Cognitive computing

Modha, Dharmendra S.; Ananthanarayanan, R.; Esser, Steven K.; Ndirango, Anthony; Sherbondy, Anthony J.; Singh, R. V.

doi:10.1145/1978542.1978559

Cited by 261 publications

(103 citation statements)

References 26 publications

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“…For making this possible, there is a need to make machines exhibit human-like cognitive skills. The idea is to extract the mathematical model of a brain, imitate it in a computer, and train it to satisfy the user needs [20]. Nevertheless, it has to be mentioned that there has been a long debate about the feasibility of computational systems mimicking human-like intellect since the early origins of artificial intelligence [21] [22] [23] [24] [25].…”

Section: The Limits Of Context-aware Systemsmentioning

confidence: 99%

Engineering context-aware systems and applications: A survey

Alegre

Augusto

Clark

2016

Journal of Systems and Software

176

137

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Context-awareness is an essential component of systems developed in areas like Intelligent Environments, Pervasive & Ubiquitous Computing and Ambient Intelligence. In these emerging fields, there is a need for computerized systems to have a higher understanding of the situations in which to provide services or functionalities, to adapt accordingly. The literature shows that researchers modify existing engineering methods in order to better fit the needs of context-aware computing. These efforts are typically disconnected from each other and generally focus on solving specific development issues. We encourage the creation of a more holistic and unified engineering process that is tailored for the demands of these systems. For this purpose, we study the state-of-the-art in the development of context-aware systems, focusing on: A) Methodologies for developing context-aware systems, analysing the reasons behind their lack of adoption and features that the community wish they can use; B) Context-aware system engineering challenges and techniques applied during the most common development stages; C) Context-aware systems conceptualization.

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Section: The Limits Of Context-aware Systemsmentioning

confidence: 99%

Engineering context-aware systems and applications: A survey

Alegre

Augusto

Clark

2016

Journal of Systems and Software

176

137

View full text Add to dashboard Cite

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“…Inspired in workshops of Neuroscience and ideas of cognitive computing [21] Nowadays two students have started a master's degree in Digital Signal Processing at UNI [22]. They got scholarships from by the Peruvian National Council for Science and Technology (CONCYTEC).…”

Section: E Cognitive Neuroscience Course In Electrical Engineering Umentioning

confidence: 99%

Cognitive Neuroscience in Engineering Coursework at Peruvian University Programs

Mugruza-Vassallo¹,

Mugruza-Vassallo²

2018

IJIET

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“…The role of humans in the analysis of datasets and the interpretation of analysis results has also been recognized in other domains such as in biocuration where AI approaches are widely used to assist humans in extracting structured knowledge from text [43]. However, progress on computational creativity [45] and cognitive computing [14], i.e., the simulation of human cognitive processes, aims to reproduce human capabilities and may contribute to further pushing the boundaries of what machines can achieve in generation of scientific theories, interpretation of data, and understanding of natural language. The role that humans will play in the process of scientific discovery will likely remain a controversial topic in the future due to the increasingly disruptive impact Data Science and AI have on our society [3].…”

Section: Limits Of Data Sciencementioning

confidence: 99%

Data Science and symbolic AI: Synergies, challenges and opportunities

Hoehndorf

Queralt-Rosiñach

2017

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Editor: Tobias Kuhn (https://orcid.org/0000-0002-1267-0234) Solicited reviews: anonymous reviewer; Rinke Hoekstra (https://orcid.org/0000-0001-7076-9083); Agnieszka Ławrynowicz (https://orcid.org/0000-0002-2442-345X); Honghan Wu (https://orcid.org/0000-0002-0213-5668) Received 21 February 2017 Accepted 27 April 2017Abstract. Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable; they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information; and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists' toolbox.

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Cognitive computing

Abstract: Unite neuroscience, supercomputing, and nanotechnology to discover, demonstrate, and deliver the brain's core algorithms.

Cited by 261 publications

References 26 publications

Engineering context-aware systems and applications: A survey

Engineering context-aware systems and applications: A survey

Cognitive Neuroscience in Engineering Coursework at Peruvian University Programs

Data Science and symbolic AI: Synergies, challenges and opportunities

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