We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research.

The Corpus dataset is composed of 363,133 abstracts from computer science papers, published between 2011-2021, and sourced from arXiv.

If the required memory to train a model exceeds this limit, the device will be excluded from the training.

Apple and Google agree to change app stores after'effective duopoly' claim Apple and Google have agreed to make changes to their app stores in the UK following an intervention from the UK markets regulator. According to the Competition and Markets Authority (CMA), the tech giants have committed to not giving preferential treatment to their own apps and will be transparent about how others are approved for sale, among other agreements. It comes seven months after the regulator said Apple and Google had an effective duopoly in the UK over their dominance in the sector. The CMA's head Sarah Cardell said the proposed commitments will boost the UK's app economy and were the first of many measures. The ability to secure immediate commitments from Apple and Google reflects the unique flexibility of the UK digital markets competition regime and offers a practical route to swiftly address the concerns we've identified, she said.

This view of the data generating process is also adopted and promoted by popular existing works.

We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator satisfies the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.

How to identify the first iconic use case for an enterprise AI transformation. Many organizations rushed into generative AI, only to see pilots fail to deliver value . Now, companies want measurable outcomes--but how do you design for success? At Mistral AI, we partner with global industry leaders to co-design tailored AI solutions that solve their most difficult problems. Whether it's increasing CX productivity with Cisco, building a more intelligent car with Stellantis, or accelerating product innovation with ASML, we start with open frontier models and customize AI systems to deliver impact for each company's unique challenges and goals. Our methodology starts by identifying an iconic use case, the foundation for AI transformation that sets the blueprint for future AI solutions.

We study various types of consistency of honest decision trees and random forests in the regression setting. In contrast to related literature, our proofs are elementary and follow the classical arguments used for smoothing methods. Under mild regularity conditions on the regression function and data distribution, we establish weak and almost sure convergence of honest trees and honest forest averages to the true regression function, and moreover we obtain uniform convergence over compact covariate domains. The framework naturally accommodates ensemble variants based on subsampling and also a two-stage bootstrap sampling scheme. Our treatment synthesizes and simplifies existing analyses, in particular recovering several results as special cases. The elementary nature of the arguments clarifies the close relationship between data-adaptive partitioning and kernel-type methods, providing an accessible approach to understanding the asymptotic behavior of tree-based methods.