Eric Roland’s scientific contributions

Health Agents are introduced as the concept of a personalized AI health advisor overlay for continuous health monitoring (e.g. 1000x/minute) medical-grade smartwatches and wearables for “healthcare by app” instead of “sickcare by appointment.” Individuals can customize the level of detail in the information they view. Health Agents “speak” natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their reinforcement learning agent behavior. As an AI health interface, the agent facilitates the management of precision medicine as a service. Healthy longevity is a high-profile area characterized by the increasing acceptance of medical intervention, longevity biotech venture capital investment, and global priority as 2 billion people will be over 65 in 2050. Aging hallmarks, biomarkers, and clocks provide a quantitative measure for intervention. Some of the leading interventions include metformin, rapamycin, spermidine, NAD+/sirtuins, alpha-ketoglutarate, and taurine. AI-driven digital biology, longevity medicine, and Web3 personalized healthcare come together in the idea of Health Agents. This Web3 genAI tool for automated health management, specifically via digital-biological twins and pathway2vec approaches, demonstrates human-AI intelligence amplification and works towards healthy longevity for global well-being.

Health Agents are introduced as the concept of a personalized AI health advisor overlay for continuous health monitoring (e.g. 1000x/minute) medical-grade smartwatches and wearables for "healthcare by app" instead of "sickcare by appointment." Individuals can customize the level of detail in the information they view. Health Agents "speak" natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their reinforcement learning agent behavior. As an AI health interface, the agent facilitates the management of precision medicine as a service. Healthy longevity is a high-profile area characterized by the increasing acceptance of medical intervention, longevity biotech venture capital investment, and global priority as 2 billion people will be over 65 in 2050. Aging hallmarks, biomarkers, and clocks provide a quantitative measure for intervention. Some of the leading interventions include metformin, rapamycin, spermidine, NAD+/sirtuins, alpha-ketoglutarate, and taurine. AI-driven digital biology, longevity medicine, and Web3 personalized healthcare come together in the idea of Health Agents. This Web3 genAI tool for automated health management, specifically via digital-biological twins and pathway2vec approaches, demonstrates human-AI intelligence amplification and works towards healthy longevity for global well-being. The AI Longevity Mindset The AI Mindset The AI Stack. The AI infrastructure is evolving rapidly, particularly with genAI (generative AI which creates new data based on what it has learned from a training dataset). Activity can be ordered in the four tiers of human-interface AI assistants, reinforcement learning (RL) agents (self-driving, robotics), knowledge graphs, and artificial neural network architectures (ANNs). AI assistants and RL agents (embodied through prompting) are an intelligence amplification tool for human-AI collaborative access to the other tiers of the vast range of knowledge and computational power now available. ANNs. The first neural network architecture to deliver genAI at scale is transformers (GPTs, generative pretrained transformer neural networks), Large Language Models (LLMs) which use attention as the mechanism to process all connections in a dataset simultaneously to perform next word (any token) prediction (OpenAI 2023). LLMs treat a data corpus as a language, with syntax, semantics, and grammar, whether natural language, mathematics, computer code, or proteins. These kinds of Foundation Models are trained on broad internet-scale data for application to a wide range of use cases. Transformers are so-called because they "transform" vector-based data representations during the learning phase (using linear algebra methods). Transformer architectures are being extended with state-of-the-art LLMs released for multimodal VLMs (vision-language models) (Gemini 2023), larger context windows (e.g. genome-scale training, 1 million base pair size context window (HyenaDNA, Nguyen et al. 2023)), and longer sequential data processing with various convolutional and other methods such as SSMs (structured state space models (Mamba, Gu and Dao, 2023)) and model grafting (hybrid network architectures evolving during training, StripedHyena-7b (7 billion parameters (learned weights between data elements), Poli et al. 2023). GPTs to GNNs: 2D to 3D+. An advance in digital biology is GNNs (graph neural networks, technically a form of transformer) to process 3D data such as molecules (Bronstein et al. 2021) with attention or message-passing. The early success of GPTs is credited to the "traditional" machine learning recipe (Halevy et al. 2009) of a small set of algorithms operating on a very large dataset, with substantial computational power. GNNs require a more extensive implementation of physics to treat 3D environments. The transformations of data representations in GNNs are more closely tied to the three main symmetry transformations in physics: translation (displacement), rotation, and reflection.

The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics", using a GPT-based workflow to convert equations from literature into LaTeX and Python formats. While many digital equation representations exist, there's a lack of automated large-scale evaluation tools. LLMs are pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. Given the infinite formal possibility spaces, Math Agents, which interact with math, could potentially shift us from "big data" to "big math". Math, unlike the more flexible natural language, has properties subject to proof, enabling its use beyond traditional applications like high-validation math-certified icons for AI alignment aims. This project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology by applying multiscalar physics mathematics to disease models and genomic data. Generative AI with episodic memory could help analyse causal relations in longitudinal health records, using SIR Precision Health models. Genomic data is suggested for addressing the unsolved Alzheimer's disease problem.