Next Gen AI-Anticipatory Intelligence: Forecasting the Unexpected

Visualizing Future Geopolitical, Environmental, and Public Health Events in Data and Imagery

Jumptuit is pleased to announce significant advances in its Next Generation AI, Genesis J2T’s Anticipatory Intelligence to forecast and visualize through the mediums of data and imagery probable geopolitical, environmental, and public health events for corporate and government leaders.

Genesis J2T has substantially increased its range of coverage of global atmospheric, terrestrial, and oceanic phenomena, and human activity and artificial systems, from widely dispersed and varied sensors, across the full EM spectrum, as well as hyper-localized data sources that provide unfiltered up-to-date information on human activity and artificial systems.

Genesis J2T has advanced its ability to extract reliable cross-sector signals from a complex of diverse data sources and increased its ability to anticipate probable events, reducing exposure to incidents and, in so doing, mitigating the Cost of Risk (COR). Genesis J2T’s ever-expanding array of hyper-localized data sources removes filters, provides greater clarity, and observability of global phenomena and sector conditions as they occur for any geolocation.

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Genesis J2T synchronizes millions of realtime data endpoints via its Global Sensory Intelligence (GSI) and its Global Data Nets (GDNs) to retrieve realtime and near-realtime hyper-localized data, and to assess geopolitical, environmental, and public health event risk.

Genesis J2T removes algorithmic interference in data acquisition and modeling, and provides transparency and traceability of the data sources, variables, and processes used in generating risk index indicators and probabilities of events.

Traditional AI

The benchmark for development of traditional AI has been human intelligence, the starting point language models, and the design of neural networks, an attempt at simulating human brain functions, including complex processes of human-decision making. The inherent limitations in this approach are modeling AI on human intelligence and human characteristics and behavior, which embody a subjective view of the Universe and its adjacent biases. The AI sector remains largely focused on improving generative AI’s capabilities of mimicking human behavior, including the creation of “original content” including text, images, audio, and video. Its most effective commercial applications to date have been the automation of repetitive tasks and improving accuracy with the goal of increasing organizational efficiencies.

The Principles of Genesis J2T’s Anticipatory Intelligence

Genesis J2T: Observing the Physical Environment

Global synchronization of realtime data from sensors.

Expanding and extending the range of human sensory reception into a new single perceptual frame that manifests as a new global sensory system.

Exceeding the sensory capabilities of living organisms that perceive stimuli beyond human range.

Synchronizing global observation across spectrums and frequencies that surpass the narrow band perception through which human beings experience the Universe.

Synchronizing millions of realtime data endpoints, globally dispersed, to discover probable events through unbiased sensor observation of co-occurring variables.

Genesis J2T: Observing Human Behavior

Reliable cross-sector signals from hyper-localized data sources.

The ethical framework for Anticipatory Intelligence is the sanctity of the observation process and the protection of the data from bias.

Viewpoints are decoupled from the observation process.

Narratives are replaced with veracity through a neutral observation process.

Search engine filtration and global news organization curation are circumvented and augmented with realtime hyper-localized data sources in every region of the world.

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Forecasting Probabilities of Environmental and Geopolitical Events

Realtime Data vs. Historical Data

Genesis J2T continuously searches for comparable data sets in the present rather than search historical databases for perceived similar events in the past. From a data standpoint, the co-occurring variables or conditions leading up to and surrounding events in the past are increasingly dissimilar the further one travels back in time. Comparable data sets in the present do not require virality or causation for parallel events to occur, although Genesis J2T’s models dynamically allow for both.

There are no explicit historical patterns. There is only the next dynamic set of variables. Genesis J2T has demonstrated that when cross-sector panoptic data sets are captured, even in increments of seconds, in every instance there are variables that disappear and new variables that emerge. Therefore, the assortment and relative value of each data point is in a continuous state of flux denoting waves of probable events. To forecast probable events with reliability it is critical to capture and train on cross-sector data sets as close to the present as possible, marking an important step toward reducing dependencies on theories and models designed to substitute for an absence of data.

“Solving the data puzzle of visualizing a future event requires enough data puzzle pieces to work with in order to identify the probable geolocation, event type, actors, and time frame,” said Jumptuit CEO and founder Donald Leka. “Conformity to facts requires a decoupling of viewpoints, interpretations and biases from the observation process and the exclusion of narratives from the forecasting process. The human and financial cost of miscalculations in forecasting is immeasurable. AI-powered early-stage detection of global events that impact global financial markets, jurisdictions, sectors, industries and companies, will provide corporate and government leaders with the ability to expedite and improve decision-making, scheduling, planning and execution to proactively avoid, circumvent, and bypass event risk.”

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