What is Information? What is Intelligence?
How to Build Artificial General Intelligence (AGI) Agents?
We developed the Social AI Platform and the Information Digital Twin (IDT) to provide commercial AGI capabilities and solutions. As such, the Social AI Platform is intended to support a wide range of intelligent scenarios using the same agent: the Information Digital Twin (IDT).
The Social AI Platform technology is the realization of our novel concepts and approaches to information and intelligence.
Information and Intelligence-Unified Definitions
Artificial approaches to intelligence depend on computational models to process information and provide intelligent capabilities. Due to the lack of uniform definitions of what constitutes intelligence and what is information, the capabilities such models provide differ according to their interpretations of intelligence
and implicit assumptions about what is information. The variety of interpretations of intelligence and information also indicate that existing intelligence computational models provide specialized rather than general capabilities.
Therefore, we believe that the unified definitions of information and intelligence are the starting point for understanding the structure and algorithms necessary to enable Artificial General Intelligence (AGI) agents.
The following are the unified definitions we have developed so far and use to mature the IDT and the Social AI Platform.
The capacity of a system to predict its environment and be predictable by it-measured in bits
Information (and intelligence) are apparent through interactions. If an agent or a system does not interact with its environment, it is impossible to tell if it processes information or if it is intelligent. Interaction, or more precisely communication, is thus a prerequisite for understanding and defining concepts like information and intelligence.
To communicate, an agent relies on a set of symbols, alphabet, or code and some rules to construct messages and to send them to its environment, and to deconstruct the messages it receives from other agents.
If the various agents depend on one another in reaching their goals, then the set of symbols used by each agent to build its messages will start developing some degree of correlations. Although each agent might use a completely arbitrary set of symbols, they need to make sure to use these symbols consistently in order to convey their messages effectively. This consistency is what achieves the correlations among the various sets of symbols.
Once correlations are established, predictability follows. When an agent sends a specific message, it predicts receiving a particular return message. In the other direction, when an agent receives a message, the level of correspondence of its response message determines the environment's capacity to predict its behavior.
Our research assumption is that the level of correlations and the resulting agent-environment predictability is a unique, general property of a system interacting with its environment, similar to properties such as energy or mass. Consequently, we attempt to demonstrate that information is the metric for measuring this level of correlations, or entanglement. To formalize the information metric, we then rely on concepts from communication theory.
The activities of a system to increase its information, i.e., increase its capacity to predict and be predictable
The relationship between intelligence and information, or level of predictability as we define it here, is rooted in the nature of communication.
Communication theory (Shannon and Weaver, 1949) defines communication as "the procedures by which one mind may affect another." To include "non-mental" systems, communication is thus the procedure by which one entity influences the actions of another in a particular direction. An example is a thermostat sending a control signal to a heater to change its degree of transferring energy into heat.
Developing an effective communication system is a highly complex task. An agent needs to define a limited set of unique symbols, define rules to structure them into signals and messages (syntax), and map them to the set of concepts necessary to achieve its objectives (semantics).
However, the most challenging aspect of communication is the activity of maintaining, updating, and optimizing the symbols, syntax, and semantics rules to achieve effective communication. This aspect is where our research assumption about the nature of intelligence comes into play.
Our research assumption is that intelligence is the continuous activity of an agent to evaluate its communication effectiveness and increase its capacity to influence its environment toward achieving its objectives. This translates into monitoring the usage of its communication symbols, updating the rules to build messages out of these symbols, updating the messages to reflect its objectives, and ultimately evaluating its overall success in influencing its environment or adapting to it.
The question here is, what is the control signal that helps the system to accomplish these activities? Our assumption is that one possible way for the system to measure its effectiveness in completing these tasks is its overall information: its ability to predict and be predictable. That is, the intelligence of an agent can be assessed by its predictability capacity and if it can maintain it or increase it against changes in its environment.
The Information Digital Twin (IDT) is the technical implementation of the unified information and intelligence concepts. The IDT architecture and algorithms enable it to assist its users in measuring and assessing the information involved in their interactions with their environment. Furthermore, the IDT monitors interactions' information changes and provides recommendations to its users to maintain or increase their information.
Entanglement, Predictability & Information
Our assumptions concerning the unified definitions of information and intelligence are founded on the concept of entanglement, a fundamental characteristic in quantum mechanics. According to Erwin Schrödinger, 1948, "entanglement consists in the fact that a single observable (or set of commuting observables) of one system is uniquely determined by a single observable (or set of commuting observables) of the other"
For two entangled systems, say A and B, knowing the state of one of its parameters provides system A with complete knowledge (prediction!) of the corresponding parameter in system B. The higher the entanglement between the two systems, the higher their ability to predict and influence one another.
As we propose, information is thus an indication and a measure of the level of entanglement between two systems, which corresponds directly to their ability to predict and be predictable.
How the IDT uses Information to Enable Intelligence
As an AGI agent, the Information Digital Twin, IDT, or the human-specific version, the Human Digital Twin, HDT, enable their users to measure their level of information (entanglement or predictability) as they interact with their environment. This measurement is based on calculating the probabilities of the various parameters involved in the user-environment interactions as captured in the Reference Events Model (REM). The various use cases (Shipment Digital Twin, Human-Machine Teaming) provide examples of the REM structure used to calculate the user-environment information.
As indicated in the two examples, any change of any involved probability would change the value of the user-environment information. The IDT can then use the information change to update its search for alternative actions to maintain or increase the user-environment information, which reflects the IDT intelligence.
The guiding principle of all our research activities is how the various architectural elements and algorithms support the IDT intelligence: i.e., how they enable it to control and increase user-environment information, entanglement, and mutual predictability.
Currently, we have two pending patents covering the Social AI Platform's (and the Information Digital Twin) novel techniques, architecture, and algorithms
Current Research Focus
Our current research focuses on three main areas: developing algorithms to optimize the Social AI Platform operations (Model Optimization), developing algorithms to enable the IDT decision process (Decision Optimization), and finally, developing and refining the IDT architecture and algorithms to become independent of the Social AI Platform and operate as a stand-alone AGI agent.
We are also developing an open platform for interested customers to use and test a simplified version of the Information Digital Twin (IDT), or its special implementation with humans, the Human Digital Twin (HDT). The IDT Test Platform will provide the necessary data structures and algorithms to enable customers to develop a Reference Events Model (REM) for various use cases, quantify initial parameters dependencies and calculate the involved information value for the modeled events.
Each user on a mature Social AI Platform will participate in hundreds of different scenarios. At any given time, numerous scenarios will be active in parallel on the users' HDT. This means that the user-specific REM on its HDT will need to accommodate a large number of events and their associated parameters in a single view (a multi-dimensional information cube).
What is the required algorithm to continually update and optimize the probabilities associated with the various parameters to maintain or maximize the platform's -and all its users-information?
Assuming once more the mature environment. The user's HDT must then calculate the "optimal path" across numerous scenarios in order to provide actionable recommendations to the user. Ultimately, effective decision automation requires decision optimization.
What is the algorithm necessary for the IDT/HDT to find the ideal route for maintaining and expanding its user information in the face of changes? This research area relies on reinforcement learning concepts and methods.