AI’s Direct Link to the Physical World and Human Decision-Making: The Dawn of Autonomous Large Dynamic Reasoning Models (LDRMs)
Observation-Based Dynamic Reasoning, Forecasting, and Risk Assessment Overcoming the Inherent Limitations of Language-Centric and Human-Mimicking AI Models
The Limitations of Conventional Artificial Systems
Conventional Artificial Intelligence (AI) systems, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), primarily rely on language, pre-trained historical data, and mimicking human reasoning, leading to outdated, siloed, and biased information. These models struggle to provide real-time, hyper-localized, and unbiased insights, and are prone to “hallucinations.”
Inference-time reasoning techniques like Chain-of-Thought (CoT), while used to improve LLMs, can systematically reduce performance on complex tasks involving implicit statistical learning, non-linguistic stimuli, or rules with exceptions, and may lead to “overthinking.” Similarly, Large Reasoning Models (LRMs) fail to develop generalizable problem-solving capabilities, with their performance collapsing at certain complexity thresholds. Multimodal Large Language Models (MLLMs) often exhibit shortcut reasoning, failing to truly integrate visual and textual understanding for complex multimodal tasks, with most errors arising in visual reasoning. Furthermore, Spoken Language Models (SLMs) struggle with generating plausible and coherent long-form speech due to architectural and memory limitations.
One issue across these conventional AI approaches is their starting point: attempting to understand the world from digitized text and media and mimicking human behavior rather than directly observing natural phenomena. This creates a need for an improved solution that provides a live, observation-based link to the physical world for dynamic forecasting and risk assessment. The breakthrough development of the LDRM represents AI’s missing link to the physical world, enabling AI to advance and provide real-world value.
The Great AI Reset
The Jumptuit Team is proud to announce today the arrival of the Large Dynamic Reasoning Model (LDRM), which represents a fundamentally different approach to Artificial Intelligence compared to traditional language-centric and human-mimicking AI models. The LDRM is engineered to establish a direct, observation-based link to the physical world, moving beyond digital and theoretical realms to dynamically observe non-visible real-time variable interactions that precede natural phenomena, including human behavior.
The core objective of the LDRM is to dynamically and autonomously forecast events, assess risks, and provide objective insights to enhance decision-making by mitigating the biases inherent in language-based AI systems. This is achieved by prioritizing non-verbal, dynamic quantitative data as its primary input and reasoning foundation. The LDRM is designed to provide human and conventional Artificial Intelligence with a live link to the physical world to dynamically observe non-visible real-time variable interactions that are precursors to natural phenomena, including human behavior.
The LDRM comprises a sophisticated architecture and a suite of interconnected components designed to process vast amounts of dynamic, real-world data and execute a flexible, multi-modal reasoning process.
Dynamic Reasoning Processes
The LDRM employs a dynamic reasoning process that is highly flexible, allowing for fluid movement and hand-offs between different reasoning types or returning to previous stages, based on incoming stimuli and the specific problem context. This contrasts with static, linear reasoning patterns. By improving the currency, veracity and multi-modality of cross-sector data for analysis, LDRMs reduce the noise and bias inherent to verbal reasoning, and re-weight verbal reasoning processes in relation to non-verbal forms of reasoning. The system integrates several types of reasoning (e.g., sign, comparative, causal, analogical), some of which are shared with other living organisms, providing a strong connection to physical world observations.
Innate Curiosity and Urgency Drivers
The LDRM is equipped with innate curiosity and urgency drivers, enabling it to operate as an autonomous intelligence system with minimal human intervention. Innate curiosity is the inherent capability that allows the LDRM to spontaneously search for and discover missing information or new data sources when existing data is inadequate for an assessment.
Urgency drivers complement innate curiosity by accelerating data capture in rapid succession (e.g., in seconds or minutes) when high-risk events are detected. Urgency is directly driven by the risk metric and magnitude of the risk, ensuring that the system focuses computational resources on critical situations without relying on scheduled searches. This dynamic adaptation allows for real-time adjustments without human intervention and optimizes computational costs by avoiding unnecessary searches when risk levels are not severe. The urgency driver acts as an accelerator for innate curiosity.
“The LDRM’s dynamic reasoning process is highly flexible, allowing for fluid movement between different reasoning types based on incoming stimuli and the specific problem context,” said Inventor and Jumptuit Founder and CEO, Donald Leka. “The starting point is observing the natural order and intelligent structure of the universe, and that includes the unfiltered observation of human activity in the physical world.”
Synchronized Global Observation
Across Spectrums and Frequencies
Anticipatory Intelligence
Synchronizes millions of realtime data endpoints via its Global Sensory Intelligence (GSI), and Global Data Nets (GDNs).
Retrieves realtime hyper-localized data to assess geopolitical, environmental, and public health event risk.
Encompasses Atmospheric, Terrestrial, and Oceanic Conditions, and Human Activity and Artificial Systems.
Global Sensory Intelligence (GSI)
Accesses millions of globally dispersed realtime data endpoints.
Synchronizes global observation across spectrums and frequencies.
Discovers probable events through sensor observation and dynamic analysis and forecasting of co-occurring variables.
Global Data Nets (GDNs)
Unfiltered observation of human activity and systems, unbiased data in its original state.
Replacing narrative-based analysis and reports with the veracity of a neutral observation process.
Bypassing search engine filtration and global news organization curation. Accessing realtime hyper-localized data in every region of the world.
Anticipatory Intelligence
Minimizes reliance on centralized processing, historical data, and manual intervention.
Enables continuous, high-resolution analysis of diverse and complex datasets.
Allows for a critical understanding of precedent conditions. Improves the ability to anticipate and respond to emerging developments.
The Building Blocks of Anticipatory Intelligence
​​​​​​​​​​​​​​Synchronizing Live Global Sensory Observation (e.g., Atmospheric, Terrestrial, and Oceanic Conditions, and Human Activity and Artificial Systems)
Expanding the 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 the Human Spatial Experience With the Environment
Improving Decision Making, Not Making Decisions for People
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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.
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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.
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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.​