A number of prestigious AAAI awards were presented during the official opening ceremony of the Fortieth AAAI Conference on Artificial Intelligence (AAAI 2026) in Singapore, on Thursday 22 January. 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 Shakir Mohamed Shakir has been recognised for . The Robert S. Engelmore Memorial Award recognises outstanding contributions to automated planning, machine learning and robotics, their application to real-world problems and extensive service to the AI community. The annual AAAI/EAAI Outstanding Educator award was created to honour a person (or group of people) who has made major contributions to AI education that provide long-lasting benefits to the AI community and society as a whole.

Andrew Barto (barto@cs.umass.edu) is Professor Emeritus of computer science at the University of Massachusetts Amherst. He is also a co-recipient of the 2024 ACM A.M Turing Award.

If you stayed awake in science class as a kid, the payoff comes when you get a good laugh out of Freya McGhee's jokes. Stop me if you've heard this one before. An aspiring chemist goes to college, realizes she's not good at chemistry, and bombs her dissertation. She takes a class in standup comedy and decides the best way to talk about STEM is to make jokes at its expense. Based in London, the comedian had a strong interest in science as a kid, but after attending the University of Brighton to study chemistry, she realized that she liked learning science more than she liked applying it. Her thesis dissertation--"Synthesis of Iron Nitroxide radical species using radical derivatized ligands and its use as a single-molecule magnet"--flopped.

The growing number of pretrained models in Machine Learning (ML) presents significant challenges for practitioners. Given a new dataset, they need to determine the most suitable deep learning (DL) pipeline, consisting of the pretrained model and the hyperparameters for finetuning to it. Moreover, as models grow in scale, the increasing reliance on real-world data poses a bottleneck for training and requires leveraging data more effectively. Addressing the first challenge often involves manual model selection and hyperparameter tuning. At the same time, as models grow larger and more and more of the available human-generated data is being used for training, data augmentation and synthetic data become critical elements. Automated machine learning offers a path to address these challenges but is traditionally designed for tabular data and classical ML methods. This dissertation adopts meta-learning to extend automated machine learning to the deep learning domain. We propose empirical approaches to automate DL pipeline selection for Computer Vision tasks using prior task knowledge to learn surrogate models for pipeline ranking. Extending these methods to the language domain, we learn to finetune large language models. As a result, we show that our approach can outperform finetuning foundation models. Additionally, we meta-learn data augmentation and synthetic data to enhance performance in up-stream and down-stream tasks. We empirically show the underestimated importance of data augmentation when using Self-Supervised Learning and meta-learn advanced data augmentation strategies. Leveraging synthetic data, we also propose to meta-learn neural synthetic data generators as proxies for Reinforcement Learning (RL) environments. Additionally, we learn a multiple-environment world model in an in-context learning fashion by purely using synthetic, randomly sampled data.

Progress in Natural Language Processing (NLP) has been dictated by the rule of more: more data, more computing power and more complexity, best exemplified by the Large Language Models. However, training (or fine-tuning) large dense models for specific applications usually requires significant amounts of computing resources. This \textbf{Ph.D. dissertation} focuses on an under-investi\-gated NLP data engineering technique, whose potential is enormous in the current scenario known as Instance Selection (IS). The IS goal is to reduce the training set size by removing noisy or redundant instances while maintaining the effectiveness of the trained models and reducing the training process cost. We provide a comprehensive and scientifically sound comparison of IS methods applied to an essential NLP task -- Automatic Text Classification (ATC), considering several classification solutions and many datasets. Our findings reveal a significant untapped potential for IS solutions. We also propose two novel IS solutions that are noise-oriented and redundancy-aware, specifically designed for large datasets and transformer architectures. Our final solution achieved an average reduction of 41\% in training sets, while maintaining the same levels of effectiveness in all datasets. Importantly, our solutions demonstrated speedup improvements of 1.67x (up to 2.46x), making them scalable for datasets with hundreds of thousands of documents.

This paper will present textual corpora for Serbian (and Serbo-Croatian), usable for the training of large language models and publicly available at one of the several notable online repositories. Each corpus will be classified using multiple methods and its characteristics will be detailed. Additionally, the paper will introduce three new corpora: a new umbrella web corpus of Serbo-Croatian, a new high-quality corpus based on the doctoral dissertations stored within National Repository of Doctoral Dissertations from all Universities in Serbia, and a parallel corpus of abstract translation from the same source. The uniqueness of both old and new corpora will be accessed via frequency-based stylometric methods, and the results will be briefly discussed.

In a series of interviews, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In this latest interview, we met Salena Torres Ashton and found out about her work focusing on causality and natural language. I am a PhD student at the School of Information at the University of Arizona. Information Science can mean a lot of things, but the easiest way that I like to describe it would be "working with computer science with people in mind".

If you squint and tilt your head, you can see some similarities in the blurry shapes that are Harvard and OpenAI. Each is a leading institution for building minds, whether real or artificial--Harvard educates smart humans, while OpenAI engineers smart machines--and each has been forced in recent days to stare down a common allegation. Namely, that they are represented by intellectual thieves. Last month, the conservative activist Christopher Rufo and the journalist Christopher Brunet accused then–Harvard President Claudine Gay of having copied short passages without attribution in her dissertation. Gay later admitted to "instances in my academic writings where some material duplicated other scholars' language, without proper attribution," for which she requested corrections. The two cases share common ground, yet many of the responses to them could not be more different.

Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field.

We propose multiple techniques for automatic document order generation for (1) curriculum development and for (2) creation of optimal reading order for use in learning, training, and other content-sequencing applications. Such techniques could potentially be used to improve comprehension, identify areas that need expounding, generate curricula, and improve search engine results. We advance two main techniques: The first uses document similarities through various methods. The second uses entropy against the backdrop of topics generated through Latent Dirichlet Allocation (LDA). In addition, we try the same methods on the summarized documents and compare them against the results obtained using the complete documents. Our results showed that while the document orders for our control document sets (biographies, novels, and Wikipedia articles) could not be predicted using our methods, our test documents (textbooks, courses, journal papers, dissertations) provided more reliability. We also demonstrated that summarized documents were good stand-ins for the complete documents for the purposes of ordering.