Abstract
AbstractThis chapter identifies the differences between natural and artifical cognitive systems. Benchmarking robots against brains may suggest that organisms and robots both need to possess an internal model of the restricted environment in which they act and both need to adjust their actions to the conditions of the respective environment in order to accomplish their tasks. However, computational strategies to cope with these challenges are different for natural and artificial systems. Many of the specific human qualities cannot be deduced from the neuronal functions of individual brains alone but owe their existence to cultural evolution. Social interactions between agents endowed with the cognitive abilities of humans generate immaterial realities, addressed as social or cultural realities. Intentionality, morality, responsibility and certain aspects of consciousness such as the qualia of subjective experience belong to the immaterial dimension of social realities. It is premature to enter discussions as to whether artificial systems can acquire functions that we consider as intentional and conscious or whether artificial agents can be considered as moral agents with responsibility for their actions.
Highlights
In natural systems the model of the world is to a large extent inherited, i.e. the relevant information has been acquired by selection and adaptation during evolution, is stored in the genes and expressed in the functional anatomy of the organism and the architecture of its nervous systems
Numerous experimentally identified phenomena lack a cohesive theoretical framework. This is true for the dynamic phenomena reviewed here because they cannot be accommodated in the prevailing concepts that emphasize serial feed-forward processing and the encoding of relations by conjunction-specific neurons
That natural systems exploit the computational power offered by the exceedingly complex, high-dimensional and non-linear dynamics that evolve in delay-coupled recurrent networks
Summary
By iterating this strategy across multiple layers in hierarchically structured feed-forward architectures complex relational constructs (cognitive objects) can be represented by conjunction-specific nodes of higher order This basic strategy for the encoding of relations has been realized independently several times during evolution in the nervous systems of different phyla (molluscs, insects, vertebrates) and reached the highest degree of sophistication in the hierarchical arrangement of processing levels in the cerebral cortex of mammals (Felleman and van Essen 1991; Glasser et al 2016; Gross et al 1972; Tsao et al 2006; Hirabayashi et al 2013; Quian Quiroga et al 2005). The highly successful recent developments in the field of artificial intelligence, addressed as “deep learning networks”
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