The insurance company uses different factors to classify the policyholders. In this study, we apply several machine learning models such as nearest neighbour and logistic regression to the Actuarial Challenge dataset used by Qazvini (2019) to classify liability insurance policies into two groups: 1 - policies with claims and 2 - policies without claims. The applications of Machine Learning (ML) models and Artificial Intelligence (AI) in areas such as medical diagnosis, economics, banking, fraud detection, agriculture, etc, have been known for quite a number of years. ML models have changed these industries remarkably. However, despite their high predictive power and their capability to identify nonlinear transformations and interactions between variables, they are slowly being introduced into the insurance industry and actuarial fields.

Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image. To approach such different information-seeking demands, we introduce UniIR, a unified instruction-guided multimodal retriever capable of handling eight distinct retrieval tasks across modalities. UniIR, a single retrieval system jointly trained on ten diverse multimodal-IR datasets, interprets user instructions to execute various retrieval tasks, demonstrating robust performance across existing datasets and zero-shot generalization to new tasks. Our experiments highlight that multi-task training and instruction tuning are keys to UniIR's generalization ability. Additionally, we construct the M-BEIR, a multimodal retrieval benchmark with comprehensive results, to standardize the evaluation of universal multimodal information retrieval.

Cyclists prefer to use infrastructure that separates them from motorized traffic. Using a traffic light to segregate car and bike flows, with the addition of bike-specific green phases, is a lightweight and cheap solution that can be deployed dynamically to assess the opportunity of a heavier infrastructure such as a separate bike lane. To compensate for the increased waiting time induced by these new phases, we introduce in this paper a deep reinforcement learning solution that adapts the green phase cycle of a traffic light to the traffic. Vehicle counter data are used to compare the DRL approach with the actuated traffic light control algorithm over whole days. Results show that DRL achieves better minimization of vehicle waiting time at almost all hours. Our DRL approach is also robust to moderate changes in bike traffic. The code of this paper is available at https://github.com/LucasMagnana/A-DRL-solution-to-help-reduce-the-cost-in-waiting-time-of-securing-a-traffic-light-for-cyclists.

The regions of Alpes-Maritimes, Var, Bouches-du-Rhône, Ardèche, Rhône, Haute-Savoie, Vendée, Maine-et-Loire and Morbihan were part of the trial - but tax officials say it may now be rolled out nationwide.

The discovery of thousands of undeclared private swimming pools in France has provided an unexpected windfall for French tax authorities. Following an experiment using artificial intelligence (AI), more than 20,000 hidden pools were discovered. They have amassed some €10m ($9.9; £8.5m) in revenue, French media is reporting. Pools can lead to higher property taxes because they boost property value, and must be declared under French law. The software, developed by Google and French consulting firm Capgemini, spotted the pools on aerial images of nine French regions during a trial in October 2021.

France experienced its worst drought on record last month, and officials have since been cracking down on conserving water. Now a new artificial intelligence (AI) technology could be added to their arsenal, after it successfully uncovered 20,356 illegally-built private swimming pools. The country's tax authority announced yesterday that the system allowed it to collect about €10 million (£8.5 million) from homeowners who failed to report the facilities. Developed by Google and Capgemini, the AI software was trained to spot pools in aerial images of nine French departments. The results of a trial run last October were then cross-checked with land registry databases, before Direction generale des Finances Publiques (DGFiP) took action.

The regions of Alpes-Maritimes, Var, Bouches-du-Rhône, Ardèche, Rhône, Haute -Savoie, Vendée, Maine-et-Loire and Morbihan were part of the trial - but tax officials say it may now be rolled out nationwide.