Core Viewpoint - The authenticity of mineral resource reserve reports is crucial for maintaining order in resource development and ensuring market fairness [2][9]. Group 1: New Regulations - The China Mining Rights Appraisal Association has introduced the "Solid Mineral Resource Reserve Reporting Rules (2025)" to address issues of false or misleading mineral resource reserve reports [2][5]. - The rules emphasize three principles: transparency, substance, and competence, applicable to capital and securities markets [2][6]. - Transparency requires that reports provide sufficient information to investors and their advisors, ensuring clarity and preventing misunderstandings [2][6]. - Substance mandates that reports include all relevant information that investors reasonably expect to make informed judgments about exploration results and resource estimates [2][6]. - Competence stipulates that reports must be based on work conducted by qualified professionals, ensuring the integrity of the information presented [2][6]. Group 2: Industry Challenges - The mining rights evaluation process has faced challenges, including instances of false reporting and misrepresentation of resource reserves, which can lead to significant financial losses for mining rights holders [3][6]. - Issues such as inadequate professional standards, lack of accountability, and intentional misreporting have been identified as contributing factors to the problem [6][7]. - Specific cases, such as the Qinghai Province copper-arsenic mine report, illustrate the consequences of inaccurate data, including inflated resource estimates and misrepresentation of mining conditions [6][7]. Group 3: Importance of Accurate Reporting - Accurate mineral resource reserve reporting is essential for determining the value of mining rights and ensuring fair market transactions [3][9]. - Misrepresentation of reserves can lead to discrepancies between the actual value and the market price, affecting both primary and secondary market transactions [9]. - The government has recognized the importance of mining rights evaluation, implementing measures to enhance the quality and reliability of reserve assessments [8][9].

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]