Abstract
The question of whether artificial beings or machines could become self-aware or conscious has been a philosophical question for centuries. The main problem is that self-awareness cannot be observed from an outside perspective and the distinction of being really self-aware or merely a clever imitation cannot be answered without access to knowledge about the mechanism's inner workings. We investigate common machine learning approaches with respect to their potential ability to become self-aware. We realize that many important algorithmic steps toward machines with a core consciousness have already been taken.
Highlights
The question of understanding consciousness is in the focus of philosophers and researchers for more than two millennia
In the 1990s, Baars introduced the concept of a virtual “Global Workspace” that emerges by connecting different brain areas (Figure 1) to describe consciousness (Newman and Baars, 1993; Baars, 1994, 2007; Baars and Newman, 1994)
It has been demonstrated recently that artificial neural networks trained on image processing can be subject to the same visual illusions as humans (Gomez-Villa et al, 2018; Watanabe et al, 2018; Benjamin et al, 2019)
Summary
The question of understanding consciousness is in the focus of philosophers and researchers for more than two millennia. Once we try to imagine to be another species, as Nagel describes in his seminal work “What is it like to be a Bat?”(Nagel, 1974), we immediately fail to follow such experience consciously. Another significant issue is that we are not able to determine consciousness by means of behavioral observations as Searle demonstrates in his thought experiment (Searle, 1980). All the messages and questions passed to the room are answered correctly as a Chinese person would. We start with the philosophical perspective, and put a special emphasis on the description of the most important arguments and positions from the philosophy of mind
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