Takashi Kido’s scientific contributions

A physical system formalism treating points as spaces with examples in Categorical Cryptoeconomics and Categorical Longevity. Categorical Field Theory is introduced as a category-theoretic formalism for physical systems, including as a Categorical Field Theory for Smart Networks to provide an integrated view of the computational infrastructure. Theoretical foundations for formal systems have been lacking. A categorical field theory is a categorical field-theoretic treatment of physical systems as formal systems of spaces governed by energy-entropy, scaling laws, and processual rates. The categorical field theory for smart networks joins existing categorical field theory approaches in quantum physics, synthetic differential geometry, and tensor category spaces together with existing smart network field theories.

Blockchains are formal systems for equipping objects with value, transacting their exchange, and creating domain-specific event histories. Categorical cryptoeconomics is the application of category-theoretic methods to blockchain study with formalisms which pertain to blockchains and generalize to the programmable computational infrastructure more broadly. Section 1 provides an overview of twenty categorical cryptoeconomic primitives (in algebraic topology (persistent cohomology, semitopology), logic, sheaves, set theory, group theory, optics, and blockchain Petri nets) and their use in consensus, ledger construction, mining, and smart contract platforms. Section 2 introduces four progressively higher categorical cryptoeconomic formulations: HoTT (homotopy type theory) blockchains, Petri net computad ledgers, coregulator DAOs (decentralized autonomous organizations), and cohomology ZKPs (zero-knowledge proofs). The progression is first, nodes as themselves simplicial sets, fibrations, and 2-Segal spaces, second, nodes switched as gradients, third, time-modulated node and path propagation, and fourth, physics-agnostic node and path multiplexing. A 2-category of smart network technologies is envisioned with object instances of blockchains, AI, deep learning, robotics, autonomous vehicles, and digital biology health twins, and morphisms as structure-preserving functors.

Categorical Longevity is introduced as a computational systems biology approach which applies category theory to unify and formalize the activity, metrics, and interventions of biological systems in addressing the accelerated multisystem decline characteristic of human aging. By providing a comprehensive and computation-ready structure, this method integrates diverse mathematical models and data representations, enabling a deeper understanding of biological dynamics across scales. While advancements such as hallmarks of aging , biomarkers, epigenetic clocks, and organ-specific aging metrics have proliferated, the absence of a standardized framework limits their joint analysis and practical application. Categorical Longevity addresses this gap, offering tools for multiscalar analysis, robust dynamical modeling, and the integration of computational technologies such as blockchain to facilitate scalable, secure population-level deployment.

Categorical Longevity is introduced as a computational systems biology approach which applies category theory to unify and formalize the activity, metrics, and interventions of biological systems in addressing the accelerated multisystem decline characteristic of human aging. By providing a comprehensive and computation-ready framework, this method integrates diverse mathematical models and data representations, enabling a deeper understanding of biological dynamics across scales. While advancements such as hallmarks of aging , biomarkers, epigenetic clocks, and organ-specific aging metrics have proliferated, the absence of a standardized framework limits their joint analysis and practical application. Categorical Longevity addresses this gap, offering tools for multiscalar analysis, robust dynamical modeling, and the integration of computational technologies such as blockchain to facilitate scalable, secure population-level deployment. Category Theory and Longevity A modern scientific trend is formalization, meaning the extensive use of mathematical models, algorithmic analysis, and computational instantiation. This is seen in deep learning as a foundational technology used in many fields, with the concept technology connoting active processes involving compute, algorithms, and data. Category theory (Eilen-berg and MacLane 1945) provides a universal language for formalizing the modeling of physical, chemical, and biological systems in a computation-amenable way (Spivak 2014). In biology, for example, categorical genomics attempts to organize vast data volumes into a coherent whole. One project identifies Dist (distance) as a category for genes, a structure unifying the descriptive gene-related sub-categories of open Petri nets (gene regulatory networks), preorder (gene orders), operad (gene trees), and olog (gene ontology) (Wu 2023). Another project specifies a genetic algebra with the categories Set and Semimodules to study genotype, phenotype , and haplotype groups (Tuyeras 2018). Longevity entails potentially curing the diseases of aging. Aging is the primary driver of many leading causes of death,

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.

At the AAAI Spring Symposium 2024, we explore the important challenges facing Generative Artificial Intelligence (GenAI) concerning both social structures and individual welfare. Our discussion revolves around two perspectives. Individual Impact of GenAI on Well-being: This perspective focuses on the design of AI systems with keen consideration for individual well-being. It seeks to understand how digital experiences influence emotions and the quality of life at a personal level. By examining the effects of AI technologies on individuals, we aim to tailor solutions to enhance personal welfare and fulfillment. Social Impact of GenAI on Well-being: Here, emphasis shifts to the broader societal implications of GenAI. We strive for decisions and implementations that foster fairness and benefit all members of society. This perspective acknowledges the interconnectedness of individuals within social structures and seeks to ensure that GenAI advancements positively contribute to collective well-being. In this paper, we provide an overview of the motivations driving our exploration, elucidate key terms essential for understanding the discourse, outline the primary areas of focus of our symposium, and pose research inquiries that will guide our discussions. Through this comprehensive approach, we aim to address the multifaceted challenges and opportunities presented by GenAI in promoting both social and individual well-being.

After the significant performance of Large Language Models (LLMs) was revealed, their capabilities were rapidly expanded with techniques such as Retrieval Augmented Generation (RAG). Given their broad applicability and fast development, it's crucial to consider their impact on social systems. On the other hand, assessing these advanced LLMs poses challenges due to their extensive capabilities and the complex nature of social systems. In this study, we pay attention to the similarity between LLMs in social systems and humanoid robots in open environments. We enumerate the essential components required for controlling humanoids in problem solving which help us explore the core capabilities of LLMs and assess the effects of any deficiencies within these components. This approach is justified because the effectiveness of humanoid systems has been thoroughly proven and acknowledged. To identify needed components for humanoids in problem-solving tasks, we create an extensive component framework for planning and controlling humanoid robots in an open environment. Then assess the impacts and risks of LLMs for each component, referencing the latest benchmarks to evaluate their current strengths and weaknesses. Following the assessment guided by our framework, we identified certain capabilities that LLMs lack and concerns in social systems.

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.