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This guide to AI data visualization breaks down the essentials of understanding and improving complex datasets with examples, tools, and proven strategies to support better model development and decision-making. When it comes to AI, large and complex datasets are a necessary evil. To build accurate and reliable AI models, it is important to truly understand the data being used. This is where data visualization becomes key. Visualization helps AI teams explore the data, spot errors or missing values, understand data distribution, and see relationships between features. Instead of just looking at raw numbers, visual tools like histograms, scatter plots, graphs and heatmaps make it easier to detect patterns and outliers. Good data visualization is key to improving AI performance. It helps to make better choices when cleaning, labeling, or selecting features for training. Choosing the right visualization tools can make complex AI data easier to understand and guide better model development from the start. What is Data Visualization? In modern AI workflows, data visualization is more than just a way to make information easier to look at — it's a functional, high-leverage tool that helps teams work faster, detect errors earlier, and explain model behavior more clearly. At its core, data visualization is the graphical representation of information using elements like charts, heatmaps, scatter plots, or dashboards. AI teams today deal with large, high-dimensional, often unstructured datasets. Visualization becomes a hands-on method for exploring, debugging, and understanding these datasets across various modalities, including tabular, image, video, and text. Rather than relying on abstract metrics or logs alone, visualizations make AI pipelines visible and interpretable, both during development and after deployment. One of the key use cases is Exploratory data analysis (EDA), or the stage where teams evaluate the structure, quality, and distribution of their data before building models. During EDA, visualization tools help uncover trends, spot imbalances, and identify data integrity issues. For example: Scatter plots and histograms can reveal feature distributions and outliers. Correlation heatmaps show how variables relate. Interactive dashboards allow filtering, subsetting, and exploring data points in real time. These tasks are typically handled with tools like Tableau and Looker for structured data, or FiftyOne and Encord for unstructured image and video datasets. The ability to zoom in on mislabeled objects, filter by metadata, or visually flag edge cases makes these tools crucial during the dataset curation and preparation stage. Once model training begins, visualization continues to play a key role. Tools like TensorBoard, Encord, or integrated dashboards in BI platforms allow teams to track and interpret model behavior: Loss and accuracy curves visualize learning progress. Confusion matrices and receiver operating characteristic (ROC) curves help evaluate classification performance. Prediction overlays and saliency maps support visual model debugging, especially in domains like computer vision and medical imaging. Data visualization also enhances the interpretability of AI models. Explainable AI uses visualization techniques such as feature importance plots, heatmaps, decision trees, and visual explanations generated through frameworks like SHAP and LIME. Data visualization is essential for real-time monitoring and debugging of AI models in production environments. Visual dashboards provide continuous insights into model performance metrics, drift detection, prediction accuracy, latency, and resource consumption. It becomes easy to identify problems and diagnose issues like data drift or model degradation by visually tracking these parameters which further helps in taking corrective actions promptly. In computer vision applications, data visualization directly helps in interpreting visual model outputs. Techniques like bounding boxes, segmentation masks, keypoint annotations, and overlays on images or videos allow to visually assess AI-driven image analysis. Similarly in NLP, data visualization helps in enhancing tasks by transforming complex textual information into easily digestible visual formats. Word clouds, sentiment analysis graphs, topic modeling visualizations (e.g., LDA visualizations), and interactive dashboards help in the interpretation of large textual datasets. The power of visualization here isn’t just in simplifying metrics, but in bringing explainability and transparency into model development. Rather than treating the model as a black box, visual outputs give teams insight into why a model behaves a certain way — whether it's overfitting, misclassifying, or biased. As models move to production, visualization supports another critical layer: monitoring and communication. Teams need ways to summarize results, flag anomalies, and share insights with stakeholders. Here, visualization tools help package AI outputs into intuitive dashboards and reports, enabling business, product, and operations teams to act on AI-driven insights. Ultimately, data visualization in AI is not a luxury — it’s a requirement for responsible, explainable, and high-performing AI systems. Whether you're cleaning data, interpreting models, or explaining predictions to executives, the right visualization tool makes these tasks clearer, faster, and more collaborative. Data Visualization in TensorBoard (Source) Why Data Visualization is Essential for AI AI relies on large amounts of data and complex algorithms to spot patterns, make predictions, and provide useful insights. But without clear visualization the AI systems seem like mysterious "black boxes" which are hard to understand or explain. Data visualization turns complicated data into easy-to-understand visuals which help to make better decisions. Data visualization is a key part of building and using AI effectively. Following are the reasons why visualization matters so much for AI. Enhanced Data Understanding Before AI models are built it is important to understand the data. Data visualization makes this easier by turning complex datasets into clear, visualization formats like charts, graphs, and heatmaps. Tools like scatter plots, histograms, and correlation matrices help to quickly spot trends, patterns, and oddities in the data. For example, visualizing data can show imbalances, missing values, or unusual outliers which helps in cleaning and preparing the data properly. Without good visualization hidden problems in data might go unnoticed which can lead to inaccurate or biased AI models. Better data understanding through visualization leads to stronger and more reliable AI. Model Interpretability AI models can be hard to understand because of their complexity. Data visualization helps to understand these AI models. Tools like feature importance charts, decision trees, and heatmaps show how and why an AI model makes certain choices. For example, in medical imaging, a heatmap can highlight which parts of an X-ray led the AI to detect a disease which helps doctors and patients understand the reasoning behind the result. By turning complex AI logic into visual explanations, data visualization builds trust and makes AI more transparent for everyone. Communication of Insights The main goal of AI is to turn data into useful insights to help make better decisions and achieve better results. Visualization is a great way to share these insights clearly even to those who are from non-technical background. Things like interactive dashboards, easy-to-read charts, live visual updates, and simple summaries help explain complex AI results in a way that is easy to understand. This makes it easier to make quick decisions. For example, a sales forecasting dashboard can show future sales in a visual way which helps to see trends and decide how to use resources wisely. Data visualization plays a key role in the success of AI projects. It helps to understand the data better and makes AI results more transparent and easier to explain. Data visualization improves how insights are shared. It helps represent complex data and model results into easy to understand visuals. Important Features of AI Data Visualization Tools Data visualization tools for AI must have capabilities to handle complex, multimodal, and dynamically changing data. Effective visualization not only simplifies complex data but also enhances AI model interpretability, collaboration, and communication of insights. Following are the critical features of a robust AI data visualization tool. Interactive Visualizations Interactivity is one of the most essential features. An AI visualization tool should enable users to explore data dynamically through interactive dashboards, filters, zoom-in and zoom-out capabilities, drill-down options, and real-time manipulation of data. Such interactions allow users to deeply understand complex AI outcomes, customize views, and answer specific questions without requiring additional analysis. Real-time Data Integration An effective AI visualization tool should be able to integrate with real-time data streams and dynamically update visualizations accordingly. Real-time integration ensures that the visualized data remains current and reflects live model outputs and predictions. This is especially critical for use cases like predictive maintenance, anomaly detection, IoT monitoring, or real-time sentiment analysis. Scalability and Performance Visualization tools must efficiently handle large datasets of AI projects without performance degradation. Important features include optimized data rendering, fast-loading visuals, and efficient processing of massive data volumes. Scalability ensures that tools remain responsive even with high-dimensional data or millions of data points to maintain user productivity and insight clarity. Advanced Visualization Techniques Data visualization tools for AI must support advanced visualization techniques such as heatmaps, scatter plot matrices, 3D plots, hierarchical visualizations, dimensionality reduction visualizations (PCA, t-SNE, UMAP) etc. These sophisticated visualizations are essential for accurately representing high-dimensional data, complex relationships, clustering outcomes, and feature importance in AI models. Explainability and Model Interpretation Data visualization tools for AI should offer features that enable easy interpretation of AI model decisions. This includes visualization of metrics like confusion matrices, mAP, ROC curves and many others. These capabilities promote transparency, trust, and regulatory compliance by clearly demonstrating how AI systems arrive at specific decisions. Ease of Use and Customization A good AI visualization tool should be both powerful and easy to use. Data visualization tools should make it easy to label data accurately, set up training workflows, and organize datasets without needing deep technical knowledge. Clear instructions, visual tools, and documentation can help speed up the process and reduce errors. This allows teams to focus more on building great AI models and less on dealing with complicated tools. Collaboration and Sharing Collaboration and sharing are important when multiple users or teams are working on the same dataset for an AI model where users can easily share and label data, and track changes in one place. Visualization plays a key role in collaboration. It helps teams clearly see the progress of labeling, training results, and model performance. Visual dashboards and charts make it easier to understand what’s happening and make decisions together, even if not everyone has a technical background. Good visualization tools for AI should provide a balance between powerful features and easy-to-use design. It should support interactive use, work well with large amounts of data and should also be able to help explain AI results clearly. It should also make it easy for teams to work together.  