Anticipatory Intelligence
Genesis J2T continuously learns from live cross-sector data through its Global Sensory Intelligence (GSI) and Global Data Nets (GDNs). Genesis J2T synchronizes global observation of atmospheric, terrestrial, and oceanic conditions, and human activity and artificial systems across every geographic region, jurisdiction, and sector. Genesis J2T continuously collects real-time data in increments of milliseconds, seconds, minutes, hours, days, weeks, and months.
This autonomous process allows for continuous learning without human intervention for data classification and annotation derived through a process of uninterrupted observation of cross sector conditions, active collection of data through dynamically generated queries in response to observed changes in conditions, live assessments of the cross-sector data collected, and dynamic observation-based forecasting of future conditions, risks, and opportunities.
The Building Blocks of Anticipatory Intelligence
Constant Live Synchronized Global Sensory Observation (e.g., Atmospheric, Terrestrial, and Oceanic Conditions, and Human Activity and Artificial Systems)
Expanding Electromagnetic Spectrum Range
Discerning Pertinent Data
Understanding Cross-Discipline, Cross-Sector Relationships in Data
Analyzing Complex Situations
Considering Future Outcomes
Exhibiting Innate Inquisitiveness, Generating Spontaneous Queries
Questioning Sources and Classifications of Data
Exhibiting Strategic Thinking
Forecasting Risks and Opportunities
Augmenting Human Spatial Interactions With Their Environment
Improving Human Decision Making, Not Making Decisions for People
Jumptuit has recently been granted groundbreaking AI and Blockchain Patents by the U.S. Patent and Trademark Office (USPTO). These USPTO-granted patents are part of a larger Intellectual Property (IP) protection strategy consisting of patents and trade secrets that constitute the underlying systems and methodologies of The Jumptuit Group's AI.
Contrasting Genesis J2T Anticipatory Intelligence With Generative AI
Generative AI typically relies on a large body of historical training data that is labeled by humans to train the AI to carry out a task, and the feedback loop for new learning/retraining typically requires sending the results generated by AI to annotators for relabeling, and then retraining the AI using the reviewer-labeled data.
A global industry has emerged to support Generative AI that funnels training data to millions of human data annotators around the world for labeling/relabeling. This global data annotation workforce, manually generating billions of labels for Generative AI to consume, is recruited largely from countries in the global south, including Kenya, Madagascar, South Africa, Colombia, Mexico, Venezuela, Cambodia, Indonesia, Vietnam, the Philippines, and India, operating from schools and technical training facilities, repurposed call centers, and as freelancers working from home.
It is therefore not logistically feasible for Chatbots powered by Generative AI to provide live real-time monitoring, information, assessments, and forecasting of dynamic global events; furthermore, Generative AI is prone to disseminating disinformation and misinformation due to human intervention and bias at all stages.
An exemplar is global systemic risk resulting from geopolitical uncertainty. Global events cannot be anticipated or addressed in a timely or accurate way by current Generative AI systems that rely on human intervention from data classification, to content moderation, to data annotation and labeling historical content. Chatbots powered by Generative AI can only be backward-looking from a data standpoint, not forward-looking, especially considering the dependence on the human intervention feedback loop that is too slow to respond to dynamic geopolitical events and forecast with any degree of accuracy.
Geopolitical events can occur suddenly with immediate consequences for the global order, impacting cross-border dynamics in a matter of seconds, minutes, or hours. But impellent cross-sector factors precede events. Genesis J2T allows for live assessments of changing conditions and dynamic forecasting through continuous learning without human biases inherent in label classifications and human error in data labeling, and with 100% transparency of sources.