The Interplay of Language Processing, Reasoning and Decision-Making in Cognitive Computing

2015 ◽  
pp. 167-179 ◽  
Author(s):  
Sergei Nirenburg ◽  
Marjorie McShane
Keyword(s):  
Decision Making ◽  
Language Processing ◽  
Cognitive Computing

Cognitive Processing of Miscommunication in Interactive Listening: An Evaluation of Listener Indecision and Cognitive Effort

2021 ◽  
pp. 1-17
Author(s):  
Jennifer M. Roche ◽  
Arkady Zgonnikov ◽  
Laura M. Morett
Keyword(s):  
Decision Making ◽  
Cognitive Processing ◽  
Language Processing ◽  
Cognitive Effort ◽  
Mouse Tracking ◽  
Computer Mouse ◽  
Decision Making Processes ◽  
Computer Mouse Tracking ◽  
Listening Task ◽  
Processing Effort

Purpose The purpose of the current study was to evaluate the social and cognitive underpinnings of miscommunication during an interactive listening task. Method An eye and computer mouse–tracking visual-world paradigm was used to investigate how a listener's cognitive effort (local and global) and decision-making processes were affected by a speaker's use of ambiguity that led to a miscommunication. Results Experiments 1 and 2 found that an environmental cue that made a miscommunication more or less salient impacted listener language processing effort (eye-tracking). Experiment 2 also indicated that listeners may develop different processing heuristics dependent upon the speaker's use of ambiguity that led to a miscommunication, exerting a significant impact on cognition and decision making. We also found that perspective-taking effort and decision-making complexity metrics (computer mouse tracking) predict language processing effort, indicating that instances of miscommunication produced cognitive consequences of indecision, thinking, and cognitive pull. Conclusion Together, these results indicate that listeners behave both reciprocally and adaptively when miscommunications occur, but the way they respond is largely dependent upon the type of ambiguity and how often it is produced by the speaker.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

Towards the Cognitive Informatics of Natural Language

2013 ◽  
Vol 7 (3) ◽  
pp. 25-45 ◽  
Author(s):  
J. M. Taylor ◽  
V. Raskin
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Cognitive Processes ◽  
Computational Analysis ◽  
Computer Experiment ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Cognitive Behaviors ◽  
Computational Humor ◽  
The Relationship

This paper deals with a contribution of computational analysis of verbal humor to natural language cognition. After a brief introduction to the growing area of computational humor and of its roots in humor theories, it describes and compares the results of a human-subject and computer experiment. The specific interest is to compare how well the computer, equipped with the resources and methodologies of the Ontological Semantic Technology, a comprehensive meaning access approach to natural language processing, can model several aspects of the cognitive behaviors of humans processing jokes from the Internet. The paper, sharing several important premises with cognitive informatics, is meant as a direct contribution to this rapidly developing transdisciplinary field, and as such, it bears on cognitive computing as well, especially at the level of implementation of computational humor in non-toy systems and the relationship to human cognitive processes of understanding and producing humor.

Big Data and Data Analytics Research

Web Services ◽  
2019 ◽  
pp. 2161-2171
Author(s):  
Miltiadis D. Lytras ◽  
Vijay Raghavan ◽  
Ernesto Damiani
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Internet Technology ◽  
Cognitive Computing ◽  
New Paradigm ◽  
Web Science ◽  
New Business ◽  
Value Propositions

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

Can neural models of cognition benefit from the advantages of connectionism?

2006 ◽  
Vol 29 (1) ◽  
pp. 86-87 ◽  
Author(s):  
Friedrich T. Sommer ◽  
Pentti Kanerva
Keyword(s):  
Language Processing ◽  
Associative Memories ◽  
Cognitive Computing ◽  
Neural Models ◽  
Combinatorial Structures ◽  
Mathematical Properties ◽  
Neural Representations ◽  
Target Article ◽  
Representational Power ◽  
Reduced Representations

Cognitive function certainly poses the biggest challenge for computational neuroscience. As we argue, past efforts to build neural models of cognition (the target article included) had too narrow a focus on implementing rule-based language processing. The problem with these models is that they sacrifice the advantages of connectionism rather than building on them. Recent and more promising approaches for modeling cognition build on the mathematical properties of distributed neural representations. These approaches truly exploit the key advantages of connectionism, that is, the high representational power of distributed neural codes and similarity-based pattern recognition. The architectures for cognitive computing that emerge from these approaches are neural associative memories endowed with additional mapping operations to handle invariances and to form reduced representations of combinatorial structures.

scholarly journals From WOM to aWOM – the evolution of unpaid influence: a perspective article

2019 ◽  
Vol 75 (1) ◽  
pp. 314-318 ◽  
Author(s):  
Nigel L. Williams ◽  
Nicole Ferdinand ◽  
John Bustard
Keyword(s):  
Decision Making ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Distribution Systems ◽  
Word Of Mouth ◽  
Customer Engagement ◽  
Content Type ◽  
Dominant Source ◽  
Source Of Information