Encord: A Multimodal Data Visualization Tool (Source) Data Visualisation Tools for Visualizing Unstructured Data Encord  Encord is a powerful data development platform designed to manage, curate, and annotate multimodal data including images, videos, audio, documents, text, and DICOM files for AI model training and fine-tuning. Following are the features of Encord related to data visualization for AI. Interactive Visualizations: Encord offers interactive dashboards and visualization tools that enable users to explore and analyze large datasets effectively. Real-time Data Integration: The platform supports integration with various data sources, allowing for real-time data synchronization. This ensures that the most current data is available for analysis and model training. Scalability and Performance: Encord is built to handle large-scale datasets and support the management of large amounts of data files across different modalities. Its architecture ensures efficient performance even with extensive data volumes. ​ Advanced Visualization Techniques: The platform provides advanced visualization techniques, such as embedding plots, which allow users to visualize high-dimensional data in two dimensions. This aids in understanding complex data structures and relationships. Explainability and Model Interpretation: Encord Active, an open-source toolkit within the platform that enables users to test, validate, and evaluate models. It offers model explainability reports, helping users understand model decisions and identify areas for improvement. Ease of Use and Customization: Encord provides an intuitive interface with customizable annotation workflow which makes it accessible for users with varying technical expertise. Collaboration and Sharing: Encord offers collaborative tools that enable multiple users to work simultaneously on data curation and annotation tasks. Data Embedding Plot in Encord FiftyOne ​FiftyOne is an open-source tool developed by Voxel51 to enhance the management, visualization, and analysis of computer vision datasets. Following are an overview of its key features related to data visualization. Interactive Visualizations: FiftyOne offers dynamic interfaces that allow users to visualize datasets, including images and videos, along with their annotations. Users can filter, sort, and query data. These changes are reflected instantly in the visual interface which helps in efficient data exploration and analysis. Real-time Data Integration: The platform supports integration with various data sources to enable real-time data synchronization. Scalability and Performance: Designed to handle large-scale datasets, FiftyOne can manage millions of data samples across diverse formats and modalities, including images, videos, and 3D point clouds. Advanced Visualization Techniques: FiftyOne provides advanced visualization techniques, such as embedding projections which allow users to visualize high-dimensional data in lower dimensions.  Explainability and Model Interpretation: The platform includes tools for evaluating and analyzing model performance. Users can compute detailed metrics, visualize predictions alongside ground truth labels and explore failure cases to improve model performance. Ease of Use and Customization: FiftyOne features a rich user interface and a powerful Python API, allowing users to programmatically control and manipulate data. Collaboration and Sharing: The platform supports collaboration that enable multiple users to work simultaneously on data curation and annotation tasks. Data Visualisation Tools for Business intelligence with AI/ML integrations ThoughtSpot ​ThoughtSpot is an AI analytics platform which is designed to explore and analyze data through natural language queries and interactive visualizations. Following are its key features in relation to data visualization. Interactive Visualizations: ThoughtSpot's Liveboards offer real-time, interactive dashboards that allow users to visualize and explore data.  Real-time Data Integration: The platform connects with various data sources, including cloud data warehouses like Snowflake, Google BigQuery, and Amazon Redshift and many more. Scalability and Performance: ThoughtSpot is built to handle large-scale data environments and provide fast query responses even with extensive datasets. Advanced Visualization Techniques: ThoughtSpot offers advanced visualization through features like SpotIQ which automatically detects patterns, anomalies, and trends in the data. Explainability and Model Interpretation: ThoughtSpot's AI enabled analytics provide transparent insights by allowing users to see the underlying data and logic behind visualizations. Ease of Use and Customization: With its natural language search interface, ThoughtSpot makes data exploration accessible to users easily. The platform also offers customization options to customize dashboards and reports to their specific needs. Collaboration and Sharing: ThoughtSpot facilitates collaboration by enabling users to share Liveboards and reports. ThoughtSpot Visualization (Source) ​​Domo  ​Domo is a cloud-based business intelligence (BI) platform that supports features such as real-time data integration, visualization, and analytics capabilities. Following are its key features: in related to data visualization. Interactive Visualizations: Domo offers a powerful charting engine that enables users to create interactive and easy-to-use visualizations. Real-time Data Integration: The platform supports integration with a wide range of data sources, including databases, files, and cloud services. Scalability and Performance: Domo is designed to handle large volumes of data and provides a scalable solution that maintains performance as data complexity and size grow. Advanced Visualization Techniques: Beyond standard charts and graphs, Domo offers advanced visualization options such as interactive dashboards and custom apps. These tools help users to present complex data in an understandable and actionable format. Explainability and Model Interpretation: The AI capabilities of Domo such as AI Chat and AI Agents provide users with conversational interfaces to query data and receive explanations. This enhances the interpretability of data models and supports informed decision-making. Ease of Use and Customization:Domo provides drag-and-drop interface with customization options to allow build dashboards, reports, and apps to meet specific requirements. Collaboration and Sharing: Domo facilitates collaboration through features that enable users to share dashboards and reports securely within their organization Domo data visualization (Source) The data visualization tools  (i.e. Encord, FiftyOne, Tableau, Looker Studio, ThoughtSpot, and Domo) discussed here offer robust features that can be used to visualize both source data and model outputs. They enable users to create interactive and insightful visualizations to help exploration of raw datasets, identification of patterns, and monitoring of model performance and thus assisting in enhancing data-driven decision-making processes. Selecting the appropriate data visualization tool is crucial for effectively analyzing and presenting data. Here are a few points for consideration. Define Your Objectives: Determine whether the tool will be used for exploratory data analysis, explanatory presentations, or real-time monitoring. Different tools excel in different areas. Data Compatibility and Integration: Assess the tool's ability to connect with various data sources  and ensure whether the tool can handle data size and complexity without performance issues. Ease of Use: The tool should have an easy to use user interface. Variety of Visualization: The tool should provides a wide range of visualization options to represent your data effectively.​ Collaboration and Sharing: The tool should allow to set permissions and control who can view or edit visualizations and enable easy sharing of data and visualization. Performance and Scalability: The tool should process and render visualizations quickly, even with large datasets. Security and Compliance: Ensure the tool complies with security policies and industry regulations especially if handling sensitive information. Data Visualisation Tool for Interactive Dashboards for Collaboration  Tableau Tableau is a leading data visualization and business intelligence tool that enables users to analyze, visualize, and share data insights across an organization. Here is an overview of its key features related to data visualization. Interactive Visualizations: Tableau offers a user-friendly, drag-and-drop interface that allows users to create a wide range of interactive visualizations including bar charts, line graphs, maps, and more. These visualizations enable users to explore data dynamically, facilitating deeper insights. Real-time Data Integration: Tableau supports connections to various data sources, such as spreadsheets, databases, cloud services, and web data connectors. Scalability and Performance: Tableau is designed to handle large volumes of data and maintains high performance and responsiveness. Advanced Visualization Techniques: Tableau offers advanced visualization options like treemaps, heatmaps, box and whisker plots, and geographic maps. These tools help users explore and find complex patterns and trends within their data. Explainability and Model Interpretation: Tableau provides features such as trend lines, forecasting, and integration with statistical tools like R and Python. The Aible extension for Tableau enables users to build predictive AI models. Ease of Use and Customization: Tableau provides an easy to use interface with drag-and-drop functionality. It offers various customization options for data visualizations and dashboards to meet specific requirements. Collaboration and Sharing: Tableau enables collaboration to allow users to share dashboards and reports securely within their organization. Data Visualization in Tableau (Source) ​Looker Studio ​Looker Studio (formerly known as Google Data Studio) is a free, cloud-based business intelligence and data visualization tool that enables users to create interactive reports and dashboards. Following are key features of looker studio related to data visualization. Interactive Visualizations: Looker Studio offers a wide range of customizable charts and tables, including bar charts, line graphs, geo maps, and more. Users can create interactive reports that helps to explore data dynamically and get deeper insights. ​ Real-time Data Integration: The platform supports connections to a large number of data sources, such as Google Analytics, Google Ads, BigQuery, and various databases. Scalability and Performance: Looker studio is designed to handle datasets of varying sizes and maintains same performance and responsiveness. Its integration with Google's infrastructure allows for efficient data processing and visualization for both small businesses and large enterprises. Advanced Visualization Techniques: Beyond standard visualization tools, Looker Studio provides advanced visualization options like geo maps and treemaps etc. Explainability and Model Interpretation: While primarily a data visualization tool, Looker Studio can integrate with platforms like Vertex AI to incorporate machine learning models into reports. Looker Studio can connect to data sources that contain the outputs of machine learning models deployed on Vertex AI. Ease of Use and Customization: Looker Studio offers customization options to allow users to customize visualizations and dashboards to meet specific requirements.  Collaboration and Sharing: Looker Studio enables collaboration via team workspace that allow multiple users to edit reports simultaneously and offers flexible sharing options. It enables efficient teamwork and broad dissemination of data insights. Data Visualization in Looker Studio (Source) Key Takeaways Data visualization is the graphical representation of data using charts, graphs, maps, and dashboards to make complex information easier to understand. It is essential in AI to explore datasets, identify patterns or anomalies, monitor model performance, and communicate insights clearly. Data visualization is essential in AI for understanding, cleaning, and exploring data effectively. It helps identify patterns, trends, outliers, and missing values through visual formats like charts and heatmaps. Visualization helps in model development by tracking training progress with tools like accuracy/loss curves and confusion matrices. It improves model interpretability and trust using visual explanations such as feature importance and heatmaps. Good visualization tools should support interactivity, real-time data integration, scalability, advanced plots, explainability, ease of use, and collaboration. Tools like Encord, FiftyOne, Tableau, Looker Studio, ThoughtSpot, Zoho Analytics, and Domo offer powerful visualization features for AI workflows. Choosing the right tool depends on your project needs, data types, performance requirements, and team collaboration preferences.

