Can a neural network write good riddles? Maybe, but I sure didn’t prove it with this post. If you have a taste for the terrible, read on:
The What Is It Riddle
What’s the first letter of the word ‘E’?
Answer: E.
The Why Does It Look Like A Man Riddle:
Why does it look like a man’s head?
Answer: It might be a man’s head.
Placeholder for a later post.
For now just linking some tweets.
Worm is a web serial written by Wildbow. You can read it on
parahumans.wordpress.com. If you have not read Worm turn away now because even silly neural network bits will spoiler you.
If you insist on reading anyway for the love of God at least only look at the first half the blog post. The second half contains paragraphs of text from Worm itself – a greatest hits of spoilers.
These are scenes generated by the GPT-2 neural network. The first section has unconditional scenes – where the network is just told ‘write something’; the second section has prompted scenes – where the network is given an existing Worm scene and asked to complete it.
Continue reading “Scenes That Never Happened In The The Web Serial WORM”
I wish I could tell you I had a good reason why.
Anyway let’s use the GPT-2 345M model to recreate the viral (but not real) “I Forced a Bot” tweets that I named this site after… but with a trained model based on Star Trek.
I was going to do many different training materials and cover more of the original viral tweets, but the Star Trek Olive Garden Commercial samples are just killing me by themselves. I honestly think I could do nothing with GPT-2 but generate Olive Garden commercials from different models and never get bored. It deserves it’s own post!
Continue reading “I Forced A Bot To Watch Over 1,000 Hours Star Trek Episodes And Then Asked It To Write 1000 Olive Garden Commercials.”
When I was working on the first post about D&D spells from a neural network I generally let the network run wild and create the spells from nothing, which also created the spell names. But I did try ‘prompting’ the network with the spell names from @JanelleCShane’s neural network D&D spell names post and asking it to fill in the rest of the spell information.
I made a ton but they were a bit harder to skim through since you can’t rely on a catchy spell name to jump out. I was going to make better sifting tools but figured I’ll post what I’ve got for now. Thanks to my friend Sam for picking out some good ones. Be warned a lot these samples were from a terrible model and it went way off the rails and just generated absolute nonsense — but it also gave us such delights as a spell that is just “No,No,No,No” over and over.
Continue reading “Ensntalice! What Would a ‘True Steake’ Spell Do? Prompted D&D Spells”
In my last post I trained GPT-2 to write Star Trek scripts. Lately I’ve been experimenting with Dungeons and Dragons spells with some amazing results.
I like tabletop roleplaying material for generation because tabletop rules often require a good faith effort at human interpretation anyway. That same effort can make some sense of the silliest of machine generated rules.
I picked out a bunch of of my favorites and there are a lot more spells at the bottom of this blog post for anyone who wants to hunt for some more good stuff. Also I’ll be posting more on my twitter.
Continue reading “Dungeons and Dragons Spells from a Neural Network Are Bonescrackling”
In my last post I trained OpenAI’s neural network on the Star Trek: The Next Generation and Deep Space Nine scripts and had it write some its own.
I used a version that GPT-2 that lets you feed it additional training material. This is why the output looks like a TV script. But even without fine-tuning GPT-2 knows a whole lot about Star Trek because GPT-2 was trained on a huge sample of the internet, and internet knows a lot about Star Trek. So what happens if you ask regular GPT-2 to predict the next word in the sequence This episode of Star Trek: The Next Generation opens with a scene where?
This episode of Star Trek: The Next Generation opens with a scene where The Enterprise's fuel cells are destroyed, and the crew and crew of the Enterprise are not rescued. As they are being evacuated, Picard uses the transporter device to activate the emergency access and communication system, and a rescue crew arrives to rescue the crew.Continue reading “You Don’t Need to Force a Bot to Watch Star Trek to Write Its Own Episodes”
The transporter device is activated, and the crew is saved. Later on, in a flashback scene, Picard describes how the Enterprise was going to be destroyed: "We had to determine what we could do with the fuel and the crew and the power that we had."
The Enterprise is finally able to reach the planet, and the crew and crew escape. While the power is still available through the emergency access, the power is being used to launch an attack on the Klingon capital.
You may have seen “I forced a bot to watch” posts where someone claims to have “forced a bot” to read or watch hours of video and written it’s own script. Those weren’t real but in recent history it has become possible to do it for real in a pretty convincing way with OpenAI’s GPT-2 system.
This is an unbroken, unedited sample of GPT-2 ‘fine-tuned’ on all the TNG and DS9 scripts. Note that television scripts have have distinct formatting and style and all of that is copied perfectly by the bot. It even *almost* gets understands page numbers (in the first example – 28, 28, 28A).
Proudly powered by WordPress | Theme: Baskerville 2 by Anders Noren.