Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry, but their full impact on software engineering remains insufficiently explored. Despite their growing adoption, there is a notable lack of formal, qualitative assessments of how LLMs are applied in real-world software development contexts. To fill this gap, we conducted semi-structured interviews with sixteen early-adopter professional developers to explore their use of LLMs throughout various stages of the software development life cycle. Our investigation examines four dimensions: people - how LLMs affect individual developers and teams; process - how LLMs alter software engineering workflows; product - LLM impact on software quality and innovation; and society - the broader socioeconomic and ethical implications of LLM adoption. Thematic analysis of our data reveals that while LLMs have not fundamentally revolutionized the development process, they have substantially enhanced routine coding tasks, including code generation, refactoring, and debugging. Developers reported the most effective outcomes when providing LLMs with clear, well-defined problem statements, indicating that LLMs excel with decomposed problems and specific requirements. Furthermore, these early-adopters identified that LLMs offer significant value for personal and professional development, aiding in learning new languages and concepts. Early-adopters, highly skilled in software engineering and how LLMs work, identified early and persisting challenges for software engineering, such as inaccuracies in generated content and the need for careful manual review before integrating LLM outputs into production environments. Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development, with their benefits, limitations, and ongoing implications.

The study of representation universality in AI models reveals growing convergence across domains, modalities, and architectures. However, the practical applications of representation universality remain largely unexplored. We bridge this gap by demonstrating that safety interventions can be transferred between models through learned mappings of their shared activation spaces. We demonstrate this approach on two well-established AI safety tasks: backdoor removal and refusal of harmful prompts, showing successful transfer of steering vectors that alter the models' outputs in a predictable way. Additionally, we propose a new task, \textit{corrupted capabilities}, where models are fine-tuned to embed knowledge tied to a backdoor. This tests their ability to separate useful skills from backdoors, reflecting real-world challenges. Extensive experiments across Llama, Qwen and Gemma model families show that our method enables using smaller models to efficiently align larger ones. Furthermore, we demonstrate that autoencoder mappings between base and fine-tuned models can serve as reliable ``lightweight safety switches", allowing dynamic toggling between model behaviors.

This paper presents a game master AI for single-player role-playing games. The AI is designed to deliver interactive text-based narratives and experiences typically associated with multiplayer tabletop games like Dungeons & Dragons. We report on the design process and the series of experiments to improve the functionality and experience design, resulting in two functional versions of the system. While v1 of our system uses simplified prompt engineering, v2 leverages a multi-agent architecture and the ReAct framework to include reasoning and action. A comparative evaluation demonstrates that v2 as an agentic system maintains play while significantly improving modularity and game experience, including immersion and curiosity. Our findings contribute to the evolution of AI-driven interactive fiction, highlighting new avenues for enhancing solo role-playing experiences.

Years before she became the peculiar central thread linking a double homicide in Pennsylvania, the fatal shooting of a federal agent in Vermont and the murder of an elderly landlord in California, a computer programmer bought a sailboat. The programmer was known to friends, foes and followers as Ziz. She had come to the San Francisco Bay Area in 2016 as part of an influx of young people arriving to study the dangers that artificial intelligence could pose to humanity. In one of the most expensive regions of the United States, however, it is difficult to save the world when you can't make rent. So she bought a boat for 600 and moored it next to a friend's vessel in a marina. For five years, she used it as an occasional, cramped bunk. In her waking hours, she worked on a blog of provocative and increasingly extreme ideas about confrontation and retaliation. At night, she fell asleep as the boat rocked back and forth, drifting with the flotsam of greater Silicon Valley. Then, on the night of 19 August 2022, her sister and a friend reported that they saw her fall overboard. The Coast Guard and local authorities scrambled boats and aircraft. After a nearly 30-hour search, neither Ziz nor her body could be found. A newspaper in Alaska, where she was born, published a short obituary referring to her by her birth name: "Jack Amadeus LaSota left our lives but not our hearts on Aug 19 after a boating accident. Loving adventure, friends and family, music, blueberries, biking, computer games and animals, you are missed." Ziz's ideas did not die in the waters of the California coast. She had faked her drowning and gone underground, before being arrested last month in western Maryland and charged with trespassing and illegal transportation of a firearm. The targets of Ziz's ire, who include some of Silicon Valley's most prominent intellectuals, have taken security precautions. "Ziz is not stupid," someone familiar with her, who asked to remain anonymous, told me. "This is a very smart person – both smart and crazy." Ziz's writing had polarized members of a niche but influential movement of AI theorists and tech bloggers who call themselves the "rationalists". The movement is less about specific ideas than it is about an ethos – applying rigorous, mathematically informed thinking to AI, philosophy, psychology and the big questions of our time. Rationalists are odd, though often charming, people. They tend to be fantasy and sci-fi geeks, use lots of jargon and think intensely about things other people barely think about at all.