Imagine a self-driving car driving up to a busy intersection as the light slowly turns to yellow and then red. In that instant, it is critical that the model understands the environment, the color of the lights and the cars around it, in order to manoeuvre the vehicle safely. This is the perfect example of the importance of successful physical AI models. Physical AI, or AI models that interact directly with the physical world, are powering the next generation of technologies across domains such as robotics, autonomous vehicles, drones, and advanced medical devices. These systems rely on high-fidelity machine learning models trained to interpret and act within dynamic, real-world environments. A foundational component in building these models is data annotation — the process of labeling raw data so it can be used to train supervised learning algorithms. For Physical AI, the data involved is often complex, multimodal, and continuous, encompassing video feeds, LiDAR scans, 3D point clouds, radar data, and more. Given the real-world stakes, safety, compliance, real-time responsiveness, selecting the right annotation tools is not just a technical decision, but a strategic one. Performance, scalability, accuracy, and support for safety-critical environments must all be factored into the equation. What Is Data Annotation for Physical AI? Data annotation for Physical AI goes beyond traditional image labeling. These systems operate in environments where both space and time are critical, requiring annotations that reflect motion, depth, and change over time. For example, labeling a pedestrian in a video stream involves tracking that object through multiple frames while adjusting for occlusions and changes in perspective. Another key element is multimodality. Physical AI systems typically aggregate inputs from several sources, such as combining different video angles of a single object. Effective annotation tools must allow users to overlay and synchronize these different data streams, creating a coherent representation of the environment that mirrors what the AI system will ultimately "see." The types of labels used are also more sophisticated. Rather than simple image tags or bounding boxes, Physical AI often requires: 3D volume rendering: allows physical AI to "see" not just surfaces, but internal structures, occluded objects, and the full spatial context. Segmentation masks: provide pixel-level detail about object boundaries, useful in tasks like robotic grasping or surgical navigation. These requirements introduce several unique challenges. Maintaining annotation accuracy and consistency over time and across modalities is difficult, especially in edge cases like poor lighting, cluttered scenes, or fast-moving objects. Additionally, domain expertise is often necessary. A radiologist may need to label surgical tool interactions, or a robotics engineer may need to review mechanical grasp annotations. This further complicates the workflow. Key Criteria for Evaluating Physical AI Annotation Tools Choosing a data annotation tool for Physical AI means looking for more than just label-drawing features. The platform must address the full spectrum of operational needs, from data ingestion to model integration, while supporting the nuanced requirements of spatial-temporal AI development. Multimodal Data Support The most critical capability is support for multimodal datasets. Annotation tools must be able to handle a range of formats including video streams, multi-camera setups, and stereo images, to name a few. Synchronization across these modalities must be seamless, enabling annotators to accurately label objects as they appear in different views and data streams. Tools should allow annotators to visualize in 2D, 3D, or both, depending on the task. Automation and ML-Assisted Labeling Given the scale and complexity of physical-world data, AI-assisted labeling is a necessity. Tools that offer pre-labeling using machine learning models can significantly accelerate the annotation process. Even more effective are platforms that support active learning, surfacing ambiguous or novel samples for human review. Some systems allow custom model integration, letting teams bring their own detection or segmentation algorithms into the annotation workflow for bootstrapped labeling. Collaboration and Workflow Management In enterprise model development, annotation is often a team-based process. Tools should offer robust collaboration features, such as task assignment, label versioning, and detailed progress tracking. Role-based access control is essential to manage permissions across large annotation teams, particularly when domain experts and quality reviewers are involved. Comprehensive audit trails ensure transparency and traceability for every annotation made. Quality Assurance and Review Pipelines Maintaining label quality is paramount in safety-critical systems. The best annotation tools support built-in QA workflows, such as multi-pass review. These checks can help catch common errors, while human reviewers can resolve more subtle issues. Review stages should be clearly defined and easy to manage, with options to flag, comment on, and resolve discrepancies. Security and Compliance For applications in healthcare, defense, and transportation, security and regulatory compliance are non-negotiable. Annotation tools should offer end-to-end encryption, granular access controls, secure data storage, and audit logging. Compliance with frameworks like HIPAA, GDPR, and ISO 27001 is essential, especially when working with sensitive patient data or proprietary robotics systems. On-premise or VPC deployment options are often necessary for organizations with strict data handling policies. Top Data Annotation Tools for Physical AI (2025 Edition) Encord Encord provides a purpose-built solution for labeling and managing high-volume visual datasets in robotics, autonomous vehicles, medical devices, and industrial automation. Its platform is designed to handle complex video workflows and multimodal data — accelerating model development while ensuring high-quality, safety-critical outputs. Encord offers a powerful, collaborative annotation environment tailored for Physical AI teams that need to streamline data labeling at scale. With built-in automation, real-time collaboration tools, and active learning integration, Encord enables faster iteration on perception models and more efficient dataset refinement. At the core of Encord’s platform is its automated video annotation engine, purpose-built to support time-sensitive, spatially complex tasks. Physical AI teams can label sequences up to six times faster than traditional manual workflows, thanks to AI-assisted tracking and labeling automation that adapts over time. Benefits & Features AI-Powered Labeling Engine: Encord leverages micro-models and automated object tracking to drastically reduce manual labeling time. This is critical for teams working with long, continuous sequences from robots, drones, or AVs. Multimodal Support: In addition to standard visual formats like MP4 and WebM, Encord natively supports modalities relevant to Physical AI — including medical DICOM imaging and video. Annotation Types Built for Real-World Perception: The platform supports a wide array of labels such as bounding boxes, segmentation masks, keypoints, polylines, and classification — enabling granular understanding of objects and motion across frames. Dataset Quality Evaluation: Encord includes tools to assess dataset integrity using metrics like frame object density, occlusion rates, lighting variance, and duplicate labels — helping Physical AI teams identify blind spots in model training data. Collaborative Workflow Management: Built for large-scale operations, Encord includes dashboards for managing annotators, tracking performance, assigning QA reviews, and ensuring compliance across projects. Ideal For: ML and robotics teams building spatial-temporal models that rely on video Medical AI developers working with procedural videos and DICOM files Autonomy and perception teams looking to scale annotation pipelines with quality assurance baked in Data operations leads who need a platform to manage internal and outsourced annotation efforts seamlessly Modalities Supported: Video & Images DICOM (Medical Imaging) SAR (Radar Imagery) Documents Audio CVAT CVAT (Computer Vision Annotation Tool) has become a trusted open-source platform for image and video annotation. Available under the MIT license, CVAT has evolved into an independent, community-driven project supported by thousands of contributors and used by over a million practitioners worldwide. For Physical AI applications, where large volumes of video data and frame-by-frame spatial reasoning are common, CVAT provides a solid foundation. Its feature set supports the annotation of dynamic scenes, making it especially useful for tasks such as labeling human motion for humanoid robotics, tracking vehicles across intersections, or defining action sequences in industrial robots. CVAT Benefits & key features:  Open-Source and Free to Use: Its source code can be self-hosted and extended to fit custom workflows or integration needs. Video Annotation Capabilities: Tailored features like frame-by-frame navigation, object tracking, and interpolation make it effective for annotating time-based data in robotics and autonomous vehicle use cases. Wide Community Support: Being under the OpenCV umbrella gives CVAT users access to a vast ecosystem of machine learning engineers, documentation, and plugins — helpful for troubleshooting and extending functionality. Semi-Automated Labeling: CVAT supports integration with custom models to assist in labeling, reducing manual effort and accelerating the annotation process. Basic Quality Control Features: While not enterprise-grade, CVAT includes fundamental review tools and validation workflows to help teams maintain annotation accuracy. Best for:  Research labs and early-stage robotics teams who need to manage perception datasets on a budget Organizations with in-house engineering resources capable of configuring, hosting, and extending an open-source platform Modalities Covered: Image  Video  Scale AI  Scale is positioned as the AI data labeling and project/workflow management platform for “generative AI companies, US government agencies, enterprise organizations, and startups.”  While often associated with natural language and generative applications, Scale’s platform also brings powerful capabilities to the physical world, supporting AI systems in robotics, autonomous vehicles, aerial imaging, and sensor-rich environments. Scale, an enterprise-grade data engine and generative AI platform Benefits & key features:  Synthetic data generation tools: With built-in generative capabilities, teams can create synthetic edge cases and rare scenarios — useful for physical AI models that must learn to handle uncommon events or extreme environmental conditions. Quality assurance and delivery speed: Scale is known for its fast turnaround on complex labeling tasks, even at enterprise scale, thanks to its managed workforce and internal quality control systems. Data aggregation: The platform helps organizations extract value from previously siloed or unlabeled datasets, accelerating development timelines for real-world AI applications. Best for:  Government agencies and defense contractors working with sensitive or national security-related sensor data Modalities Covered: Image  Video  Test Documents  Audio Dataloop Dataloop is especially good for teams working with high-volume video datasets in robotics, surveillance, industrial automation, and autonomous systems. Dataloop combines automated annotation, collaborative workflows, and model feedback tools to help Physical AI teams build and scale real-world computer vision models more efficiently. Through a combination of AI-assisted labeling and automated QA workflows, Dataloop allows for faster iteration without compromising on label accuracy. Dataloop Benefits & key features:  Multi-format video support: supports various video file types, making it easier to work with raw footage from drones, AVs, or industrial cameras without time-consuming conversions. Integrated quality control: Built-in consensus checks, annotation review tools, and validation metrics help teams ensure label integrity — essential for Physical AI systems where edge cases and environmental noise are common. Interoperability with ML Tools:integrates with ML platforms and frameworks, making it easy to move labeled data directly into training pipelines  Best for:  ML, data ops, enterprise AI teams, and managing video annotation workflows with outsourced teams. Modalities Covered: Image  Video  Supervisely Supervisely positions itself as a “unified operating system” for computer vision, with video annotation tools, support for 3D data, and customizable plugin architecture. Its intuitive interface and support for visual data make