But as a linguist who studies how English has changed over the centuries, I can promise that, while it might feel like nails screeching on a blackboard, the use of nonliteral 'literally' developed as an organic and dynamic outgrowth of the very human desire to communicate emotion and intensity. The word literal first appeared in English in the late 14th century, borrowed from French. In turn, French 'literal' came from Latin 'littera,' with the original meaning of 'pertaining to alphabetic letters.' It is this same root that delivered to English the words 'literate' and 'literature,' both harking back to the idea of knowing one's 'letters.' In early English use, literal referred to the straightforward meaning recoverable from reading a religious text, as in this example from the Wycliffe Bible dating to 1383, 'Holy scripture hath iiij vndirstondingis; literal, allegorik, moral, and anagogik.' The word literal as used here contrasts a direct ' literal ' reading of scripture's meaning to other more symbolic or metaphorical ones....

When GitHub Copilot launched and started autocompleting lines of code ' and, later, entire code snippets ' the question many people were asking was: How long until we can just describe an app in natural language and Copilot will build it for us' We've seen quite a few experiments in this arena in recent months, but now, GitHub itself is throwing its weight behind this idea with the announcement of GitHub Spark at the company's annual GitHub Universe conference in San Francisco. Spark, which is officially an experiment the company is launching out of its GitHub Next labs, allows you to quickly build a small web app using nothing but natural language. Experienced developers can still see and edit the code ' and underneath it all is a GitHub repository, GitHub Actions, and Microsoft's Azure CosmosDB as the default database for applications that need one ' but that's optional. Ideally, you'll be able to use a chat-like experience to create a prototype and then refine it in subsequent steps....

ChatGPT has already wreaked havoc on classrooms and changed how teachers approach writing homework, since OpenAI publicly launched the generative AI chatbot in late 2022. School administrators rushed to try to detect AI-generated essays, and in turn, students scrambled to find out how to cloak their synthetic compositions. But by focusing on writing assignments, educators let another seismic shift take place in the periphery: students using AI more often to complete math homework too. Right now, high schoolers and college students around the country are experimenting with free smartphone apps that help complete their math homework using generative AI. One of the most popular options on campus right now is the Gauth app, with millions of downloads. It's owned by ByteDance, which is also TikTok's parent company. The Gauth app first launched in 2019 with a primary focus on mathematics, but soon expanded to other subjects as well, like chemistry and physics. It's grown in relevance, and neared the top of smartphone download lists earlier this year for the education category. Students seem to love it. With hundreds of thousands of primarily positive reviews, Gauth has a favorable 4.8 star rating in the Apple App Store and Google Play Store....

When you watch this year's English Premier League soccer games, there's a high chance you may get mad at some of the offside calls. However, unlike past seasons, your anger won't be because the call, or the lack thereof, was obviously lousy. That's because the League's new offside-detection system is apparently able to spot a player's position on the field, and call them offside, with more accuracy than ever'and it's all powered by iPhones. The League's rollout of this new semiautomated offside tech later in the 2024'25 season won't just provide long-awaited placation for players and fans frustrated by years of problems with previous video-assistant referee (VAR) systems, from extensive delays and human process errors to concerns about the precision of in-game calls due to limitations of the existing technology. The system utilizes dozens of iPhones, using the cameras to capture high-frame-rate video from multiple angles. Dragon's custom machine intelligence software supposedly allows the smartphones to effectively communicate and work together to process all the visual data collected by the multiple cameras....