As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and downtime. Many AI applications in wind energy, such as in condition monitoring and power forecasting, may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Collaborative distributed AI which preserves data privacy holds a strong potential for these applications. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We explore federated learning in wind turbine condition monitoring, specifically for fault detection using normal behaviour models. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model. Our case study results indicate that federated learning across multiple wind turbines consistently outperforms models trained on a single turbine, especially when training data is scarce. Moreover, the amount of historical data necessary to train an effective model can be significantly reduced by employing a collaborative federated learning strategy. Finally, our findings show that extending the collaboration to multiple wind farms may result in inferior performance compared to restricting learning within a farm, specifically when faced with statistical heterogeneity and imbalanced datasets.

Multi-modal object tracking (MMOT) is an emerging field that combines data from various modalities, \eg vision (RGB), depth, thermal infrared, event, language and audio, to estimate the state of an arbitrary object in a video sequence. It is of great significance for many applications such as autonomous driving and intelligent surveillance. In recent years, MMOT has received more and more attention. However, existing MMOT algorithms mainly focus on two modalities (\eg RGB+depth, RGB+thermal infrared, and RGB+language). To leverage more modalities, some recent efforts have been made to learn a unified visual object tracking model for any modality. Additionally, some large-scale multi-modal tracking benchmarks have been established by simultaneously providing more than two modalities, such as vision-language-audio (\eg WebUAV-3M) and vision-depth-language (\eg UniMod1K). To track the latest progress in MMOT, we conduct a comprehensive investigation in this report. Specifically, we first divide existing MMOT tasks into five main categories, \ie RGBL tracking, RGBE tracking, RGBD tracking, RGBT tracking, and miscellaneous (RGB+X), where X can be any modality, such as language, depth, and event. Then, we analyze and summarize each MMOT task, focusing on widely used datasets and mainstream tracking algorithms based on their technical paradigms (\eg self-supervised learning, prompt learning, knowledge distillation, generative models, and state space models). Finally, we maintain a continuously updated paper list for MMOT at https://github.com/983632847/Awesome-Multimodal-Object-Tracking.

High-risk pregnancy (HRP) is a pregnancy complicated by factors that can adversely affect outcomes of the mother or the infant. Health insurers use algorithms to identify members who would benefit from additional clinical support. We aimed to build machine learning algorithms to identify pregnant patients and triage them by risk of complication to assist care management. In this retrospective study, we trained a hybrid Lasso regularized classifier to predict whether a patient is currently pregnant using claims data from 36735 insured members of Independence Blue Cross (IBC), a health insurer in Philadelphia. We then train a linear classifier on a subset of 12,243 members to predict whether a patient will develop gestational diabetes or gestational hypertension. These algorithms were developed in cooperation with the care management team at IBC and integrated into the dashboard. In small user studies with the nurses, we evaluated the impact of integrating our algorithms into their workflow. We find that the proposed model predicts an earlier pregnancy start date for 3.54% (95% CI 3.05-4.00) for patients with complications compared to only using a set of pre-defined codes that indicate the start of pregnancy and never later at the expense of a 5.58% (95% CI 4.05-6.40) false positive rate. The classifier for predicting complications has an AUC of 0.754 (95% CI 0.764-0.788) using data up to the patient's first trimester. Nurses from the care management program expressed a preference for the proposed models over existing approaches. The proposed model outperformed commonly used claim codes for the identification of pregnant patients at the expense of a manageable false positive rate. Our risk complication classifier shows that we can accurately triage patients by risk of complication.

At Civis Analytics, we bring objective, data-driven truth to organizational decision-making--from the boardroom to the world's largest progressive social causes. This mission isn't an aspiration: it's something we see realized every day, and it brings purpose to everything we're working on. We combine a sophisticated SaaS product with our specialized data science consultancy to empower tens of thousands of active users to make millions of data-driven decisions every month. From joining disparate data sources and automating reporting, to building more elaborate models of targeted audiences and optimizing messages for them, organizations like the Bill and Melinda Gates Foundation, the City of Boston, and iHeart Radio trust Civis's technology to make their most critical decisions. Core to our product and consulting offerings is Civis Platform, which makes it easy to import, manage, transform, analyze, and report on data.

We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable. At the heart of what we do is help clients manage risk. Verisk (Nasdaq: VRSK) provides data and insights to our customers in insurance, energy and the financial services markets so they can make faster and more informed decisions. Our global team uses AI, machine learning, automation, and other emerging technologies to collect and analyze billions of records.

We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable. The Data Engineering and Analytics Lab (DEAL) is a team of technical actuaries responsible for the design and implementation of our core statistical data-systems including data ingestion, data integration, data transformation, data analysis, and analytic dataset construction. We're an innovation group that is charged with visualizing the future of our organization's operations and leveraging our expertise in data, technology, P&C insurance, and process optimization to provide a first-class analytics environment to our data-collection, data-management, actuarial, and data-analytics colleagues. The DEAL team is looking to hire an experienced Lead ETL Data Engineer, ideally having a good combination of an analytical/innovative mindset, technical aptitude, business accumen, communication skills, and a passion for mentoring.

Nuro exists to better everyday life through robotics. We have an elite team of entrepreneurs, engineers, designers, and scientists. We believe AI and robotics are at the cusp of transforming daily life and we are dedicated to building meaningful products with this technology. Join us and play a critical role in our mission. Our team is growing and we are looking for talented engineers to join us.

AI for Managers is a 16-month long programme comprising 11 online modular courses stacked together based on the order of their sequence in a learning curve. It aims to make the knowledge of Artificial Intelligence and its components such as Statistical Learning, Machine Learning, and Deep Learning accessible to a large number of interested candidates from fresh graduates to senior managers who aspire to become competent Decision Makers. Understand foundations of data science on which the AI models are built. Understand and apply machine learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning algorithms to solve problems across various functional areas of management. Apply AI techniques to solve problems in various sectors such as Aerospace, Banking financial services and insurance (BFSI), E-commerce, Manufacturing, Retail, Sports and Services.

Cognilytica is running our Artificial Intelligence (AI) and Machine Learning (ML) training and certification based on the best practices CPMAI methodology, with a live virtual training starting the week of March 25, 2020 that will teach you how to apply the CPMAI Methodology for your projects. This live, instructor-led training is conducted completely online at designated times with live trainers. The course is run as a series of eight (8) sessions conducted over four (4) weeks. This vendor neutral course is an intensive, interactive, real-world based "fire hose" that prepares you to succeed with your AI & ML efforts, whether you're just beginning them or are well down the road with implementation, reflecting the best thinking and research that Cognilytica produces. Cognilytica's CPMAI AI & ML Project Management Certification has no prerequisites, and is designed for people managing AI & ML projects but appropriate for people with different roles and levels of expertise.

Joint replacement is the most common inpatient surgical treatment in the US. We investigate the clinical pathway optimization for knee replacement, which is a sequential decision process from onset to recovery. Based on episodic claims from previous cases, we view the pathway optimization as an intelligence crowdsourcing problem and learn the optimal decision policy from data by imitating the best expert at every intermediate state. We develop a reinforcement learning-based pipeline that uses value iteration, state compression and aggregation learning, kernel representation and cross validation to predict the best treatment policy. It also provides forecast of the clinical pathway under the optimized policy. Empirical validation shows that the optimized policy reduces the overall cost by 7 percent and reduces the excessive cost premium by 33 percent.