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(TeeStocker/Shutterstock) As businesses recognize the need for more responsive, efficient, and personalized customer service, the demand for intelligent automation is growing The customer service industry is witnessing a shift from basic, rule-based chatbots to sophisticated AI agents.  Salesforce, one of the leading cloud-based CRM providers, has jumped into the AI agent world by announcing its first fully autonomous AI agent – Einstein Service Agent. Powered by GenAI, the new agent offers customers a self-service experience.  According to Salesforce, the new agent can understand and take action on a broad range of services without needing pre-programmed scenarios. Unlike traditional chatbots, which…

Forecasting models are critical for many businesses to predict future trends, but their accuracy depends heavily on the quality of the input data. Poor quality data can lead to inaccurate forecasts that result in suboptimal decisions. This is where Databricks Lakehouse Monitoring comes in - it provides a unified solution to monitor both the quality of data flowing into forecasting models as well as the model performance itself.Monitoring is especially crucial for forecasting models. Forecasting deals with time series data, where the temporal component and sequential nature of the data introduce additional complexities. Issues like data drift, where the statistical…

arXiv:2407.12112v1 Announce Type: new Abstract: Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware graph learning methods, which blocks practitioners from choosing appropriate ones for broader real-world applications. In this paper, we present an extensive benchmark on ten representative fairness-aware graph learning methods. Specifically, we design a systematic evaluation protocol and conduct experiments on seven real-world datasets to evaluate these methods from multiple perspectives, including group fairness, individual fairness, the balance between different fairness criteria, and computational efficiency. Our in-depth analysis reveals key insights into the strengths…

arXiv:2407.12138v1 Announce Type: new Abstract: This work presents a novel approach to monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, symmetries, occlusions, and lack of annotated real-world data. The method leverages synthetic data generation and domain adaptation techniques to overcome these obstacles. The proposed approach consists of three main components: (1) synthetic data generation using 3D modeling of surgical tools with articulation rigging and physically-based rendering; (2) a tailored pose estimation framework combining object detection with pose estimation and a hybrid geometric fusion strategy; and (3) a training strategy that utilizes both…

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