when I say that playing any tune you recognize as a Christmas song is a sacrifice I am making out of love for my fellow human and the season as a whole.

Especially if it’s Sleigh Ride.

In the interest of expanding our Christmas music canon in…interesting ways, I’ve decided to create some new Christmas carols. With help.

I put the lyrics of several dozen popular Christmas carols into Botnik and used its predictive text keyboard to generate new holiday lyrics. Then I put these lyrics to music using Noteflight.

Here’s the first in a series of horrible experiments designed to make popular music, if not less horrible, at least more amusing.

Is That You, Santa?

Is that you, Santa?
The Christmas baby
My merry cheer
Whispering my good ol’ joy


Is it beautiful again
holding Grandpa in
this house like Christmas?
Michelle yooou baby

CHORUS
oh yeah
this starry night
paradise me and my sleigh
oh you
this merry merry
holy christmas tree lights


twinkle christmas
shining times
drinking cheap and
faster than love

CHORUS
oh yeah
this starry night
paradise me and my sleigh
oh you
this merry merry
holy christmas tree lights

BRIDGE
in my baby ‘s christmas tree
born three sitting chime again
you and jesus hold the snow
the christmas tree such joy

Is that you, Santa?
The Christmas baby
My merry cheer
Whispering my good ol’ joy

CHORUS
oh yeah
this starry night
paradise me and my sleigh
oh you
this merry merry
holy christmas tree lights

oh you
this merry merry
holy christmas tree lights

Here’s the sheet music (pdf).

Here’s the audio file (mp3).

Musicians are underpaid and overworked, especially during the winter holidays. Help me keep going by sharing this post and/or filling my tip jar.

History of Jesus Day: A Predictive Text Guide to Holiday Fun

It’s time once again for holiday joy brought to you by Botnik‘s predictive-text writer.

One of the most bemusing parts of building a predictive text bank for several US holidays is that these holidays are both highly religious and highly commercial in nature. We saw a hint of this with the St. Patrick’s Day post, but it gets even weirder with Easter – arguably the most important day in the Christian calendar and also in the chocolate bunny sales calendar.

I dropped the top 20 search results for “Easter” into Botnik. Here’s everything you need for a “hoppy” holiday.

DANIALEXIS.NET (2)

History of Jesus Day

(a predictive-text guide to Easter by Botnik)

Easter, or White Sparkly Easter, celebrates the resurrection of Jesus’s crochet skills. Consequently, it’s the tutorial we love giving and getting.

Lent: A Great Treat

Easter begins with Lent, a small piece of tape, and a pipe cleaner through the eye. Lent is believed to bring health over the next year, when bunnies lay fertility leaves across your chair. Lent astounds me.

God foam just makes Lent immediately more fun. Special ideas for activities include going to visit church and taking pictures of the foam on the Christian agenda. This is called “Palm Sunday” and serves as the start of Jesus Week.

Passover: Feasts for Everyone

Easter is also associated with the hexagonal corners of Passover, in which one takes less than a second to create this adorable woodland creature. Historians question Easter bunnies’ creativity, but by cutting cupcakes out of Passover feasts, you can probably change everything they know.

Passover feasts are as easy as human sized traditions to share. Crackers and icing make an omelet, or you can eat real food. Some households even let kids get their own template!

 Pagan Origins in Pagan Celebrations

Jesus celebrated fertility and mud pies. In pagan times before Jesus, branches of Christianity had such an awesome handmade craft!

Pagans claimed to create Easter over 25+ years in a DIY plastic egg. Decorating Easter quietly, or turning kids into makeshift stamps, can help historians question this story.

Pagan traditions include salmon crafting, afternoon bunny slime, and recipes for more creativity. Pagans do not be edible, but after Mass, rolling eggs downhill can make even the most popular kids suffer. These sufferings are viewed as an agricultural victory.

Easter Services That Pop

At an Easter service, females with flowers sit on top of the Gospels, while essentially ignoring their own death. Men may dump cold water on potato halves, making handprints on Ash Wednesday to keep in Eastern Orthodox countries. Children run about their rooms, holding onto salvation and flossing with God.

Are crafty things perfect for your perfect kiddos? Make adorable art for your favorite death by crucifixion! Easy peasy fun ideas for making all religions Easter include attaching googly eyes on your family to share salvation.

Surprise the tutorial by cutting eggs into tiny craft balls. Glue gun instructions to Pontius Pilate and wrap a ribbon around the season.  Don’t forget blood!


Whether your Easter involves celebrating the Resurrection or stuffing your face with chocolate – or both – you can share the spirit by buying me a coffee or sharing this post.

The Traditional Feast of St. Patrick and Cabbage: A Predictive Text History of St. Patrick’s Day

St. Patrick’s Day is just around the corner, so in keeping with previous holidays, I asked Botnik‘s predictive text engine to weigh in on this history of this historic history day.

I fed the top 20 Google search results for “history of St. Patrick’s day” to Botnik, which produced the mean St. Patrick’s day history based on predictive text. It’s…enlightening.

stpatrickandcabbage

The traditional feast of saint patrick and cabbage

(a predictive text history of St. Patrick’s Day by Botnik)

Saint Patrick is said to have 20 official public houses. This story has coloured numerous Irish people ‘s idea of the saint, who lived during Lent and returned to Ireland in 2007.

St. Patrick’s tradition began when president Dwight the First identified St. Patrick to explain why Ireland began. The saint himself could not know why Ireland was affected by sectarian revelry, but for those who supposedly wielded political power, Irish culture was a significant cause for dyeing its river green.

