Duality

What is the difference between Falcon's Share and EDU licenses?

The EDU license is a non-commercial Falcon license that grants users access to the full functionality of FalconEditor and includes an educational subscription designed to teach digital twin simulation and AI model training.

The free Share license allows users to access FalconCloud and only run the available demo scenarios.

What background or skills to I need to make use of EDU?

Our guided exercise were designed to teach successful AI vision model training to anyone. This means that, outside of installing software in Windows, there is no technical background required to learn and master the content in the EDU examples! We truly want everyone to be able to learn how these models are trained.

For those looking to create their own projects with FalconEditor, familiarity with Python, Unreal Engine, and basic simulation principles will be helpful. To make this easier, the EDU subscription also includes in-depth classes, video tutorials, and Q&As on these subjects to help all learners build skills and create their own simulations.

How long do the AI exercises take?

This depends on what you wish to get from your EDU subscription. Completing the first exercise, which includes an orientation to Falcon, takes about 4 hours. With every new exercise, the time commitment will vary based on how much independent work each learner wants to do. Our goal is to give learners more and more freedom to experiment with and customize each scenario they are introduced to. As the exercises become more complex, learners will be spending more time fine-tuning and testing their models to get the specific results they are interested in.

I am just learning for fun. What are some applications of what I'll learn with EDU?

AI is rapidly transforming out world, and how we interact with it. For most people, how AI operates, and what determines how good it is at its work, remains a black box. By getting hands on with some of the most ubiquitous models on the world today you will gain a strong understanding of how AI models are trained and used. Furthermore, you will gain foundational skills that will enable you to think about what kind of models you may want to create for yourself. This can take the shape of anything from interactive art projects, to utilitarian models that can help you with household tasks.
Imagine: you could train a vision model to help you keep track of where you put your keys! What you can do is limited only by your imagination and your curiosity to learn more of Falcon capabilities.

I want to use Falcon in my lab. Does the EDU license cover academic research?

Applications of digital twin simulation in research is one of the main reason Falcon's EDU license was created. We encourage interested academic parties to contact our EDU team directly at edu@duality.ai. Our team can make sure Falcon is a good fit for your projects, arrange financial accommodations, confirm that your research meets our non-commercial requirements, discuss potential academic partnerships with Duality, and help with options for group licenses.

I want to use EDU with my class. Can I subscribe a whole group?

Yes! Using EDU in an educational setting is one of the main reasons we created this subscription. We work with educational groups of all types and sizes. Educators and universities/learning institution affiliates are encouraged to contact Duality to discuss arrangements, questions, and financial accommodations. Send us an email: edu@duality.ai .

What are the hardware and software requirements for running Falcon?

‍Computer: Modern PC, desktop or laptop

Operating System (Windows): Windows 10 or higher

RAM: 16GB+

Storage: SSD or fast HDD with 100+ GB available storage space

Graphics Card: Nvidia GeForce series 2070 minimum. 3070 or higher suggested.

Graphics Libraries: DirectX 12

‍

How much does EDU cost? Can I cancel at any time?

Every EDU subscription starts with a risk-free, 30-day free trial. You can cancel your subscription at any point during your trial without incurring any charges. After the free-trial the price of the EDU subscription is $20/month with the first charge processed on the 30th day of your initial trial. Cancellations of the EDU subscription can be initiated at any point in the billing cycle, and you will retain access until the end of that billing cycle.

‍

Who is the EDU license for?

Falcon’s EDU license was specifically designed for learning new skills and workflows. Anyone interested in learning the skills necessary for using digital twin simulation to train and test AI models will find the EDU subscription a worthwhile investment. This includes teachers exploring AI curriculums, engineering students learning simulation and synthetic data, 3D artists looking to translate their skills to the AI tech sector, hobbyists interested in experimenting with latest AI vision models, and many more.

On the licensing front this includes classroom and self-taught learning, non-commercial academic research projects, and all other non-commercial uses.

What is USD?

