Applications of Cognitive Computing Systems and IBM Watson

2017 ◽  
Keyword(s):  
Cognitive Computing ◽  
Computing Systems ◽  
Ibm Watson

Cloud-Based Intelligent Accounting Applications: Accounting Task Automation Using IBM Watson Cognitive Computing

2018 ◽  
Vol 15 (1) ◽  
pp. 199-215 ◽  
Author(s):  
Thomas Edward Marshall ◽  
Sherwood Lane Lambert
Keyword(s):  
Data Analytics ◽  
Cognitive Task ◽  
Future Research ◽  
Cognitive Computing ◽  
Knowledge Model ◽  
Job Roles ◽  
Computing Model ◽  
Cognitive Services ◽  
Ibm Watson ◽  
The Impact

ABSTRACT This paper presents a cognitive computing model, based on artificial intelligence (AI) technologies, supporting task automation in the accounting industry. Drivers and consequences of task automation, globally and in accounting, are reviewed. A framework supporting cognitive task automation is discussed. The paper recognizes essential differences between cognitive computing and data analytics. Cognitive computing technologies that support task automation are incorporated into a model delivering federated knowledge. The impact of task automation on accounting job roles and the resulting creation of new accounting job roles supporting innovation are presented. The paper develops a hypothetical use case of building a cloud-based intelligent accounting application design, defined as cognitive services, using machine learning based on AI. The paper concludes by recognizing the significance of future research into task automation in accounting and suggests the federated knowledge model as a framework for future research into the process of digital transformation based on cognitive computing.

Early experience with IBM Watson for Oncology (WFO) cognitive computing system for lung and colorectal cancer treatment.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 8527-8527 ◽  
Author(s):  
S.P. Somashekhar ◽  
Martín-J. Sepúlveda ◽  
Andrew D Norden ◽  
Amit Rauthan ◽  
Kumar Arun ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Rectal Cancer ◽  
Computing System ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Metastatic Colon Cancer ◽  
Cognitive Computing ◽  
Evidence Based Treatment ◽  
Tumour Board ◽  
Ibm Watson

8527 Background: IBM Watson for Oncology is an artificial intelligence cognitive computing system that provides confidence-ranked, evidence-based treatment recommendations for cancer. In the present study, we examine the level of agreement for lung and colorectal cancer therapy between the multidisciplinary tumour board from Manipal Comprehensive Cancer Centre in Bangalore, India, and Watson for Oncology. Methods: Watson for Oncology is a Memorial Sloan Kettering Cancer Center (New York, USA) trained cognitive computing system that uses natural language processing and machine learning to provide treatment recommendations. It processes structured and unstructured data from medical literature, treatment guidelines, medical records, imaging, lab and pathology reports, and the expertise of Memorial Sloan Kettering experts to formulate therapeutic recommendations. Treatment recommendations are provided in three categories: recommended, for consideration and not recommended. In this report we provide the results of the independent and blinded evaluation by the multidisciplinary tumour board and Watson for Oncology of 362 total cancer cases comprised of 112 lung, 126 colon and 124 rectal cancers seen at the Centre within the last three years. The recommendations of the two agents were compared for agreement and considered concordant when the tumour board recommendation was included in the recommended or for consideration categories of the treatment advisor. Results: Overall, treatment recommendations were concordant in 96.4% of lung, 81.0% of colon and 92.7% of rectal cancer cases. By tumour stage, treatment recommendations were concordant in 88.9% of localized and 97.9% of metastatic lung cancer, 85.5% of localized and 76.6% of metastatic colon cancer, and 96.8% of localized and 80.6% of metastatic rectal cancer. Conclusions: Treatment recommendations made by the Manipal multidisciplinary tumour board and Watson for Oncology were highly concordant in the cancers examined. This cognitive computing technology holds much promise in helping oncologists make information intensive, evidence based treatment decisions.

Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores

2013 ◽  
Author(s):  
Steve K. Esser ◽  
Alexander Andreopoulos ◽  
Rathinakumar Appuswamy ◽  
Pallab Datta ◽  
Davis Barch ◽  
...  
Keyword(s):  
Cognitive Computing ◽  
Computing Systems

Cognitive Computing Systems in Reactive Multimedia Applications Developed in CÉU

2016 ◽  
Author(s):  
Rodrigo C. M. Santos ◽  
Francisco SantAnna ◽  
Marcio F. Moreno ◽  
Noemi Rodriguez ◽  
Renato Cerqueira
Keyword(s):  
Multimedia Applications ◽  
Cognitive Computing ◽  
Computing Systems

Cognitive innovation: top performers share their best practices

2018 ◽  
Vol 46 (1) ◽  
pp. 30-35 ◽  
Author(s):  
Glenn Finch ◽  
Brian Goehring ◽  
Anthony Marshall
Keyword(s):  
Best Practices ◽  
Design Methodology ◽  
Management Systems ◽  
Cognitive Computing ◽  
Computing Systems ◽  
Content Type ◽  
Revenue Growth ◽  
Top Performers ◽  
Research Findings ◽  
Practical Implications

Purpose The authors address how a combination of artificial intelligence (AI) and cognitive computing --- adaptive data management systems that monitor, analyze, make decisions and learn -- will transform businesses, work and customer offerings. Design/methodology/approach A survey of 6,050 C-suite executives worldwide identified a small group of cognitive innovators and revealed what they are doing differently. Findings Cognitive innovators identify customer satisfaction, retention, acquisition and revenue growth as the primary rationale for embracing cognitive technologies. Practical implications Cognitive computing systems are already helping make sense of the deluge of data spawned by ordinary commerce because they are able to adapt and learn. Originality/value The authors offer a four-step approach to cognitive computing innovation based on their research findings.

Strong Cognitive Symbiosis: Cognitive Computing for Humans

2017 ◽  
Author(s):  
Csaba Veres
Keyword(s):  
Language Processing ◽  
Alternative Interpretation ◽  
Software Systems ◽  
Cognitive Computing ◽  
Computing Systems ◽  
The Public ◽  
Technological Advances ◽  
Media Hype ◽  
New Perspective ◽  
The Public Sphere

Cognitive Computing has become somewhat of a rallying call in the technology world, with the promise of new smart services offered by industry giants like IBM and Microsoft. The recent technological advances in Artificial Intelligence (AI) have thrown into the public sphere some old questions about the relationship between machine computation and human intelligence. Much of the industry and media hype suggests that many traditional challenges have been overcome. On the contrary, our simple examples from language processing demonstrate that present day Cognitive Computing still struggles with fundamental, long-standing problems in AI. An alternative interpretation of cognitive computing is presented, following Licklider's lead in adopting man-computer symbiosis as a metaphor for designing software systems that enhance human cognitive performance. A survey of existing proposals on this view suggests a distinction between weak and strong versions of symbiosis. We propose a Strong Cognitive Symbiosis which dictates an interdependence rather than simply cooperation between human and machine functioning, and introduce new software systems which were designed for cognitive symbiosis. We conclude that strong symbiosis presents a viable new perspective for the design of cognitive computing systems.

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