it especially useful in domains where multi-sensor inputs and spatial-temporal precision are key to performance and safety. Supervisely Benefits & key features:  End-to-end video annotation support: Supervisely handles full-length video files natively, so teams can annotate continuous footage without breaking it into frame sets. Its multi-track timelines and object tracking tools make it easy to manage annotations across time. Advanced annotation types: From bounding boxes and semantic segmentation to 3D point clouds and DICOM formats, Supervisely is equipped to handle the modalities critical to physical-world AI, including healthcare imaging and autonomous navigation. Custom scripting and extensibility: Teams with specialized needs can build their own plugins and scripts, tailoring the platform to match niche requirements or integrate with proprietary systems. Best for:  ML, data ops, and AI teams in Fortune 500 companies and computer vision research teams. Modalities Covered:  Image  Video Point-Cloud  DICOM Feature Comparison Summary Legend: ✅ = Fully supported ⚠️ = Partially or indirectly supported ❌ = Not supported Why Physical AI Teams Are Choosing Encord As Physical AI grows more complex, many teams are moving away from general-purpose annotation tools. Encord stands out as a purpose-built platform designed specifically for real-world, multimodal AI — making it a top choice for teams in robotics, healthcare, and industrial automation. Built for Real-World AI Encord was designed from the ground up for computer vision data with native video rendering. It supports complex formats, allowing annotators to seamlessly switch between views within a single workspace. Scales from R&D to Production Encord adapts to your project’s lifecycle. It supports fast, flexible annotation during experimentation and scales to enterprise-grade workflows as teams grow. You can integrate your own models, close the loop between training and labeling, and continuously refine datasets using real-world feedback. Trusted in High-Stakes Domains Encord is proven in safety-critical fields like surgical robotics and industrial automation. Built-in tools for QA, review tracking, and compliance help meet strict regulatory standards — ensuring high-quality, traceable data at every step. Quality and Feedback at the Core Encord includes integrated quality control features and consensus checks to enforce annotation standards. You can surface low-confidence predictions or model errors to guide re-annotation — speeding up model improvement while minimizing labeling waste. Real-World Application: Encord for Physical AI Data Annotation Pickle Robot, a Cambridge-based robotics company, is redefining warehouse automation with Physical AI. Their green, mobile manipulation robots can unload up to 1,500 packages per hour, handling everything from apparel to tools with speed and precision. But to achieve this, they needed flawless training data. The Challenge: Incomplete Labels & Inefficient Workflows Before Encord, Pickle Robot relied on outsourced annotation providers with inconsistent results: Low-quality labels (e.g., incomplete polygons) Time-consuming audit cycles (20+ mins per round) Limited support for complex semantic segmentation Unreliable workflows that slowed model development For robotics, where millimeter-level accuracy matters, these issues directly impacted grasping performance and throughput. The Solution: A Robust, Integrated Annotation Stack with Encord Partnering with Encord gave Pickle Robot: Consolidated data curation & labeling Nested ontologies & pixel-level annotations AI-assisted labeling with human-in-the-loop (HITL) Seamless integration with their Google Cloud infrastructure The Results: Faster Models, Smarter Robots Since switching to Encord, Pickle Robot has achieved:

Imagine you are listening to the recording of an important team meeting that you missed. The conversation flows naturally and different voices chime in, ideas bounce back and forth, questions are asked and answered. But as the minutes tick by you find yourself frustrated and you may question yourself,  “Who’s talking right now? Was that John’s suggestion or James’s?”  “Wait, was it the client or the product manager who raised that concern?”  Without knowing who said what, it’s just a sea of words. Now imagine if the recording could automatically tell you: Suddenly, the conversation has structure, clarity, and meaning. That is the speaker diarization, a technology that teaches machines to separate and label voices in an audio stream, just like your brain does in real life. Speaker diarization is the process of partitioning an audio stream into homogeneous segments according to the identity of the speaker. In simple terms we can say that  it answers the question, "Who spoke when?"  Speaker diarization (Source) This technology is important for analyzing multi-speaker audio recordings, such as meetings, phone calls, interviews, podcasts, and even surveillance audio. Speaker diarization involves the segmentation and clustering of an audio signal into distinct parts where each part is associated with a unique speaker. It does not require prior knowledge about the number of speakers or their identities. The typical output of a speaker diarization system looks like following, It essentially adds structure to unstructured audio, providing metadata that can be used for further analysis, indexing, or transcription. Why Speaker Diarization Matters in Audio and AI In our increasingly audio-driven world, (i.e. from smart assistants and call centers to podcasts and meetings) it is not enough for machines to just hear what is being said. They need to understand who is speaking. Speaker diarization adds this critical layer of intelligence to audio to make it easier to understand, organize, and work with audio in smart ways across numerous real-world applications. Following are some important points why it matters. Enhances Speech Recognition: In Automatic Speech Recognition (ASR), speaker diarization improves transcription accuracy by associating text with individual speakers. This makes the transcript more readable and contextually meaningful, especially in overlapping conversations. Boosts Conversational AI Systems: Conversational AI (like virtual assistants or call center bots) get benefits from diarization by better understanding user intent in multi-speaker conversations. It helps systems differentiate between users and agents and respond more appropriately. Critical in Meeting Summarization: Speaker diarization is essential for generating intelligent meeting notes. It enables systems to group speech by speaker which is important for action-item tracking, sentiment analysis, and speaker-specific summaries. Privacy and Security: Speaker diarization helps isolate speakers for identity verification, anomaly detection, or behavior analysis without always needing to know who the speaker is in surveillance and legal audio analysis. Content Indexing and Search: Speaker diarization enables better indexing and retrieval of audio content for media houses, podcasts, and broadcasting companies. Users can search based on speaker turns or speaker-specific quotes. Speaker Identification vs Diarization Although both speaker identification and speaker diarization deal with analyzing who is speaking in an audio clip. However, both solve different problems and are used in different scenarios. Let’s understand the difference between the two. What is Speaker Identification? In speaker identification,  the voice of a person in an audio is recognized and real identity is assigned to it. In other words, it answers the question “Who is speaking right now?”. Speaker Identification (Source) Speaker identification is a supervised task in which a pre-enrolled list of known speakers with voice samples is required. The system matches the speaker’s voice against the list and identifies them. Speaker identification systems typically work by extracting voice features and comparing them to stored voice profiles. The system knows the possible speakers ahead of time.  For example, imagine a voice-controlled security system at home. When a user says, “Unlock the door,” the system not only recognizes the command but also checks who said it. If it matches the voice to an authorized user, the door unlocks. Here, the system is identifying the user's voice by comparing it to known voices it has in its database. What is Speaker Diarization? In Speaker Diarization, different voices in an audio are separated and labelled without necessarily knowing who the speakers are. It answers the question “Who spoke when?”. Speaker Diarization (Source) Speaker diarization is an unsupervised task which does not need prior enrollment of speaker speaker data. It simply separates the audio into segments and assigns labels like "Speaker 1", "Speaker 2", etc. Therefore, the system doesn’t know who the speakers are. For example, suppose you have a recording of a team meeting for which you want to create a transcript. You do not care about matching voices to specific names, you just want to understand the flow of conversation and know when one speaker stopped and another started. So the system outputs: You can now read the transcript with clear speaker turns, even if you don’t know who the actual speakers are. Speaker identification is used when there is a need to verify or recognize who is speaking, such as in voice-based login systems, forensic voice matching, or personalizing voice assistants etc. Speaker Diarization on the other hand is used when there is a need to analyze conversations with multiple people, such as transcribing meetings, analyzing group discussions or organizing podcast interviews etc. In real-world applications, these two techniques are often used together. For example, in a customer service call, speaker diarization can first separate the customer and agent voices. Then, speaker identification can confirm which agent handled the call, allowing for quality review and personalization. Applications of Speaker Diarization Speaker diarization plays an important role in audio understanding by breaking down conversations into “who spoke when”. even when the speakers are not known in advance. Following are key applications of speaker diarization in real-world use cases. Meeting Transcription and Summarization In corporate settings, meetings often involve multiple people contributing ideas, sharing updates, and making decisions. Speaker diarization helps separate speaker voices, making transcriptions clearer and summaries more meaningful. For example, a team uses a meeting transcription tool like Otter.ai or Microsoft Teams that applies speaker diarization to tag each speaker’s contribution. This allows team members to see who said what. It automatically generates action items per speaker which provides easy review of discussions for absent participants. Otter.ai transcription (Source) Call Center Analytics Customer service calls often involve two speakers i.e. the agent and the customer. Speaker diarization helps monitor conversations, measuring things like agent performance, customer satisfaction, and detecting service issues by separating who is talking. For example, in a customer service center of a telecom  company the recordings of support calls are diarized. The system analyzes if the agent followed the troubleshooting script, if the customer sounded frustrated (detected through emotional analysis on customer segments), and how much time the agent vs. customer spoke. This helps improve customer service quality. Observe.AI uses diarization in customer-agent calls to measure agent speaking time, detect interruptions, track emotional tone per speaker, and improve coaching for call center agents based on how well they interact with customers. Observe AI speaker diarization (Source) Broadcast Media Processing News broadcasts, interviews, and talk shows involve multiple speakers. Diarization is used to automatically label and separate speech segments for archiving, searching, subtitling, or content moderation. For example, during a TV political debate, speaker diarization automatically segments speech between Candidate A, Candidate B and Moderator. Later, when a journalist searches for "closing statement by Candidate A" the system quickly retrieves it because it knows who spoke when. Veritone Media applies speaker diarization on radio talk shows and TV interviews to archive and search by speaker. Podcast and Audiobook Indexing Podcasts and audiobooks often feature multiple hosts, guests, or characters. Speaker diarization helps in indexing content by speaker. This makes it easy to search and navigate long audio recordings for required information. For example, a podcast episode features three hosts discussing technology. Speaker diarization allows listeners to jump directly to Host 2's thoughts on AI, and view a timeline showing when each speaker talks. This makes podcasts more interactive and searchable, like