Our Science is our Strength

AI Technical Design

Rejuve has developed its own AI platform, applying logical, evolutionary and neural net algorithms integrated within SingularityNET to meet the needs of longevity science. Using this platform, scientists constrasdasdasduct a multi-resolutional, mechanistic, dynamic model of the body with which to infer longevity treatments.

The Rejuve platform comprises two distinct components, interlinked using customized AI methods. The first is the OpenCog AGI engine, incorporating probabilistic evolutionary learning (MOSES), probabilistic logical inference (PLN), stochastic pattern mining and other methods that act on Bio-Atomspace, a hypergraph store integrating biological knowledge from databases, ontologies and datasets.

The second is a deep neural embedding framework, which combines multiple neural architectures to create embedding vectors representing aspects of each body. This is connected to OpenCog via a biomedical-AI customized deployment of the DeepWalk graph embedding algorithm, which maps nodes in Bio-Atomspace into embedding vectors to be processed by deep neural networks.

AI platform biodata comes from Members as well as other biological and medical databases. Members contribute different types and amounts of data. One may contribute data from a wearable while others contribute nutritional interviews, blood test results or supplement data. Data from past studies and contributions help AI interpret smaller amounts of data.

Rejuve addresses many common issues prevalent in healthcare data, including how to deal with overlapping, sparse and multi-modal observational data. Health studies struggle to deal with issues in observational data which mask causal connections.

Three Principles

The ability of the Rejuve AI strategy to handle the vast amounts of biodata and deliver the goods when it comes to anti-aging cures and therapies is based on three principles. They combine together to provide a top of class AI solution for the medical world designed to be a disruptive technology that delivers results.

1. Data Integration

A powerful aspect of the Rejuve approach is to effectively integrate patterns found in multiple types of data. The volume of data and computational power needed to solve complex health problems is only recently available to researchers. AI is improving rapidly though most gains exists in silos applied to healthcare claims, biomarkers, genomics and imaging. These siloed applications are not enough to analyze complex systems like the human body.

Rejuve combines specific information and insights to develop a high-level understanding of the body and join these silos in a platform that allows for deeply synergistic data integration. Analysis tools fuse multiple data channels to facilitate interpretation of sparsely overlapping observational data. When a dataset contains patients with measurable variables A and B and patients with B and C, Rejuve can interpret variables A and C by using overlapping variable B.

3. Decentralization

Modelling and understanding the body is too difficult a task for any one entity or organization to perform. These constraints can be circumvented through crowdsourcing and Rejuve develops methods by which researchers come to consensus on models of complexity while exploring multiple combinations of models and datasets.

Metrics of consonance, formally measuring the sense models and data make together, facilitate automated complexification in a decentralized manner. The models submitted by researchers can be combined in an automated way or with researcher participation.

2. Causation

Deep neural networks, today’s most popular data modeling method, learn a system in amazing detail but are often poor at generalizing significantly beyond their training data. Neural networks struggle with suggesting the best treatment in the models they have made of the world, mainly because they are correlational rather than causal.

Correlation is fine for some commercial AI applications, but when predicting treatments principles of causation matter. Rejuve’s AI prioritizes causation, leveraging tailored neural techniques combined with probabilistic causal logical inference methods.

Research Projects

The Rejuve research initiative leverages the strengths of its internal team to develop and conduct world class research projects. These strengths are compounded by deep and long relationships with other research bodies, universities and key government bodies in the medical space.

Ongoing Science Team Research Projects

AI-Driven Systems Biology Human Aging Model

Dr. Ben Goertzel, Michael Duncan, Dr. Deborah Duong & Lin Zheng

AI-Driven Systems Biology Model of COVID-19

Dr. Ben Goertzel, Michael Duncan

P53 and Other Cancer Preventives and Therapies: A Researchers Retrospective and Call to Action ​

Dr. Dominic Man-Kit Lam et al.​

Novel Targeted Immuno-Stimulant Modulations for Prevention of Infectious Diseases, Cancer and Other Diseases​ ​​

Dr. Dominic Man-Kit Lam et al.​

Application of Evolutionary and Probabilistic AI to Learn Stem Cell Differentiation and Somatic Trans-differentiation Protocols​ ​​​

Dr. Ben Goertzel & Michael Duncan​​

Immunotherapy and Gene Therapy​

Dr. Dominic Man-Kit Lam et al.​