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Melania Trump

Melania is working another weird project. She is now working with the United Nations to cause children all across this world to adopt AI. See below....

First lady Melania Trump launches global coalition on tech education for kids

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• Purpose: The coalition works to empower young people to be designers of their own future. It aims to integrate the voices of youth and civil society into global decisions, particularly those related to the Sustainable Development Goals (SDGs) and other critical issues impacting future generations.
• Membership: It brings together some of the world's largest youth- and child-focused organizations, including youth-led groups, civil society networks, and global institutions.
• Key initiatives: The coalition is guided by its Road to 2100 strategy, which outlines collective action for the SDG Summit in 2027 and future global governance efforts. It also includes the Next Generation Fellows program, which trains young people to be leaders.

• Global Movement for Children (GMC): A broad worldwide movement of organizations and individuals advocating for child rights and holding governments accountable to their children.
• Global Coalition to End Child Poverty: Initiated by organizations like UNICEF and Save the Children, this initiative raises awareness and supports actions to alleviate child poverty.
• Global Education Coalition (UNESCO): Created to address education disruptions caused by the COVID-19 pandemic, this coalition has grown to over 220 institutional partners working to advance inclusive and equitable quality education.
• Global Partnership to End Violence Against Children: Formed after world leaders committed to ending violence against children by 2030 (as part of the SDGs), this initiative builds political will and accelerates action to protect children.
• International Youth Foundation (IYF): This organization has supported tailored youth development strategies for decades to help young people gain job skills, start careers, and improve their future.

Unlock the Future

PRESIDENT DONALD J. TRUMP

AI PANDEMIC ID PROGRAM

Donald Trump recently announced a new international effort involving an AI verification system to enforce the United Nations' rules on biological weapons. The announcement was made during a speech at the UN General Assembly in September 2025. 

Key details of the announcement:

• Preventing pandemics: Trump stated that the project was intended to prevent potential disasters like the COVID-19 pandemic, which he suggested was the result of "reckless experiments overseas".

• Enforcement mechanism: The system would use artificial intelligence to help verify compliance with the Biological Weapons Convention, which prohibits the development, production, and stockpiling of biological weapons.

• International cooperation: The Trump administration will be meeting with international leaders to discuss the project, with the hope that the UN will play a constructive role. 

This initiative is the latest in a series of AI-related announcements from the Trump administration in 2025, which have largely focused on removing regulatory barriers and promoting American leadership in AI innovation.

The Trump administration plans to create a verification system that would use artificial intelligence to enforce the United Nations. 

At the U.N., President Donald Trump announced his administration will pioneer an AI verification system to enforce the Biological Weapons 

• Real-time disease surveillance: Using natural language processing (NLP), AI systems scan multiple open-source data streams—including social media, news reports, and official public health advisories—to identify early signals of potential outbreaks. Projects like HealthMap and EPIWATCH have used this method to detect outbreaks before official alerts were released.
• Data verification: Not all signals identified by AI are credible. Some systems, like Boston University's BEACON network, use a "human-in-the-loop" model where AI-generated signals are sent to human experts for verification before alerts are distributed. This helps ensure accuracy and minimize the spread of misinformation.
• Predictive modeling: AI systems can process epidemiological, environmental, and mobility data to forecast the spread and impact of infectious diseases. During the COVID-19 pandemic, AI models helped predict outbreak trends and assess the effectiveness of interventions like lockdowns.
• Genomic analysis: AI tools are being developed to help forecast viral evolution. For instance, the EVEscape tool created by researchers at Harvard and Oxford uses a generative AI model to predict which variants of a virus are most likely to occur, which can assist in vaccine and therapy development.
• Resource optimization: AI-driven models can guide the efficient distribution of medical supplies, hospital beds, and healthcare workers during an outbreak. These systems analyze population health data to predict disease risk and guide proactive resource allocation. 

• BEACON: Developed by Boston University and Boston Children's Hospital, this open-source platform combines AI with human expertise to detect emerging infectious diseases globally in near real-time.
• BlueDot: A commercial analytics company that uses AI to analyze global data, including airline ticketing information, to track and predict the spread of infectious diseases. It gained prominence for detecting the initial COVID-19 outbreak before many public health agencies raised alarms.
• EPIWATCH: An AI-driven early warning system from the University of New South Wales that scans open-source data to provide alerts ahead of official announcements. It detected an unusual increase in respiratory illness in China before the WHO officially identified a Mycoplasma pneumoniae outbreak in late 2023.
• Pandemic Preparedness Engine: The Coalition for Epidemic Preparedness Innovations (CEPI) is developing a global AI platform for pandemic preparedness that focuses on accelerated vaccine development. 

• Data privacy: The use of extensive personal data for surveillance, such as from wearable devices or contact tracing apps, raises significant privacy and ethical concerns. Frameworks must balance data security with the need for timely epidemic response.
• Misinformation: The same open-source data that provides early warning signals can also contain misinformation. AI systems must be designed to effectively filter and validate information to avoid acting on false or amplified data.
• Bias and equity: AI models can contain biases, particularly if trained on data from specific regions like the Global North, which can lead to inequitable health outcomes. Ensuring fairness and local context is crucial for responsible AI deployment.
• Trust and adoption: Public health agencies have been slow to adopt AI systems due to concerns over reliability, transparency, and integration with existing workflows. Fostering public trust and strengthening data-sharing policies are essential for broader implementation. 

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