Data Modeling for Artificial Intelligence

To create a model for Artificial intelligence, it is important that we first understand natural intelligence, because our task is to simply mimic and exhibit intelligence using digital data. So we put forth a possible data structure employed by the brain to process information, in line with the Adaptive Resonance Theory (ART) developed by Stephen Grossberg and Gail Carpenter.

It would be logical to say that the brain employs a very easy structure in order to store and synthesize quickly, which is demonstrated in our thinking and actions. A simple storage and retrieval mechanism backed by simpler computation techniques helping us compare, reason and deduce answers, would also imply that the brain’s output (intelligence) is designed to achieve maximum energy optimization.

Even learning employs simpler methods to establish information and intelligence using the basic concepts of mimicking. You can see mimicking as a general rule of learning among all cellular models. Mimicking, which can be further broken down into sequencing, relationship, and strength is by itself a linear process which gives the ability to replicate an action with available data parameters collected. Imagine a brain having to employ traditional latent machine learning techniques to extract patterns for every learning exercise. It would drain all its energy computing these techniques and eventually hang up the machine.

As the data touch-points are limited, we can be sure that the data entities are limited and the entire knowledge structure is created using this attributes. The human brain structures knowledge based on data collected through touch-points (eyes, ears, nose, tongue and skin) by building relationships and applying weights for simpler computations.

The brain employs a most optimized data architecture with zero redundancy, by storing data by unique sensory inputs, which enables it to build any complex structures using minimum attributes. A hypothetical hierarchical relationship structure may be built in order to learn and process intelligence in real-time.

The idea that the unified model is formed from the sensory inputs are tagged to an object which in turn is tagged to a temporal frame, which in turn, rolls up to events (memories). Intelligence which works using the combination of all sensory parameters needs to be unified to make learning more accurate, similar to the methods employed by the brain.

The model is not designed around topic areas as we see in traditional information architecture. Here it is modeled around unique sensory input parameter, which builds a relationship between parameters based on a timestamp automatically. These relationships cover both spatial (between parameters available at a specific time) and temporal relationships (between similar parameters over a period of time)

The Data model that goes into building an intelligent machine is utmost crucial for rendering instant learning and response selection. Using sensory data (collected from sensors), it is important to lay out a structure for incoming data to form patterns which can be matched, weighted and synthesized, all in real-time.

The data model for AI needs to be centered on the object node, which acts as the pivot between macro-clusters (frame, objects) and micro-clusters(shape, depth, color, etc). This entire data relationship of an object is available as a string. These strings are used to match with the incoming dataset and the differences and similarities are used for auto-classification and auto-labeling. Click here to know more on how it works

The right data relationship defines the truth behind accurate intelligence. If the holistic relationship of a data entity is not computed to the fullest, there is every possibility that the robot/machine can end up on an erroneous note, which we see that occurring naturally even among naturally intelligent models

The ominous relationship backed with appropriate weights are the two important aspects of accuracy in deducing right responses. Without this, we could see artificially intelligent machines failing in their learning methods and end up being far from intelligent.


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