Weather foundation models (WFMs) have recently set new benchmarks in global forecast skill, yet their concrete value for the weather-sensitive infrastructure that powers modern society remains largely unexplored. In this study, we fine-tune Silurian AI's 1.5B-parameter WFM, Generative Forecasting Transformer (GFT), on a rich archive of Hydro-Québec asset observations--including transmission-line weather stations, wind-farm met-mast streams, and icing sensors--to deliver hyper-local, asset-level forecasts for five grid-critical variables: surface temperature, precipitation, hub-height wind speed, wind-turbine icing risk, and rime-ice accretion on overhead conductors. Across 6-72 h lead times, the tailored model surpasses state-of-the-art NWP benchmarks, trimming temperature mean absolute error (MAE) by 15%, total-precipitation MAE by 35%, and lowering wind speed MAE by 15%. Most importantly, it attains an average precision score of 0.72 for day-ahead rime-ice detection, a capability absent from existing operational systems, which affords several hours of actionable warning for potentially catastrophic outage events. These results show that WFMs, when post-trained with small amounts of high-fidelity, can serve as a practical foundation for next-generation grid-resilience intelligence.

We consider two-player zero-sum concurrent stochastic games (CSGs) played on graphs with reachability and safety objectives. These include degenerate classes such as Markov decision processes or turn-based stochastic games, which can be solved by linear or quadratic programming; however, in practice, value iteration (VI) outperforms the other approaches and is the most implemented method. Similarly, for CSGs, this practical performance makes VI an attractive alternative to the standard theoretical solution via the existential theory of reals. VI starts with an under-approximation of the sought values for each state and iteratively updates them, traditionally terminating once two consecutive approximations are $ε$-close. However, this stopping criterion lacks guarantees on the precision of the approximation, which is the goal of this work. We provide bounded (a.k.a. interval) VI for CSGs: it complements standard VI with a converging sequence of over-approximations and terminates once the over- and under-approximations are $ε$-close.

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.

The Space-Air-Ground-Sea integrated network calls for more robust and secure transmission techniques against jamming. In this paper, we propose a textual semantic transmission framework for robust transmission, which utilizes the advanced natural language processing techniques to model and encode sentences. Specifically, the textual sentences are firstly split into tokens using wordpiece algorithm, and are embedded to token vectors for semantic extraction by Transformer-based encoder. The encoded data are quantized to a fixed length binary sequence for transmission, where binary erasure, symmetric, and deletion channels are considered for transmission. The received binary sequences are further decoded by the transformer decoders into tokens used for sentence reconstruction. Our proposed approach leverages the power of neural networks and attention mechanism to provide reliable and efficient communication of textual data in challenging wireless environments, and simulation results on semantic similarity and bilingual evaluation understudy prove the superiority of the proposed model in semantic transmission.

Montreal, March 20, 2023 – Mila – Quebec Artificial Intelligence Institute and the United Nations Educational, Scientific and Cultural Organization (UNESCO) today unveiled at Mila a joint book on the urgent need for a better governance of artificial intelligence (AI) in the face of unprecedented technological change. The book, Missing Links in AI Governance, includes 18 articles on AI governance written by academics, civil society representatives, innovators and policy makers at a time when technological revolutions provide new scientific, economic and social opportunities while raising ethical questions and posing regulatory challenges. The book explores themes such as the influence of AI on indigenous and LGBTI communities, the necessary inclusion of Southern countries in global governance or the use of AI to support innovation for socially beneficial purposes. It maps out possible solutions to foster an AI development that is ethical, inclusive, and respectful of human rights. The authors also warn against the use of AI in potentially harmful contexts like autonomous weapons or the manipulation of digital content for social destabilization, deplore the increasing centralization of decision-making power in the development of AI systems and biases embedded in them, and the lack of transparency and accountability in the industry.

We establish a dataset of over $1.6\times10^4$ experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About $33~\%$ of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and OD as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.

To collaborate well with robots, we must be able to understand their decision making. Humans naturally infer other agents' beliefs and desires by reasoning about their observable behavior in a way that resembles inverse reinforcement learning (IRL). Thus, robots can convey their beliefs and desires by providing demonstrations that are informative for a human learner's IRL. An informative demonstration is one that differs strongly from the learner's expectations of what the robot will do given their current understanding of the robot's decision making. However, standard IRL does not model the learner's existing expectations, and thus cannot do this counterfactual reasoning. We propose to incorporate the learner's current understanding of the robot's decision making into our model of human IRL, so that a robot can select demonstrations that maximize the human's understanding. We also propose a novel measure for estimating the difficulty for a human to predict instances of a robot's behavior in unseen environments. A user study finds that our test difficulty measure correlates well with human performance and confidence. Interestingly, considering human beliefs and counterfactuals when selecting demonstrations decreases human performance on easy tests, but increases performance on difficult tests, providing insight on how to best utilize such models.

The use artificial intelligence (AI) is exploding across the planet as it evolves into an essential tool for a myriad of fields and industries. But to successfully implement the technology into business operations, IT leaders need to surround themselves with global experts while building a state-of-the-art ecosystem. The place to start looking for such experts in Canada is Québec, the nation's AI powerhouse. Seventh in the world – that's where the province ranks in the Global AI Index published by the British firm Tortoise Media, a ranking of the most competitive countries in AI. Canada overall comes in fourth place – a remarkable achievement of which Québec is a real driving force.

If your company is looking for an anti-spam and anti-phishing solution, Gatefy will solve your problem. Gatefy Email Security (GES) is a solution that protects your company against different types of email threats, such as spam, phishing, ransomware, virus, BEC (Business Email Compromise), and social engineering. GES is compatible with several email providers, such as Office 365, G Suite, Exchange, and Zimbra. In practice, it adds an advanced layer of protection to your line of defense, offering great value for money. As we're talking about a security and data protection tool, Gatefy anti-spam and anti-phishing solution also helps your company to comply with laws and regulations, as is the case with LGPD in Brazil, GDPR in Europe, and CCPA in California. Email is the primary vector used by hackers to compromise companies.

Younghoo Lee is a Senior Data Scientist at Sophos. Together with Joshua Saxe, Sophos Chief Scientist, he recently presented these findings at DEFCON 28 AI Village. Business Email Compromise (BEC), is a form of targeted phishing where attackers disguise themselves as senior executives to dupe employees into doing something they absolutely shouldn't, like wire money. It started out as an evolution of the fraudulent international money transfer scams, and the messages were often riddled with poor punctuation and grammar, misspelt names and more that made them relatively easy to identify. Yet they still made money.