What robots want? Hearing the inner voice of a robot

Pipitone, Arianna; Chella, Antonio

doi:10.1016/j.isci.2021.102371

Cited by 23 publications

(11 citation statements)

References 18 publications

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“…Thus, AI systems lack experiential understanding and cannot claim awareness in the second dimension. Though cognitive architectures with inner speech models are available (Pipitone & Chella, 2021), it lacks 'self-awareness' and it can claim only 'awareness' (Chella et al, 2020).…”

Section: Programming Ethical Guidance In Ai Systemsmentioning

confidence: 99%

Artificial Intelligent Systems and Ethical Agency

Cheruvalath

2022

Journal of Human Values

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The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the subjective nature of ethical decision-making. The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it.

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Section: Programming Ethical Guidance In Ai Systemsmentioning

confidence: 99%

Artificial Intelligent Systems and Ethical Agency

Cheruvalath

2022

Journal of Human Values

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“…Interaction by voice commands with robots is another field of research interest in speech recognition [7]- [9]. The use of voice assistants such as Amazon's Alexa [10] or Google's [11], allow to obtain a more natural method of human-robot interaction.…”

Section: Introductionmentioning

confidence: 99%

Deep learning speech recognition for residential assistant robot

Moreno

Estepa

2023

IJ-AI

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This work presents the design and validation of a voice assistant to command robotic tasks in a residential environment, as a support for people who require isolation or support due to body motor problems. The preprocessing of a database of 3600 audios of 8 different categories of words like “paper”, “glass” or “robot”, that allow to conform commands such as "carry paper" or "bring medicine", obtaining a matrix array of Mel frequencies and its derivatives, as inputs to a convolutional neural network that presents an accuracy of 96.9% in the discrimination of the categories. The command recognition tests involve recognizing groups of three words starting with "robot", for example, "robot bring glass", and allow identifying 8 different actions per voice command, with an accuracy of 88.75%.

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“…Accordingly, social robotics aims at developing “[…] autonomous or semiautonomous robots that are natural and intuitive for the general public to interact with, communicate with, work with as partners, and teach new capabilities” (Breazeal et al, 2016 ; cf. Pipitone & Chella, 2021 for a recent example). Usually, three types of social robots are distinguished (Sandry, 2015a , 2015b ): human-like or android robots , animal-like as well as machine-like or “overtly other” robots.…”

Section: Introductionmentioning

confidence: 99%

Joint Interaction and Mutual Understanding in Social Robotics

Schleidgen

Friedrich

2022

Sci Eng Ethics

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Social robotics aims at designing robots capable of joint interaction with humans. On a conceptual level, sufficient mutual understanding is usually said to be a necessary condition for joint interaction. Against this background, the following questions remain open: in which sense is it legitimate to speak of human–robot joint interaction? What exactly does it mean to speak of humans and robots sufficiently understanding each other to account for human–robot joint interaction? Is such joint interaction effectively possible by reference, e.g., to the mere ascription or simulation of understanding? To answer these questions, we first discuss technical approaches which aim at the implementation of certain aspects of human–human communication and interaction in social robots in order to make robots accessible and understandable to humans and, hence, human–robot joint interaction possible. Second, we examine the human tendency to anthropomorphize in this context, with a view to human understanding of and joint interaction with social robots. Third, we analyze the most prominent concepts of mutual understanding and their implications for human–robot joint interaction. We conclude that it is—at least for the time being—not legitimate to speak of human–robot joint interaction, which has relevant implications both morally and ethically.

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What robots want? Hearing the inner voice of a robot

Cited by 23 publications

References 18 publications

Artificial Intelligent Systems and Ethical Agency

Artificial Intelligent Systems and Ethical Agency

Deep learning speech recognition for residential assistant robot

Joint Interaction and Mutual Understanding in Social Robotics

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