chapters in a book. Descript applies speaker diarization to podcasts so that users can edit episodes easily such as remove filler words or edit a specific guest’s section without disturbing the flow. Courtroom Proceedings and Legal Documentation In legal settings, accurate attribution of who spoke is critical. Speaker diarization enables transcripts to properly record testimony, objections, and judicial rulings without manual effort. For example, during a court trial, speaker diarization can help distinguish between instructions by a Judge, arguments by  a defense attorney, and testimonies by a witness. It produces a clean transcript necessary for official legal records and appeals, ensuring fairness and accountability. Verbit specializes in legal transcription. It uses speaker diarization to separate attorneys, judges, witnesses in court recordings automatically, helping produce official court transcripts with clear speaker attribution. Health and Therapy Session Monitoring In mental health counseling and therapy, speaker diarization can help therapists analyze sessions, track patient participation, and even assess changes in patient speech patterns over time. For example, a psychologist records therapy sessions with consent. Speaker diarization can show that the patient spoke 60% of the time answering open-ended questions by the therapist. Over months, analysis reveals the patient started speaking longer and more confidently which is a sign of progress. Eleos Health records therapy sessions (with client consent) and diarizes who is speaking, therapist or client. It analyses engagement metrics like speaking ratios, pauses, emotional markers, helping therapists understand client progress over time. Eleos Health records therapy sessions (Source) Speaker diarization can be used in many other applications across various domains. It has become a critical enabler for making audio and voice-driven systems more intelligent, personal, and practical. From automating meeting notes and customer service analytics to powering smarter healthcare systems and legal services, speaker diarization plays a foundational role wherever "who is speaking" matters. Criteria to evaluate Speaker Diarization  Once the speaker diarization system is built, it should be evaluated how well it performs to get the best speaker diarization. When evaluating speaker diarization, you are basically checking how accurately the system splits and labels speech into different speakers over time. There are three popular metrics to evaluate the speaker diarization. Diarization Error Rate (DER) The Diarization Error Rate (DER) is the traditional and most widely used metric for evaluating the performance of speaker diarization systems. DER measures the proportion of the total recording time that is incorrectly labeled by the system. It is computed as the sum of three different types of errors,  False alarms (FA) - speech detected when none exists) Missed speech - speech present but not detected Speaker confusion - speech correctly detected but attributed to the wrong speaker  The formula for DER is: To ensure fair speaker label matching between the system output and the ground truth, the Hungarian algorithm is used to find the best one-to-one mapping between hypothesis speakers and reference speakers. Additionally, the evaluation allows for a 0.25-second "no-score collar" around reference segment boundaries to account for annotation inconsistencies and timing errors by human annotators. This collar means that slight boundary mismatches are not penalized. While DER is widely accepted, it has some limitations. DER can exceed 100% if the system makes severe errors, and dominant speakers may disproportionately affect the score. Therefore, while DER is highly correlated with overall system performance, it sometimes fails to reflect fairness across all speakers. Jaccard Error Rate (JER) The Jaccard Error Rate (JER) was proposed in the DIHARD II evaluation, to overcome some of the shortcomings of DER. JER aims to equalize the contribution of each speaker to the overall error, treating all speakers fairly regardless of how much they talk. Instead of calculating a global error over all time segments, JER first calculates per-speaker error rates and then averages them across the number of reference speakers. For each speaker, JER is computed by summing the speaker’s false alarm and missed speech errors, and dividing by the total speaking time of that speaker. It is mathematically expressed by: Where N is the number of reference speakers. Importantly, speaker confusion errors that appear in DER are reflected in the false alarm component in JER calculation. Unlike DER, JER is bounded between 0% and 100%, making it more interpretable. However, if a subset of speakers dominates the conversation, JER gives higher error rates than DER. JER provides a balanced and speaker-centric evaluation method that complements DER. Word-Level Diarization Error Rate (WDER) WDER is a metric designed to evaluate the performance of systems that jointly perform Automatic Speech Recognition (ASR) and Speaker Diarization (SD). Unlike traditional metrics that assess errors based on time segments, WDER focuses on the accuracy of speaker labels assigned to each word in the transcript. This word-level evaluation is particularly relevant for applications where both the content of speech and the identity of the speaker are crucial, such as in medical consultations or legal proceedings. Where  SIS (Substitutions with Incorrect Speaker tokens): The number of words where the ASR system incorrectly transcribed the word and assigned it to the wrong speaker.​ CIS (Correct words with Incorrect Speaker tokens): The number of words correctly transcribed by the ASR system but assigned to the wrong speaker.​ S (Substitutions): The total number of words where the ASR system incorrectly transcribed the word.​ C (Correct words): The total number of words correctly transcribed by the ASR system.​ This metric specifically evaluates the accuracy of speaker assignments for words that were either correctly or incorrectly recognized by the ASR system. However, it does not account for insertions or deletions, as these errors do not have corresponding reference words to compare against. Therefore, WDER should be considered alongside the traditional Word Error Rate (WER) to obtain a comprehensive understanding of system performance. How Encord is Used for Speaker Diarization ​Encord is a comprehensive multimodal AI data platform that facilitates efficient management, curation, and annotation of large-scale unstructured datasets, including audio files. Its audio annotation tool is particularly adept at handling complex tasks like speaker diarization, which involves segmenting audio recordings to identify and label individual speakers. Following are the features of Encord in annotating data for speaker diarization. Encord Audio Annotation (Source) Precise Temporal Annotation Encord allows annotators to label audio segments with millisecond-level precision. This is important  for accurately marking the start and end times of each speaker's voices. ​ Support for Overlapping Speech In real-world scenarios like meetings or interviews, speakers often talk over each other. Encord platform supports overlapping annotations, enabling annotators to label multiple speakers speaking simultaneously. This feature ensures that models trained on such data can handle crosstalk and interruptions effectively. Layered Annotations  Beyond identifying who spoke when, Encord allows for layered annotations, where additional information such as speaker emotion, language, or background noise can be tagged alongside speaker labels.  AI-Assisted Pre-Labeling Encord integrates with state-of-the-art models like OpenAI's Whisper and Google's AudioLM. These models can generate preliminary transcriptions and speaker labels, which annotators can then review, refine and use thus reducing manual effort. ​ Collaborative Annotation Environment Encord platform is designed for team collaboration that allows multiple annotators and reviewers to work on the same project simultaneously. Features like real-time progress tracking, change logs, and review workflows ensure consistency and quality across large annotation projects. Scalability and Integration Encord supports various audio formats, including WAV, MP3, FLAC, and EAC3, and integrates with cloud storage solutions like AWS, GCP, and Azure. This flexibility allows organizations to scale their annotation efforts efficiently and integrate Encord into their existing data pipelines. Key Takeaways Speaker diarization separates an audio recording into segments based on who is speaking, answering "Who spoke when?" without needing to know their identities. Speaker diarization adds structure to audio, improves transcription accuracy, enhances conversational AI. Speaker identification matches a voice to a known person, while diarization only separates and labels speakers without requiring pre-known identities. Speaker diarization used in meetings, call centers, podcasts, legal transcription, media broadcasting, and healthcare monitoring to organize and analyze conversations. Speaker diarization systems are evaluated using metrics like DER, JER, and WDER Encord helps streamline audio annotation for building speaker diarization models.

Imagine your voice assistant flawlessly transcribing every word in a noisy meeting or a security system instantly detecting the sound of a potential threat like a gunshot. Audio segmentation is the crucial element that is turning such ideas into reality, leveraging artificial intelligence (AI)  to process different sound types.  This technology is driving significant advancements in the audio AI industry, fuelling the demand for several audio AI solutions. For instance, according to MarketsandMarkets, the current global speech and voice recognition market is projected to reach USD 73.49 billion by 2030.  The core concept behind audio segmentation is to split audio recordings into distinct, homogeneous segments. It enables AI to interpret between various audio components, such as speech, music, and environmental sounds.  While it may sound straightforward in principle, audio segmentation presents several challenges, such as overlapping sounds, poor audio quality, and the need for carefully annotated datasets.  In this post, we will explore audio segmentation and its techniques, applications, and challenges. We will also see how tools like Encord can help developers segment audio to build scalable audio AI systems. Audio Segmentation - A Brief Overview Audio segmentation divides an audio signal into contiguous segments for AI to process. It identifies parts of the audio where the sound stays relatively consistent, like speech, music, or silence. Each segment should ideally contain a single type of sound event or acoustic characteristic. For example, in a conversation recording, segmentation can identify speech segments from different speakers, periods of silence, or any background noise present. Audio segmentation relies on several key concepts: Segments: These are the audio units resulting from segmentation, each representing a specific part of the recording. Boundaries: These are the temporal points that mark the start and end of a segment, defining where one acoustic event ends and another begins. Labels/Categories: After identifying a segment, it is usually given a label or category that describes its content. This might include the speaker's name, the nature of the sound event (e.g., "dog bark," "car horn"), or a description of the acoustic environment (e.g., "office," "park"). Boundaries segments Types of Audio Segmentation  Audio segmentation categorizes audio signals into distinct types for targeted AI processing. Below are some key types: Speaker Diarization: This type focuses on answering "Who spoke when?". It includes segmenting an audio stream to identify individual speakers and determine the time intervals each person speaks. This is useful in meetings, interviews, and multi-party conversations for indexing and understanding the flow of dialogue. Environmental Sound Event Detection: The goal is to identify and label specific audio events occurring within an audio signal. Examples include detecting the sound of a car horn, a dog barking, or glass breaking. Effective sound event detection depends on algorithms that distinguish these events from general background noise within the audio files. Music Structure Analysis: This includes segmenting a piece of music into its constituent structural elements, including the intro, verse, chorus, bridge, and outro sections. Music information retrieval uses this type of audio segmentation to understand the composition and organization of musical pieces by analyzing patterns in the waveform and other features of the audio data. Speech Segmentation: This type is fundamental to automatic speech recognition (ASR) and aims to divide spoken language into smaller, linguistically meaningful units. These units range from individual phonemes (the smallest sound units) to words or even entire sentences.  