At least it shows its symptoms clearly: inflammation and swelling. In the first film, Gemma (Allison Williams), a robotics engineer, becomes the guardian to her orphaned niece, Cady (Violet McGraw), and tests a new invention, the titular A.I.-powered robot-doll, on her. Cady grows attached to the responsive doll, which is programmed to protect the child and takes to the mission with a mechanical perfection, slaughtering anyone who expresses hostility--and does so with snarky pride in her absolute power. At its core, though, "M3GAN" (like the sequel, directed by Gerard Johnstone) is a family melodrama centered on Gemma's struggles with parenting and Cady's need to bond--plus the robot's quick embrace of human cruelty. The film's failures are painful because its setup is fruitful.

Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretability. Moreover, these systems offer limited control to users over their recommendations. Inspired by recent work, we introduce TExtuAl Representations for Scrutable recommendations (TEARS) to address these challenges. Instead of representing a user's interests through a latent embedding, TEARS encodes them in natural text, providing transparency and allowing users to edit them. To do so, TEARS uses a modern LLM to generate user summaries based on user preferences. We find the summaries capture user preferences uniquely. Using these summaries, we take a hybrid approach where we use an optimal transport procedure to align the summaries' representation with the learned representation of a standard VAE for collaborative filtering. We find this approach can surpass the performance of three popular VAE models while providing user-controllable recommendations. We also analyze the controllability of TEARS through three simulated user tasks to evaluate the effectiveness of a user editing its summary.

Did you ever get to the end of Wizard of Oz and have notes – the nagging intuition that you could have taken down all those pesky flying monkeys or handled the backstabbing intricacies of Munchkin guild politics more effectively than Dorothy and her band of misfits did in the books? Thanks to the new AI storytelling platform Hidden Door, which plops players into TTRPG-like adventures based in their favorite literary universes, you'll soon have the chance to walk the Yellow Brick Road however you see fit. Hidden Door is both the company and the game. Hidden Door, the company, was co-founded by Hilary Mason, who is also CEO, and Matt Brandwein in 2020 with a mission to "inspire creativity through play with narrative AI." The staff is split nearly evenly between machine learning engineers and traditional game designers, Mason told Engadget.

Anthropic, an AI startup co-founded by former employees of OpenAI, has quietly begun testing a new, ChatGPT-like AI assistant named Claude. The team at Anthropic was gracious enough to grant us access, and updates to Anthropic's social media policies mean we can now share some of our early, informal comparison findings between Claude and ChatGPT. To show how Claude is different, we'll begin by asking ChatGPT and Claude to introduce themselves with the same prompt. Short and to the point -- ChatGPT is an assistant made to answer questions and sound human. The interface to Claude is a Slack channel using a bot that edits messages to make text appear word-by-word. This causes "(edited)" to appear.

As a writer collective, we've had AI on the brain--from my last piece on AI companion bots to Evan's excellent essay on the AI value chain to Nathan's exploration of the infinite AI article. Every has also been building Lex, a word processor with AI baked in. I started working on this piece before we launched Lex, but testing out this tool (among others) has shaped my perspective on the role of AI writing assistants for creatives. Try it for yourself: watch the demo and sign-up to join the waitlist (Every's paid subscribers have priority access, so subscribe to skip the line). In 2016, filmmaker Oscar Sharp and AI researcher Ross Goodwin created an experimental short sci-fi film written entirely by a neural network.

The Three Laws of Robotics are iconic in the science fiction world, and have become a symbol within the AI and robotics community of how difficult it is to properly design a system that is foolproof. To fully comprehend the importance of these three laws, we must first learn about the brilliant mind who conceived of these laws the late science fiction author Isaac Asimov. We must then understand how to adapt these laws and have them evolve to protect humanity. Isaac Asimov was born in Russia on January 2, 1920, and immigrated to the United States at age three. He grew up in Brooklyn, New York, and graduated from Columbia University in 1939.

Dr Lara Martin wants to teach artificial intelligence how to tell a tale and tell it well. Lara is a Computing Innovation Fellow postdoctoral researcher at the University of Pennsylvania, where she teaches AI to generate stories and produce language that is natural and human-like. She reveals why we need to train machines how to be storytellers and what Dungeons & Dragons has to do with it all. People have been telling stories since before we could write; we're natural storytellers. So if machines were able to tell and understand stories as well, we'd be able to communicate with them more naturally.

Automated story plot generation is the task of generating a coherent sequence of plot events. Causal relations between plot events are believed to increase the perception of story and plot coherence. In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning. We demonstrate C2PO, an approach to narrative generation that operationalizes this concept through Causal, Commonsense Plot Ordering. Using human-participant protocols, we evaluate our system against baseline systems with different commonsense reasoning reasoning and inductive biases to determine the role of soft causal relations in perceived story quality. Through these studies we also probe the interplay of how changes in commonsense norms across storytelling genres affect perceptions of story quality.

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and person-alisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL is able to learn action sequences to complete a game, and provided insights towards generating behaviour more similar to specific players.