Robohub.org
 

Can artificial intelligence learn to scare us?


by
28 October 2017



share this:

Just in time for Halloween, a research team from the MIT Media Lab’s Scalable Cooperation group has introduced Shelley: the world’s first artificial intelligence-human horror story collaboration.

Shelley, named for English writer Mary Shelley — best known as the author of “Frankenstein: or, the Modern Prometheus” — is a deep-learning powered artificial intelligence (AI) system that was trained on over 140,000 horror stories on Reddit’s infamous r/nosleep subreddit. She lives on Twitter, where every hour, @shelley_ai tweets out the beginning of a new horror story and the hashtag #yourturn to invite a human collaborator. Anyone is welcome to reply to the tweet with the next part of the story, then Shelley will reply again with the next part, and so on. The results are weird, fun, and unpredictable horror stories that represent both creativity and collaboration — traits that explore the limits of artificial intelligence and machine learning.

“Shelley is a combination of a multi-layer recurrent neural network and an online learning algorithm that learns from crowd’s feedback over time,” explains Pinar Yanardhag, the project’s lead researcher. “The more collaboration Shelley gets from people, the more and scarier stories she will write.”

Shelley starts stories based on the AI’s own learning dataset, but she responds directly to additions to the story from human contributors — which, in turn, adds to her knowledge base. Each completed story is then collected on the Shelley project website.

“Shelley’s creative mind has no boundaries,” the research team says. “She writes stories about a pregnant man who woke up in a hospital, a mouth on the floor with a calm smile, an entire haunted town, a faceless man on the mirror … anything is possible!”

One final note on Shelley: The AI was trained on a subreddit filled with adult content, and the researchers have limited control over her — so parents beware.



tags:


MIT News

            AUAI is supported by:



Subscribe to Robohub newsletter on substack



Related posts :

Developing active and flexible microrobots

  13 May 2026
This class of robots opens up possibilities for biomedical applications.

How to teach the same skill to different robots

  11 May 2026
A new framework to teach a skill to robots with different mechanical designs, allowing them to carry out the same task without rewriting code for each.

Robot Talk Episode 155 – Making aerial robots smarter, with Melissa Greeff

  08 May 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Melissa Greeff from Queen's University about autonomous navigation and learning for drones.

New understanding of insect flight points way to stable flapping-wing robots

  07 May 2026
The way bugs and birds flap their wings may look effortless, but the dynamics that keep them aloft are dizzyingly complex and difficult to quantify.

Robotically assembled building blocks could make construction more efficient and sustainable

  05 May 2026
Research suggests constructing a simple building from interlocking subunits should be mechanically feasible and have a much smaller carbon footprint.

Robot Talk Episode 154 – Visual navigation in insects and robots, with Andrew Philippides

  01 May 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Andrew Philippides from the University of Sussex about what we can learn from ants and bees to improve robot navigation.

Ultralightweight sonar plus AI lets tiny drones navigate like bats

  29 Apr 2026
Researchers develop ultrasound-based perception system inspired by bat echolocation.

Gradient-based planning for world models at longer horizons

  28 Apr 2026
What were the problems that motivated this project and what was the approach to address them?



AUAI is supported by:







Subscribe to Robohub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence