PCWorld highlights Microsoft's new agentic AI features for Outlook that go beyond basic email drafting to advanced inbox and calendar management automation. These tools can identify unreplied emails, summarize missed content, draft follow-ups, reschedule meetings, and create agendas to save significant time. Access requires a Microsoft 365 Copilot for Business account and IT approval, potentially revolutionizing productivity for business users. I never really thought I'd welcome AI as a part of my ongoing business day. But Microsoft's ongoing productivity updates to Outlook actually have me tempted. By now, drafting an email using AI is old hat, and something that I generally wouldn't do. But Microsoft has begun adding agentic AI to Outlook via its experimental "Frontier" program and it actually sounds like something that could really save time and energy.

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What are Russia's gains from the Iran war? 'We are not losers; we are winners' Ukrainian drones strike Russia's Tuapse refinery for third time NewsFeed Ukrainian drones strike Russia's Tuapse refinery for third time Ukraine has targeted a major Russian oil refinery in the Black Sea port city of Tuapse for the third time in less than two weeks, setting off a fresh blaze and prompting authorities to evacuate local residents. Qatar says using Hormuz Strait as political weapon is'unacceptable' Australia's top diplomat visits China to talk energy security

The United Arab Emirates has announced it's withdrawing from OPEC and OPEC+. Al Jazeera's Michael Appel outlines the significance of the announcement and its likely impact on the energy market. Ukrainian drones strike Russia's Tuapse refinery for third time Qatar says using Hormuz Strait as political weapon is'unacceptable' Australia's top diplomat visits China to talk energy security

A source says the contract doesn't give the company any veto power over how the tech is used by the government. Google has signed a deal that allows the US Department of Defense to use its AI models for any lawful government purpose. This is according to a report by, which also notes that the full details of the contract are classified. An anonymous source within the company has suggested that the two entities have agreed that the search giant's AI tech shouldn't be used for domestic mass surveillance or autonomous weapons without appropriate human oversight and control. However, the contract also reportedly doesn't give Google any right to control or veto anything the government decides to do.

The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research. We introduce TensorNet, an innovative O(3)-equivariant message-passing neural network architecture that leverages Cartesian tensor representations. By using Cartesian tensor atomic embeddings, feature mixing is simplified through matrix product operations. Furthermore, the cost-effective decomposition of these tensors into rotation group irreducible representations allows for the separate processing of scalars, vectors, and tensors when necessary. Compared to higher-rank spherical tensor models, TensorNet demonstrates state-of-the-art performance with significantly fewer parameters. For small molecule potential energies, this can be achieved even with a single interaction layer. As a result of all these properties, the model's computational cost is substantially decreased. Moreover, the accurate prediction of vector and tensor molecular quantities on top of potential energies and forces is possible. In summary, TensorNet's framework opens up a new space for the design of state-of-the-art equivariant models.

Kernel design is a pivotal but challenging aspect of time series analysis, especially in the context of small datasets. In recent years, Reservoir Computing (RC) has emerged as a powerful tool to compare time series based on the underlying dynamics of the generating process rather than the observed data. However, the performance of RC highly depends on the hyperparameter setting, which is hard to interpret and costly to optimize because of the recurrent nature of RC. Here, we present a new kernel for time series based on the recently established equivalence between reservoir dynamics and Nonlinear Vector AutoRegressive (NVAR) processes. The kernel is non-recurrent and depends on a small set of meaningful hyperparameters, for which we suggest an effective heuristic. We demonstrate excellent performance on a wide range of real-world classification tasks, both in terms of accuracy and speed. This further advances the understanding of RC representation learning models and extends the typical use of the NVAR framework to kernel design and representation of real-world time series data.

Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-economic, ecological, or biomedical systems. Non-trivial dependencies between groups of more than two variables can play a significant role in the analysis and modelling of such systems, yet extracting such high-order interactions from data remains challenging. Here, we introduce a hierarchy of d-order interaction measures, increasingly inclusive of possible factorisations of the joint probability distribution, and define non-parametric, kernel-based tests to establish systematically the statistical significance of d-order interactions. We also establish mathematical links with lattice theory, which elucidate the derivation of the interaction measures and their composite permutation tests; clarify the connection of simplicial complexes with kernel matrix centring; and provide a means to enhance computational efficiency. We illustrate our results numerically with validations on synthetic data, and through an application to neuroimaging data.

Phylogenetic inference, grounded in molecular evolution models, is essential for understanding the evolutionary relationships in biological data. Accounting for the uncertainty of phylogenetic tree variables, which include tree topologies and evolutionary distances on branches, is crucial for accurately inferring species relationships from molecular data and tasks requiring variable marginalization. Variational Bayesian methods are key to developing scalable, practical models; however, it remains challenging to conduct phylogenetic inference without restricting the combinatorially vast number of possible tree topologies. In this work, we introduce a novel, fully differentiable formulation of phylogenetic inference that leverages a unique representation of topological distributions in continuous geometric spaces. Through practical considerations on design spaces and control variates for gradient estimations, our approach, GeoPhy, enables variational inference without limiting the topological candidates. In experiments using real benchmark datasets, GeoPhy significantly outperformed other approximate Bayesian methods that considered whole topologies.