Beyond AI: Multi-Intelligence (MI) Combining Natural and Artificial Intelligences in Hybrid Beings and Systems

Fox, Stephen

doi:10.3390/technologies5030038

Cited by 7 publications

(6 citation statements)

References 62 publications

(110 reference statements)

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“…For example, physical production is dependent upon embodied natural intelligence and artificial intelligence among individuals and groups [63][64][65], which carry out physical work in order to survive in markets. Apropos, active inference provides a framework for embodied perception, action and learning, which can be applied to natural life and to artificial life [66]: for example, to evolutionary robotics [67].…”

Section: Active Inference As a Unifying Frameworkmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

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Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

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Section: Active Inference As a Unifying Frameworkmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

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“…Birds demonstrate a high level of intelligence leading to the study of cognitive ornithology and avian intelligence [33][34][35]. Fox in [36] recognize that avian intelligence play an important role in the design of future multiintelligence systems. In [36], the combination of human and avian intelligence is used to design mechanisms enabling the design of useful techniques for drones.…”

Section: Proposed Solution -Artificial Intelligencementioning

confidence: 99%

“…Fox in [36] recognize that avian intelligence play an important role in the design of future multiintelligence systems. In [36], the combination of human and avian intelligence is used to design mechanisms enabling the design of useful techniques for drones. This combination requires the use of learning mechanisms motivated by human and avian intelligence.…”

Section: Proposed Solution -Artificial Intelligencementioning

confidence: 99%

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Periola¹,

Obayiuwana²

2020

Nig. J. Tech.

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The increased use of drones and aerial vehicles in applications poses challenges of airspace safety for aviation organizations. It is important to ensure the safety of the airspace when a significant number of unmanned aerial vehicles are deployed by civilian users. A solution that meets this requirement is important to promote innovation in the commercialization of air space for civilian users deploying unmanned aerial vehicle. The discussion in this paper proposes a mechanism that uses artificial intelligence to address this challenge. The proposed mechanism utilizes a low altitude platform (LAP) and entities in terrestrial wireless networks. The low altitude platform (LAP) observes, develops insights and training data (with human aid). The training data is used to develop learning mechanisms which determine the suitable unmanned aerial vehicles flight parameters in different scenarios. The use of the LAP reduces the burden of communicating with terrestrial base stations. The unmanned aerial vehicles have a reduced altitude between the LAPs in comparison to terrestrial base stations. This reduces the free space path loss and rain-induced attenuation. The performance benefit of the proposed mechanism in comparison to existing solution is examined via MATLAB simulations. Evaluation shows that the proposed mechanism reduces the network access costs by up to 90% on average. The proposed mechanism also increases available flight power and improves airspace safety by 37.3% and up to 53.2% on average respectively. Keywords: Autonomous unmanned aerial vehicles, Intelligence Paradigm; Aviation Safety, Capital Constrained Aviation Organizations.

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“…Humans can be described as cyborgs when their natural capabilities are restored or enhanced by the addition of technologies [ 25 , 63 , 64 , 65 ]. Humans who are enhanced by technologies can already have autonomous psychomotor work skills.…”

Section: Workers: Plpa and Human Cyborg Robot Psychomotor Skillsmentioning

confidence: 99%

“…Here, cyborgs are humans who are enhanced by permanent implanting and/or persistent wearing of work technologies [ 24 ]. Together, the combination of human workers, cyborg workers and robot workers can introduce hybrid intelligence systems that deploy natural and artificial intelligence [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Principle of Least Psychomotor Action: Modelling Situated Entropy in Optimization of Psychomotor Work Involving Human, Cyborg and Robot Workers

Fox¹,

Kotelba²

2018

Entropy

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Entropy in workplaces is situated amidst workers and their work. In this paper, findings are reported from a study encompassing psychomotor work by three types of workers: human, cyborg and robot; together with three aspects of psychomotor work: setting, composition and uncertainty. The Principle of Least Psychomotor Action (PLPA) is introduced and modelled in terms of situated entropy. PLPA is founded upon the Principle of Least Action. Situated entropy modelling of PLPA is informed by theoretical studies concerned with connections between information theory and thermodynamics. Four contributions are provided in this paper. First, the situated entropy of PLPA is modelled in terms of positioning, performing and perfecting psychomotor skills. Second, with regard to workers, PLPA is related to the state-of-the-art in human, cyborg and robot psychomotor skills. Third, with regard to work, situated entropy is related to engineering of work settings, work composition and work uncertainty. Fourth, PLPA and modelling situated entropy are related to debate about the future of work. Overall, modelling situated entropy is introduced as a means of objectively modelling relative potential of humans, cyborgs, and robots to carry out work with least action. This can introduce greater objectivity into debates about the future of work.

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Beyond AI: Multi-Intelligence (MI) Combining Natural and Artificial Intelligences in Hybrid Beings and Systems

Cited by 7 publications

References 62 publications

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Principle of Least Psychomotor Action: Modelling Situated Entropy in Optimization of Psychomotor Work Involving Human, Cyborg and Robot Workers

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