Universal Scene Description (USD) composition system lets users combine scene description from multiple sources (layers) into a single “composed” view. This is extremely useful for Digital Twin and scenario creation pipelines because it lets us modularly build up large assets from smaller ones. This is a significant benefit for simulation since it allows for the continual recomposing of scenarios without having to start from scratch. It also gives you the ability to perform non-destructive overrides, so you can change how an asset might look without actually changing the data in the asset itself. The reusability created by this form of augmentation significantly increases the long-term value of your Digital Twins.

What skill-sets are needed to use Falcon?

While high-level familiarity with the concepts of simulation is helpful, working with Falcon is customizable to your requirements. We can tailor Falcon to your specific use-case with a user-friendly interface. More technical users can customize their Falcon experience via Python scripting, Unreal Engine plugins, and more. Falcon's flexibility rivals that of open-source tools, while benefiting from enterprise-level support.

Is Falcon for us? Are there specific industries and applications Falcon is aimed at?

Digital twin simulation and the end-to-end workflows enabled by Falcon can be leveraged for any use case. Falcon's always expanding library of virtual sensors and ability model dynamics, from hydro- to aero- and everything in between, puts most simulation scenarios within reach. Though Falcon has shown utility in scenarios ranging from Smart Cities to wind energy, we are currently focused on the Autonomous Robotics, Government, and Consumer Packaged Goods sectors. Our customers and partners include F500 companies, cutting edge startups, and university research groups.

What makes Duality's Synthetic Data relevant and valuable?

Synthetic data is only valuable if it provides novel information that is representative of real-world scenarios and outcomes. Highly realistic and relevant synthetic data has been shown to match and augment real-world data leading to robust and deployable ML models. Since the quality of our synthetic data helps predict successful ML model training, we developed a system for quantifying the quality of our data and guide its generation via 3 interconnected criteria: Indistinguishability, Information Richness, and Intentionality. Collectively, they are referred to as The Three I's, and you can learn about them here.

What is a Digital Twin?

Digital Twins are virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. Simply put - they are accurate versions of their real-world counterparts that possess all the same properties. For example: a Digital Twin of an autonomous robot behaves identically to its physical twin - its physics is the same and it runs the same software. This means we can use Digital Twins to develop, learn about, improve and test products in a safe, more cost-effective and efficient way. Since Digital Twins are reusable and modular (and they aren't tied to any specific simulation) we use them to create and simulate diverse varieties of scenarios, and we generate vast amounts of high-quality synthetic data that has vital uses in the real world.

What is a Digital Twin Integration Platform? And what is an end-to-end Digital Twin workflow?

A Digital Twin Integration Platform like Falcon centralizes every aspect of working with Digital Twin in one place. This centralization enables a true end-to-end workflow: With Falcon you can (1) easily acquire interoperable Digital Twins from most 3D model sources. You can (2) edit and manage your Digital Twins and their associated scenarios. You can (3) modularly compose new scenarios and (4) run simulations to generate synthetic perception data using customizable virtual sensors. And you can (5) store, share, stream, and annotate your data in anyway that is of most use to your project.

What is the difference between Predictive Digital Twins and Internet-Of-Things Digital Twins?

While there is overlap between the two, Predictive Digital Twins are used for simulation while IOT Digital Twins are used for monitoring and controlling concurrently happening events. In other words, IOT Digital Twins show the present while Predictive Digital Twins are used to generating synthetic data to answer question and explore complex scenarios in ways that are too difficult to effectively execute in the real world. While Falcon’s simulation capability leverages predictive twins, it can also support IOT twins and their various applications.

What is the difference between a 3D asset and a Digital Twin?

Beyond looking realistic, Digital Twins also need to be “simulation-ready”, which means they need to function like their real-world counterparts. They need to carry information and exhibit properties tied to their ontology and physics (and more). In every way possible, they must be grounded in real-world data. And this data structure needs to be accessible and interoperable. A Digital Twin can be viewed as an encapsulation of all the relevant data that the utilization of that Digital Twin may require.

How is all that different from a standard 3D asset created by an artist or downloaded from a marketplace? A good 3D asset is still only a 3D mesh with a realistic texture. It carries no information regarding size, mass, stiffness, etc., — all the vital information and properties required for effective simulation.

Frequently Asked Questions