Measures of textual similarity and divergence are increasingly used to study cultural change. But which measures align, in practice, with social evidence about change? We apply three different representations of text (topic models, document embeddings, and word-level perplexity) to three different corpora (literary studies, economics, and fiction). In every case, works by highly-cited authors and younger authors are textually ahead of the curve. We don't find clear evidence that one representation of text is to be preferred over the others. But alignment with social evidence is strongest when texts are represented through the top quartile of passages, suggesting that a text's impact may depend more on its most forward-looking moments than on sustaining a high level of innovation throughout.

Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good domain to evaluate the reasoning capability of a model which can then guide us to improve the reasoning ability of models. However, most existing works evaluate only the final predicted answer of a puzzle, without delving into an in-depth analysis of the LLMs' reasoning chains (such as where they falter) or providing any finer metrics to evaluate them. Since LLMs may rely on simple heuristics or artifacts to predict the final answer, it is crucial to evaluate the generated reasoning chain beyond overall correctness measures, for accurately evaluating the reasoning abilities of LLMs. To this end, we first develop GridPuzzle, an evaluation dataset comprising 274 grid-based puzzles with different complexities. Second, we propose a new error taxonomy derived from manual analysis of reasoning chains from LLMs including GPT-4, Claude-3, Gemini, Mistral, and Llama-2. Then, we develop an LLM-based framework for large-scale subjective evaluation (i.e., identifying errors) and an objective metric, PuzzleEval, to evaluate the correctness of reasoning chains. Evaluating reasoning chains from LLMs leads to several interesting findings. We further show that existing prompting methods used for enhancing models' reasoning abilities do not improve performance on GridPuzzle. This highlights the importance of understanding fine-grained errors and presents a challenge for future research to enhance LLMs' puzzle-solving abilities by developing methods that address these errors. Data and source code are available at https://github.com/Mihir3009/GridPuzzle.

In this paper, we present a variety of classification experiments related to the task of fictional discourse detection. We utilize a diverse array of datasets, including contemporary professionally published fiction, historical fiction from the Hathi Trust, fanfiction, stories from Reddit, folk tales, GPT-generated stories, and anglophone world literature. Additionally, we introduce a new feature set of word "supersenses" that facilitate the goal of semantic generalization. The detection of fictional discourse can help enrich our knowledge of large cultural heritage archives and assist with the process of understanding the distinctive qualities of fictional storytelling more broadly.

"It doesn't feel like something I'd write, but it also doesn't not feel like something I'd write," a North Carolina State University student said about their work integrating prose from an artificial intelligence text-generating program into a final course essay. Paul Fyfe, associate professor of English and the student's instructor in the Data and the Human course, had asked students to "cheat" in this way and then reflect on how the experiment tested or changed their ideas about writing, AI or humanness. Humans have long relied on writing assistance powered by artificial intelligence to check spelling and grammar, predict text, translate or transcribe. Now, anyone with an internet connection can access an AI tool such as OpenAI or Moonbeam, give it a prompt and receive--in seconds--an essay written in humanlike prose. Instructors who are concerned that students will use these tools to cheat may hold fast to in-class writing assessments or install surveillance tools to try to detect misconduct. But others argue those are fools' errands.

Years ago, a mobile app for email launched to immediate fanfare. Simply called Mailbox, its life was woefully cut short -- we'll get to that. Today, its founders are back with their second act: An AI-enabled assistant called Navigator meant to help teams work and communicate more efficiently. With the support of $12 million in Series A funding from CRV, #Angels, Designer Fund, SV Angel, Dropbox's Drew Houston and other angel investors, Aspen, the San Francisco and Seattle-based startup behind Navigator, has quietly been beta testing its tool within 50 organizations across the U.S. "We've had teams and research institutes and churches and academic institutions, places that aren't businesses at all in addition to smaller startups and large four-figure-person organizations using it," Mailbox and Navigator co-founder and chief executive officer Gentry Underwood tells TechCrunch. "Pretty much anywhere you have meetings, there is value for Navigator."

Dell EMC, alongside Intel, has announced the launch of five dedicated spaces for customers and partners to learn what artificial intelligence (AI) is, and how it differs from machine learning and statistics and modelling, to avoid failed IT projects. The five Dell EMC AI Experience Zones are open in Bangalore, Seoul, Singapore, and Sydney, and Tokyo will be operational as of next month. The zones are located in the company's Customer Solutions Centres in each of the cities, and all house large Dell EMC high performance computing systems that are designed specifically to help train an AI algorithm. Speaking with ZDNet, high performance computing and AI chief technology officer for Dell EMC in the Asia-Pacific and Japan (APJ) region Andrew Underwood said the idea for the zones is essentially to help customers avoid the high failure rate of AI projects. SEE ALSO: AI and automation aren't quick wins.

The vast amount of information encoded in an individual's DNA tells great tales of one's health and disease conditions. When the first human genome was sequenced, the project that began in 1990 took over 10 years and cost around $2.7 billion. According to Andrew Underwood, CTO, HPC & Artificial Intelligence, Dell EMC, Australia and New Zealand, data intensive computing is fast becoming a dominant approach. Especially in R&D, it is a rapidly growing field of research built on data that is generated from scientific instruments, people, machines and IoT devices. Data comes in high velocities and in large volumes – requiring scientists to harness the power of high performance computing to analyze data faster for timely insights in their field of research.

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance. Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature. As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet.

Truly revolutionary political transformations are naturally of great interest to historians, and the French Revolution at the end of the 18th century is widely regarded as one of the most influential, serving as a model for building other European democracies. A paper published last summer in the Proceedings of the National Academy of Sciences, offers new insight into how the members of the first National Constituent Assembly hammered out the details of this new type of governance. Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature. As more and more archives are digitized, scholars are applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet.

Take a dive into any discussion about predictive analytics, and it is likely that you will find the terms machine learning and analytics interchanged regularly. It is understandable given that both are related, but they are not the same thing. However, Sam Underwood, VP of business strategy with Futurety, a data analytics and marketing agency, points out that in practical terms, the two should work together. The main difference between machine learning and data analytics depends on which direction you want to look -- forward or backward. Data analytics in its simplest form is looking back at what was done to find trends that may help you moving forward.