DADA X is the only commercial software platform that uses the Rapide language. It was written in large part by Dr. John Kenney, who is a key member of our leadership team.
Rapide is a programming language that is specifically designed for complex event processing using causal analysis and branching time logics.
The superiority of Rapide lies in its ability to handle the complexity of real-time event streams and to provide a comprehensive set of features for performing real-time event monitoring and control, now.
>> The restriction today with current programming languages that we can get away from is simple: we don’t have to rely on data storage.
The foundational limitation of the data-processing approach for large language models and other AI systems in achieving AGI is that these models are primarily based on statistical pattern recognition and correlation rather than understanding causality or developing an internal model of the world. LLMs are trained on vast amounts of data (trillions of tokens) to find patterns and correlations but often fail to capture causal relationships. Understanding causality is critical for achieving AGI, as it enables better reasoning, generalization, and decision making.
Events in a distributed system can happen independently of one another; they can happen at the same time or at different times; or they can happen in sequence, in which one causes another. Consequently, Rapide was designed to model not only the timing of events as they were created but also their causal relationships or their independence. In order to be able to model multi-layered architectures, Rapide captures levels of events with timing and membership relations between the events at different levels. Event patterns are used as triggers and outputs of component state transitions.
Rapide is implementation independent. The intended usage of the components of a system must be ensured. Rapide enables a specification of pattern constrains on event posets (partially ordered sets) that are generated and observed from a component’s interface. Mapping correspondence models in Rapide provides constraints on valid refinement of a configuration. The ability to specify and define these constraints is key. If the model is formally valid, mapping constraints will produce a valid configuration.
By maintaining the causal relationships between events, our platform enables the user to better understand and therefore manage, control and secure their complex systems. By contrast, AI does data integration but not at the system level, and without the ability to maintain context and causal connections.