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Create a project using the NVIDIA Jetson Nano developer kit and submit it by September 30, 2022 for a chance to win a Machine Learning at Home Kit.
Create a project using the NVIDIA Jetson Nano developer kit and submit it by September 30, 2022 for a chance to win a Machine Learning at Home Kit.
Join us on August 11, 2022 to learn how to design edge deployments for future-proof scale, best practices for optimizing multiple deployments on edge systems,…
Join us on August 11, 2022 to learn how to design edge deployments for future-proof scale, best practices for optimizing multiple deployments on edge systems, and tips for remotely repairing systems and applications.
How do I start a career as a deep learning engineer? What are some of the key tools and frameworks used in AI? How do I learn more about ethics in AI? Everyone has questions, but the most common questions in AI always return to this: how do I get involved? Cutting through the hype Read article >
The post How to Start a Career in AI appeared first on NVIDIA Blog.
NVIDIA today announced selected preliminary financial results for the second quarter ended July 31, 2022.
Imagine driving along a road and an obstacle suddenly appears in your path. How quickly can you react to it? How does your reaction speed change with the time…
Imagine driving along a road and an obstacle suddenly appears in your path. How quickly can you react to it? How does your reaction speed change with the time of day, the color of the obstacle, and where it appears in your field of view?
The ability to react quickly to visual events is valuable to everyday life. It is also a fundamental skill in fast-paced video games. A recent collaboration between researchers from NVIDIA, NYU, and Princeton—winner of a SIGGRAPH 2022 Technical Paper Award—explores the relationship between image features and the time it takes for an observer to react.
With so many recent advances in display technology, human reaction times have become a primary bottleneck in the graphics pipeline. Response times for communicating with remote servers, rendering and displaying images, and collecting and processing mouse or keyboard input are all typically tens of milliseconds or less.
By contrast, the pipeline for human perception is much slower, and can range from 100 to 500 milliseconds depending on the complexity of the visual input. This research aims to simplify and optimize images to reduce our reaction time as much as possible.
Visual contrast and spatial frequency are well-known features that influence low-level vision. Further, human vision is not uniform over the entire field of view. The amount of contrast needed to boost reaction time varies depending on eccentricity, or visual angle (where an object is located relative to center gaze) and spatial frequency (whether an object is a solid color or a complex pattern, for example). Reaction time is a combination of many neural processes, and the proposed model includes all of these factors.
Reaction time measurements are based on the onset latency of voluntary rapid eye movements called saccades. The “reaction time clock” starts ticking as soon as the target appears on the screen. Once the target is identified, a saccade is initiated towards it.
To build a perceptually accurate model for reaction time prediction, researchers conducted a series of experiments with human observers, collecting over 11,000 reaction times for varying image features.
Inspired by how the human brain perceives information and makes decisions, the researchers designed a model for reaction time prediction, accounting for contrast, frequency, and eccentricity, as well as the inherent randomness in human reaction speed.
In this model, a measure of “decision confidence” is accumulated over time, and once enough confidence has been accumulated, a saccade is made. The rate at which confidence accumulates over time is inconsistent, as shown in the video below.
Hence, instead of predicting a single reaction time with full certainty, the model provides a likelihood of exhibiting various reaction times. The average rate of confidence accumulation is influenced by image features and results in a change in the likelihood of reaction times, as shown in the video below.
Two validation experiments confirm that this model can be applied to images that might be seen, including video games and natural photographs.
Applications for this saccadic reaction time model include, for example, a smart drive-assist system estimating whether a driver can safely react to pedestrians and other vehicles, and turn on appropriate assistance features. Similarly, e-sports game designers can use this model to understand the fairness of their game’s visual design, avoiding bias in competitive outcomes.
Ambitious gamers can also use this model to fine-tune their setup for maximum performance–by choosing an optimal skin for the target 3D object, for example.
In future work, the research team plans to explore how other image features like color and temporal effects influence human reaction time, and how to train humans to increase the speed at which they react to on-screen or real-world events.
For more details, read the paper, Image Features Influence Reaction Time: A Learned Probabilistic Perceptual Model for Saccade Latency. You can also visit the gaze-timing project on GitHub.
The paper’s authors, Budmonde Duinkharjav, Praneeth Chakravarthula, Rachel Brown, Anjul Patney, and Qi Sun will present this work at SIGGRAPH 2022 on August 11 in Vancouver, British Columbia.
Discover how to detect cyber threats using machine learning and NVIDIA Morpheus, an open-source AI framework.
Discover how to detect cyber threats using machine learning and NVIDIA Morpheus, an open-source AI framework.
