Version 1
: Received: 18 April 2024 / Approved: 18 April 2024 / Online: 19 April 2024 (14:57:53 CEST)
How to cite:
Mikkilineni, R.; Kelly, W.P. Machine Intelligence with Associative Memory and Event-Driven Transaction History. Preprints2024, 2024041298. https://doi.org/10.20944/preprints202404.1298.v1
Mikkilineni, R.; Kelly, W.P. Machine Intelligence with Associative Memory and Event-Driven Transaction History. Preprints 2024, 2024041298. https://doi.org/10.20944/preprints202404.1298.v1
Mikkilineni, R.; Kelly, W.P. Machine Intelligence with Associative Memory and Event-Driven Transaction History. Preprints2024, 2024041298. https://doi.org/10.20944/preprints202404.1298.v1
APA Style
Mikkilineni, R., & Kelly, W.P. (2024). Machine Intelligence with Associative Memory and Event-Driven Transaction History. Preprints. https://doi.org/10.20944/preprints202404.1298.v1
Chicago/Turabian Style
Mikkilineni, R. and W. Patrick Kelly. 2024 "Machine Intelligence with Associative Memory and Event-Driven Transaction History" Preprints. https://doi.org/10.20944/preprints202404.1298.v1
Abstract
Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation.
In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
