Variational Autoencoder/Transformer (Transformer VAE)

Modelers also have the option of submitting a generative neural network for the GCN to employ in a composition. To forecast health states that are crucial to lifespan, we have developed a generative neural network, the third native AI service, a Transformer Variational Autoencoder. We begin with the longevity app's age calculator. It's a compositional neural network that takes the same self-supervised learning method that prioritizes understanding the interplay between variables over fixing specific problems as do the widely used Stable Diffusion and DALL-E transformer networks. Generative neural networks are used to “fill in” missing data in a plausible manner, improving partial network data of those that can not afford wearables and DNA analyses with that of those who can afford them.

Given that the values of all variables influence those of all others, this network is resistant to a number of issues plaguing biological data, such as the fact that it often does not overlap or match. Several biological applications, such as protein folding and antibiotic therapy, have found success using a neural net model based on a "mixture of experts." It also facilitates collaborative scientific efforts to develop "deep real" realistic simulations of the causal processes occurring within the human body.