AI Dungeon, a text-based fantasy simulation that runs on OpenAI's GPT-3, has been churning out weird tales since May 2019. Reminiscent of early text adventure games like Colossal Cave Adventure, you get to choose from a roster of formulaic settings--fantasy, mystery, apocalyptic, cyberpunk, zombies--before picking a character class and name, and generating a story. Here was mine: "You are Mr. Magoo, a survivor trying to survive in a post-apocalyptic world by scavenging among the ruins of what is left. You have a backpack and a canteen. You haven't eaten in two days, so you're desperately searching for food."

The premise of this article is that a basic understanding of the composition and functioning of large language models is critically urgent. To that end, we extract a representational map of OpenAI's GPT-2 with what we articulate as two classes of deep learning code, that which pertains to the model and that which underwrites applications built around the model. We then verify this map through case studies of two popular GPT-2 applications: the text adventure game, AI Dungeon, and the language art project, This Word Does Not Exist. Such an exercise allows us to test the potential of Critical Code Studies when the object of study is deep learning code and to demonstrate the validity of code as an analytical focus for researchers in the subfields of Critical Artificial Intelligence and Critical Machine Learning Studies. More broadly, however, our work draws attention to the means by which ordinary users might interact with, and even direct, the behavior of deep learning systems, and by extension works toward demystifying some of the auratic mystery of "AI." What is at stake is the possibility of achieving an informed sociotechnical consensus about the responsible applications of large language models, as well as a more expansive sense of their creative capabilities-indeed, understanding how and where engagement occurs allows all of us to become more active participants in the development of machine learning systems.

Before OpenAI's ChatGPT emerged and captured the world's attention for its ability to create compelling sentences, a small startup called Latitude was wowing consumers with its AI Dungeon game that let them use artificial intelligence to create fantastical tales based on their prompts. But as AI Dungeon became more popular, Latitude CEO Nick Walton recalled that the cost to maintain the text-based role-playing game began to skyrocket. AI Dungeon's text-generation software was powered by the GPT language technology offered by the Microsoft-backed AI research lab OpenAI. The more people played AI Dungeon, the bigger the bill Latitude had to pay OpenAI. Compounding the predicament was that Walton also discovered content marketers were using AI Dungeon to generate promotional copy, a use for AI Dungeon that his team never foresaw, but that ended up adding to the company's AI bill.

If you've been closely following the progress of Open AI, the company run by Sam Altman whose neural nets can now write original text and create original pictures with astonishing ease and speed, you might just skip this piece. If, on the other hand, you've only been vaguely paying attention to the company's progress and the increasing traction that other so-called "generative" AI companies are suddenly gaining and want to better understand why, you might benefit from this interview with James Currier, a five-time founder and now venture investor who cofounded the firm NFX five years ago with several of his serial founder friends. Currier falls into the camp of people following the progress closely -- so closely that NFX has made numerous related investments in "generative tech" as he describes it, and it's garnering more of the team's attention every month. In fact, Currier doesn't think the buzz about this new wrinkle on AI isn't hype so much as a realization that the broader startup world is suddenly facing a very big opportunity for the first time in a long time. "Every 14 years," says Currier, "we get one of these Cambrian explosions. We had one around the internet in '94.

Update: AI Dungeon's CEO has confirmed that, for speed's sake, the game does not generate images on the fly. Instead it uses "AI to match to a library of AI created images that were pre-generated." Original Story: I love me a good AI Dungeon (opens in new tab) session, and I'm happy to announce the devs have just made their AI image generator free to use with the base AI. At the same time though, it looks like the free version now includes ads so, swings and roundabouts. If you've never heard of AI Dungeon before, think D&D but with an AI as your dungeon master.

And then the Guardian editors took the best outputs and spliced them together into this one large op-ed." Still, according to Porr, the Guardian editors reported that the process was easier than, or at least comparable to, working with a human writer. But Parrish, an assistant arts professor at NYU who uses AI to craft verse, argues that computer-generated poetry is a new frontier in literature, allowing for serendipitous connections beyond anything human brains can create. While GPT-3 is not available for use by the general public, according to Porr, I could interact with it through a story-generating game called AI Dungeon, run by Latitude, a company purporting to make "AI a tool of creativity and freedom for everyone." Using the program, I could enter a line of text and it would produce a follow-up line that would presumably advance the plot. It's like the writing game "exquisite corpse," where a group of writers passes around a folded piece of paper on which only the previous line is visible as they construct the next line, but in this case, my co-conspirator was a computer program, trawling the internet for patterns in human-generated text, alchemizing gigabytes of language from the web into narrative. So, in fact, this tool is useless."

Not all of us have the talent to whip up a piece of art at a moment's notice. But algorithms using machine learning are learning how to create "AI art" based on text prompts--and you can use them, too. Algorithms like DALL-E (and eventually, DALL-E 2), DALL-E mini, Craiyon, Midjourney, and more are learning how to take publicly available art and learn what makes them art. Or, at least, digest the various elements and style of a photo or artistic work and recombine them into something new. Sure, you can argue whether or not they're, in fact, "art," but the creations are unique, original, and compelling. Simply put, AI art uses a text prompt: something specific like McDonalds at the bottom of the sea, for example, or a bit more generic like the castle of time -- the prompt that generated the art at the top of this story.

Latitude, the startup behind text game AI Dungeon, is expanding into a new artificial intelligence-powered game platform called Voyage. The company announced the closed beta on Friday, opening a waitlist for current AI Dungeon users. It's the next step for a company that began with a university hackathon project, but that ultimately hopes to help other people create their own games using trained AI models. AI Dungeon, which launched as AI Dungeon 2 in 2019, is powered by OpenAI's GPT-2 and GPT-3 text generation algorithms. To start, you generate some introductory text or write your own adventure setup.

In December 2019, Utah startup Latitude launched a pioneering online game called AI Dungeon that demonstrated a new form of human-machine collaboration. The company used text-generation technology from artificial intelligence company OpenAI to create a choose-your-own adventure game inspired by Dungeons & Dragons. When a player typed out the action or dialog they wanted their character to perform, algorithms would craft the next phase of their personalized, unpredictable adventure. Last summer, OpenAI gave Latitude early access to a more powerful, commercial version of its technology. In marketing materials, OpenAI touted AI Dungeon as an example of the commercial and creative potential of writing algorithms.\

I've put together a list of the data I want to record and analyse. I've also put together a checklist of the things I'll need to run the D&D campaign that will actually be generating all that beautiful data. Now I need to start operationalising this bad boy. First up, how am I actually going to export the data from all the sources I've identified? I'd rather not be messing around with data scraping, so would prefer (where possible) any tools I use to natively export to convenient file formats.