Comparing Input Sensors in an Immersive Mixed-Reality Environment for Human-Computer Symbiosis

Negri, Paolo; Omedas, Pedro; Chech, Luca; Pluchino, Patrik; Minelle, Filippo; Verschure, Paul F. M. J.; Freeman, Jonathan; Spagnolli, Anna; Gamberini, Luciano

doi:10.1007/978-3-319-24917-9_12

Cited by 2 publications

(1 citation statement)

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“…The combination of input and output is a key differentiated advantage for SMEs [55][56][57][58]. In this way, hybrid reality technology can directly affect the workflow of employees and help them improve their work efficiency and innovation ability [59][60][61]. Hybrid reality technology will be more and more widely used in our life, changing the way people work, communicate, learn and entertain [62,63].…”

Section: Application Of Computer In Mixed Reality Technologymentioning

confidence: 99%

Interdisciplinary Evolution of the Machine Brain

Wang

Cai

Deng

et al. 2021

Research on Intelligent Manufacturing

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This chapter finally presents a characterization of interdisciplinary evolution of the machine brain. Perspective schemes for rebuilding a real vision brain in the future are analyzed, along with the major principles to construct the machine brain, are presented, which include memory, thinking, imagination, feeling, speaking and other aspects associated with machine vision, machine touch and machine minds. This explicitly developed the theoretical framework of brain-inspired intelligence from the vision brain hypothesis, the vision-minds hypothesis and the skin brain hypothesis. Based on Chaps. 2-5, development of machine intelligence during the past decades have experienced three stages-machine computation, learning and understanding. Machine leaning includes data mining. Environmental sensing helps to acquire the data. Pattern analysis and scene understanding are significant parts of machine understanding. Scientists have taught machine how to collect and treat data and discover knowledge from the data. Evolution of machine brain will experience another two stages-machine meta-learning (learning to learn) and selfdirected development (improving the capability of machine brain utilizing the learned knowledge). There are still great challenges in realization of the dream.

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