AGI will be Event Driven
An event-driven platform with autonomous anomaly detection can play a crucial role in developing an integrated artificial general intelligence (AGI) system. By leveraging real-time event processing and anomaly detection, AGI systems can adapt, learn, and respond to dynamic environments more efficiently.
One of the critical aspects of AGI is its ability to adapt to changing circumstances in real-time. DADA X event-driven platform can support this by reacting to new information as it becomes available, enabling the AGI system to adjust its strategies and actions accordingly.
Incorporating an event-driven platform with autonomous anomaly detection in AGI can significantly contribute to its metacognitive abilities. The platform enables the AGI system to self-monitor, self-regulate, and learn from experience, fostering continuous improvement and adaptation to changing environments. This heightened ability to evaluate and optimize its decision-making processes are essential for achieving true AGI.
Rethink Information Entropy
Current mindset sees the event phase of the data lifecycle as the highest entropy state.
Data Collection adds timestamps, removes context. The event is flattened.
Processed Data Logs: where dormant data must be resurrected or decay.
When the architecture is changed to implement an event-driven approach where events are monitored for correctness of operation, timing, and causality before entering the system, the information entropy dynamics within the lifecycle change. With our event-driven approach of monitoring and auditing of events at run-time before entering the system there is a lower initial information entropy, as well as dramatic improvements in the areas of latency and scalability.
In this modified architecture, the initial uncertainty and randomness in the events are reduced through the live monitoring and auditing process. This ‘pre-processing’ step helps to ensure that the events entering the system are more accurate, reliable, and causally correct. As a result, the information entropy is lower at the beginning of the lifecycle compared to the original architecture.
Each event is checked against the deployed causal event pattern predefined by the model logic.
Clean, noiseless, secure data is generated by DADA X and allowed into the system or anomaly triggers remediation.
Any logged data is now structured with fuller context and provides superior, actionable insights.
Another major concern with autoregressive LLMs is energy inefficiency. GPT models are computationally intensive, requiring significant processing power to generate responses. They require massive computational resources for both training and inference. This approach is not scalable, and a more efficient method is needed to achieve AGI.
Event-driven AGI will be more powerful while reducing energy use. Our AGI architecture which does not rely on traditional data processing will be more efficient and resource-friendly compared to current approaches.