Acoustic Scene Classification: This type of audio classification focuses on identifying the overall acoustic environment of an audio recording. Algorithms analyze the characteristics of the audio stream to classify the recording as taking place in an office, a park, a restaurant, or another defined acoustic scene. This has important applications in context-aware systems and multimedia analysis. Learn how speech-to-text AI works How Audio Segmentation Works The process of audio segmentation involves several stages. It begins with the pre-processing step, which cleans up the audio signal by reducing noise and normalizing the audio levels. This enhances the quality of the audio data and prepares it for subsequent analysis.  Next, feature extraction techniques are applied to the preprocessed audio stream. The goal here is to extract relevant information from the waveform that can be used to differentiate between different acoustic events or segments.  Acoustic waveform characteristics Common feature extraction methods include Mel-Frequency Cepstral Coefficients (MFCCs), which represent the short-term power spectrum of sound.  Another method is spectrograms, which visually depict the audio signal's frequency content over time. These extracted features are represented as vectors, which are numerical representations of an audio signal. These vectors distill complex audio data into manageable forms that ML algorithms process and analyze effectively. Illustration of spectrograms After feature extraction, segmentation methods identify the boundaries between different segments based on some criteria. Audio segmentation methods can be classified into two primary approaches: supervised and unsupervised methods. Below, we’ll explore each approach and the techniques within them. Supervised Methods Supervised methods rely on labeled training data, where each segment is annotated with its class or boundary information. These methods use this data to train algorithms and to predict segment boundaries in new audio streams. While effective, they require significant resources to create large, annotated datasets. Within supervised learning, several techniques are used: ML-Based Techniques: Hidden Markov Models (HMMs): These model the statistical properties of audio sequences, learning transitions between segments. They’re widely used in tasks like speaker diarization. Gaussian Mixture Models (GMMs): These treat observed data as a mix of Gaussian distributions, each representing a cluster in feature space, aiding in segment classification. Deep Learning Approaches: Convolutional Neural Networks (CNNs): These analyze spectrograms for pattern recognition, excelling in tasks like acoustic event detection. Recurrent Neural Networks (RNNs): Including Long Short-Term Memory (LSTM) units, RNNs capture temporal dependencies in audio signals. For example, a study at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) demonstrated Bidirectional LSTMs with Attention mechanisms effectively segmenting heart sounds. Advanced Deep Learning Methods: Mamba-Based Segmentation Models: The Mamba architecture space model with attention-like capabilities processes long audio sequences with reduced memory requirements. This makes it suitable for identifying speaker turns in extended recordings. ​ You Only Hear Once (YOHO) Algorithm: YOHO treats audio segmentation as a regression problem, predicting the presence and boundaries of audio classes directly. This approach improves speed and accuracy over traditional frame-based classification methods. ​ Audio Spectrogram Transformer (AST): AST applies transformer models to audio spectrograms for classification tasks. Due to their self-attention mechanisms, ASTs are computationally intensive. Audio spectrogram transformer (AST) architecture Unsupervised Methods Unsupervised methods don’t use labeled data. Instead, they identify segment boundaries by detecting patterns or changes in the audio signal, often through clustering or similarity analysis. While they’re valuable when labeled data is unavailable, they may lack the precision of supervised methods due to the absence of training guidance. Common techniques include: Threshold-Based Segmentation: This compares feature values against predefined thresholds or metrics (e.g., similarity between adjacent windows) to detect changes, with local maxima indicating segment boundaries. Clustering Algorithms: Methods like K-means or hierarchical clustering group similar audio frames based on feature similarity, revealing natural transitions. These are often applied in music structure analysis or environmental sound detection. Applications of Audio Segmentation Across Industries Audio segmentation drives multiple industries by helping analyze and interpret audio data. Its applications cover various sectors, improving functionality and user experience.  Speech Technology Audio segmentation helps various speech-based technologies. Transcription services depend on segmenting audio files into smaller units to convert speech to text. Voice assistants use it to isolate and process user commands from background noise.  Call centers use audio segmentation for analytics, such as identifying periods of silence, speaker changes, and key phrases within customer interactions.  Speaker diarization Security and Surveillance In security systems, audio segmentation helps detect specific sound events that may indicate anomalies or threats. For instance, algorithms can be trained to identify the distinct waveform of a gunshot or the sound of breaking glass within an audio recording, triggering alerts for real-time response. Media and Entertainment Audio segmentation benefits the media and entertainment industry significantly. It powers automated music information retrieval systems that can analyze and categorize vast music libraries based on their structure, identifying intros and choruses.  Similarly, sound event detection through segmentation methods allows for efficient indexing and retrieval of specific sound effects in multimedia content. Healthcare Healthcare professionals are using audio segmentation for various analytical purposes. They can identify patterns indicative of certain medical conditions by segmenting patient vocalizations. Another growing application is monitoring respiratory sounds, such as coughs or wheezes, through audio stream analysis. Education Educational platforms can use audio segmentation capabilities to enhance learning experiences. Analyzing student participation in online discussions by segmenting individual contributions can provide insights into engagement levels.  Furthermore, automated feedback on pronunciation can be facilitated by segmenting spoken words into phonemes and comparing them against a reference, often in conjunction with ASR technology. Challenges in Audio Segmentation ​Audio segmentation faces several challenges that impact its accuracy and effectiveness:​ Overlapping Sounds: In real-world environments, multiple audio sources can overlap, making it difficult to distinguish individual sound events. For example, sounds such as doorbells, alarms, and conversations can overlap in a home setting, complicating the segmentation process. Variability in Audio Quality: Differences in recording devices, environments, and conditions lead to inconsistencies in audio quality. Factors such as background noise, echo, and distortion can degrade the performance of segmentation algorithms, especially those relying on subtle audio features. ​ Need for High-Quality Annotated Datasets: Training effective audio segmentation models requires large datasets with precise annotations. However, creating these datasets is labor-intensive and time-consuming. The lack of standardized, high-quality annotated data hampers the development and evaluation of robust segmentation systems. How Encord is Used for Audio Segmentation An advanced annotation tool like Encord can help overcome the challenges mentioned above. Encord is a data curation, annotation and evaluation platform for AI. It’s audio annotation feature segments audio files for speaker recognition and sound event detection applications. Its capabilities enable the precise classification of audio attributes and accurate temporal annotations.​ Comprehensive Audio File Format Support The platform supports various audio formats, including .mp3, .wav, .flac, and .eac3, allowing seamless integration with existing data workflows. You can upload audio files through Encord's interface or SDK, connecting to cloud storage solutions like AWS, GCP, Azure, or OTC for efficient data management. ​ Precision Labeling and Layered Annotations Encord's label editor supports detailed classification with millisecond-level precision, allowing annotators to accurately label sound events, emotional tone in speech, languages, and speaker identities.  Its ability to handle layered and overlapping annotations is particularly effective for applications involving complex audio streams, such as audio classification tasks where multiple events may co-occur. This functionality supports advanced use cases in multimedia indexing, sound event detection, and speech segmentation. Temporal Classification for AI Training Another key feature is temporal classifications, which allow annotators to label specific time segments corresponding to individual speakers or sound events. This helps enhance AI training and model optimization in applications like transcription services, virtual assistants, and security systems. ​ AI-Assisted Annotation for Efficiency Encord also offers AI-assisted annotation tools that automate parts of the labeling process, increasing efficiency and accuracy. These tools can pre-label audio data, identifying spoken words, pauses, and speaker identities, thereby reducing manual effort.  Foundational models, such as OpenAI’s Whisper and Google’s AudioLM, can achieve breakthrough performance in several actions to accelerate audio curation and annotation workflows.   Label complex audio using Flixble Ontologies  AI teams can use Encord Agents to integrate with new models as well as their own, orchestrating automated audio transcription, pre-labeling, and quality control to significantly enhance the efficiency and quality of their audio data pipelines. Collaborative Annotation and Quality Control Integrated collaboration tools within Encord facilitate team-based projects by providing features like real-time progress tracking, change logs, and review workflows. This ensures teams can work together effectively, maintaining high-quality annotations across complex audio datasets.  Audio Applications of Encord  Encord's platform provides a robust environment for annotating audio data, directly supporting the development and enhancement of various audio-centric AI applications. Development of Voice Assistants and Chatbots  Encord can help create high-performing voice assistants and chatbots by enabling the accurate annotation of speech audio data. Its precise temporal labeling features help label spoken words and phrases, crucial for training automatic speech recognition (ASR) models.  Encord helps build more context-aware and personalized conversational AI agents by enabling detailed annotation of who is speaking and when (speaker diarization).  Furthermore, the ability to annotate various audio attributes helps developers train models that can understand not just the content of the speech but also its nuances and characteristics. Speaker Annotation Enhancing Emotion Recognition Systems Encord can significantly improve the accuracy of emotion recognition systems. This detailed annotation of sentiment and emotion in audio files provides the high-quality training set required for deep learning models. These models can accurately identify and classify a wide range of emotions from audio data. Handling overlapping annotations is particularly valuable in emotion recognition, where multiple emotions or intensities might be present simultaneously in an audio stream. Sentiment or Emotion Annotation Want to accurately curate and label audio data? Explore Encord's Audio modality. Key Takeaways Audio segmentation transforms industries through AI to process audio signals accurately. It powers transcription, security, and healthcare with precise segment labeling.  Best Use Cases for Audio Segmentation: It excels in speaker recognition for voice assistants, sound event detection for surveillance, and emotion analysis in call centers. Challenges in Audio Segmentation: Overlapping sounds, poor audio quality, and dataset annotation demands hinder performance.  Encord for Audio Segmentation: Encord’s tools enhance audio data quality with AI-assisted annotation and temporal precision. It streamlines datasets for deep learning, ensuring scalable, high-performing audio AI systems.

