Core Viewpoint - The recent AI Governance Dialogue held in Geneva emphasized the need for a sustainable and inclusive governance framework for artificial intelligence, with a focus on health and mental health applications [1][3][5]. Group 1: AI Governance Framework - The dialogue attracted over 10,000 stakeholders from more than 170 countries, including government officials, industry leaders, and academics, to discuss building a trustworthy AI governance system [1]. - The conference highlighted the importance of moving beyond slogans to implement actionable and sustainable policies for AI governance [5]. - A flexible and inclusive governance framework is necessary, along with adaptive regulatory mechanisms and technical standards to ensure AI development aligns with social, economic, and environmental responsibilities [5]. Group 2: Health Sector Focus - The health sector is identified as having significant potential for AI applications, particularly in addressing global mental health challenges [3]. - AI can provide powerful tools for early intervention, improved diagnostics, and expanded access to healthcare services, especially in mental health [3][5]. - There is a notable gap in mental health treatment availability, with many individuals suffering from mental health issues lacking sufficient access to professionals [3]. Group 3: Multi-Stakeholder Participation - Effective AI governance requires participation from multiple stakeholders, including governments, industry, academia, civil society, and international organizations [5]. - There is a call to support capacity building in developing countries to ensure equitable participation in AI governance [5]. - Transparency is deemed essential for building trust, and governance frameworks should reflect diversity and address the digital divide [5]. Group 4: Sustainability and Environmental Impact - The dialogue acknowledged the increasing energy consumption associated with AI and its environmental impact, necessitating the integration of energy and environmental policies into governance frameworks [5]. - The need for green data centers and the use of renewable energy in AI projects was emphasized to ensure sustainable development without overwhelming local infrastructure [5][6]. Group 5: Future Directions - The consensus from the dialogue is that future AI governance should be driven by innovation, guided by inclusivity, and aimed at sustainable development, ensuring that AI benefits everyone globally [6].

Pain Points in Traditional Prediction Industry - Traditional platforms suffer from a lack of fairness and transparency, including issues like manipulating odds and delaying payments [1] - Security and trust are major concerns due to fund freezes and platforms disappearing [1] - Traditional platforms lack effective incentive mechanisms to enhance user loyalty [4] - Traditional prediction ecosystems are closed, lacking asset appreciation channels [7] Hash Epoch's Blockchain Solutions - Blockchain technology ensures data transparency and fairness through immutable smart contracts and hash-based random number generation [1] - Smart contracts manage user funds, and a 1:1 collateral mechanism ensures payment capability [2][3] - A multi-dimensional incentive system rewards users with HEST tokens for tasks, social sharing, and staking [5] - A loss compensation mechanism provides HEST token rewards to users who lose predictions, reducing risk [6] - DeFi integration allows users to earn interest by investing idle funds in liquidity pools [8] - Users can participate in node validation by staking HEST, sharing platform revenue [8]