On the probabilistic mind of a robot

In this article, we discuss what differentiates an artificial mind from a human one, during the process of making a choice. We do this without any intention to debunk famous arguments according to which there are aspects of human consciousness and expertise that cannot be simulated by artificial humanlike cognitive entities (like a robot, for example). We will first put in evidence that artificial minds, built on top of Deep Neural Network (DNN) technologies, are probabilistic in nature, and follow a very clear line of reasoning. Simply told, their reasoning style can be assimilated to a process that starts from a bunch of example data and learns to point to the most likely output, where the meaning of likely, here, is neither vague or fuzzy, but it obeys well-known probability theories. Nonetheless, as such, choices could be made by those intelligent entities that fail to pass human plausibility criteria, even if those chosen are those with high probability values. We will provide an (obvious) explanation for this apparent paradox, and we will demonstrate that an artificial mind based on a DNN can be driven to translate probabilities into choices that humans judge as plausible. Finally, we will show how an artificial probabilistic mind can be made to learn from its errors, till the point where it exhibits a cognitive behavior comparable to that of a human being.