Stifling artificial intelligence: Human perils

Gürkaynak, Gönenç; Yılmaz, İlay; Haksever, Gunes

doi:10.1016/j.clsr.2016.05.003

Cited by 95 publications

(59 citation statements)

References 4 publications

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“…We realized the birth of a reshaping of the perception of the law, as how occurred with disruptive innovations in the past [58]. The heterogenous nature of the professional profiles involved in the debate evinces the complexity and maturity with which the topic is being studied.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance

Almeida¹,

Santos²,

Farias³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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This article presents a meta-framework for Artificial Intelligence (AI) regulation that encompasses all stages of international public policy-making, from formulation to sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2009 and 2019, a dispersed body of knowledge organized under the label "framework" was identified, containing 15 unique frameworks and several different theories that created a complex scientific scenario for research and practice. Theories and principles as diverse as Agile and Ethics were found. Thus, a structured analytical method was followed to integrate this bulk of knowledge into a cohesive, synthetic, and generic theoretical tool. The resulting "AIR framework" provides a trustworthy lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the novel framework organizes the latest developments in the area in a format that allows future research to be framed in and added to the published literature. The (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds in terms of terminology, governance, and social values.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance

Almeida¹,

Santos²,

Farias³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…For a final outlook towards the impact of especially future AI developments [82][83][84][85][86][87][88][89] in the supply chain and business domain a metaphor might be adapted: Bostrom started his 2014 book about superintelligence-hence AI-with a fable of sparrows looking out for an owl, symbolizing humans on the verge of developing AI instances (see [14], p. v). However, this unfinished fable lacks three important features: First, sparrows and owls are-though quite different-still both quite similar bird species.…”

Section: Discussionmentioning

confidence: 99%

Innovation Potentials and Pathways Merging AI, CPS, and IoT

Klumpp

2018

ASI

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Recent advances in the areas of Artificial Intelligence (AI) in the informatics field, Cyber-Physical Systems (CPS) in the production field, and Internet of Things (IoT) in the logistics and transportation field have induced a tremendous growth and innovation potential for global value chain setups. The question is not if further innovation and automation will happen but when-sooner than later-and how. Independent of physical production innovations (additive manufacturing) the information integration and decision autonomy tendencies themselves will drive new supply chain and customer interaction designs and business models. This article presents a technology forecast model based on extensive descriptions of developments by field as well as interaction traits. Results suggest that the crucial element in AI and technology application in logistics will be the human factor and human-artificial cooperation capacities and attitudes.

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“…The cell body adds the input signals and if the sum achieves a certain value the neuron sends a signal, which will propagate through the axon into other neurons [13]. Neuronal networks are computational programs that implement detection of sophisticated patterns and learning algorithms, allowing to build predictive models from wide data basis [15]. Their name derives from the parallelism established with the architecture of the human brain.…”

Section: Ann Principlesmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

Guiné¹,

Dets²

2019

ijfe

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 -Artificial neural networks (ANN) aim to solve problems of artificial intelligence, by building a system with links that simulate the human brain. This approach includes the learning process by trial and error. The ANN is a system of neurons connected by synaptic connections and divided into incoming neurons, which receive stimulus from the external environment, internal or hidden neurons and output neurons, that communicate with the outside of the system. The ANNs present many advantages, such as good adaptability characteristics, possibility of generalization and high noise tolerance, among others. Neural networks have been successfully used in various areas, for example, business, finance, medicine, and industry, mainly in problems of classification, prediction, pattern recognition and control. In the food industry, food processing, food engineering, food properties or quality control, statistical tools are frequently present, and ANNs can process more efficiently data comprising multiple input and output variables. The objective of this review was to highlight the application of ANN to food processing, and evaluate its range of use and adaptability to different food systems. For that a systematic review was undertaken from the scientific literature and the selection of the information was based on inclusion criteria defined. The results indicated that ANN is widely used for modelling and prediction in food systems, showing good accuracy and applicability to a wide range of situations and processes in food engineering.

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Stifling artificial intelligence: Human perils

Cited by 95 publications

References 4 publications

Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance

Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance

Innovation Potentials and Pathways Merging AI, CPS, and IoT

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

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