Groundbreaking Artificial Intelligence
The Era of AI Scenario Forecasting Has Begun
Jumptuit Technology
Collectively, the Jumptuit Group’s Intellectual Property (IP) represents a comprehensive new approach in the development of Artificial Intelligence. Its goal is not to mimic and amplify human knowledge, sensibilities, and intelligence, but rather to complement human thinking and decision making by continuously observing ‘states’ free of human constructs and observing and learning new correlations and causations in relation to cross-sector variables that constitute future events – a non-human approach to help humans chart a future free from avoidable crises and conflict.
The Jumptuit Group’s interconnecting AI modules that exchange data and insights across sectors collectively provide unprecedented visibility into future events and unrivaled augmentation of human decision making to mitigate risk and improve outcomes.
Genesis J2T AI Scenario Forecasting
Jumptuit provides transformative AI Scenario Forecasting applications for the Finance, Insurance, Transport and Logistics, Energy, Critical Minerals, Health and Travel Sectors, among others.
Cross-regional and cross-sector events continually threaten to disrupt global economic and business activity, and early signals are critical. Jumptuit’s Genesis J2T AI discovers the cross-sector elements and coactions that constitute the genesis of global events, and continuously generates adaptive scenario forecasting. Genesis J2T is based on Jumptuit’s Event Genesis Intelligence (EGI) that identifies the time and place of future events and generates adaptive scenario forecasting, based on continuous analyses of Real-Time-Cross-Spectrum-Data (RTCSD) captured via Jumptuit’s Global Data Nets (GDNs).
Jumptuit’s Genesis J2T presents a new dynamic cross-sector index for identifying coalescing elements across sectors of the economy, political system, and other components of society, and external geopolitical, regional, and environmental factors to forecast events. The index measures the respective degree of each element and the collective value of the array of cross-sector elements to forecast the probability of an event.