cognitive scientist
, also known as “the wizard of consciousness”(born 1973 in Weimar, Germany) is a cognitive scientist focusing on cognitive architectures, models of mental representation, emotion, motivation and sociality. Achievements include research in novel data compression algorithm using concurrent entropy models; development of microPsi cognitive architecture for modeling emotion, motivation, mental representation. In 2000, Bach graduated with a diploma in Computer Science from Berlin, followed by a Doctor of Philosophy at Osnabrück University, Germany, in 2006.
Before joining , he worked as a visiting researcher at the and the Harvard Program for Evolutionary Dynamics. Fact finding reports by the and found that Bach’s research was supported with more than $150,000 by the Foundation.
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In cognitive science, we currently have two major families of architectures... One, the classical school... characterized as Fodorian Architectures, as... the manipulation of a language of thought, usually expressed as a set of rules and capable of . ...The other family favors distributed approaches and constrains a dynamic system with potentially astronomically many until... behaviors [of] general intelligence are left. This may seem more "natural" and well-tuned... Yet many functional aspects of intelligence... as planning and language, are... much harder to depict using the dynamical systems approach.
[M]otivation... does not arise from intelligence itself, but from a motivational system underlying all directed behavior. ...[T]here is no reason that could let us take behavioral tendencies such as self-preservation, energy conservation, altruistic behavior for granted—they... have... to be designed [including by evolutionary methods] into the system...
General intelligence is not only the ability to reach a given goal (and usually, there is some very specialized, but non-intelligent way to reach a singular fixed goal, such as winning a game of chess), but includes the setting of novel goals, and most important of all, about exploration. ...[A]n environment with fixed tasks, scaled by an agent with pre-defined goals is not going to make a good benchmark problem for AGI.
Robots are... not going to be the singular route to achieving AGI, and successfully building robots that are performing well in a physical environment does not necessarily engender the solution of the problems of AGI. Whether robotics or virtual agents will be first to succeed in the quest of achieving AGI remains an open question.
For all practical purposes, the universe is a pattern generator, and the mind "makes sense" of these patterns by encoding them according to the regularities it can find. Thus, the representation of a concept in an intelligent system is not a pointer to a "thing in reality", but a set of hierarchical constraints over (for instance perceptual) data.
Early AI systems tended to constrain themselves to micro-domains that could be sufficiently described using simple ontologies and binary predicate logics, or restricted themselves to hand-coded ontologies altogether. ...AI systems will probably have to be perceptual symbol systems, as opposed to amodal symbol systems...
AI’s gradual demotion from a science of the mind to the nerdy playpen of engineering was accompanied not by utterances of disappointment, but by a chorus of glee, uniting those wary of human technological hubris with the same factions of society that used to oppose evolutionary theory or materialistic monism...
Attempts in psychology at overarching theories of the mind have been all but shattered by the influence of behaviorism, and where cognitive psychology has sprung up in its tracks, it rarely acknowledges that there is something as "intelligence per se", as opposed to the individual performance of a group of subjects in an isolated set of experiments.
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When the Artificial Intelligence (AI) movement set off fifty years ago, it bristled with ideas and optimism, which have arguably both waned since. The field has regressed into a multitude of relatively well insulated domains like logics, neural learning, case based reasoning, artificial life, robotics, agent technologies, semantic web... each with their own goals and methodologies. The decline of the idea of studying intelligence per se, as opposed to designing systems that perform tasks that would require some measure of intelligence in humans, has progressed to such a degree that we must now rename the original AI idea into Artificial General Intelligence.