3D content creators are clamoring for NVIDIA Instant NeRF, an inverse rendering tool that turns a set of static images into a realistic 3D scene. Since its debut earlier this year, tens of thousands of developers around the world have downloaded the source code and used it to render spectacular scenes, sharing eye-catching results on Read article >
The post NVIDIA Instant NeRF Wins Best Paper at SIGGRAPH, Inspires Creative Wave Amid Tens of Thousands of Downloads appeared first on NVIDIA Blog.
The Acceleration Agency, a digital innovation and product design firm, is working on an active digital twin framework and toolkit called Project Gemini….
The Acceleration Agency, a digital innovation and product design firm, is working on an active digital twin framework and toolkit called Project Gemini. Inspired by the United States space program of the same name, Project Gemini uses active sensor fabric data and a wide range of data from sources like Google Sheets and Customer Relationship Management (CRM) platforms to replicate real-world settings in the virtual world.
The active digital twin framework and toolkit will be fully connected to NVIDIA Omniverse–a scalable platform for design and collaboration–using Universal Scene Description (USD).
The project launched with a digital replication of The Acceleration Agency’s main office located in Austin, Texas. Instrumented with a dense sensor fabric for real-time and historical spatial computation, the digital twin of the office includes employees and employee information (job title, ID#, gender, and date of birth) provided by Salesforce. It also tracks inventory items on site and can display information such as quantity, date of last interaction, temperature, and orientation.
With NVIDIA Omniverse real time, true-to-reality physics from PhysX, and physically accurate RTX rendering capabilities, the team anticipates that the Gemini active digital twin can be simulated with an unprecedented level of visual and physical fidelity and with complex simulations.
Leveraging USD and Omniverse Nucleus, users of the Project Gemini digital twin platform will be able to update content in a variety of tools in real time collaboratively instead of having to wait for new builds.
Multiple abstraction layers and a sensor fabric layer allow a variety of sensors, databases, CRMs and object integration tools to connect to Omniverse. The connection allows real-time updates to inventory objects and information like temperature, humidity, and location.
To accomplish this, the team created a simple Omniverse Kit Extension enabled by a Python script that reads data from a Google Sheet and attaches the data to an object in Omniverse Kit. It allows someone to control the location, scale, and rotation of any selected object in Omniverse applications like Omniverse Code or Omniverse Create using the metadata in the spreadsheet. You can access the AccelerationAgency/omniverse-extensions through GitHub.
Using database and CRM tools with the extension makes the task of manipulating object data more scalable. When building digital twins at the scale of factories, stadiums, warehouses, and even cities, hundreds, thousands, and even millions of objects may need to be manipulated rapidly.
The Acceleration Agency loaded the USD version of their office digital twin into the Omniverse stage and used the extension to select and manipulate object data.
The images below show an example of how this process was done for a Tesla in the parking lot outside the agency office. Building this was fairly straightforward and only took a few days for a single developer to create. It can be extended to any data source.
Watch the extension in action with Starr Long, Executive Producer at The Acceleration Agency:
The next step for Project Gemini is to render in real time with the NVIDIA RTX Renderer and allow for real-time modifications through Nucleus. The real-time modifications are one of the advantages of working with the powerful USD 3D framework and composition engine. This will be coupled with historical recordings of real data which when played back can be mixed with these modifications to try different scenarios. Some of the use cases the team is targeting include construction sites, hospitals, and live event venues. To learn more, visit the Project Gemini website.
Learn more about building custom USD-based applications and extensions for NVIDIA Omniverse in the Omniverse Resource Center and with these USD-specific resources.
Don’t miss NVIDIA at SIGGRAPH, August 8-11, 2022. Watch the Omniverse community livestream at SIGGRAPH on August 9 at noon, Pacific time, to learn how NVIDIA Omniverse and other design and visualization solutions are driving breakthroughs in graphics and GPU-accelerated software.
You’re also invited to enter the inaugural #ExtendOmniverse developer contest, open through August 19, 2022. Create an Omniverse Extension using Omniverse Code for a chance to win an NVIDIA RTX GPU.
Follow NVIDIA Omniverse on Instagram, Twitter, YouTube and Medium for additional resources and inspiration. Check out the Omniverse forums, and join our Discord server and Twitch channel to chat with the community.
Join us September 19-22 for a deep dive into the latest advances in edge AI, from reimagined shopping experiences to industrial automation.
Join us September 19-22 for a deep dive into the latest advances in edge AI, from reimagined shopping experiences to industrial automation.
Innovative technologies in AI, virtual worlds and digital humans are shaping the future of design and content creation across every industry. Experience the latest advances from NVIDIA in all these areas at SIGGRAPH, the world’s largest gathering of computer graphics experts, running Aug. 8-11. At the conference, creators, developers, engineers, researchers and students will see Read article >
The post Dive Into AI, Avatars and the Metaverse With NVIDIA at SIGGRAPH appeared first on NVIDIA Blog.