Bricout, J. C, Sharma, B. B, Baker, P. M.A., Behal, A., and L. Bölöni

Learning Futures with Mixed Sentience


Cite as:

Bricout, J. C, Sharma, B. B, Baker, P. M.A., Behal, A., and L. Bölöni . Learning Futures with Mixed Sentience. Futures, 87:91–105, Elsevier, 2017.

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Abstract:

Attaching a particular date or event to the beginning of a future; or perhaps as Winston Churchill might have it, the end of the beginning, is always fraught. In 1996 IBM's Big Blue supercomputer defeated the reigning chess master, Gary Kasparov – presumably by learning and executing superior strategies. Early in 2016 Alpha Go beat three-time European Go champion Fan Hui, and in that same year Google took on the mantle of a ``machine learning company first'' to acknowledge its commitment to deep machine learning and artificial intelligence (AI) (Backchannel, 2016). This is consistent with the branching possibility of a future envisioned by noted computer scientist Pedro Domingos, whose book Master Algorithm describes the paradigm shift that machine learning is bringing about, with an emphasis upon the learning that can take place without human intervention, or to riff from an Alfred North Whitehead quote in the book's leaf – thoughts without (humans) thinking about them. The goal of this paper is to explore an alternate future for human and machine intelligence, one in which human and machine learners together inform ``operations'' or actions in a reciprocal and mutual fashion, with the particular goal of augmenting human capabilities; especially those of people with disabilities. These augmented capabilities can be made possible through robotics integrated into a learning community with humans as co-sentient entities, changing the way people with disabilities, and indeed people generally, live in the world. This foreshadows a paradigm shift, enabling mutually constructed human-machine learning encounters with the external world. While such a future is yet over the horizon, we can see incipient forms of robot-human collaborations, and nascent learning networks. This paper will review the roles played by assistive technologies in augmenting learning and action for people with disabilities, while also describing current and emerging trends in augmentive technologies and artificial intelligence, with a special emphasis upon socially assistive robotics (SAR). SAR is posed as a technology can augment the capabilities of people with disabilities by acting in concert with human users in learning networks. The paper then considers three possible scenarios for the development of human-robot learning communities, contingent upon how human users engage the networking capacity of those communities. Finally, the ethical implications of human-robot learning communities in each scenario are assessed in the light of how user capabilities are augmented.

BibTeX:

@article{Bricout-2017-Futures,
    title={Learning Futures with Mixed Sentience},
    author={Bricout, J. C and Sharma, B. B and Baker, P. M.A. and Behal, A. and L. B{\"o}l{\"o}ni },
    journal={Futures},
    volume = "87",
    xxxnumber = "xxx",
    pages = "91-105",
    year={2017},
    doi = "http://dx.doi.org/10.1016/j.futures.2016.10.001",
    publisher={Elsevier},
    abstract = {
    Attaching a particular date or event to the beginning of a future; or perhaps as Winston Churchill might have it, the end of the beginning, is always fraught. In 1996 IBM's Big Blue supercomputer defeated the reigning chess master, Gary Kasparov – presumably by learning and executing superior strategies. Early in 2016 Alpha Go beat three-time European Go champion Fan Hui, and in that same year Google took on the mantle of a ``machine learning company first'' to acknowledge its commitment to deep machine learning and artificial intelligence (AI) (Backchannel, 2016). This is consistent with the branching possibility of a future envisioned by noted computer scientist Pedro Domingos, whose book Master Algorithm describes the paradigm shift that machine learning is bringing about, with an emphasis upon the learning that can take place without human intervention, or to riff from an Alfred North Whitehead quote in the book's leaf – thoughts without (humans) thinking about them. The goal of this paper is to explore an alternate future for human and machine intelligence, one in which human and machine learners together inform ``operations'' or actions in a reciprocal and mutual fashion, with the particular goal of augmenting human capabilities; especially those of people with disabilities. These augmented capabilities can be made possible through robotics integrated into a learning community with humans as co-sentient entities, changing the way people with disabilities, and indeed people generally, live in the world. This foreshadows a paradigm shift, enabling mutually constructed human-machine learning encounters with the external world. While such a future is yet over the horizon, we can see incipient forms of robot-human collaborations, and nascent learning networks. This paper will review the roles played by assistive technologies in augmenting learning and action for people with disabilities, while also describing current and emerging trends in augmentive technologies and artificial intelligence, with a special emphasis upon socially assistive robotics (SAR). SAR is posed as a technology can augment the capabilities of people with disabilities by acting in concert with human users in learning networks. The paper then considers three possible scenarios for the development of human-robot learning communities, contingent upon how human users engage the networking capacity of those communities. Finally, the ethical implications of human-robot learning communities in each scenario are assessed in the light of how user capabilities are augmented.
    }
}

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