The Chinese video game giant Tencent is now building some of the world's best 3D AI models. This could have implications far outside game design. The video game Valorant, a fast-paced team-based shooter, has recently become a testing ground for a promising new direction in artificial intelligence research. The game's developers at Riot Games (a Tencent subsidiary) are using 3D-native AI models to prototype new characters, scenes, and storylines, according to a researcher familiar with the company's efforts who spoke on the condition of anonymity. While many AI models can generate text, images, and video, Tencent's Hunyuan (混元 or "first mix") family of models can dream up 3D objects and interactive scenes.

Encouraged early on by Nobel laureate Enrico Fermi, the "Queen of Carbon" laid the foundation for countless advances in nanotechnology--and mentored countless young scientists along the way. At MIT, Mildred Dresselhaus became a beloved professor who pushed her students to be their very best and provided support in ways big and small. Institute Professor Mildred "Millie" Dresselhaus forever altered our understanding of matter--the physical stuff of the universe that has mass and takes up space. Over 57 years at MIT, Dresselhaus also played a significant role in inspiring people to use this new knowledge to tackle some of the world's greatest challenges, from producing clean energy to curing cancer. Although she became an emerita professor in 2007, Dresselhaus, who taught electrical engineering and physics, remained actively involved in research and all other aspects of MIT life until her death in 2017. She would have been 95 this November.

It's surprisingly easy to stumble into a relationship with an AI chatbot Looking for help with her art project, she strikes up a conversation with her assistant. One thing leads to another, and suddenly she has a boyfriend she's introducing to her friends and family. Her new companion is an AI chatbot. The first large-scale computational analysis of the Reddit community r/MyBoyfriendIsAI, an adults-only group with more than 27,000 members, has found that this type of scenario is now surprisingly common. In fact, many of the people in the subreddit, which is dedicated to discussing AI relationships, formed those relationships unintentionally while using AI for other purposes. Researchers from MIT found that members of this community are more likely to be in a relationship with general-purpose chatbots like ChatGPT than companionship-specific chatbots such as Replika.

When I was growing up, I watched a lot of sci-fi movies about aliens that come to Earth. The extraterrestrials in popular culture, however, always looked so familiar that I found them far-fetched. What are the chances that E.T., the Predator, or ALF would develop arms and legs, a humanlike face, and opposable thumbs? Perhaps as a result, I associated alien life more with fantasy than with science, and I never gave much thought to what a visit would really look like. But my attitude started to change in 2020, when I read Liu Cixin's "The Three-Body Problem" and its two sequels.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. As a classically trained singer preparing for a professional career, Erin Perry can see quite clearly how artificial intelligence is upending her field--all the way down to the classroom. Perry just completed her first year as a graduate student in voice performance at the Peabody Institute, the prestigious music conservatory run by Johns Hopkins University. It's been rewarding so far: She's been learning how to navigate the modern classical music sector and confronting the relevant impacts of generative A.I., having taken on a project to study the major record labels' lawsuit against the Amazon-backed A.I. startup Anthropic, which trained its models on songwriters' lyrics sans permission or compensation. Understandably, Perry's rather skeptical of A.I.'s artistic applications, and fearful of the sweeping effects it could have on her chosen field, especially as generative-music startups like Suno and Udio are programmed to replicate specific artists and musical styles.

Large Language Models (LLMs) have demonstrated impressive performances in tasks related to coreference resolution. However, previous studies mostly assessed LLM performance on coreference resolution with nouns and third person pronouns. This study evaluates LLM performance on coreference resolution with indexical like I, you, here and tomorrow, which come with unique challenges due to their linguistic properties. We present the first study examining how LLMs interpret indexicals in English, releasing the English Indexical Dataset with 1600 multiple-choice questions. We evaluate pioneering LLMs, including GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and DeepSeek V3. Our results reveal that LLMs exhibit an impressive performance with some indexicals (I), while struggling with others (you, here, tomorrow), and that syntactic cues (e.g. quotation) contribute to LLM performance with some indexicals, while they reduce performance with others. Code and data are available at: https://github.com/metehanoguzz/LLMs-Indexicals-English.

One open question in the study of Large Language Models (LLMs) is whether they can emulate human ethical reasoning and act as believable proxies for human judgment. To investigate this, we introduce a benchmark dataset comprising 196 real-world ethical dilemmas and expert opinions, each segmented into five structured components: Introduction, Key Factors, Historical Theoretical Perspectives, Resolution Strategies, and Key Takeaways. We also collect non-expert human responses for comparison, limited to the Key Factors section due to their brevity. We evaluate multiple frontier LLMs (GPT-4o-mini, Claude-3.5-Sonnet, Deepseek-V3, Gemini-1.5-Flash) using a composite metric framework based on BLEU, Damerau-Levenshtein distance, TF-IDF cosine similarity, and Universal Sentence Encoder similarity. Metric weights are computed through an inversion-based ranking alignment and pairwise AHP analysis, enabling fine-grained comparison of model outputs to expert responses. Our results show that LLMs generally outperform non-expert humans in lexical and structural alignment, with GPT-4o-mini performing most consistently across all sections. However, all models struggle with historical grounding and proposing nuanced resolution strategies, which require contextual abstraction. Human responses, while less structured, occasionally achieve comparable semantic similarity, suggesting intuitive moral reasoning. These findings highlight both the strengths and current limitations of LLMs in ethical decision-making.

Nearly three months into President Donald Trump's term, the future of American AI leadership is in jeopardy. Basically any generative-AI product you have used or heard of--ChatGPT, Claude, AlphaFold, Sora--depends on academic work or was built by university-trained researchers in the industry, and frequently both. Today's AI boom is fueled by the use of specialized computer-graphics chips to run AI models--a technique pioneered by researchers at Stanford who received funding from the Department of Defense. They rely on a training method called "reinforcement learning," the foundations of which were developed with National Science Foundation (NSF) grants. "I don't think anybody would seriously claim that these [AI breakthroughs] could have been done if the research universities in the U.S. didn't exist at the same scale," Rayid Ghani, a machine-learning researcher at Carnegie Mellon University, told me.

Imagine grasshoppers distributed evenly across a dry field. As the temperature rises, the grasshoppers start to sweat. The field catches fire in a few spots and starts to spread. What kind of burn pattern will appear in the field? If the grasshoppers sweat more, or less, or if the fire spreads faster, or slower--how will that alter the burn pattern?

We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin. In response to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University, were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience.