We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease. The model achieves 0.9375 accuracy and was validated by comparing large-scale UN data with detailed firm-level data, confirming that the risk signatures are consistent. This scalable tool provides customs authorities with a transparent, data-driven method to shift from conventional to priority-based inspection protocols, translating complex data into actionable intelligence to support international environmental policies.

House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.

Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning. We propose an interpretable role-based clustering approach for multi-layer financial networks, designed to identify the functional positions of institutions across different market segments. Our method follows a general clustering framework defined by proximity measures, cluster evaluation criteria, and algorithm selection. We construct explainable node embeddings based on egonet features that capture both direct and indirect trading relationships within and across market layers. Using transaction-level data from the ECB's Money Market Statistical Reporting (MMSR), we demonstrate how the approach uncovers heterogeneous institutional roles such as market intermediaries, cross-segment connectors, and peripheral lenders or borrowers. The results highlight the flexibility and practical value of role-based clustering in analyzing financial networks and understanding institutional behavior in complex market structures.

LG TVs will soon leverage an artificial intelligence model built for showing advertisements that more closely align with viewers' personal beliefs and emotions. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. The company plans to incorporate a partner company's AI tech into its TV software in order to interpret psychological factors impacting a viewer, such as personal interests, personality traits, and lifestyle choices. The aim is to show LG webOS users ads that will emotionally impact them.

Item-to-Item (I2I) recommendation is an important function in most recommendation systems, which generates replacement or complement suggestions for a particular item based on its semantic similarities to other cataloged items. Given that subsets of items in a recommendation system might be co-interacted with by the same set of customers, graph-based models, such as graph neural networks (GNNs), provide a natural framework to combine, ingest and extract valuable insights from such high-order relational interactions between cataloged items, as well as their metadata features, as has been shown in many recent studies. However, learning GNNs effectively for I2I requires ingesting a large amount of relational data, which might not always be available, especially in new, emerging market segments. To mitigate this data bottleneck, we postulate that recommendation patterns learned from existing mature market segments (with private data) could be adapted to build effective warm-start models for emerging ones. To achieve this, we propose and investigate a personalized federated modeling framework based on GNNs to summarize, assemble and adapt recommendation patterns across market segments with heterogeneous customer behaviors into effective local models. Our key contribution is a personalized graph adaptation model that bridges the gap between recent literature on federated GNNs and (non-graph) personalized federated learning, which either does not optimize for the adaptability of the federated model or is restricted to local models with homogeneous parameterization, excluding GNNs with heterogeneous local graphs.

Artificial intelligence (AI), often recognized as machine intelligence, is an area of computer science that emphasizes developing and managing technology that can learn to make choices and can separately carry out transactions on behalf of humans. The banking, financial services, and insurance segments experience substantial expansion during the estimated period. A substantial amount of client data or transaction records are produced owing to the rising digital revolution in banking and the augmented use of mobile payment, real-time money transfers, e-banking, and mobile banking applications. The global Artificial Intelligence Service Market size was valued at USD 93.5 billion in 2021 growing at the CAGR of 38.1% from 2022 to 2032. Evolve Business Intelligence provides an in-dept research study that contains the ability to focus on the major market dynamics in several region across the globe.

Visiongain has published a new report entitled the Artificial Intelligence (AI) in Drug Discovery 2022-2032. It includes profiles of Artificial Intelligence (AI) in Drug Discovery and Forecasts Market Segment by Offering, (AI Software, AI Services) Market Segment by Technology, (Deep Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning, Other Technology) Market Segment by Applications, (Oncology, Infectious Diseases, Neurological Disorders, Metabolic Diseases, Cardiovascular Diseases, Other Applications) Market Segment by Type, (Target Identification, Molecule Screening, Drug Design and Drug Optimization, Preclinical and Clinical Testing) PLUS COVID-19 Impact Analysis and Recovery Pattern Analysis (V-shaped, W-shaped, U-shaped, L-shaped) Profiles of Leading Companies, Region and Country. The global artificial intelligence (AI) in drug discovery market was valued at US$791 million in 2021 and is projected to grow at a CAGR of 30.7% during the forecast period 2022-2032. AI makes use of the most recent developments in biology and computation to create cutting-edge drug discovery algorithms. AI has the potential to level the playing field in drug research, with to rapid increases in computing capacity and lower processing costs.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Nvidia released results today against new MLPerf industry-standard artificial intelligence (AI) benchmarks for its AI-targeted processors. While the results looked impressive, it is important to note that some of the comparisons they make with other systems are really not apples-to-apples. For instance, the Qualcomm systems are running at a much smaller power footprint than the H100, and are targeted at market segments similar to the A100, where the test comparisons are much more equitable.

The report is an in-depth Deep Learning in Security market research pertaining to recent developments in the market, financial analysis, trends analysis, and evaluation of the perception of the investors. The report advocates the distribution of the Deep Learning in Security industry globally, the different market segments, and the behavior of the consumers. Additionally, the study puts forward the market strategies that may be profitable for Deep Learning in Security industry businesses. The study sheds light on the supportive policy environment and supportive measures undertaken by the governments across the world to facilitate growth of the market players and the overall Deep Learning in Security industry. The study is helpful to the economic operators, investors, and policy-makers seeking to understand the economic status of the global Deep Learning in Security market.

The following paper, "Simba: Scaling Deep-Learning Inference with Chiplet-Based Architecture," by Shao et al. presents a scalable deep learning accelerator architecture that tackles issues ranging from chip integration technology to workload partitioning and non-uniform latency effects on deep neural network performance. Through a hardware prototype, they present a timely study of cross-layer issues that will inform next-generation deep learning hardware, software, and neural network architectures. Chip vendors face significant challenges with the continued slowing of Moore's Law causing the time between new technology nodes to increase, sky-rocketing manufacturing costs for silicon, and the end of Dennard scaling. In the absence of device scaling, domain specialization provides an opportunity for architects to deliver more performance and greater energy efficiency. However, domain specialization is an expensive proposition for chip manufacturers.