AI systems are increasingly moving beyond text and images into the physical world like robots, autonomous vehicles, and embodied agents. This makes the need for robust multimodal reasoning at scale critical. Last week’s webinar, Building Physical AI: How to Enable Multimodal Reasoning at Scale, brought together experts from Encord and Archetype AI. The session explored what it really takes to build AI systems that can perceive, reason, and act in complex real-world environments. Here’s a recap, in case you missed it. What is Physical AI? Physical AI refers to AI systems that can operate in and reason about the physical world. These systems not only process images or pre-processed text, they also interpret sensory inputs from multiple sources like video, audio, and LiDAR. This combination of AI with physical systems makes decisions in real time in order to navigate real environments.   For more information, read the blog What is Physical AI At the core of Physical AI is the ability to combine visual, auditory, spatial, and temporal data to understand what’s happening and what actions should follow. That is multimodal reasoning.  Multimodal reasoning requires more than just robust AI models. It needs new data infrastructure, scalable annotation workflows and evaluation pipelines that mimic real world environments, not just benchmark accuracy. Why It’s Hard to Build Physical AI Systems Building AI systems deployed in the real world adds a new layer of complexity. Here are few gaps: High-dimensional, unstructured input: You’re not dealing with curated datasets anymore. You’re working with noisy sensor streams, long video sequences, and time-synced data from multiple sources. No clear ground truth: Especially for downstream tasks like navigation, the “correct” label is often contextual, ambiguous, or learned through feedback. Fragmented workflows: Most annotation tools, model training frameworks, and evaluation platforms aren’t built to handle multimodal input in a unified way. Key Takeaways from the Webinar Encord provides tools built for multimodal annotation and model development. Our latest webinar “Building Physical AI: Enabling Multimodal Reasoning at Scale”, was in collaboration with Archetype AI, “a physical AI company helping humanity make sense of the world”. They have developed Newton, an AI model that can understand the physical world using multimodal sensor data.  In this webinar, we outline key challenges and show how to build Physical AI systems that use rich multimodal context for multi-step reasoning. The aim is to build AI models which are safer and provide reliable real-world performance. Here are the key takeaways for teams scaling multimodal AI. Multimodal Annotation The first challenge in Physical AI is aligning and labeling data across modalities. The annotation pipelines must handle: Video-native interfaces: Unlike static images, video requires temporal awareness. Annotators need to reason about motion, persistence, and cause-effect relationships between frames. Ontologies that reflect the real world: Events, interactions, and object properties need structured representation. For example, knowing that “Person A hands object to Person B” involves spatial and temporal coordination, not just bounding boxes. Multifile support: A key Encord feature highlighted was the ability to annotate and synchronize multiple data streams such as RGB video, depth maps, and sensor logs within a single interface. This enables richer context without switching tools. Scalable Automated Labeling Once a strong ontology is in place, it becomes possible to automate large parts of the annotation process. The Encord team outlined a two-step loop: Use ontologies for consistent labeling: The structure enforces what can be labeled, how relationships are defined, and what types of attributes are expected. This reduces ambiguity and improves inter-annotator agreement. Add model-in-the-loop tools: After initial manual labeling, models can be trained to pre-label data in the future as well. This cuts the annotation time dramatically. Annotators then shift to verification and correction, speeding up throughput. This hybrid approach balances the need for quality via human input with the need for scale via automation. It’s particularly useful in domains with long-tail edge cases like industrial robotics or medical imaging, where manual annotation alone doesn’t scale. Agentic Workflows Traditional ML workflows are often rigid. They collect data, train a model, evaluate, repeat. But the real-world environment is dynamic. In the webinar, the speakers introduce the idea of agentic workflows. The pipelines are modular that can: React to new data in real time Orchestrate multiple model components Include humans in the loop during key stages Be reused across tasks, domains, or hardware setups Encord’s agentic workflow builder is designed for this kind of modularity. It lets teams compose workflows using building blocks like models, data sources, annotation tools, evaluation criteria and run them in structured loops. This helps AI systems that can not only perceive but also act to evaluate their own performance. They can also trigger re-labeling or retraining when needed. Evaluation Metrics Most existing ML benchmarks fall short when applied to Physical AI. Accuracy, mAP, and F1 scores don’t always correlate with real-world performance especially when the task is “did the robot successfully hand over the object?” or “did the system respond to the sound cue correctly?”  “Evaluation is no longer just a number. It’s whether your robot does what it’s supposed to do.” - Frederik H. What’s needed instead are: Behavioral benchmarks: To measure whether the system can accomplish end-to-end tasks in physical environments. Continuous evaluation: Instead of one-time test sets, build systems that constantly monitor themselves and flag drift or errors. Task-aware success criteria: For example, a model that misses 3% of bounding boxes might still succeed in object tracking but fail miserably in manipulation if it loses track of a key object for just a moment. The teams building physical AI systems should think beyond classic metrics and focus on holistic evaluation that spans perception, reasoning, and action. The webinar also included a product walkthrough showing how Encord supports: Video-native annotation tools with time-based tracking, object permanence, and multimodal synchronization Multifile annotation interfaces where audio, LiDAR, or sensor data can be aligned with video Reusable workflows that integrate models into the annotation process and kick off retraining pipelines Agentic workflows for tasks like active learning, error correction, and feedback loops For teams used to stitching together open-source tools, spreadsheets, and Python scripts, this kind of unified, GUI-driven interface could dramatically simplify experimentation and iteration. What This Means for Teams Building Physical AI If you’re building AI systems for the physical world whether that’s drones, manufacturing bots, self-driving cars, or AR agents, here’s what you should take away: Start with a strong data ontology that reflects the semantics of your task Choose annotation tools that support multimodal data natively and avoid fragmented workflows Use model-in-the-loop setups to scale annotation cost-effectively Design workflows that are modular, composable, and agent-driven Define success using task-based or behavioral metrics, not just classification scores Physical AI isn’t just a new application domain. It’s a fundamental shift in how we collect, train, and evaluate AI systems.  Want to go deeper? You can watch the full webinar. Building Physical AI: How to Enable Multimodal Reasoning at Scale Conclusion Physical AI represents the next frontier in machine learning, where models aren’t just answering questions, but interacting with the world. But to reach that future, we need more than smarter models. We need better data workflows, richer annotations, and tools that can keep up with the complexity of real-world signals. The teams that win in this space won’t just be good at training models. They’ll be the ones who know how to structure, label, and scale the messy multimodal data that Physical AI depends on.

Another day, another episode of "Behind the Enc-urtain", where we go behind the scenes with the Encord team and learn more about their life and work! Today we sit down with Alex Winstone, Account Executive here at Encord. As one of our first AEs, Alex has worn many hats in these first 6 months — he's brought onboard some of the leading AI research labs, F500 organizations and fast growing scale-ups, run industry roundtables in 5+ countries, helped onboard 4 new members of the UK Sales team... and somehow also managed to almost never miss an Encord Thursday bar! PS. We are hiring! We are looking for AEs to join our London and San Francisco teams - you can find more about the London AE role here and SF AE role here. Let's start with a quick introduction — can you share a bit about your background and how you found your way to Encord? I joined Encord after spending 4 years at an AI scale-up, joining as the first Sales hire and seeing their growth from seed to a $45M Series A and beyond. It was an incredible journey. As I was thinking about what was next for me, I had some key criteria in mind. I was firstly looking for a great sales team I could learn from and develop with. From the outset, I was impressed by Leo and the team, and was certain this was an environment within which I could continue developing. Having been first on the ground previously, I wanted to ensure I could take ownership and have a tangible impact on the company's outcomes. Secondly, I wanted to find a deeply interesting problem-area with huge growth potential. I was looking for a company with product market fit (or strong signs of it). This was potentially the hardest criteria to fulfil, as often companies where you can have meaningful impact are yet to achieve PMF. I was convinced through my conversations that Encord was delivering true value to their clients and have now seen this first-hand in these first 6 months! What does a 'day in the life' of Alex look like?   Each day is quite different! Around two thirds of my day is usually spent running demos, presentations and 1:1 meetings with companies who are exploring Encord. I get to work with firms at the cutting-edge of AI, consultatively showcasing the Encord platform and working with the Solutions Engineering team to solve their pains around data curation, annotation, RLHF and model evaluation. Internally, I also work very closely with our Commercial Associate team — who identify companies where Encord can really move the needle and solve MLOps bottlenecks — and the Product team, sharing feedback and ideas from customers, and seeing them turn into reality. What kinds of companies or personas make the right Encord buyers? We typically work with ML and AI teams — from engineers and data scientists, to CTOs and Heads of AI. Encord is industry-agnostic, so I might work with healthcare or logistics companies in the morning, and sports analytics and robotics teams in the afternoon! We also work with organizations of various sizes, from large Fortune 500 orgs to big tech companies and fast-growing scale-ups. No two conversations are ever identical, but it's interesting to see so many similar pain points. What advice would you give to someone who wants to join Encord as our next AE? Reach out to me on LinkedIn (..we have a referral bonus! 😉) Jokes aside, I'd say be prepared to get stuck in, learn quickly, and be a team-player. I'd join as many calls as you can in your early weeks and really absorb everything. If you’re at a point in your career where you are looking for a sales team to grow in, a fast-paced environment and strong signs of PMF, I'd wholeheartedly recommend Encord. And now for a rapid fire round... What 3 words would you use to describe the Encord culture? Focused, collaborative & transparent. Which fictional character would make the best Encord hire and why?  Mystery Incorporated (Scooby-Doo and the gang). You’ll always be getting to the bottom of mysteries (bottlenecks in MLOps and data pipelines) and it’s a dog friendly office. What is one thing you found surprising or different about Encord when you joined? How customer-focused the team is. Every idea or bit of feedback that could improve a client's 'life' is meaningfully fed back to the product and engineering team, where it's considered, discussed and often implemented. It’s also great to see the founders regularly share their vision for the platform (as well as historic views) and see how these materialize in real time.  You can find Alex on Linkedin here. See you at the next episode of “Behind the Enc-urtain” 👋

Imagine a busy factory, where all the machines are running and sensors are tracking every detail of how they run. The key technology of this factory is a digital twin, a virtual copy of the whole factory. Meet Alex, the plant manager, who starts his day by checking the digital twin of the factory on his tablet. In this virtual model, every conveyor belt, robotic arm, and assembly station is shown in real time. This digital replica is not just a static image. It is a dynamic, live model that replicates exactly what is happening within the factory. Earlier in the week, a small vibration anomaly was detected on one of the robotic arms. In the digital twin, Alex saw the warning signals and quickly zoomed in on the problem area. By comparing the current data with historical trends stored in the model, the system predicted that the robotic arm might experience a minor malfunction in the next few days if not serviced. Alex then called a meeting with the maintenance team using the insights from the digital twin. The team planned a repair to ensure minimal disruption to production. The digital twin not only helped predict the issue but also allowed the team to simulate different repair scenarios and choose the most efficient one without stopping the production line. As production increases, the digital twin continues to act as a silent guardian monitoring energy use, optimizing machine settings, and suggesting improvements to reduce waste. It is like having a virtual copy of the factory in the cloud that constantly learns and adapts to make the physical world more efficient. Digital Twin in Factory (Source) What is a Digital Twin? A Digital Twin is a virtual representation of a physical object, system, or process that reflects its real-world version in real-time or near-real-time. It uses data from sensors, IoT devices, or other sources to simulate, monitor, and analyze the behavior, performance, or condition of the physical entity. This concept is widely used in industries like manufacturing, healthcare, urban planning, and more to help improve decision-making, predictive maintenance, and optimization. Digital twin fundamental technologies (Source) A Digital Twin is a dynamic, digital copy that grows and changes along with its physical counterpart. It combines data (whether from the past, in real-time, or predictive) with advanced technologies like AI, machine learning, and simulation tools. This allows it to provide insights, predict outcomes, or test scenarios without the need to directly interact with the