Canadian Prime Minister Mark Carney has championed the "Buy Canadian" movement, saying the power lies in "decisions taken at the kitchen table, not the boardroom table." Canadians are responding en masse, embracing patriotic shopping, even with the challenges it can present.

Alarm bells within the company should have stopped model training from going forward. By now you've probably heard about last month's major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on a test. On Wednesday, OpenAI released a postmortem technical report on the incident, which I wrote about here . The day before OpenAI released that report, I spoke with David Krueger, a computer science professor and prominent alignment expert who took leave from the University of Montreal to found and lead an AI safety nonprofit called Evitable. He said what he had really hoped to see in the report was an analysis of the human factors behind the incident. "When you look at accidents and incidents, oftentimes people try to find the technical source of failure, but that can give a very inaccurate and misleading sense of why the failure occurred," he said.

Dr François Pachet is an AI researcher and musician, and one of the most influential figures in AI and music. His innovative contributions have defined the field over the past decades through creative systems such as the Continuator, and Flow Machines, among others. After leading the Spotify Creator Technology Research Lab and the Sony Computer Science Lab, he went on to create his own companies: Imagine All The People and Ynosound. In the context of IJCAI2025, he spoke about what deep learning changed, and what still remains wide open. He explains why tools like Suno and Udio--ChatGPT-like platforms for music generation--can produce astonishing results that still feel unsatisfying; why the next step for music generation requires combining sampling with search; and why the most important problems in artistic domains are, by nature, ill-defined--because there is no loss function to determine what is "good". Above all, he defends the importance of researcher autonomy: work on the questions that genuinely fascinate you, even when they fall outside prevailing trends--perhaps especially then. Thank you for joining me for this interview. Could you begin by telling us when was your first IJCAI and a memory related to it? I think the first IJCAI I attended was in Montreal in '95. I was there for a couple of workshops, one of them was about music and AI, and the other one I think was on advisor systems, something like that. And I remember there was a French colleague who was there also, at the time he was doing his PhD. And there was this researcher called Herbert Simon, who is a Nobel Prize pioneer of AI. I remember having chatted a little bit with this French guy who was very bold, and he just went up to Simon, said "Hey," and he started a conversation with him. And I was very impressed by the fact that you could meet those kinds of guys informally in a corridor or something at this conference.

Plus: Why the OpenAI hack is the scariest AI mishap yet. The power line that could reshape New York's grid is hitting snags During a heat wave on July 3, New York State's grid imported enough electricity from Canada to meet about 9% of its total demand that day. Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens. It opened in May and is officially the longest underground transmission line in North America. It could provide up to 20% of New York City's electricity demand, largely with abundant hydropower from Quebec. One wrinkle: The line has been down for most of this month, and some experts are concerned about how drought will affect the power supply feeding it.

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Thi Kieu Khanh Ho who is studying time-series anomaly detection. We found out more about her research, and what inspired her to study AI, and what she plans to work on next. Tell us a bit about your PhD -- where are you studying, and what is the topic of your research? I am doing my PhD at McGill University and Mila - Québec AI Institute, in the Department of Electrical and Computer Engineering, supervised by Professor Narges Armanfard. My research focuses on time-series anomaly detection, the problem of teaching AI systems to recognize when something unusual or abnormal is happening in complex, real-world data streams, without relying on large amounts of labeled examples.

Temporal Graph Learning (TGL) aims to discover patterns in evolving networks or temporal graphs and leverage these patterns to predict future interactions. However, most existing research focuses on learning from a single network in isolation, leaving the challenges of within-domain and cross-domain generalization largely unaddressed. In this study, we introduce a new benchmark of 84 real-world temporal transaction networks and propose Temporal Multi-network Transfer (MiNT), a pre-training framework designed to capture transferable temporal dynamics across diverse networks. We train MiNT models on up to 64 transaction networks and evaluate their generalization ability on 20 held-out, unseen networks. Our results show that MiNT consistently outperforms individually trained models, revealing a strong relation between the number of pre-training networks and transfer performance. These findings highlight scaling trends in temporal graph learning and underscore the importance of network diversity in improving generalization. This work establishes the first large-scale benchmark for studying transferability in TGL and lays the groundwork for developing Temporal Graph Foundation Models.

Performative prediction is a framework accounting for the shift in the data distribution induced by the prediction of a model deployed in the real world. Ensuring convergence to a stable solution--one at which the post-deployment data distribution no longer changes--is crucial in settings where model predictions can influence future data. This paper, for the first time, extends the Repeated Risk Minimization (RRM) algorithm class by utilizing historical datasets from previous retraining snapshots, yielding a class of algorithms that we call Affine Risk Minimizers that converges to a performatively stable point for a broader class of problems. We introduce a new upper bound for methods that use only the final iteration of the dataset and prove for the first time the tightness of both this new bound and the previous existing bounds within the same regime. We also prove that our new algorithm class can surpass the lower bound for standard RRM, thus breaking the prior lower bound, and empirically observe faster convergence to the stable point on various performative prediction benchmarks. We offer at the same time the first lower bound analysis for RRM within the class of Affine Risk Minimizers, quantifying the potential improvements in convergence speed that could be achieved with other variants in our scheme.

We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by data complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional stochastic gradient descent with momentum (SGD-M) yields identical scaling law exponents to SGD, dimension-adapted Nesterov acceleration (DANA) improves these exponents by scaling momentum hyperparameters based on model size and data complexity. This outscaling phenomenon, which also improves compute-optimal scaling behavior, is achieved by DANA across a broad range of data and target complexities, while traditional methods fall short. Extensive experiments on high-dimensional synthetic quadratics validate our theoretical predictions and large-scale text experiments with LSTMs show DANA's improved loss exponents over SGD hold in a practical setting.

Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can lead to multiple plausible outputs. To generate diverse images reflecting this diversity, traditional methods often modify random seeds, making it difficult to discern meaningful differences between samples, or diversify the input prompt, which is limited in verbally interpretable diversity. We propose Rainbow, a novel conditional image generation framework, applicable to any pretrained conditional generative model, that addresses inherent condition/prompt uncertainty and generates diverse plausible images. Rainbow is based on a simple yet effective idea: decomposing the input condition into diverse latent representations, each capturing an aspect of the uncertainty and generating a distinct image. First, we integrate a latent graph, parameterized by Generative Flow Networks (GFlowNets), into the prompt representation computation. Second, leveraging GFlowNets' advanced graph sampling capabilities to capture uncertainty and output diverse trajectories over the graph, we produce multiple trajectories that collectively represent the input condition, leading to diverse condition representations and corresponding output images. Evaluations on natural image and medical image datasets demonstrate Rainbow's improvement in both diversity and fidelity across image synthesis, image generation, and counterfactual generation tasks.

This work introduces a novel approach, Pairwise Epistemic Estimators (PairEpEsts), for epistemic uncertainty estimation in ensemble models for regression tasks using pairwise-distance estimators (PaiDEs). By utilizing the pairwise distances between model components, PaiDEs establish bounds on entropy. We leverage this capability to enhance the performance of Bayesian Active Learning by Disagreement (BALD). Notably, unlike sample-based Monte Carlo estimators, PairEpEsts can estimate epistemic uncertainty up to 100 times faster and demonstrate superior performance in higher dimensions. To validate our approach, we conducted a varied series of regression experiments on commonly used benchmarks: 1D sinusoidal data, Pendulum, Hopper, Ant, and Humanoid, demonstrating PairEpEsts' advantage over baselines in high-dimensional regression active learning.