The International Prize in Statistics Foundation has awarded Grace Wahba the 2025 prize for "her groundbreaking work on smoothing splines, which has transformed data analysis and machine learning". Professor Wahba was among the earliest to pioneer the use of nonparametric regression modeling. Recent advances in computing and availability of large data sets have further popularized these models, especially under the guise of machine learning algorithms such as gradient boosting and neural networks. Nevertheless, the use of smoothing splines remains a mainstay of nonparametric regression. In seminal research that began in the early 1970s, Wahba developed theoretical foundations and computational algorithms for fitting smoothing splines to noisy data.

Some of the flashiest achievements in artificial intelligence in the past decade have come from a technique by which the computer acts randomly from a set of choices and is rewarded or punished for each correct or wrong move. It's the technique most famously employed in AlphaZero, Google DeepMind's 2016 program that achieved mastery at the games of chess, shogi, and Go in 2018. The same approach helped the AlphaStar program achieve "grandmaster" play in the video game Starcraft II. On Wednesday, two AI scholars were rewarded for advancing so-called reinforcement learning, a very broad approach to how a computer proceeds in an unknown environment. Andrew G. Barto, professor emeritus in the Department of Information and Computer Sciences at the University of Massachusetts, Amherst, and Richard S. Sutton, professor of computer science at the University of Alberta, Canada, were jointly awarded the 2025 Turing Award by the Association for Computing Machinery.

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 AAAI 2025 outstanding paper awards were announced during the opening ceremony of the 39th Annual AAAI Conference on Artificial Intelligence on Thursday 27 February. Papers are recommended for consideration during the review process by members of the Program Committee. This year, three papers have been selected as outstanding papers, with a further paper being recognised in the special track on AI for social impact. Abstract: A fundamental task in multi-agent systems is to match agents to alternatives (e.g., resources or tasks). Often, this is accomplished by eliciting agents' ordinal rankings over the alternatives instead of their exact numerical utilities.

A number of prestigious AAAI awards were presented during the official opening ceremony of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2025) on 27 February. Some of the winners will also be giving invited talks as part of the programme. The AAAI Award for Artificial Intelligence for Humanity recognises the positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways with long-lived effects. The winner of this year's award is Stuart J. Russell (University of California, Berkeley, USA). Stuart has been recognised for "work on the conceptual and theoretical foundations of provably beneficial AI and his leadership in creating the field of AI safety".

We propose a novel Two-Stage framework for Structured Pruning (2SSP) for pruning Large Language Models (LLMs), which combines two different strategies of pruning, namely Width and Depth Pruning. The first stage (Width Pruning) removes entire neurons, hence their corresponding rows and columns, aiming to preserve the connectivity among the pruned structures in the intermediate state of the Feed-Forward Networks in each Transformer block. This is done based on an importance score measuring the impact of each neuron over the output magnitude. The second stage (Depth Pruning), instead, removes entire Attention submodules. This is done by applying an iterative process that removes the Attention submodules with the minimum impact on a given metric of interest (in our case, perplexity). We also propose a novel mechanism to balance the sparsity rate of the two stages w.r.t. to the desired global sparsity. We test 2SSP on four LLM families and three sparsity rates (25\%, 37.5\%, and 50\%), measuring the resulting perplexity over three language modeling datasets as well as the performance over six downstream tasks. Our method consistently outperforms five state-of-the-art competitors over three language modeling and six downstream tasks, with an up to two-order-of-magnitude gain in terms of pruning time. The code is available at available at \url{https://github.com/FabrizioSandri/2SSP}.

A former ByteDance intern who was allegedly dismissed for professional misconduct, including sabotaging colleagues' work, was announced as a winner of one of the most prestigious annual awards for AI research this week. Keyu Tian, whose LinkedIn and Google Scholar pages list him as a master's student in computer science at Peking University, is the first author of one of two papers chosen Tuesday for the main "Best Paper Award" at the Neural Information Processing Systems (NeurIPS) conference, the largest gathering of machine learning researchers in the world. The paper, titled "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction," presents a new method for creating AI-generated images that Tian and four coauthors--all affiliated with either ByteDance or Peking University--claim is faster and more efficient than its predecessors. "The overall quality of the paper presentation, experimental validation and insights (scaling laws) give compelling reasons to experiment with this model," the NeurIPS Best Paper Award committee wrote in a statement. The committee's decision to grant the honor to Tian, whom ByteDance reportedly sued for over 1 million in damages last month, claiming deliberate sabotage of other company research projects, quickly became the focus of wider discussions online about how NeurIPS is run and the way top AI researchers evaluate the work of their colleagues.

This simple, intuitive methodology allows autoregressive (AR) transformers to learn visual distributions fast and generalize well: VAR, for the first time, makes GPT-style AR models surpass diffusion transformers in image generation. On ImageNet 256 256 benchmark, VAR significantly improve AR baseline by improving Frechet inception distance (FID) from 18.65 to 1.73, inception score (IS) from 80.4 to 350.2, with around 20x faster inference speed. It is also empirically verified that VAR outperforms the Diffusion Transformer (DiT) in multiple dimensions including image quality, inference speed, data efficiency, and scalability. Scaling up VAR models exhibits clear power-law scaling laws similar to those observed in LLMs, with linear correlation coefficients near -0.998 as solid evidence.

This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compared directly to those produced by graduate-level human participants. By focusing on two Nobel Prize-winning short stories, 'Nine Chapters' by Han Kang, the 2024 laureate, and 'Friendship' by Jon Fosse, the 2023 laureate, the research explores the extent to which AI can engage with complex literary elements such as thematic analysis, intertextuality, cultural and historical contexts, linguistic and structural innovations, and character development. Given the Nobel Prize's prestige and its emphasis on cultural, historical, and linguistic richness, applying LLMs to these works provides a deeper understanding of both human and AI approaches to interpretation. The study uses qualitative and quantitative evaluations of coherence, creativity, and fidelity to the text, revealing the strengths and limitations of AI in tasks typically reserved for human expertise. While LLMs demonstrate strong analytical capabilities, particularly in structured tasks, they often fall short in emotional nuance and coherence, areas where human interpretation excels. This research underscores the potential for human-AI collaboration in the humanities, opening new opportunities in literary studies and beyond.