Generative AI to transform Laboratory Testing

Dr. Tong

About the author

Picture of Metaphysic

Metaphysic

Share This Post

In recent years, hyperreal media has gained widespread attention for its ability to transform the entertainment industry, pop culture and visual effects. A new area of transformation is within laboratory testing, and Metaphysic is honored to have inspired advancements in science by Dr. Weida Tong, a lead researcher at the FDA’s National Center for Toxicological Research and Director of its Bioinformatics and Biostatistics division. 

Generative AI can transform Laboratory Testing

Dr. Tong has dedicated his career to finding alternatives to animal testing and found that AI can learn from past animal data to generate new data. He was inspired by our America’s Got Talent performance and seeing the power of AI technology in action. In his work, he created a virtual lab rat using the same deep generative adversarial network (GAN)-based framework to do synthetic animal testing. Dr. Tong and his team were able to generate new data results from existing animal studies and found that there was an 87% agreement between the results from the virtual lab rat compared to real data. 

Weida Tong
Credits: Arkansas Money & Politics

‘Elvis’ to help transform Laboratory Testing

At Metaphysic, we are always inspired and honored to be a part of all technological advancements to better the world and help people find new ways to own and control their biometric data. Our recent appearance on America’s Got Talent, the very one that inspired Dr. Tong in his research, showcased how hyperreal media can potentially change the entertainment industry. It can bring stars back to the stage, such as our work to bring back Elvis, or allow actors to transform their appearance to age or de-age. Metaphysic’s proprietary technology incorporates machine learning, algorithms, and software to create hyperreal media at scale. 

We advocate for people to know about the capabilities of hyperreal media and find ways for it to be a part of everyday life. To propel this mission, we launched Every Anyone, a platform that allows anyone to upload their image and create a hyperreal avatar that is securely backed by their biometric data. Metaphysic also formed Synthetic Futures, a community of individuals dedicated to educating people on ethics of synthetic media.

As we continue to build AI generation tools and infrastructure, we hope more people, such as Dr. Tong, will be inspired by the limitless possibilities in the industries of science, technology, entertainment and more. 

Full article from Dr. Weida Tong PHD here.

More To Explore

LayGa - Source: https://arxiv.org/pdf/2405.07319
AI ML DL

Editable Clothing Layers for Gaussian Splat Human Representations

While the new breed of Gaussian Splat-based neural humans hold much potential for VFX pipelines, it is very difficult to edit any one particular facet of these characters, such as changing their clothes. For the fashion industry in particular, which has a vested interest in ‘virtual try-ons’, it’s essential that this become possible. Now, a new paper from China has developed a multi-training method which allows users to switch out garments on virtual people.

A film grain effect applied to a stock image - source: https://pxhere.com/en/photo/874104
AI ML DL

The Challenge of Simulating Grain in Film Stocks of the Past

Hit shows like The Marvelous Mrs. Maisel and WandaVision use some cool tricks to make modern footage look like it was shot in the 1960s, 70s, and various other eras from film and TV production. But one thing they can’t quite pull off convincingly is reproducing the grainy film stocks of yesterday – a really thorny problem that’s bound up with the chemical processes of emulsion film. With major directors such as Denis Villeneuve and Christopher Nolan fighting to keep the celluloid look alive, it would be great if AI could lend a hand. In this article, we look at the challenges involved with that.

It is the mark of an educated mind to be able to entertain a thought without accepting it.

Aristotle