Causation

Rejuve’s AI prioritizes causation, leveraging tailored neural techniques combined with probabilistic causal logical inference methods.

Deep neural networks, today’s most popular data modeling method, can learn a system in amazing detail, but at the same time they are poor at making recommendations. Neural networks struggle with suggesting the best treatment from their models of the world, mainly because these models are correlational rather than causal. Correlation is useful for general prediction, but when trying to predict how a treatment will work, principles of causation matter. Rejuve’s AI platform prioritizes understanding causation, leveraging appropriately tailored neural techniques combined with probabilistic causal logical inference methods.

Healthcare data analyses must be causal in order to deal with longevity treatments, but should also exhibit generality. This generality helps them be accurate, but moreover allows what is learned in a larger dataset to be applied to another smaller dataset. Not all persons present the same kinds of health data for analyses; some will be power users that regularly take readings of health data from many wearables, have genomic sequencing and medical imaging done, take biomarker tests and try out many supplements, and others will do only a few of those. Rejuve allows people who’ve tracked less data to learn from those who have tracked more. For example, we can interpret genomic and nutritional data from small sets of supercentenarians using knowledge transferred from larger sets of genetic and nutritional data.