Pioneering Decentralized Longevity Research
The state of biomedical research has largely remained unchanged over the past decades. Fragmented. Non-representative. And driven by perverse incentives. It’s exactly why humanity still doesn’t have an answer for aging.
We may not have the answer, but at Rejuve.AI, we hold the keys to get us there: technology and decentralized science.
We crowdsource data and research models to forge a future of cutting-edge treatments and personalized longevity plans — for everyone. Our mission is to create a robust, transparent, and unbiased scientific ecosystem which follows this simple principle:
The more the diverse data → The stronger the research → The closer we are to solving aging
Our AI Models: How We Make It Happen
At the heart of our innovative approach to longevity lies a suite of cutting-edge artificial intelligence models that automate the collaborative effort of scientific progress.
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These decentralized models take in vast amounts of biomedical data — from wearable to ‘omic’ data — and transform them into actionable insights and scientific breakthroughs.
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Our collaboration with SingularityNET not only amplifies these AI capabilities but also integrates their renowned models into our solutions.
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Ultimately, we aim to create a multi-resolution simulation of the human body, applying generative models to Rejuve.AI’s vast volume of data to derive mechanistic movies of aging processes.
Bayes Expert
Bayes Expert is our revolutionary approach to integrating diverse scientific studies into a coherent and holistic understanding of health risks and interventions. It automates community contribution and validation of new scientific knowledge while reducing bias by measuring the applicability of inputs.
Systems within Bayes Expert
Bayesian Network
The Bayesian Network integrates hundreds of meta-analyses, randomized controlled trials, and observational studies, using consensus algorithms to evaluate their quality. Harnessing this rich array of research, the self-constructing Network computes risk scores for personalized longevity insights that power our Rejuve Longevity App. This is only the initial seed, as our Bayesian Network is open for the science community to access and contribute to via GitHub.
Systems within Bayes Expert
Markov Decision Processes (MDPs)
Markov Decision Processes (MDPs) are used to approach health in trajectories — assessing every user’s unique journey of healthy and unhealthy actions. Leveraging longitudinal data, MDPs can probabilistically identify critical tipping points in a user’s trajectory and stop them from falling into vicious cycles by suggesting tailored preventative strategies that evolve over time.
Generative Cooperative Network (GCN)
The Generative Cooperative Network (GCN) is our groundbreaking approach to crowdsourcing various types of AI models — including diffusion models, generative versions of V-Jepa, Variational Autoencoders, and Large Language Models — for longevity research and accelerating scientific discovery. The decentralized GCN algorithm enables intelligent agents to integrate crowdsourced models, organizing them into a cohesive system where models have an emergent role within a unified consensus.
Systems within GCN
Variational Autoencoder
Our Variational Autoencoder is meticulously engineered to forecast pivotal health states directly linked to healthspan and infer upon missing data. One of its core applications is embedded within the Rejuve Longevity App's age estimator, ensuring precise and valuable insights for users about the speed of their biological aging — even for users who can’t afford expensive biomarker testing.
Systems within GCN
LongevityGPT
LongevityGPT lets individuals in on the numerous intricacies that shape their health — from dietary habits and allergies to work dynamics and environmental contexts. It then has the task of explaining the generated probabilistic score outputs to the user in comprehensive reports and creating personalized health plans.
Quantum Longevity Science
We’re exploring the transformative potential of quantum machine learning and quantum biology to enhance the precision of our models. We are investigating phenomena such as proton tunneling and quantum effects in enzymatic processes to uncover new insights into aging mechanisms at the most fundamental physical level.
This effort represents a significant leap forward in longevity science, aiming to identify novel pathways and interventions that traditional research might overlook. While integration with quantum technologies is in the early stages, this exploration reflects our commitment to advancing longevity frontiers.
THE PINNACLE OF COMPREHENSIVE AGI DEVELOPMENT
OpenCog Hyperon AGI Engine
SingularityNET’s OpenCog's Hyperon AGI Engine is a trailblazing open-source platform meticulously designed to revolutionize artificial general intelligence development. OpenCog masterfully amalgamates a spectrum of avant-garde AI techniques.​
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Central to OpenCog's functionality is the 'Bio-Atomspace', an innovative hypergraph knowledge store that seamlessly integrates a plethora of biological knowledge, amalgamating data from various databases, ontologies, and datasets.
In collaboration with Rejuve.Bio, we will use this groundbreaking technology in hypothesis generation and therapeutic discovery.
Realizing the Vision: Decentralized N-of-1 Trials
One of our first milestones in realizing the power of our core technologies is building functionality for decentralized N-of-1 trials. These trials offer a reliable way to generate evidence on longevity optimization strategies — tailored to every individual.
With the forthcoming Rejuve Longevity App, health enthusiasts can anticipate unprecedented accessibility to N-of-1 trials!
Our Bayes Expert Markov Decision Process enables the optimization of multiple longevity goals. We’ve compiled hundreds of biomarkers to help users track a variety of health outcomes. Optimal levels for all biomarkers will be provided, enabling users to track how each intervention brings them closer to their health targets. This robust infrastructure will give everyone the tools to run decentralized and N-of-1 trials directly from the App.
Additionally, our Bayes Expert will be able to integrate the results of these trials into its large body of data. The more users conduct N-of-1 trials on the platform, the greater the generalizability of the generated data to the wider population. This approach supports our goal of simultaneously fostering healthier individuals and populations.
