Category: News

AI in Human Resources: the real operational impactAI News Human Resources is an area in many organisations where AI can have significant operational impact. The technology is now being embedded into day-to-day operations, in activities like answering employees’ questions and supporting training. The clearest impact appears where organisations can measure the tech’s outcomes, typically in time saved and the numbers of queries successfully resolved.
The post AI in Human Resources: the real operational impact appeared first on AI News.

 Human Resources is an area in many organisations where AI can have significant operational impact. The technology is now being embedded into day-to-day operations, in activities like answering employees’ questions and supporting training. The clearest impact appears where organisations can measure the tech’s outcomes, typically in time saved and the numbers of queries successfully resolved.
The post AI in Human Resources: the real operational impact appeared first on AI News. Read More  

Wall Street’s AI gains are here — banks plan for fewer peopleAI News By December 2025, AI adoption on Wall Street had moved past experiments inside large US banks and into everyday operations. Speaking at a Goldman Sachs financial-services conference in New York on 9 December, bank executives described AI—particularly generative AI—as an operational upgrade already lifting productivity across engineering, operations, and customer service. The same discussion also
The post Wall Street’s AI gains are here — banks plan for fewer people appeared first on AI News.

 By December 2025, AI adoption on Wall Street had moved past experiments inside large US banks and into everyday operations. Speaking at a Goldman Sachs financial-services conference in New York on 9 December, bank executives described AI—particularly generative AI—as an operational upgrade already lifting productivity across engineering, operations, and customer service. The same discussion also
The post Wall Street’s AI gains are here — banks plan for fewer people appeared first on AI News. Read More  

A fine-grained look at causal effects in causal spacescs.AI updates on arXiv.org arXiv:2512.11919v2 Announce Type: replace-cross
Abstract: The notion of causal effect is fundamental across many scientific disciplines. Traditionally, quantitative researchers have studied causal effects at the level of variables; for example, how a certain drug dose (W) causally affects a patient’s blood pressure (Y). However, in many modern data domains, the raw variables-such as pixels in an image or tokens in a language model-do not have the semantic structure needed to formulate meaningful causal questions. In this paper, we offer a more fine-grained perspective by studying causal effects at the level of events, drawing inspiration from probability theory, where core notions such as independence are first given for events and sigma-algebras, before random variables enter the picture. Within the measure-theoretic framework of causal spaces, a recently introduced axiomatisation of causality, we first introduce several binary definitions that determine whether a causal effect is present, as well as proving some properties of them linking causal effect to (in)dependence under an intervention measure. Further, we provide quantifying measures that capture the strength and nature of causal effects on events, and show that we can recover the common measures of treatment effect as special cases.

 arXiv:2512.11919v2 Announce Type: replace-cross
Abstract: The notion of causal effect is fundamental across many scientific disciplines. Traditionally, quantitative researchers have studied causal effects at the level of variables; for example, how a certain drug dose (W) causally affects a patient’s blood pressure (Y). However, in many modern data domains, the raw variables-such as pixels in an image or tokens in a language model-do not have the semantic structure needed to formulate meaningful causal questions. In this paper, we offer a more fine-grained perspective by studying causal effects at the level of events, drawing inspiration from probability theory, where core notions such as independence are first given for events and sigma-algebras, before random variables enter the picture. Within the measure-theoretic framework of causal spaces, a recently introduced axiomatisation of causality, we first introduce several binary definitions that determine whether a causal effect is present, as well as proving some properties of them linking causal effect to (in)dependence under an intervention measure. Further, we provide quantifying measures that capture the strength and nature of causal effects on events, and show that we can recover the common measures of treatment effect as special cases. Read More  

Causal Structure Learning for Dynamical Systems with Theoretical Score Analysiscs.AI updates on arXiv.org arXiv:2512.14361v1 Announce Type: cross
Abstract: Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time — leading to poor performance on irregularly sampled data — or ignore the underlying causality. We propose CaDyT, a novel method for causal discovery on dynamical systems addressing both these challenges. In contrast to state-of-the-art causal discovery methods that model the problem using discrete-time Dynamic Bayesian networks, our formulation is grounded in Difference-based causal models, which allow milder assumptions for modeling the continuous nature of the system. CaDyT leverages exact Gaussian Process inference for modeling the continuous-time dynamics which is more aligned with the underlying dynamical process. We propose a practical instantiation that identifies the causal structure via a greedy search guided by the Algorithmic Markov Condition and Minimum Description Length principle. Our experiments show that CaDyT outperforms state-of-the-art methods on both regularly and irregularly-sampled data, discovering causal networks closer to the true underlying dynamics.

 arXiv:2512.14361v1 Announce Type: cross
Abstract: Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time — leading to poor performance on irregularly sampled data — or ignore the underlying causality. We propose CaDyT, a novel method for causal discovery on dynamical systems addressing both these challenges. In contrast to state-of-the-art causal discovery methods that model the problem using discrete-time Dynamic Bayesian networks, our formulation is grounded in Difference-based causal models, which allow milder assumptions for modeling the continuous nature of the system. CaDyT leverages exact Gaussian Process inference for modeling the continuous-time dynamics which is more aligned with the underlying dynamical process. We propose a practical instantiation that identifies the causal structure via a greedy search guided by the Algorithmic Markov Condition and Minimum Description Length principle. Our experiments show that CaDyT outperforms state-of-the-art methods on both regularly and irregularly-sampled data, discovering causal networks closer to the true underlying dynamics. Read More  

