Intelligence, Control and the Artificial Mind

Abstract

Artificial intelligence (AI) seems to be at an impasse. The old vision of AI which started as the search for a computer-based approximation of the human mind is not delivering. The initial hype opened the door to ample criticism following failures to fulfill some bold predictions. Cognitive-systems research (CSR) has replaced AI at the forefront of this research programme. But CSR is really just a new name for the same set of objectives, designed to elude the tag of failure. The problem with this programme may not be in the methods but in the naive conceptualizations that have driven and are still driving the research. Indeed, AI has not been a failure. Many AI technologies are routinely used with enormous success in domains from credit-card authentication to nozzle design and language understanding. And beyond the focused applications of concrete AI technologies, its big objective remains an ongoing success. However, the realization of AI is not to be found in the domain of robotics—still in its infancy—but in the uncontroversially materialistic and practical world of industrial-processing plants. The challenges posed today by these complex technical systems set the proper stage for continuing the pursuit of the old dream of AI: the artificial mind. Current research delves into topics such as perception, understanding, self, and consciousness: not for human-like robots, but for plainly alien systems like refineries or electrical infrastructures. Intelligent control (IC) started as a process of technologically immersing AI into the world of control systems. For process control systems,1, 2 the availability of reusable inference engines led to implementation of expert systems exploiting the knowledge of human operators. At first, these systems were only usable as decision-support systems for humans. But with the development of real-time expert-system shells, one could use inference engines to implement closed-loop real-time controllers. At the same time, developments in fuzzy logic and fuzzy control technology enabled construction of systems embracing vagueness with better results than those obtained with other mechanisms Figure 1. Typical functional layering in a complex industrial-processcontrol system.