Purpose Advances in artificial intelligence (AI) natural language processing may see the emergence of algorithmic word of mouth (aWOM), content created and shared by automated tools. As AI tools improve, aWOM will increase in volume and sophistication, displacing eWOM as an influence on customer decision-making. The purpose of this paper is to provide an overview of the socio technological trends that have encouraged the evolution of informal infulence strategies from WOM to aWOM. Design/methodology/approach This paper examines the origins and path of development of influential customer communications from word of mouth (WOM) to electronic word of mouth (eWOM) and the emerging trend of aWOM. The growth of aWOM is theorized as a result of new developments in AI natural language processing tools along with autonomous distribution systems in the form of software robots and virtual assistants. Findings aWOM may become a dominant source of information for tourists, as it can support multimodal delivery of useful contextual information. Individuals, organizations and social media platforms will have to ensure that aWOM is developed and deployed responsibly and ethically. Practical implications aWOM may emerge as the dominant source of information for tourist decision-making, displacing WOM or eWOM. aWOM may also impact online opinion leaders, as they may be challenged by algorithmically generated content. aWOM tools may also generate content using sensors on personal devices, creating privacy and information security concerns if users did not give permission for such activities. Originality/value This paper is the first to theorize the emergence of aWOM as autonomous AI communication within the framework of unpaid influence or WOM. As customer engagement will increasingly occur in algorithmic environments that comprise person–machine interactions, aWOM will influence future tourism research and practice.

Concordance assessment of a cognitive computing system in Thailand.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6589-6589 ◽  
Author(s):  
Suthida Suwanvecho ◽  
Harit Suwanrusme ◽  
Montinee Sangtian ◽  
Andrew D Norden ◽  
Alexandra Urman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gastric Cancer ◽  
Language Processing ◽  
Treatment Plan ◽  
Treatment Options ◽  
Decision Support Tool ◽  
Computing System ◽  
Cognitive Computing ◽  
Support Tool ◽  
Lung Cancer Patients

6589 Background: IBM Watson for Oncology (WFO) was trained by Memorial Sloan Kettering and is a cognitive computing system that uses natural language processing to ingest patient data in structured and unstructured formats. The system provides physicians with treatment options that are derived from established guidelines, the medical literature, and training from patient cases. In this study, we assessed the degree of concordance between treatment recommendations proposed by WFO and oncologists at Bumrungrad International Hospital (BIH). BIH is a 580-bed multispecialty hospital in Bangkok, Thailand. Methods: Data from breast, colorectal, gastric, and lung cancer patients treated at BIH were entered into WFO in 2015 and 2016. Retrospective cases were entered after a treatment plan had been determined, and prospective cases were entered during patients’ treatment planning sessions. WFO recommendations were provided in 3 categories: “Recommended”, “For Consideration”, and “Not Recommended.” Concordance was analyzed by comparing the decisions made by the oncologists to those proposed by WFO. Concordance was achieved when the oncologist’s treatment suggestion was in the “Recommended” or “For Consideration” categories given by WFO. Results: A total of 211 cases were assessed, 92 were retrospective and 119 were prospective. The overall concordance rate was 83%; 89% for colorectal, 91% for lung, 76% for breast, and 78% for gastric cancer. Similar concordance rates were observed when retrospective and prospective cases were analyzed separately. Discordance was attributable in part to local oncologists’ preferences for non-U.S. guidelines for certain cancers, especially gastric cancer. Conclusions: There was a high degree of concordance between WFO treatment options and the decisions made by local oncologists. Similar results were recently reported in a breast cancer concordance study conducted using WFO in India (San Antonio Breast Cancer Symposium 2016, Somashekhar et al). WFO’s capabilities as a cognitive decision support tool can be further improved by incorporating regional guidelines. Future work will analyze reasons for discordance such as cost, insurance requirements, and patient and physician preference.

scholarly journals An Ontological Metro Accident Case Retrieval Using CBR and NLP

2020 ◽  
Vol 10 (15) ◽  
pp. 5298
Author(s):  
Haitao Wu ◽  
Botao Zhong ◽  
Benachir Medjdoub ◽  
Xuejiao Xing ◽  
Li Jiao
Keyword(s):  
Decision Making ◽  
Language Processing ◽  
Economic Loss ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Average Precision ◽  
Emergency Plans ◽  
Rule Based ◽  
Ontological Model ◽  
Accident Case

Metro accidents are apt to cause serious consequences, such as casualties or heavy economic loss. Once accidents occur, quick and accurate decision-making is essential to prevent emergent accidents from getting worse, which remains a challenge due to the lack of efficient knowledge representation and retrieval. In this research, an ontological method that integrates case-based reasoning (CBR) and natural language processing (NLP) techniques was proposed for metro accident case retrieval. An ontological model was developed to formalize the representation of metro accident knowledge, and then, the CBR aimed to retrieve similar past cases for supporting decision-making after the accident cases were annotated by the NLP technique. Rule-based reasoning (RBR), as a complementary of CBR, was used to decide the appropriate measures based on those that are recorded in regulations, such as emergency plans. A total of 120 metro accident cases were extracted from the safety monthly reports during metro operations and then built into the case library. The proposed method was tested in MyCBR and evaluated by expert reviews, which had an average precision of 91%.

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