The Final Exam, a recently released video game in which you play as a student caught amid a school shooting, lasts for around ten minutes, about the length of a real shooting event in a U.S. school. The game opens in an empty locker room. You hear distant gunfire, screams, harried footsteps, and the thudding of heavy furniture being overturned. The sense of disharmony is immediate: a familiar scene of youth and learning is grimly debased into one of peril. As the lockers surround you, their doors gaping, you feel caged: get me out of here. Moments later, as you enter the gymnasium, a two-minute countdown flashes on screen.

Text is a vehicle to convey information that reflects the writer's linguistic style and communicative patterns. By studying these attributes, we can discover latent insights about the author and their underlying message. This article uses such an approach to better understand patent applications and their inventors. While prior research focuses on patent metadata, we employ machine learning and natural language processing to extract hidden information from the words in patent applications. Through these methods, we find that inventor gender can often be identified from textual attributes - even without knowing the inventor's name. This ability to discern gender through text suggests that anonymized patent examination - often proposed as a solution to mitigate disparities in patent grant rates - may not fully address gendered outcomes in securing a patent. Our study also investigates whether objective features of a patent application can predict if it will be granted. Using a classifier algorithm, we correctly predicted whether a patent was granted over 60% of the time. Further analysis emphasized that writing style - like vocabulary and sentence complexity - disproportionately influenced grant predictions relative to other attributes such as inventor gender and subject matter keywords. Lastly, we examine whether women disproportionately invent in technological areas with higher rejection rates. Using a clustering algorithm, applications were allocated into groups with related subject matter. We found that 85% of female-dominated clusters have abnormally high rejection rates, compared to only 45% for male-dominated groupings. These findings highlight complex interactions between textual choices, gender, and success in securing a patent. They also raise questions about whether current proposals will be sufficient to achieve gender equity and efficiency in the patent system.

This study examines the integration of generative AI in schools, assessing its benefits and risks. As AI use by students grows, it's crucial to understand its impact on learning and teaching practices. Generative AI, like ChatGPT, can create human-like content, prompting questions about its educational role. The article differentiates large language models from traditional search engines and stresses the need for students to develop critical source evaluation skills. Although empirical evidence on AI's classroom effects is limited, AI offers personalized learning support and problem-solving tools, alongside challenges like undermining deep learning if misused. The study emphasizes deliberate strategies to ensure AI complements, not replaces, genuine cognitive effort. AI's educational role should be context-dependent, guided by pedagogical goals. The study concludes with practical advice for teachers on effectively utilizing AI to promote understanding and critical engagement, advocating for a balanced approach to enhance students' knowledge and skills development.

When faced with a bit of downtime, many of my friends will turn to the same party game. It's based on the surrealist game Exquisite Corpse, and involves translating brief written descriptions into rapidly made drawings and back again. One group calls it Telephone Pictionary; another refers to it as Writey-Drawey. The internet tells me it is also called Eat Poop You Cat, a sequence of words surely inspired by one of the game's results. As recently as three years ago, it was rare to encounter text-to-image or image-to-text mistranslations in daily life, which made the outrageous outcomes of the game feel especially novel. But we have since entered a new era of image-making. With the aid of AI image generators like Dall-E 3, Stable Diffusion and Midjourney, and the generative features integrated into Adobe's Creative Cloud programs, you can now transform a sentence or phrase into a highly detailed image in mere seconds. Images, likewise, can be nearly instantly translated into descriptive text.

The field of text-to-image (T2I) generation has garnered significant attention both within the research community and among everyday users. Despite the advancements of T2I models, a common issue encountered by users is the need for repetitive editing of input prompts in order to receive a satisfactory image, which is time-consuming and labor-intensive. Given the demonstrated text generation power of large-scale language models, such as GPT-k, we investigate the potential of utilizing such models to improve the prompt editing process for T2I generation. We conduct a series of experiments to compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process. We found that GPT-k models focus more on inserting modifiers while humans tend to replace words and phrases, which includes changes to the subject matter. Experimental results show that GPT-k are more effective in adjusting modifiers rather than predicting spontaneous changes in the primary subject matters. Adopting the edit suggested by GPT-k models may reduce the percentage of remaining edits by 20-30%.

March 31, 2023 - Artificial Intelligence suddenly seems to be everywhere. ChatGPT is writing human-sounding sermons, news updates, and answers to law school exam questions, while Dall·E is generating images ranging from the lifelike to the surreal in response to virtually any prompt. With much less fanfare, AI has already become ubiquitous in myriad ways. AI curates social media feeds and generates purchasing suggestions to fill internet shopping carts. AI saves lives by identifying potential pharmaceutical compounds and by quickly and accurately interpreting medical scans and images.

An actual, if elderly and ailing, Havanese is looking up at me as I work, and an Avedon portrait book is open on my desk. What could be more beguiling than combining the two? Then my laptop stutters and pauses, and there it is, eerily similar to what Richard Avedon would have done if confronted with a Havanese. The stark expression, the white background, the implicit anxiety, the intellectual air, the implacable confrontational exchange with the viewer--one could quibble over details, but it is close enough to count. My Havedon is, of course, an image produced by an artificial-intelligence image generator--DALL-E 2, in this case--and the capacity of such systems to make astonishing images in short order is, by now, part of the fabric of our time, or at least our pastimes.

The Federal Circuit recently held as a matter of statutory interpretation that an artificial intelligence system cannot be named as an inventor on a US patent application. This holding, which effectively excludes AI systems from the category of "individuals" eligible to be named as inventors, may complicate the intellectual property strategies of innovators who use advanced AI for research and development. Here's what happened and why it matters. The Federal Circuit was asked to determine whether an AI system called DABUS could be named as the inventor on two separate patent applications. The first disclosed a light source that was calibrated with a specific frequency corresponding to, among other characteristics, certain human brainwave activity.

I've been dying to try DALL·E 2 ever since I first saw this artificially generated image of a "Shiba Inu Bento Box". For those of you unfamiliar, DALL·E 2 is a system created by OpenAI that can generate original images from text. It's currently in closed Beta -- I signed up for the waitlist in early May and got access at the end of July. During the Beta, users receive credits (50 free in the first month, 15 credits every month after that) where every use costs 1 credit, and each use results in 3–4 images. You can also purchase 115 credits for US$15.

News is essential to a functioning society. It keeps people informed on important subject matters and enables them to formulate an opinion on those topics. Thus, the information people consume can be immensely influential. While this puts the onus on media outlets to establish trust and credibility, these values are only part of the equation when it comes to discerning real news from fake news. Unfortunately, there is no cure for fake news.