The Networks of Learning in Technological Innovation : The Emergence of Collaboration Across Fields of Expertise

This study addresses four issues concerning technological product innovations. First, the nature of the very early phases or "embryonic stages" of technological innovation is addressed. Second, this study analyzes why and by what means people initiate innovation processes outside the technological community and the field of expertise of the established industry. In other words, this study addresses the initiation of innovation that occurs without the expertise of established organizations, such as technology firms, professional societies and research institutes operating in the technological field under consideration. Third, the significance of interorganizational learning processes for technological innovation is dealt with. Fourth, this consideration is supplemented by considering how network collaboration and learning change when formalized product development work and the commercialization of innovation advance. These issues are addressed through the empirical analysis of the following three product innovations: Benecol margarine, the Nordic Mobile Telephone system (NMT) and the ProWellness Diabetes Management System (PDMS). This study utilizes the theoretical insights of cultural-historical activity theory on the development of human activities and learning. Activity-theoretical conceptualizations are used in the critical assessment and advancement of the concept of networks of learning. This concept was originally proposed by the research group of organizational scientist Walter Powell. A network of learning refers to the interorganizational collaboration that pools resources, ideas and know-how without market-based or hierarchical relations. The concept of an activity system is used in defining the nodes of the networks of learning. Network collaboration and learning are analyzed with regard to the shared object of development work. According to this study, enduring dilemmas and tensions in activity explain the participants' motives for carrying out actions that lead to novel product concepts in the early phases of technological innovation. These actions comprise the initiation of development work outside the relevant fields of expertise and collaboration and learning across fields of expertise in the absence of market-based or hierarchical relations. These networks of learning are fragile and impermanent. This study suggests that the significance of networks of learning across fields of expertise becomes more and more crucial for innovation activities.

Learning challenges of NGOs in development : Co-operation of Finnish NGOs in Morogoro, Tanzania

Non-governmental organisations (NGOs) have gained an important role in development co-operation during the last two decades. The development funding channelled through NGOs has increased and the number of NGOs engaged in development activities, both North and South, has been growing. Supporting NGOs has been seen as one way to strengthen civil society in the South and to provide potential for enhancing more effective development than the state, and to exercise participatory development and partnership in their North-South relationships. This study focuses on learning in the co-operation practices of small Finnish NGOs in Morogoro, Tanzania. Drawing on the cultural-historical activity theory and the theory of expansive learning, in this study I understand learning as a qualitative change in the actual co-operation practices. The qualitative change, for its part, emerges out of attempts to deal with the contradictions in the present activity. I use the concept of developmental contradiction in exploring the co-operation of the small Finnish NGOs with their Tanzanian counterparts. Developmental contradiction connects learning to actual practice and its historical development. By history, in this study I refer to multiple developmental trajectories, such as trajectories of individual participants, organisations, co-operation practices and the institutional system in which the NGO-development co-operation is embedded. In the empirical chapters I explore the co-operation both in the development co-operation projects and in micro-level interaction between partners taking place within the projects. I analyse the perceptions of the Finnish participants about the different developmental trajectories, the tensions, inclusions and exclusions in the evolving object of co-operation in one project, the construction of power relations in project meetings in three projects, and the collision of explicated partnership with the emerging practice of trusteeship in one project. On the basis of the empirical analyses I elaborate four developmental contradictions and learning challenges for the co-operation. The developmental contradictions include: 1) implementing a ready-made Finnish project idea vs. taking the current activities of Tanzanian NGO as a starting point; 2) gaining experiences and cultural interaction vs. access to outside funding; 3) promoting the official tools of development co-operation in training vs. use of tools and procedures taken from the prior activities of both partners in actual practice; and 4) asymmetric relations between the partners vs. rhetoric of equal partnership. Consequently, on the basis of developmental contradictions four learning challenges are suggested: a shift from legitimation of Finnish ideas to negotiation, transcending the separate objects and finding a partly joint object, developing locally shared tools for the co-operation, and identification and reflection of the power relations in the practice of co-operation. Keywords: activity theory; expansive learning; NGO development co-operation; partnership; power

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.