In 2017, soon after Google researchers invented a new kind of neural network called a transformer, a young OpenAI engineer named Alec Radford began experimenting with it. What made the transformer architecture different from that of existing A.I. systems was that it could ingest and make connections among larger volumes of text, and Radford decided to train his model on a database of seven thousand unpublished English-language books--romance, adventure, speculative tales, the full range of human fantasy and invention. Then, instead of asking the network to translate text, as Google's researchers had done, he prompted it to predict the most probable next word in a sentence. The machine responded: one word, then another, and another--each new term inferred from the patterns buried in those seven thousand books. Radford hadn't given it rules of grammar or a copy of Strunk and White.

The debate over the risks and harms of artificial intelligence often focuses on what governments can or should do. However, just as important are the choices that AI researchers themselves make. This week, in Singapore, more than 100 scientists from around the world proposed guidelines for how researchers should approach making AI more "trustworthy, reliable, and secure." The recommendations come at a time when the giants of generative AI, such as OpenAI and Google, have increasingly reduced disclosures about their AI models, so the public knows less and less about how the work is conducted. The guidelines grew out of an exchange among the scholars last month in Singapore, in conjunction with one of the most prestigious conferences on AI, the International Conference on Learning Representations -- the first time a major AI conference has taken place in Asia.

Gabfest Reads is a monthly series from the hosts of Slate's Political Gabfest podcast. Recently, Maggie Smith talked with John Dickerson about her new book Dear Writer: Pep Talks & Practical Advice for the Creative Life. Maggie's first love is poetry, and they discuss how to tell when your creative endeavor is complete. This partial transcript has been edited and condensed for clarity. John Dickerson: What does it feel like when you've arrived with a poem--when you think it's "done?"

The rapid proliferation of AI in our lives introduces new challenges around authorship, authenticity, and ethics in work and art. But it also offers a particularly human problem in narrative: How can we make sense of these machines, not just use them? And how do the words we choose and stories we tell about technology affect the role we allow it to take on (or even take over) in our creative lives? Both Vara's book and The Uncanny Muse, a collection of essays on the history of art and automation by the music critic David Hajdu, explore how humans have historically and personally wrestled with the ways in which machines relate to our own bodies, brains, and creativity. At the same time, The Mind Electric, a new book by a neurologist, Pria Anand, reminds us that our own inner workings may not be so easy to replicate.

Power grid operation is becoming increasingly complex due to the rising integration of renewable energy sources and the need for more adaptive control strategies. Reinforcement Learning (RL) has emerged as a promising approach to power network control (PNC), offering the potential to enhance decision-making in dynamic and uncertain environments. The Learning To Run a Power Network (L2RPN) competitions have played a key role in accelerating research by providing standardized benchmarks and problem formulations, leading to rapid advancements in RL-based methods. This survey provides a comprehensive and structured overview of RL applications for power grid topology optimization, categorizing existing techniques, highlighting key design choices, and identifying gaps in current research. Additionally, we present a comparative numerical study evaluating the impact of commonly applied RL-based methods, offering insights into their practical effectiveness. By consolidating existing research and outlining open challenges, this survey aims to provide a foundation for future advancements in RL-driven power grid optimization.

The new essay collection "Searches: Selfhood in the Digital Age," by Vauhini Vara, opens with a transcript. "If I paste some writing here, can we talk about it?" Her interlocutor, the large language model ChatGPT, responds, "Of course!" The chatbot asks what specific themes it should focus on. "Nothing in particular," Vara replies.

The Hugo Awards began honoring video games for the first time back in 2021. This week, the organization revealed the list of six finalists for the 2025 awards ceremony. Let's go over the nominations. Two AAA titles are up for the award. The gameplay involves summoning monsters and items to solve puzzles and do battle.

R1 raised the concern that the abstract visual reasoning tasks considered in this paper "... seem a bit unintuitive

These authors made history with their powerful books. March is Women's History Month, a time dedicated to honoring the powerful, inspiring and trailblazing women who have contributed amazing things to our world. What better way to celebrate this month than by diving into books written by women? Female authors have written a diverse range of books, from novels to memoirs, to science fiction and horror. Get your bookmarks ready and prepare to be captivated by these must-read books for Women's History Month. Follow an eccentric artist and her daughter through this short novel.

Speech datasets available in the public domain are often underutilized because of challenges in accessibility and interoperability. To address this, a system to survey, catalog, and curate existing speech datasets was developed, enabling reproducible evaluation of automatic speech recognition (ASR) systems. The system was applied to curate over 24 datasets and evaluate 25 ASR models, with a specific focus on Polish. This research represents the most extensive comparison to date of commercial and free ASR systems for the Polish language, drawing insights from 600 system-model-test set evaluations across 8 analysis scenarios. Curated datasets and benchmark results are available publicly.