Entrepreneurial Roles Along a Cycle of Discovery

The literature on entrepreneurship recognizes a variety of entrepreneurial roles, and the question arises what roles are played when and by whom.In this article, roles are attributed to different stages of innovation and organizational development.A central theme is the relation between discontinuity, in radical innovation (exploration), and continuity, in application, diffusion and adaptation (exploitation).Use is made of a concept of a 'cycle of discovery', which seeks to explain how exploration leads on to exploitation, and how exploitation may yield exploration, in a step-by-step development towards radical innovation.Parallel to this there are processes of organisational development.entrepreneurship;innovation;discovery;organizational learning

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)

Third-graders' problem solving performance and teachers' actions

Peer reviewe

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Aprendiendo funciones complejas con GeoGebra

In this paper we describe a teaching experiment targeting with students of Complex Analysis attending an undergraduate course of a Portuguese university. Our main goal is the understanding of the GeoGebra role with respect to visualization and as a technological mediator, according to Vygotsky theory, in the teaching and learning process of complex functions. The first step of our study was the conception of a sequence of didactical tasks and the development of GeoGebra tools related to the target didactical objectives. Here we will describe the procedure related to the tasks implementation in a classroom environment and the achieved results based on the collected data composed by written assignments produced by students, video recording the student performance during the experiment, and the student constructions with GeoGebra. All the collected data were analysed from a qualitative and interpretative paradigm.publishe

Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning

Data-driven models created by machine learning gain in importance in all fields of design and engineering. They have high potential to assists decision-makers in creating novel artefacts with better performance and sustainability. However, limited generalization and the black-box nature of these models lead to limited explainability and reusability. These drawbacks provide significant barriers retarding adoption in engineering design. To overcome this situation, we propose a component-based approach to create partial component models by machine learning (ML). This component-based approach aligns deep learning to systems engineering (SE). By means of the example of energy efficient building design, we first demonstrate better generalization of the component-based method by analyzing prediction accuracy outside the training data. Especially for representative designs different in structure, we observe a much higher accuracy (R2 = 0.94) compared to conventional monolithic methods (R2 = 0.71). Second, we illustrate explainability by exemplary demonstrating how sensitivity information from SE and rules from low-depth decision trees serve engineering. Third, we evaluate explainability by qualitative and quantitative methods demonstrating the matching of preliminary knowledge and data-driven derived strategies and show correctness of activations at component interfaces compared to white-box simulation results (envelope components: R2 = 0.92..0.99; zones: R2 = 0.78..0.93). The key for component-based explainability is that activations at interfaces between the components are interpretable engineering quantities. In this way, the hierarchical component system forms a deep neural network (DNN) that a priori integrates information for engineering explainability. ...Comment: 17 page