Parallel Domain provides synthetic data solutions designed to speed up the development of autonomous systems, such as self-driving cars and delivery drones. The company generates synthetic labeled datasets, simulation environments, and controllable sensor feeds, which help teams working on perception, machine learning, and data operations to develop, train, and test their algorithms in a safe and efficient manner. Unlike many competitors, Parallel Domain offers a user-friendly API that allows clients to easily integrate their services, reducing the need for extensive real-world testing. The company's goal is to streamline the development process for autonomous technologies by providing high-quality synthetic data, ultimately making it easier and faster for companies to bring their autonomous systems to market.

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Parallel Domain

As innovative technologies become more commonplace, the opportunity to unlock value becomes more accessible. And with the news Friday (June 14) that industrial metaverse company DataMesh raised a new funding round to develop its enterprise metaverse platform, tapping into the potential that digital ecosystems offer is top of mind for forward-thinking firms, particularly those in manufacturing and industry. That’s because a new wave of innovations that use extended reality (XR) and metaverse-native digital twins are being positioned to help businesses digitize the management of factories, optimize production, improve efficiency, and introduce new workflows

How The B2B Metaverse Opportunity Enables Tomorrow’S Industrial Economy

Parallel Domain, a pioneer in synthetic data generation and simulation for autonomous systems, today unveiled PD Replica, a groundbreaking product that generates high-fidelity digital twins from real-world photos, videos, and 3D scans, transforming the way autonomous vehicles are developed and tested.

Exclusive: Parallel Domain launches PD Replica for high-fidelity digital twins in autonomous vehicle testing

Parallel Domain, a synthetic data platform that enhances perception performance for autonomy and robotics companies, today announced Data Lab, a new API to generate high-fidelity synthetic data for training and testing of perception systems.

Parallel Domain Unveils Data Lab, a Self-Serve API for Synthetic Data Generation

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More. Synthetic data platform Parallel Domain today announced the launch of Reactor, a state-of-the-art synthetic data generation engine that integrates advanced generative AI technologies with proprietary 3D simulation capabilities. The platform aims to provide machine learning (ML) developers with control and scalability, enabling them to generate fully annotated data that enhances AI performance and fosters the creation of safer and more resilient AI systems for real-world applications.According to the company, Reactor enhances AI performance across various industries, such as autonomous vehicles and drones, by generating high-quality images. In addition, the tool harnesses the power of generative AI to produce annotated data, which is a crucial requirement for ML tasks.By generating both bounding boxes (for object detection) and panoptic segmentation annotations (which provide comprehensive/panoramic views), Reactor ensures that AI models can effectively utilize visual data, resulting in more accurate and reliable outcomes.“Our proprietary generative AI technology allows users to create and manipulate synthetic data using intuitive natural language prompts while also generating the corresponding labels required for training and testing ML models,” Kevin McNamara, CEO and founder of Parallel Domain, told VentureBeat. “Reactor’s ability to generate diverse synthetic examples has led to significant performance improvements in tasks like pedestrian segmentation and debris and baby stroller detection

Parallel Domain Unveils Reactor, A Generative Ai-Based Synthetic Data Generation Engine

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now. Artificial intelligence (AI) may be eating the world as we know it, but experts say AI itself is also starving — and needs to change its diet. One company says synthetic data is the answer

Why Ai Needs A Steady Diet Of Synthetic Data

Parallel Domain, a Palo Alto, CA-based provider of a data-generation platform for autonomous driving companies, raised $30m in Series B funding

Parallel Domain Raises $30M in Series B Funding

Achieving autonomous driving safely requires near endless hours of training software on every situation that could possibly arise before putting a vehicle on the road. Historically, autonomy companies have collected hordes of real-world data with which to train their algorithms, but it’s impossible to train a system how to handle edge cases based on real-world data alone. Not only that, but it’s time-consuming to even collect, sort and label all that data in the first place. Most self-driving vehicle companies, like Cruise, Waymo and Waabi, use synthetic data for training and testing perception models with speed and a level of control that’s impossible with data collected from the real world. Parallel Domain, a startup that has built a data-generation platform for autonomy companies, says synthetic data is a critical component to scaling the AI that powers vision and perception systems and preparing them for the unpredictability of the physical world

Parallel Domain Says Autonomous Driving Won'T Scale Without Synthetic Data

Parallel Domain, a startup developing a platform for synthesizing AI model training data, has raised $11 million.

Parallel Domain raises $11 million to generate synthetic data for AI model training

Synthetic data solutions for autonomous systems

Parallel Domain

Synthetic data solutions for autonomous systems

Parallel Domain

Synthetic data solutions for autonomous systems

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