Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

Urban Growth Machine

Whatever else organizes city politics, economies, or modes of governance, dedication to growth is the main political force at work—according to the widely influential “growth machine” thesis. Growth interests, it argues, especially dominate in countries like the United States, where land and buildings operate as private markets, rather than held in common by government or some other entity; most studies have been US based but many commentaries and analyses have followed on from research elsewhere. Researchers describe pursuit of growth in payrolls, capital spending, or construction activity. As in the classic Marxian framework, such would enhance “exchange value.” In the political sphere, growth interests form up at nested scales where they pressure for advantageous zoning and public infrastructure investments at relevant administrative levels—in roads, sewer lines, and so forth. They lobby and help finance campaigns. Opposition forms up, in turn, from those whose interest in the city is primarily for home life or shared enhancements—represented by civic groups or, increasingly, environmental organizations. They strive for gains, again from the extended Marxian lexicon, in “use value”—substantive public benefits, as in health care, libraries, schools, safety, and parks. The resulting debates, studies, and applications are subjects of hundreds of articles, reviews, and books.

Rail-to-park transformations in 21st century modern cities: Green gentrification on track

With urban greening projects increasingly sparking conflicts with environmental and social activists, rail-to-park transformations reveal how ideas of modernity in urban planning enable the perfect “green growth machine.” Here, trains and connectivity—powerful symbols of Modernity in the 19th and 20th centuries—are interlaced with greening and sustainability, motives of the current progress paradigm, and planning orthodoxy. Through a political economy and political ecology lens, we analyze the material and symbolic assembly of two recent railway transformations—Valencia Parc Central and the Atlanta Beltline—and their associated parks. We examine the actual process under which parks are created (parks as a tangible, material object, as infrastructure) and how such a process is entangled in social, political, and economic dynamics that also shape adjacent gentrification. We argue that gentrification is implicit, yet necessary, in the process of park making. Such a process and its embedded politics shape the role that parks have in their neighborhoods and their cities, and what it is expected from them socially, politically, and financially. The conflicts arising from the park making illustrate the two speeds working within 21st century cities: the fast, modern, outward-looking competitive model and the inward-looking, caring more for local revitalization and residents’ welfare.

Role of Machine Learning & Artificial Intelligence Techniques in Software Testing

This study provides information about the use of artificial intelligence (AI) and machine learning (ML) techniques in the field of software testing. The use of AI in software testing is still in its initial stages. Also the automation level is lesser compared to more evolved areas of work.AI and ML can be used to help reduce tediousness and automate tasks in software testing. Testing can be made more efficient and smarter with the help of AI. Researchers recognize potential of AI to bridge the gap between human and machine driven testing capabilities. There are still number of challenges to fully utilize AI and ML techniques in testing but it will definitely enhance the entire testing process and skills of testers and will contribute in business growth. Machine learning research is a subset of overall AI research. The life-cycle of software is increasingly shortening and becoming more complicated. There is a struggle in software development between the competing pressures of developing software and meeting deadlines. AI-powered automated testing makes conducting full test suites in a timely manner on every change. In this article a detailed overview about the various applications of AI in software testing have been demonstrated. Also the implementation of machine learning in software testing has been discussed in detail and use of different machine learning techniques has been explained as well.

On Empty Spaces, Silence, and the Pause

Before there is an aesthetic of gentrification, there is disinvestment. In between both is the production – and perception – of empty space ready to be filled. The production of empty space has a long history in New York City, from settler colonialism to urban renewal to gentrification under the neoliberal regime of today. Techniques such as filtering, investing in the aesthetic potential of aging neighbourhoods, and declaring vacancy, have helped fuel the process of gentrification. More recently, that process has accelerated to insure New York’s world city status by promising that every underutilized parcel will be filled with the tallest buildings, the greenest construction, and the densest use of land. Yet the city still has room for alternative visions that embrace a pause in the growth machine, such as cooperative centres and community gardens. These efforts, threatened though they are, provide models for inclusive cities where neoliberalism does not.