Authors: Arvind Chitra Rajasekaran
In this article we will introduce a cognitive architecture for creating a more human like and personable artificial intelligence. Recent works such as those by Marvin Minsky, Google DeepMind and cognitive models like AMBR, DUAL that aim to propose/discover an approach to commonsense AI have been promising, since they show that human intelligence can be emulated with a divide and conquer approach on a machine. These frameworks work with an universal model of the human mind and do not account for the variability between human beings. It is these differences between human beings that make communication possible and gives them a sense of identity. Thus, this work, despite being grounded in these methods, will differ in hypothesizing machines that are diverse in their behavior compared to each other and have the ability to express a dynamic personality like a human being. To achieve such individuality in machines, we characterize the various aspects that can be dynamically programmed onto a machine by its human owners. In order to ensure this on a scale parallel to how humans develop their individuality, we first assume a child-like intelligence in a machine that is more malleable and which then develops into a more concrete, mature version. By having a set of tunable inner parameters called aspects which respond to external stimuli from their human owners, machines can achieve personability. The result of this work would be that we will not only be able to bond with the intelligent machines and relate to them in a friendly way, we will also be able to perceive them as having a personality, and that they have their limitations. Just as each human being is unique, we will have machines that are unique and individualistic. We will see how they can achieve intuition, and a drive to find meaning in life, all of which are considered aspects unique to the human mind.
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