Stories are records of our experiences and their analysis reveals insights into the nature of being human. Successful analyses are often interdisciplinary, leveraging mathematical tools to extract structure from stories and insights from structure. Historically, these tools have been restricted to one dimensional charts and dynamic social networks; however, modern AI offers the possibility of identifying more fully the plot structure, character incentives, and, importantly, counterfactual plot lines that the story could have taken but did not take. In this work, we use AI to model the structure of stories as game-theoretic objects, amenable to quantitative analysis. This allows us to not only interrogate each character's decision making, but also possibly peer into the original author's conception of the characters' world. We demonstrate our proposed technique on Shakespeare's famous Romeo and Juliet. We conclude with a discussion of how our analysis could be replicated in broader contexts, including real-life scenarios.

Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human Feedback (RLHF) shows promise in aligning LLMs, its reliance on scalar rewards often limits its ability to capture diverse user preferences in real-world applications. To address this limitation, we introduce the Directional Preference Alignment (DPA) framework. Unlike the scalar-reward RLHF, DPA incorporates multi-objective reward modeling to represent diverse preference profiles. Additionally, DPA models user preferences as directions (i.e., unit vectors) in the reward space to achieve user-dependent preference control. Our method involves training a multi-objective reward model and then fine-tuning the LLM with a preference-conditioned variant of Rejection Sampling Finetuning (RSF), an RLHF method adopted by Llama 2. This method enjoys a better performance trade-off across various reward objectives. In comparison with the scalar-reward RLHF, DPA offers users intuitive control over LLM generation: they can arithmetically specify their desired trade-offs (e.g., more helpfulness with less verbosity). We also validate the effectiveness of DPA with real-world alignment experiments on Mistral-7B. Our method provides straightforward arithmetic control over the trade-off between helpfulness and verbosity while maintaining competitive performance with strong baselines such as Direct Preference Optimization (DPO).

The availability of quantitative text analysis methods has provided new ways of analyzing literature in a manner that was not available in the pre-information era. Here we apply comprehensive machine learning analysis to the work of William Shakespeare. The analysis shows clear changes in the style of writing over time, with the most significant changes in the sentence length, frequency of adjectives and adverbs, and the sentiments expressed in the text. Applying machine learning to make a stylometric prediction of the year of the play shows a Pearson correlation of 0.71 between the actual and predicted year, indicating that Shakespeare's writing style as reflected by the quantitative measurements changed over time. Additionally, it shows that the stylometrics of some of the plays is more similar to plays written either before or after the year they were written. For instance, Romeo and Juliet is dated 1596, but is more similar in stylometrics to plays written by Shakespeare after 1600. The source code for the analysis is available for free download. INTRODUCTION Being one of the most in influential authors in history, the analysis of the stylometrics of William Shakespeare has been a topic of substantial interest.

You're about to confront a pernicious new challenge that is spreading, kudzu-like, into your student writing assignments: papers augmented with artificial intelligence. The first online article generator debuted in 2005. Now, A.I.-generated text can now be found in novels, fake news articles and real news articles, marketing campaigns, and dozens of other written products. The tech is either free or cheap to use, which places it in the hands of anyone. Using an A.I. program is not "plagiarism" in the traditional sense--there's no previous work for the student to copy, and thus no original for teachers' plagiarism detectors to catch.

A product built to shuffle characters and events and generate narrative possibilities in real time, dancers using it brought a new version of the classic tragedy to life. The one-off production, R J RMX, was filmed for the Opera House's streaming platform. The "remix" was interactive: audience members were sent to a website where they could restructure the play with the touch of a button, while on stage narrators and dancers ran through numerous renditions of the story. The works of Shakespeare, surely more than those of any other writer, have been subject to interminable reworkings, as if we are at once infinitely fascinated and infinitely dissatisfied with the source material. So how does technology alter this process?

Machine learning clustering techniques are not the only way to extract topics from a text data set. Text mining literature has proposed a number of statistical models, known as probabilistic topic models, to detect topics from an unlabeled set of documents. One of the most popular models is the latent Dirichlet allocation (LDA) algorithm developed by Blei, Ng, and Jordan [i]. LDA is a generative unsupervised probabilistic algorithm that isolates the top K topics in a data set as described by the most relevant N keywords. In other words, the documents in the data set are represented as random mixtures of latent topics, where each topic is characterized by a Dirichlet distribution over a fixed vocabulary.

Among the scant books in my tiny rented room in San Francisco, I've kept a spine-worn copy of Romeo and Juliet. It's the one I read in my high school English class, the pages yellowed, the margins filled with scribbled notes. Since the play was written in the 1590s, Shakespeare's portrayal of the nature of love--irrational, all-consuming--has been told and retold in countless movie adaptations. I hold onto the book to revisit those insights, and also because I'm prone to nostalgic literary tendencies like keeping old books. I am also a personal tech writer in 2018. It's my job to keep tabs on how our rapidly shifting technology is shaping not only how we communicate, but how we empathize, trust, show affection.

In 2014, game designer Yoko Taro gave a talk about the creative process behind his cult PlayStation 3 title Nier: Replicant. He called the talk "Weird Games for Weird People". That is the best possible description of what he makes. Taro is famous for the eccentric persona he presents to the world. He rarely shows his face in public or interviews, preferring to talk from behind a sock puppet or the eerie wide grin of a mask.

Sunday night's season 1 finale of Westworld was the perfect finish to a near-perfect season of television. While many fan theories came true tonight, there were some big twists that not everyone saw coming. Better still, the finale leaves plenty of loose ends flapping in the breeze, leaving the door wide open for a second season. Perhaps the biggest twist of the night was Dr. Ford's role in...well, everything. It turns out it wasn't Arnold's voice in the hosts' heads, nor was it Arnold manipulating the hosts to awaken and rebel.

From Harry Potter and Romeo and Juliet to the stories of Oedipus and Icarus, almost every tale told conforms to one of just six plots, researchers have claimed. A major new analysis of over 1,700 stories identified the core plots'which form the building blocks of complex narratives'. Researchers used complex data-mining to locate words linked to positive or negative emotion in each story to reveal the set of arcs. A major new analysis of over 1,700 stories identified the core plots'which form the building blocks of complex narratives'. Shown, the plot of Harry Potter and the Deathly Hallows, which researchers found has the'rise, fall rise' plot.