Models that depict innovation as a smooth, well-behaved linear process badly misspecify the nature and direction of the causal factors at work. Innovation is complex, uncertain, somewhat disorderly, and subject to changes of many sorts. Innovation is also difficult to measure and demands close coordination of adequate technical knowledge and excellent market judgment in order to satisfy economic, technological, and other types of constraints—all simultaneously. The process of innovation must be viewed as a series of changes in a complete system not only of hardware, but also of market environment, production facilities and knowledge, and the social contexts of the innovation organization.

An Overview of Innovation

Applying artificial intelligence technique to predict knowledge hiding behavior

The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

The CSIRO’s ‘Tomorrow’s Digitally Enabled Workforce’ report is an important foundation for policy makers grappling with the future of work in Australia. The findings of the report underpin the work already being undertaken by the Department of Employment to address the impact of rapid technological development, new business and employment models, increasing globalisation and social change on employment and workplace relations.  The analysis of the megatrends outlined in ‘Tomorrow’s Digitally Enabled Workforce’ is a solid base for the development of future-focused strategies to enable people and organisations to take advantage of emerging opportunities – including promoting entrepreneurship, facilitating participation in the online economy, and encouraging the development of new business models. We are looking to policy settings that enable current and future generations of Australians to be well-positioned to support themselves and contribute to our economy and society. This report marks the beginning of an era of immense change in Australia. The future holds exciting opportunities for the way we work, consume and interact, and also poses some policy challenges. People who work in highly structured environments or who lack high level interpersonal skills may be particularly vulnerable to their jobs being automated. We need to get the policy settings right so that workers who lose their jobs due to automation have access to other streams of work.

Tomorrow’s Digitally Enabled Workforce: Megatrends and scenarios for jobs and employment in Australia over the coming twenty years.

The paper provides an overview of how Artificial Intelligence (AI) is applied in different government sectors. Our methodology is based on a systematic review of 74 papers retrieved from Web of Science and Scopus databases. We find that the extant literature is less focused on healthcare, ICT, education, social and cultural services, and fashion sector; while ignoring the practical implementation of AI in these sectors. We present an organizing framework stating different areas related to governance and throws light on research gaps in the extant literature that can be further worked upon for promoting the research in digital governance.

Artificial intelligence and effective governance: A review, critique and research agenda

Non-linear multiclassifier model based on Artificial Intelligence to predict research and development performance in European countries

The paper is focused on the progress toward knowledge economy (KE) of countries.Behaviour patterns on KE of the countries (clusters) are identified.Three ordinal classifiers are compared to identify the best model.The best ordinal model according its accuracy is chosen.The model can predict the classification toward KE of countries. Knowledge is a key factor of competitive advantages in the current economic crisis and uncertain environment. There are a number of indicators to measure knowledge advances, however, the benefits for stakeholders and policy makers are limited because of a lack of classification models. This paper introduces an approach to classify 54 countries (in 2007-2009) according to their progress toward a knowledge economy (KE). To achieve this, the aims of this paper are twofold: first, to find clusters of countries at a similar stage of development toward KE to test if they are meaningful; hence, it will be possible to order the clusters from early KEs (last cluster) to advanced KEs (first cluster). Second, having obtained these clusters, it is possible to build various models to detect the advancement of countries toward KE from one year to another due to its classification. Then, three ordinal classifiers from the machine-learning field were compared in order to select the classifier that performs the best and to confirm the ordinal description of the clusters. Finally, an ordinal model based on the Support Vector Ordinal Regression with Implicit Constraints was selected because of its ability to classify the patterns into the clusters, confirming the appropriateness of the clusters and their ordinal nature. The proposed ordinal classifier could be used for monitoring the progress or stage of transition to KE and for analysing whether a country changes clusters, entering one that performs better or worse.

Classification of countries' progress toward a knowledge economy based on machine learning classification techniques

Time-series segmentation can be approached by combining a clustering technique and genetic algorithm (GA) with the purpose of automatically finding segments and patterns of a time series. This is an interesting data mining field, but its application to the optimal segmentation of financial time series is a very challenging task, so accurate algorithms are needed. In this sense, GAs are relatively poor at finding the precise optimum solution in the region where the algorithm converges. Thus, this work presents a hybrid GA algorithm including a local search method, aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. A real-world time series in the Spanish Stock Market field was used to test this methodology.

Applying a Hybrid Algorithm to the Segmentation of the Spanish Stock Market Index Time Series

The discovery of characteristic time series patterns is of fundamental importance in financial applications. Repetitive structures and common type of segments can provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterisation methodology combining a maximal likelihood optimisation procedure and a clustering technique to automatically segment common patterns from financial time series and address the problem of stock market prices trends. To do so, the obtained segments are transformed into a five-dimensional space composed of five typical statistical measures in order to group them according to their statistical properties. The experimental results show that it is possible to exploit the behaviour of the stock market Ibex-35 Spanish index (closing prices) to detect homogeneous segments of the time series.

Mónica de la Paz-Marín

Papers

Non-linear multiclassifier model based on Artificial Intelligence to predict research and development performance in European countries

Classification of countries' progress toward a knowledge economy based on machine learning classification techniques

Applying a Hybrid Algorithm to the Segmentation of the Spanish Stock Market Index Time Series

Time Series Segmentation and Statistical Characterisation of the Spanish Stock Market Ibex-35 Index

Evolutionary neural network classifiers for monitoring research, development and innovation performance in european union member states