physical object or system. A Digital Twin arrangement in automotive industry (Source) Types of Digital Twins Digital twins can be categorized into different types based on the scope, complexity of what they represent and application it can perform. Here are four primary types. Component Twins Component twins are digital replicas of individual parts or components of a larger system. They focus on the specific characteristics and performance metrics of a single element. For example, imagine a jet engine where each turbine blade is modeled as a component twin. By tracking stress, temperature, and wear in real time, engineers can predict when a blade might fail and schedule maintenance before a critical issue occurs. Asset Twins Asset twins represent entire machines or physical assets. They integrate data from multiple components to provide a collective view of an asset's performance, condition, and operational history. Consider an industrial robot on a production line. Its digital twin includes data from all its moving parts, sensors, and control systems. This asset twin helps the maintenance team monitor the robot’s overall health, optimize its performance, and schedule repairs to avoid downtime. System Twins System twins extend beyond individual assets to represent a collection of machines or subsystems that interact with one another. They are used to analyze complex interactions and optimize performance at a broader scale. In a smart factory, a system twin might represent the entire production line. It integrates data from various machines, such as conveyors, robots, and quality control systems. This comprehensive model enables managers to optimize workflow, balance loads, and reduce bottlenecks throughout the entire manufacturing process. Process Twins Process twins model entire workflows or operational processes. They capture not just physical assets but also the sequence of operations, decision points, and external variables affecting the process. A supply chain process twin could represent the journey of a product from raw material sourcing to final delivery. By simulating logistics, inventory levels, and transportation routes, businesses can identify potential disruptions, optimize delivery schedules, and enhance overall supply chain efficiency. Levels of Digital Twins Digital twins evolve over time as they incorporate more data, analysis, and autonomous capabilities. Here are the 5 Levels of Digital Twins. Descriptive Digital Twin A descriptive digital twin is a basic digital replica that mirrors the current state of a physical asset. It represents real-time data and static properties without much analysis. The example of a descriptive digital twin is a digital model of a hospital MRI machine that displays its operating status, temperature, and usage statistics. It shows the current condition but does not analyze trends or predict future issues. Diagnostic Digital Twin This level enhances the descriptive twin by adding diagnostic capabilities. It analyzes data to identify deviations, errors, or early signs of malfunction. For example, consider the same MRI machine that now includes sensors and analytics that detect if its cooling system is underperforming. Alerts are generated when operating parameters deviate from normal ranges to enable identification of the issue early. Predictive Digital Twin At this stage, the digital twin uses historical and real-time data to forecast future conditions. Predictive analytics help anticipate failures or performance drops before they occur. For a surgical robot, the predictive digital twin analyzes past performance data to predict when a component might fail. This allows maintenance to be scheduled proactively which reduces the risk of unexpected downtime during critical operations. Prescriptive Digital Twin It is a more advanced twin that goes beyond prediction to recommend specific actions or solutions, often with “what-if” scenario testing. It combines predictive insights with recommendations or automated adjustments. A digital twin of a hospital’s intensive care unit (ICU) monitors various devices and patient parameters. If the twin predicts a rise in patient load, it might suggest reallocating resources or adjusting ventilator settings to optimize care which ensures the unit runs smoothly during peak times. Autonomous Digital Twin It is the most advanced level of digital twins. An autonomous digital twin not only predicts and prescribes actions but can execute them automatically in real time. It uses AI and machine learning to adapt continuously without human intervention. For example, in a fully automated pharmacy system this digital twin monitors medication dispensing, inventory levels, and patient prescriptions. When it detects discrepancies or low stock, it autonomously reorders supplies and adjusts dispensing algorithms to ensure optimal service without waiting for manual input. Do Digital Twins Use AI? Digital twins often integrate AI to transform raw data into actionable insights, optimize performance, and automate operations. The following points describe how AI enhances digital twin models: Predictive Insights AI algorithms analyze historical and real-time data gathered by the digital twin to identify patterns and trends. For example, machine learning models can predict when a critical component in a manufacturing line might fail which enables maintenance to be scheduled proactively. By continuously monitoring performance metrics, AI can detect anomalies before they rise into major issues. This early detection helps prevent costly downtime and improves overall reliability. Advanced Analytics AI can analyze huge amounts of data from sensors to find hidden patterns and insights that traditional methods might miss. This deep analysis helps create more accurate models of how physical systems work. Advanced algorithms can also simulate different operating situations to let decision-makers test possible changes in a virtual setting. This is especially useful for improving system performance without causing real-world problems. Automation Using AI, digital twins can not only suggest corrective actions but also execute them automatically. For example, if a digital twin identifies that a machine is overheating, it might automatically adjust operating parameters or shut the machine down to prevent damage. AI models embedded within digital twins continuously learn from new data. This adaptability means that the system improves its predictive and diagnostic accuracy over time and becomes more effective in managing complex operations. Imagine a virtual copy of a factory production line. AI tools built into this virtual copy keep an eye on how well the machines are working. If the AI notices a small sign that an important part is wearing out, it can predict that the part might fail soon. The system then changes the workflow to reduce any problems, plans a maintenance check, and gives the repair team detailed information about what’s wrong. By using digital twin technology with AI, industries can move from reactive to proactive management and transform how they maintain systems, predict issues, and optimize operations. Digital Twins Examples Digital twins have many use cases in different domains. Let’s discuss some example of digital twins. Digital Twin in Spinal Surgery A digital twin in spinal surgery is a detailed virtual replica of a real surgical operation. It captures both the static setup (like the operating room and patient anatomy) and the dynamic actions (like the surgeon’s movements and tool tracking) in one coherent 3D model. A digital twin is a virtual simulation that mirrors an actual surgery, created by merging data from various sensors and imaging methods. Digital photograph of a spinal surgery (left) and rendering of its digital twin (right) (Source) Following are the main components of this digital twin system. Reference Frame: A high-precision 3D map of the operating room is built using multiple laser scans. Markers are placed in the room to fuse these scans into one common coordinate system. Static Models: The operating room, equipment, and patient anatomy are modeled using photogrammetry (detailed photos) and 3D modeling software. This produces realistic textures and accurate dimensions. Dynamic Elements: Multiple ceiling-mounted RGB-D cameras capture the surgeon’s movements. An infrared stereo camera tracks the surgical instruments with marker-based tracking. Data Fusion and Integration: All captured data is registered into the same reference frame, ensuring that every element—from the static room to dynamic tools, is accurately aligned. The system is built in a modular and explicit manner, where each component is separate yet integrated. Use of AI: AI techniques enhance dynamic pose estimation (e.g., using models like SMPL-H) and help in processing the sensor data. The detailed digital twin data also provides a rich source for training machine learning models to improve surgical planning and even automate certain tasks. Comparison of the rendered digital twin with the real camera images (Source) This digital twin can help in the following tasks: Training & Education: Surgeons and students can practice procedures in a risk-free, realistic environment. Surgical Planning: Doctors can simulate and plan complex surgeries ahead of time. Automation & AI: The rich, detailed data can train AI systems to assist with surgical navigation, process optimization, and even automate some tasks. The digital twin for spinal surgery is a comprehensive 3D virtual model that integrates high-precision laser scans, photogrammetry, multiple RGB-D cameras, and marker-based tracking. This system captures the entire surgical scene and aligns them within a common reference frame. AI plays a role in enhancing dynamic data capture and processing, and the detailed model serves as a powerful tool for training, surgical planning, and automation. Digital twin in Autonomous Driving This paper on digital twins in virtual reality describes a digital twin built in a virtual reality setting to study human-vehicle interactions at a crosswalk. The digital twin recreates a real-world crosswalk and an autonomous vehicle using georeferenced maps and the CARLA simulator. Real pedestrians interact with this virtual environment through a VR interface, where an external HMI (GRAIL) on the vehicle provides explicit communication (e.g., changing colors to signal stopping). The system tests different braking profiles (gentle versus aggressive) to observe their impact on pedestrian confidence and crossing behavior. The setup uses questionnaires and sensor-based measurements to collect data, and it hints at leveraging AI for data processing and analysis. Overall, this approach offers a controlled, safe, and realistic way to evaluate and improve communication strategies for autonomous vehicles, potentially enhancing road safety. Following are the components of the system. Digital twin for human-vehicle interaction in autonomous driving. Virtual (left) and real (right) setting (Source) Digital Twin Environment: The virtual crosswalk is digitally recreated using map data to ensure it matches the real-world layout. Experiments run in CARLA, an open-source simulator that creates realistic traffic scenarios. Human-Vehicle Interaction Interface: A colored bar on the vehicle indicates if the car is about to stop or yield. Two braking styles are tested which are gentle (slow deceleration) and aggressive (sudden deceleration). Virtual Reality Setup: Participants use a VR headset and motion capture to see and interact with the virtual world. Their movements are synchronized with the simulation for accurate feedback. Data Collection & Analysis: Participants share their feelings about safety and the vehicle's actions. The system records objective data like distance, speed, and time-to-collision. Role of AI: AI analyzes both subjective feedback and sensor data to model behavior and refine communication. AI helps integrate data so the simulation responds realistically to both the vehicle and pedestrians. This digital twin system helps in following: Enhances Safety: Clear communication through the digital twin helps pedestrians understand vehicle intentions, reducing uncertainty and potential accidents. Improves Training: It offers a realistic simulation for both pedestrians and autonomous vehicles, enabling safer, hands-on training and evaluation. Informs Design: By collecting both subjective feedback and objective measurements, designers can refine vehicle behavior and HMI features for better user interaction. Supports Data-Driven Decisions: The system’s real-time data and AI processing allow for continuous improvements in autonomous driving and pedestrian safety strategies. How Encord Enhances Digital Twin Models Encord, a data management and annotation platform which can be used in digital twin applications. It is used to annotate, curate, and monitor large-scale datasets to train machine learning models for digital twin creation and optimization. Following are the important points how Encord helps in creating and enhancing Digital Twins. Encord provides tools for preparing the data needed to train machine learning models that can power digital twins.  Encord allows users to annotate and curate large datasets, ensuring the data is clean, accurate, and suitable for training machine learning models that will be used in digital twin applications.  Encord platform enables users to monitor the performance of their machine learning models and datasets, allowing for continuous improvement and optimization of the digital twin.  By using high-quality, well-curated datasets, machine learning models can achieve higher accuracy and reliability.  Encord platform can accelerate the development of digital twins by streamlining the data preparation and model training process.  Digital twins powered by machine learning models can provide valuable insights into the performance of physical systems, enabling better decision-making. Key Takeaways Digital twin technology revolutionizes industrial operations by creating a dynamic virtual replica of physical systems. This technology not only mirrors real-time activities in environments like factories and hospitals but also uses historical data and AI to predict issues, simulate repairs, and optimize processes across various industries. Real-Time Monitoring & Visualization: Digital twins provide live, interactive models that replicate every detail of a physical system that allows to quickly identify anomalies and monitor system performance continuously. Predictive Maintenance: Digital twin helps in analyzing historical and real-time data which can be used to forecast potential equipment failures and enables proactive maintenance. Enhanced Decision-Making Through Simulation: Digital twins allow to simulate repair scenarios and operational adjustments in a virtual space which ensures the most efficient solutions are chosen. Cross-Industry Applications: From factory production lines to surgical procedures and autonomous driving, digital twins are transforming how industries plan, train, and optimize their systems. AI Driven Insights: The integration of AI and machine learning empowers digital twins to offer advanced analytics, automate corrective actions, and continuously learn from new data to improve accuracy over time.