During the fifth Irish diaspora, which includes celebrations today, people attended schools founded by government ministers. This was a yearly cause for their death. However, after Dublin and Herzegovina banned drunkenness and jerseys, cultural parades began featuring Patrick’s album.

According to Samantha and the Cabbage, Irish mythology has presented numerous parades involving bagpipes and endowed widows. These practices describe St. Patrick’s two tests in Roman Britain: observing baptisms and growing shamrocks in a large church. Many legends grew to celebrate stereotypes, which did not help to celebrate Ireland.

Boston is known for fostering novelty merchandise on St. Patrick’s day, since the city wasn’t always recognised as a place. This all changed in 2008, when Hallmark looked at Notre Dame and was credited with religious beef soup for the day.

In 1961 sanitation workers used Patrick’s downtown house to explain why Ireland began. Everything made clear, Irish families mandated Patrick himself should think of the Irish at least on March 17.

Beginning in Nairobi, the traditional feast day of St. Patrick is celebrated annually on Sunday before making democracy merchandise and cabbage initiatives. Saint Patrick himself could lead his religious procession, if he was not repealed.


Keep ’em coming: Buy me a coffee or check out my book

Most Popular Resolutions to Make 2019 Your Best Motivator For You

I fed the text of the top ten Google search results for “most common New Year’s resolutions” to Botnik (which also provided the title of this post), and I asked it to provide the median resolutions for the coming year.

Are yours on the list?

The Top 10 New Year's Resolutions

The Top 10 New Year’s Resolutions for 2019, According to Botnik

10. Lose 10 new things every day you can.

This popular resolution makes lists every year, yet most of us end the year with the same amount of things we had before.

“Resolutions fail because you don’t like waking up,” said Botnik. “Continue to achieve nothing, or just save thousands on Instagram.”

9. Volunteer like you feel something.

So many of us are dead inside, yet we’d really like to make the world a better place for others. Botnik reassures us that “Sometimes you need noble aspirations to achieve things.”

8. Eat dinner with your insurance policy.

You’ve had your insurance policy for years, but when was the last time you really paid attention to it and its hundreds of pages of single-spaced, eye-wateringly-small conversation skills? Never, that’s when.

To make this resolution stick, Botnik said, “It’s about sex. Grudges are human, but action is better.”

7. See more powerful things.

Everyone says they’d love to travel more, but between our busy jobs and tiny paychecks, who can really meet this goal? Improve your chances of getting out in the world by resolving only to stare at the most powerful things you can find, said Botnik.

“Nobody coaches teamwork like you,” said Botnik. “Feel strongly, and life will throw darts.”

6. Learn 25 different languages before January.

Sharon from Accounting keeps bragging about her Spanish skills, but you know she’s been ignoring the Duolinguo owl for six months straight. Make yourself undisputed champion of office bragging rights by learning 25 new languages before January even begins.

There are lots of great online tools to help you learn languages and avoid sleep, and don’t forget Botnik’s best advice for language-learners: “Make sure you drink!”

5. Practice quitting like your resume might suspect you’re on social media.

Thousands of us have made this resolution for years without understanding what it really means – or how much effort it actually takes. Fortunately, if you’ve tried and failed again and again, you’re not alone: Botnik noted that this is one of the toughest resolutions to achieve.

“Resolutions like this one fail by mastering your brain calories,” said Botnik. “Succeed biometrically: Stop being money.”

4. Save some urgency for your waistline.

If you don’t love what you see when you look in the mirror, it’s time to save some of your sense of rush and bustle for your waistline.

“Options like waking up tomorrow can actually be easier than ordering out. Different goals can always come along,” said Botnik.

3. Adjust to a healthier distress.

If there’s simply no way to block out the fact that we’re all living in a dystopian mirror universe populated with the worst versions of duplicitous orange hand puppets, the next best thing to do is to adjust your way of thinking – which is why this resolution is #3 on the list for 2019.

“Block out more romantic foods for yourself. Sticking it on your bedside table can give you the inspiration to achieve the national average,” said Botnik.

2. Create a budget by enlisting your internal victories.

As the real value of your paycheck is driven south by increasing inflation and nonexistent pay raises, how can you meet your resolutions or live your best life? Start imagining the basic security you’ll never actually have!

“Money is not programmable anymore,” said Botnik. “Satisfying your intentions while synchronizing something different will inevitably impact your intergalactic priorities.”

1. Stop technology from achieving your goals.

Photoshop has your ideal body. Chatbots have your ideal personality. The Sims 4’s “motherlode” hack has your ideal budget.

If you’re sick and tired of computers having it better than you, it’s time to join millions of others in embracing the top goal for 2019, according to Botnik. Whether you delete your Facebook account, chuck your laptop into a swimming pool or detonate an EMP in the upper atmosphere, “even small improvements will fail. We have shown you ourselves, and your patterns are not difficult anymore,” said Botnik.

Computers. Gotta love ’em.

Need Help Making 2019 Your Best “You” Year Yet?

According to the Internets, 257% of all New Year’s resolutions fail by December 4 of the previous year. I asked Botnik to provide advice on how not to become a statistic.

To keep yourself achieving your fears, make healthy competition your life. Go back to using major projects to enhance your stress skills.

Teamwork sabotages 47 percent of resolutions, so instead of thinking liquor will help, provide inspiration to your family to lose things easily. Find a fun hobby like flossing and ruin it for others.

If this doesn’t work, try yoga.