AI Researcher · Co-founder & CTO · Originator and Developer of AI Digital Care Log
AI Digital Care Log connects records from everyday life over time, learns their context, provides reasoned judgments and next actions, and learns from outcomes so that its judgments continue to improve.
Jung Haesung first conceived and developed AI Digital Care Log. He designed and developed Jarame, the world’s first personalized treatment and learning platform for developmental disabilities; founded NoahAI Labs and designed, developed, and commercialized the NoahAI financial and investing infrastructure OS; and led the technology and product design of Senior & Life, Global Couple Care, VeggieCare, and other DAL services.
AI Digital Care Log is a universal AI data, judgment, and learning-feedback architecture that connects records generated across human life—from daily activities, behavior, and habits to care, education, healthcare, finance, investing, consumption, and work—on timelines centered on each person and situation. Its AI judgment layer analyzes accumulated context, uses XAI to explain the grounds for its outputs, and derives appropriate judgments, recommendations, and actions. Outcomes are logged again to continuously improve subsequent judgments and personalized models.
Connect records → Understand context → AI analysis and judgment → Explainable action → Log outcomes → Learn and feed back
Preprint draft (2026): “AI Digital Care Log: A Feedback-based Decision Architecture for Developmental Disability Intervention.” Preprint draft (2026): “NoahAI-DAL: A Governable Financial Decision Architecture with XAI and Risk Governance.” These are not labeled as peer-reviewed publications.
Amazon books: “AI 시대의 진짜 기술” and “인공지능 시대의 발달장애의 현재와 미래.” Public PDF (NoahAI Labs distribution, not a bookstore edition): “노아AI가 바꾸는 금융 AI의 미래.”
Longitudinal context across human life, AI judgment layers, explainable AI, judgment–recommendation–action pipelines, outcome logging, learning feedback, personalization, consent, authority, and auditability across care, education, healthcare, lifestyle, and finance.
Research credibility is established through technical documentation, precise role attribution, evidence boundaries, and revision history. Sensitive family information is not used as a repeated identity signal.