Researchers have achieved an efficiency record for transparent solar cells, paving the way for skyscraper windows to serve as power sources. A team from the University of Michigan used an organic, carbon-based design to transform sunlight into electricity with an efficiency rate of 8.1 per cent. Commercial solar cells, which are typically made using silicon rather than carbon, tend to have an efficiency rate of between 14 and 19 per cent. "Windows, which are on the face of every building, are an ideal location for organic solar cells because they offer something silicon can't, which is a combination of very high efficiency and very high visible transparency," said Stephen Forrest, a professor of engineering who led the research. The transparency of the solar cells used in the research had 43.3 per cent transparency, which is similar to the transparency of windows used in skyscrapers and tinted car windows.

The First Five Consortium, a nod to the importance of the first five minutes in responding to a natural disaster, aims to build between 10 and 30 different AI-powered systems. Microsoft will provide technological resources, including its Azure cloud for AI model training and inference. Other organizations, including public- and private-sector entities, are expected to participate. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. The announcement comes as California confronts another summer of raging wildfires, while Iowa reels from devastating windstorms.

Artificial intelligence (AI) is a subject of great debate when it comes to ethics, but one area people might not think about is its carbon footprint. A study released last year by MIT Technology Review found that training a "regular" AI using a single high-performance graphics card has the same carbon footprint as a flight across the United States. Training a more sophisticated AI was even worse, pumping five times more CO2 into the atmosphere than the entire life cycle of an American car, including its manufacturing. Whether it's the latest AI or machine learning algorithm that's active on a system, a new 5G network deployed at a commercial building or people streaming the latest Twitch gaming video, people generate and consume a lot of data. All that data must be captured, stored, analyzed and sent back out, which requires significant amounts of processing power.

Not surprisingly, researchers are working on new methods with a view to reducing the carbon footprint of these machines. In June, American company OpenAI unveiled the world's largest text generator. Called GPT-3, the new artificial intelligence (AI) model can, among other things, write creative fiction and translate legal jargon into plain English, two functions that have been achieved using deep learning. However, above and beyond these technological breakthroughs, it is important to bear in mind that the creation of this new tool generated an enormous amount of pollution. The extent to which deep learning and computing are polluting is often overlooked. A recent study by the University of Massachusetts has shown that the training of a deep learning machine, which can take several hours or even days, can produce up to 283,000 kilograms of greenhouse gas.

Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling.

Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented literature on ML for reliability and safety applications, and it can be overwhelming to navigate and integrate into a coherent whole. In this work, we facilitate this task by providing a synthesis of, and a roadmap to this ever-expanding analytical landscape and highlighting its major landmarks and pathways. We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. We then look back and review the use of ML in reliability and safety applications. We examine several publications in each category/sub-category, and we include a short discussion on the use of Deep Learning to highlight its growing popularity and distinctive advantages. Finally, we look ahead and outline several promising future opportunities for leveraging ML in service of advancing reliability and safety considerations. Overall, we argue that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications. It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident prevention.

The goal of text generation models is to fit the underlying real probability distribution of text. For performance evaluation, quality and diversity metrics are usually applied. However, it is still not clear to what extend can the quality-diversity evaluation reflect the distribution-fitting goal. In this paper, we try to reveal such relation in a theoretical approach. We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution. We also show that the commonly used BLEU/Self-BLEU metric pair fails to match any divergence metric, thus propose CR/NRR as a substitute for quality/diversity metric pair.

But they are a few problems relating to using a traditional R CNN. Mainly that processing an image takes a lot of time. As the model is extracting 2000 regions to check. Prediction with the model takes around 40 seconds. A better version of R CNN was created by the same person.

While artificial intelligence (AI) is often associated with the spawning of robots that will take our jobs, Terminator's Skynet, or the unblinking red eyes of Hal 9000 in 2001: A Space Odyssey, its true and immediate effects are best seen by simply observing the innovations -- ones that prove that software can do a variety of tasks better than humans can. If one thing is clear, it's that artificial intelligence has the potential to disrupt every industry, which leads to a big question that should matter to all of us: To what extent can a powerful technology like artificial intelligence be used to help us tackle climate change? To learn more about how we can leverage artificial intelligence to tackle climate change, I had to chat with Priya Donti, who's completing a Ph.D. in Computer Science and Public Policy at Carnegie Mellon University, focused on the role machine learning can play in climate change mitigation solutions. Donti is also a co-chair of Climate Change AI, an organization that unites "volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis." Our conversation, which has been edited for length and clarity, discusses the risks, the rewards, and the limitations of using artificial intelligence to combat climate change.