The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

Baldassarre, Gianluca; Santucci, Vieri Giuliano; Cartoni, Emilio; Caligiore, Daniele

doi:10.1017/s0140525x17000036

Cited by 8 publications

(4 citation statements)

References 293 publications

(380 reference statements)

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“…Challenges exist around the architecture of IA systems and the need for sophisticated structures to understand human cognitive flexibility, learning speed and even moral qualities (Baldassarre, Santucci, Cartoni, & Caligiore, 2017;Edwards, 2018). Sun and Medaglia (2019) reviewed the technological challenges of algorithm opacity and lack of ability to read unstructured data.…”

Section: Technological and Technology Implementation Challengesmentioning

confidence: 99%

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Dwivedi

Hughes

Ismagilova

et al. 2021

International Journal of Information Management

1,850

742

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As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

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Section: Technological and Technology Implementation Challengesmentioning

confidence: 99%

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Dwivedi

Hughes

Ismagilova

et al. 2021

International Journal of Information Management

1,850

742

View full text Add to dashboard Cite

show abstract

“…Indeed, we aim to build AI systems with general-domain intelligence by looking at the general principles that underlie intelligence in the brain. The rationale of this is that the space of possible mechanisms that might implement general-domain intelligence is huge, and thus the viable solutions are extremely difficult to find (Baldassarre et al, 2017). A way to restrict the search space is thus to look at the brain, the only known system able to express general-domain intelligence, found by evolutionary selection in millions of years.…”

Section: Our Proposal: Looking At Principles Of Intelligence In the Bmentioning

confidence: 99%

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Monett

Lewis²,

Þórisson

et al. 2020

Journal of Artificial General Intelligence

Self Cite

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Pei Wang's paper titled "On Defining Artificial Intelligence" was published in a special issue of the Journal of Artificial General Intelligence (JAGI) in December of last year (Wang, 2019). Wang has been at the forefront of AGI research for over two decades. His non-axiomatic approach to reasoning has stood as a singular example of what may lie beyond narrow AI, garnering interest from NASA and Cisco, among others. We consider his article one of the strongest attempts, since the beginning of the field, to address the long-standing lack of consensus for how to define the field and topic of artificial intelligence (AI). In the recent AGISI survey on defining intelligence (Monett and Lewis, 2018), Pei Wang's definition, The essence of intelligence is the principle of adapting to the environment while working with insufficient knowledge and resources. Accordingly, an intelligent system should rely on finite processing capacity, work in real time, open to unexpected tasks, and learn from experience. This working definition interprets "intelligence" as a form of "relative rationality" (Wang, 2008), 1. Most striking in these numbers is the glaring absence of female authors. A common reason among female academics for rejecting our invitation to contribute was overcommitment. As a community, we may want to think of new, different ways of engaging the full spectrum of AI practitioners if we value inclusion as an essential constituent of a healthy scientific growth. Self determination and willingness to participate are also essential. This is an open access article licensed under the Creative Commons BY-NC-ND License.

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“…However, the artificial intelligence recognition method used in this study still has some error detections. In future research on artificial intelligence recognition of railway steel cracks, the artificial intelligence recognition method of multi-model composite detection will be adopted in combination with the model characteristics to make corresponding improvements in terms of error detection and missing detection [33], further improve the recognition speed, analyze and use the characteristics of different models to complement each other, and improve the overall recognition ability and efficiency. In the future, we will expand the application scenarios of AI recognition technology, so that AI technology can better serve the industrial field.…”

Section: Discussionmentioning

confidence: 99%

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

et al. 2023

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A method of steel structure surface crack identification based on artificial intelligence technology is proposed to solve the problem that steel cracks can not be detected and forewarned in time when they appear in the railway industrial environment. The appearance of steel cracks greatly weakens the stability of steel structures, and will seriously endanger the safety of the railway industry if it is not detected and repaired in time. However, the common steel crack detection methods cannot achieve real-time monitoring of steel structures. In order to monitor the surface of steel structure in real-time and explore the recognition effect and model the advantages of common classification neural network models for surface cracks of railway industrial steel, this study evaluates the network model with multiple indicators and parameters under two experimental conditions. In this study, the steel surface cracks in the railway industrial environment are taken as samples, and the steel cracks are identified through the neural network model. For large-volume datasets, the recognition accuracy of the three network models has reached 97%, of which the YOLOv5 model has the best comprehensive recognition ability, and the C-Alex model has the best performance and convergence speed in small-volume datasets. This study explores the application prospects of models under different scenarios, proving that the three models can effectively detect steel surface cracks in real-time, and at the same time, it will pave the way for the development and application of artificial intelligence multi-model fusion technology in the field of the railway industry.

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The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

Cited by 8 publications

References 293 publications

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

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