scholarly journals Deep Learning, Deep Change? Mapping the Development of the Artificial Intelligence General Purpose Technology

2018 ◽  
Author(s):  
Joel Klinger ◽  
Juan C Mateos-Garcia ◽  
Konstantinos Stathoulopoulos
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
General Purpose ◽  
General Purpose Technology

How to Appropriate Value from General-Purpose Technology by Applying Open Innovation

2021 ◽  
pp. 000812562110417
Author(s):  
Jialei Yang ◽  
Henry Chesbrough ◽  
Pia Hurmelinna-Laukkanen
Keyword(s):  
Artificial Intelligence ◽  
Open Innovation ◽  
General Purpose ◽  
General Purpose Technology ◽  
Application Field ◽  
Closed Approach ◽  
Ibm Watson

Artificial intelligence increasingly attracts attention and investments. However, appropriating value from this general-purpose technology (GPT) can be difficult. To understand these challenges, this article analyzes why IBM failed to generate significant profits from IBM Watson Health despite its promising starting points. The findings suggest that, considering the characteristics of GPT, an overly closed approach for taking it to market contributed to the failure. Furthermore, conditions such as the immaturity and the complexity of the application field intensified the challenges. This study suggests that using a strong appropriability regime in open innovation can enhance the appropriation of value from a GPT.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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