Andrew Barto and Richard Sutton have won the 2024 Turing award, which is often called the Nobel prize of computing, for their fundamental work on ideas in machine learning that later proved crucial to the success of artificial intelligence models such as Google DeepMind's AlphaGo. Barto, who is now retired and lives in Cape Cod, Massachusetts, didn't even realise he was nominated for the award. "I joined a Zoom with some people and was told and I was…

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea--having machines learn, as humans and animals do, from experience. Decades on, with the technique they pioneered now increasingly critical to modern artificial intelligence and programs like ChatGPT, Barto and Sutton have been awarded the Turing Award, the highest honor in the field of computer science. Barto, a professor emeritus at the University of Massachusetts Amherst, and Sutton, a professor at the University of Alberta, trailblazed a technique known as reinforcement learning, which involves coaxing a computer to perform tasks through experimentation combined with either positive or negative feedback. "When this work started for me, it was extremely unfashionable," Barto recalls with a smile, speaking over Zoom from his home in Massachusetts. "It's been remarkable that [it has] achieved some influence and some attention," Barto adds.

The prevailing paradigm in the domain of Open-Domain Dialogue agents predominantly focuses on the English language, encompassing both models and datasets. Furthermore, the financial and temporal investments required for crowdsourcing such datasets for finetuning are substantial, particularly when multiple languages are involved. Fortunately, advancements in Large Language Models (LLMs) have unveiled a plethora of possibilities across diverse tasks. Specifically, instruction-tuning has enabled LLMs to execute tasks based on natural language instructions, occasionally surpassing the performance of human crowdworkers. Additionally, these models possess the capability to function in various languages within a single thread. Consequently, to generate new samples in different languages, we propose leveraging these capabilities to replicate the data collection process. We introduce a pipeline for generating Open-Domain Dialogue data in multiple Target Languages using LLMs, with demonstrations provided in a unique Source Language. By eschewing explicit Machine Translation in this approach, we enhance the adherence to language-specific nuances. We apply this methodology to the PersonaChat dataset. To enhance the openness of generated dialogues and mimic real life scenarii, we added the notion of speech events corresponding to the type of conversation the speakers are involved in and also that of common ground which represents the premises of a conversation.

Machine learning (ML) and artificial intelligence (AI) conferences including NeurIPS and ICML have experienced a significant decline in peer review quality in recent years. To address this growing challenge, we introduce the Isotonic Mechanism, a computationally efficient approach to enhancing the accuracy of noisy review scores by incorporating authors' private assessments of their submissions. Under this mechanism, authors with multiple submissions are required to rank their papers in descending order of perceived quality. Subsequently, the raw review scores are calibrated based on this ranking to produce adjusted scores. We prove that authors are incentivized to truthfully report their rankings because doing so maximizes their expected utility, modeled as an additive convex function over the adjusted scores. Moreover, the adjusted scores are shown to be more accurate than the raw scores, with improvements being particularly significant when the noise level is high and the author has many submissions -- a scenario increasingly prevalent at large-scale ML/AI conferences. We further investigate whether submission quality information beyond a simple ranking can be truthfully elicited from authors. We establish that a necessary condition for truthful elicitation is that the mechanism be based on pairwise comparisons of the author's submissions. This result underscores the optimality of the Isotonic Mechanism, as it elicits the most fine-grained truthful information among all mechanisms we consider. We then present several extensions, including a demonstration that the mechanism maintains truthfulness even when authors have only partial rather than complete information about their submission quality. Finally, we discuss future research directions, focusing on the practical implementation of the mechanism and the further development of a theoretical framework inspired by our mechanism.

Recent advances in large language models have demonstrated impressive capabilities in task-oriented applications, yet building emotionally intelligent chatbots that can engage in natural, strategic conversations remains a challenge. We present a novel approach called SAGE that uses latent variables to control long-horizon behavior in dialogue generation. At the core of our method is the State-Action Chain (SAC), which augments standard language model fine-tuning by introducing latent variables that encapsulate emotional states and conversational strategies between dialogue turns. During inference, these variables are generated before each response, enabling coarse-grained control over dialogue progression while maintaining natural interaction patterns. We also introduce a self-improvement pipeline that leverages dialogue tree search, LLM-based reward modeling, and targeted fine-tuning to optimize conversational trajectories. Our experimental results show that models trained with this approach demonstrate improved performance in emotional intelligence metrics while maintaining strong capabilities on LLM benchmarks. The discrete nature of our latent variables facilitates search-based strategies and provides a foundation for future applications of reinforcement learning to dialogue systems, where learning can occur at the state level rather than the token level.

A film released by The New Yorker was among the winners at Sunday's Academy Awards. "I'm Not a Robot," a darkly comic portrayal of a woman trying to convince her computer that she is human, claimed the prize for Best Live Action Short. It is the second film released by the magazine to be honored with an Oscar. The film, written and directed by Victoria Warmerdam, opens with a seemingly typical office scene that quickly unravels. When the protagonist, a music producer, fails a series of CAPTCHA tests, she begins to question her own grip on reality.