Cross-modal Turing test and embodied cognition: agency, computingстатья
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Дата последнего поиска статьи во внешних источниках: 27 апреля 2022 г.
Аннотация:Deep learning paradigm allowed computer scientists to take a fresh look at the format of knowledge representation and assimilation. Studies of artificial analogs of neurons and synaptic connections of the brain have indicated
many significant regularities in the opposition of cognitive "medium" and cognitive information. This understanding gave a new impetus to the previously developed concept of embodied cognition in various branches of artificial intelligence. "Embodiment" is usually understood as a combination of cognitive and substrate components. At the same time, there remain world-systemic connections that involve a broader context in the dynamics of correlation between the
subject of cognition (cognitive agent, bounded rationality) and the environment.
The concept of embodied cognition assumes a clash of the range of cognitive
systems, built upon different infogenesis and infotectonics (for example, different computing platforms and degrees of agency). The cross-modal Turing test is supposed to be a universal communication interface that allows “message” and
“medium” of embodied cognitive agent to test each other. The use of reciprocal,
environments and systems will allow a sequential cross-modal Turing test for two
competing modules. Such an approach may turn out to be decisive in cyberphysical systems, which are born at the junction of diverse technical-scientific engineering solutions, as well as in systems that require a high learning rate and model correction. In neural network practice, this approach can be effective in
the field of transfer learning, in which a pre-trained fragment of the network can
be correlated with fundamentally irrelevant (for a neural network) data.