As Artificial Intelligence continues to become a prominent factor in our technology, it is important to have a shared understanding of the abilities each new tool and approach has. Establishing a shared language permits all those involved to quicken their comprehension, encourage usage, support teamwork, measure advancement, and promote invention.
So far, the most well-recognized method for evaluating Artificial Intelligence is the Turing Test.
Although it has been around since 1950, the discipline of artificial intelligence has developed considerably. It is now clear that the Turing Test is not enough to measure the current and future range of AI capabilities.
The Turing Test is limited in that it only evaluates an AI’s chat/linguistic capability, disregarding other important aspects of intelligence such as problem-solving, creativity and social awareness. Furthermore, this test is based on the expectation that AI should be as intelligent as humans, which may not be necessary or relevant when assessing artificial intelligence.
The structure
It is essential to come up with a more detailed and thorough structure for assessing AI abilities in various aspects of intelligence in order to overcome these issues.
I arrived at the idea of creating the “AI Classification Framework” after I pondered the Theory of Multiple Intelligences. The ACF is a novel means of assessing AI capabilities in regards to the Theory of Multiple Intelligences.
Howard Gardner first introduced the concept of the Theory of Multiple Intelligences in 1983, contending that intelligence was not simply one singular ability, but rather a mixture of various capabilities that could be expressed in many ways. Gardner identified eight different types of intelligence and declared that individuals may be especially proficient in one or more of them, as each type of intelligence is independent from the others. The traditional notion of intelligence being a single, immovable concept was questioned by this theory, enabling new ways of researching the range of human thinking. Despite some criticism and discussion, the theory of multiple intelligences has had a significant effect on psychology and schooling, particularly when it comes to different techniques for teaching and learning.
This was an ideal starting point for the AI Categorization Structure. Applying the concept, the system facilitates the analysis of AI programs along many facets of knowledge, such as verbal, logical-mathematical, melodic, spatial, bodily-kinesthetic, interpersonal and intrapersonal intelligence.
For each intelligence measure, the structure provides a range from 1 to 5, with 1 being equivalent to the level of an infant and 5 being at a level of “Super Intelligence”, which is more than any human can achieve.
Illustrations
or
Instances
This framework consists of a comprehensive table of descriptions that is accessible here. Additionally, I made a basic visual representation for an uncomplicated overall reference. Following this are two straightforward samples of the parts that address the abilities of ChatGPT and DALL-E 2.