Masked Omics Modeling for Multimodal Representation Learning across Histopathology and Molecular Profilescs.AI updates on arXiv.org arXiv:2508.00969v2 Announce Type: replace-cross
Abstract: Self-supervised learning (SSL) has driven major advances in computational pathology by enabling the learning of rich representations from histopathology data. Yet, tissue analysis alone may fall short in capturing broader molecular complexity, as key complementary information resides in high-dimensional omics profiles such as transcriptomics, methylomics, and genomics. To address this gap, we introduce MORPHEUS, the first multimodal pre-training strategy that integrates histopathology images and multi-omics data within a shared transformer-based architecture. At its core, MORPHEUS relies on a novel masked omics modeling objective that encourages the model to learn meaningful cross-modal relationships. This yields a general-purpose pre-trained encoder that can be applied to histopathology alone or in combination with any subset of omics modalities. Beyond inference, MORPHEUS also supports flexible any-to-any omics reconstruction, enabling one or more omics profiles to be reconstructed from any modality subset that includes histopathology. Pre-trained on a large pan-cancer cohort, MORPHEUS shows substantial improvements over supervised and SSL baselines across diverse tasks and modality combinations. Together, these capabilities position it as a promising direction for the development of multimodal foundation models in oncology. Code is publicly available at https://github.com/Lucas-rbnt/MORPHEUS

 arXiv:2508.00969v2 Announce Type: replace-cross
Abstract: Self-supervised learning (SSL) has driven major advances in computational pathology by enabling the learning of rich representations from histopathology data. Yet, tissue analysis alone may fall short in capturing broader molecular complexity, as key complementary information resides in high-dimensional omics profiles such as transcriptomics, methylomics, and genomics. To address this gap, we introduce MORPHEUS, the first multimodal pre-training strategy that integrates histopathology images and multi-omics data within a shared transformer-based architecture. At its core, MORPHEUS relies on a novel masked omics modeling objective that encourages the model to learn meaningful cross-modal relationships. This yields a general-purpose pre-trained encoder that can be applied to histopathology alone or in combination with any subset of omics modalities. Beyond inference, MORPHEUS also supports flexible any-to-any omics reconstruction, enabling one or more omics profiles to be reconstructed from any modality subset that includes histopathology. Pre-trained on a large pan-cancer cohort, MORPHEUS shows substantial improvements over supervised and SSL baselines across diverse tasks and modality combinations. Together, these capabilities position it as a promising direction for the development of multimodal foundation models in oncology. Code is publicly available at https://github.com/Lucas-rbnt/MORPHEUS Read More  

Generating Artwork in Python Inspired by Hirst’s Million-Dollar Spots PaintingTowards Data Science Using Python to generate art
The post Generating Artwork in Python Inspired by Hirst’s Million-Dollar Spots Painting appeared first on Towards Data Science.

 Using Python to generate art
The post Generating Artwork in Python Inspired by Hirst’s Million-Dollar Spots Painting appeared first on Towards Data Science. Read More  

Ensuring effective AI in insurance operationsAI News Artificial intelligence has been part of the insurance sector for years – the Finance function in many businesses is often the first to automate. But what’s remarkable in the instance of AI is how directly the technology is woven into day-to-day operational work. Not sitting in the background as a niche modelling capability, AI is
The post Ensuring effective AI in insurance operations appeared first on AI News.

 Artificial intelligence has been part of the insurance sector for years – the Finance function in many businesses is often the first to automate. But what’s remarkable in the instance of AI is how directly the technology is woven into day-to-day operational work. Not sitting in the background as a niche modelling capability, AI is
The post Ensuring effective AI in insurance operations appeared first on AI News. Read More  

AstraZeneca leads big pharma’s AI clinical trials revolution with real-world patient impactAI News Big Pharma’s AI race extends across drug discovery, development, and clinical trials—but AstraZeneca has distinguished itself by deploying AI clinical trials technology at an unprecedented public health scale.  While competitors optimise internal R&D pipelines, AstraZeneca’s AI is already embedded in national healthcare systems, screening hundreds of thousands of patients and demonstrating what happens when AI
The post AstraZeneca leads big pharma’s AI clinical trials revolution with real-world patient impact appeared first on AI News.

 Big Pharma’s AI race extends across drug discovery, development, and clinical trials—but AstraZeneca has distinguished itself by deploying AI clinical trials technology at an unprecedented public health scale.  While competitors optimise internal R&D pipelines, AstraZeneca’s AI is already embedded in national healthcare systems, screening hundreds of thousands of patients and demonstrating what happens when AI
The post AstraZeneca leads big pharma’s AI clinical trials revolution with real-world patient impact appeared first on AI News. Read More