The ability to confront new questions, opportunities, and challenges is of fundamental importance to human progress and the resilience of human societies, yet the capacity of science to meet new demands remains poorly understood. Here we deploy a new measurement framework to investigate the scientific response to the COVID-19 pandemic and the adaptability of science as a whole. We find that science rapidly shifted to engage COVID-19 following the advent of the virus, with scientists across all fields making large jumps from their prior research streams. However, this adaptive response reveals a pervasive "pivot penalty", where the impact of the new research steeply declines the further the scientists move from their prior work. The pivot penalty is severe amidst COVID-19 research, but it is not unique to COVID-19. Rather it applies nearly universally across the sciences, and has been growing in magnitude over the past five decades. While further features condition pivoting, including a scientist's career stage, prior expertise and impact, collaborative scale, the use of new coauthors, and funding, we find that the pivot penalty persists and remains substantial regardless of these features, suggesting the pivot penalty acts as a fundamental friction that governs science's ability to adapt. The pivot penalty not only holds key implications for the design of the scientific system and human capacity to confront emergent challenges through scientific advance, but may also be relevant to other social and economic systems, where shifting to meet new demands is central to survival and success.
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Recent advances in cultural analytics and large-scale computational studies of art, literature and film often show that long-term change in the features of artistic works happens gradually. These findings suggest that conservative forces that shape creative domains might be underestimated. To this end, we provide the first large-scale formal evidence of the persistent association between poetic meter and semantics in 18-19th European literatures, using Czech, German and Russian collections with additional data from English poetry and early modern Dutch songs. Our study traces this association through a series of clustering experiments using the abstracted semantic features of 150,000 poems. With the aid of topic modeling we infer semantic features for individual poems. Texts were also lexically simplified across collections to increase generalizability and decrease the sparseness of word frequency distributions. Topics alone enable recognition of the meters in each observed language, as may be seen from highly robust clustering of same-meter samples (median Adjusted Rand Index between 0.48 and 1). In addition, this study shows that the strength of the association between form and meaning tends to decrease over time. This may reflect a shift in aesthetic conventions between the 18th and 19th centuries as individual innovation was increasingly favored in literature. Despite this decline, it remains possible to recognize semantics of the meters from past or future, which suggests the continuity of semantic traditions while also revealing the historical variability of conditions across languages. This paper argues that distinct metrical forms, which are often copied in a language over centuries, also maintain long-term semantic inertia in poetry. Our findings, thus, highlight the role of the formal features of cultural items in influencing the pace and shape of cultural evolution.
Developing sustainable scientific software for the needs of the scientific community requires expertise in both software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for modern software engineering practices in the scientific community, and the complexity of evolving scientific contexts for developers. These difficulties can be reduced if scientists and developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of scientific software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting the needs of an user-group while maintaining core development. We mitigated these issues by utilizing an Agile Scrum framework to orchestrate the collaboration. This promoted communication and cooperation, ensuring that the scientists had an active role in development while allowing the developers to quickly evaluate and implement the scientists' software requirements. While each system was still in an early stage, the collaboration provided benefits for each group: the scientists kick-started their development by using an existing platform, and the developers utilized the scientists' use-case to improve their systems. This case study suggests that scientists and software developers can avoid some difficulties of scientific computing by collaborating and can address emergent concerns using Agile Scrum methods.
This paper concerns the intersection of natural language and the physical space around us in which we live, that we observe and/or imagine things within. Many important features of language have spatial connotations, for example, many prepositions (like in, next to, after, on, etc.) are fundamentally spatial. Space is also a key factor of the meanings of many words/phrases/sentences/text, and space is a, if not the key, context for referencing (e.g. pointing) and embodiment. We propose a mechanism for how space and linguistic structure can be made to interact in a matching compositional fashion. Examples include Cartesian space, subway stations, chesspieces on a chess-board, and Penrose's staircase. The starting point for our construction is the DisCoCat model of compositional natural language meaning, which we relax to accommodate physical space. We address the issue of having multiple agents/objects in a space, including the case that each agent has different capabilities with respect to that space, e.g., the specific moves each chesspiece can make, or the different velocities one may be able to reach. Once our model is in place, we show how inferences drawing from the structure of physical space can be made. We also how how linguistic model of space can interact with other such models related to our senses and/or embodiment, such as the conceptual spaces of colour, taste and smell, resulting in a rich compositional model of meaning that is close to human experience and embodiment in the world.
Modern neural networks can assign high confidence to inputs drawn from outside the training distribution, posing threats to models in real-world deployments. While much research attention has been placed on designing new out-of-distribution (OOD) detection methods, the precise definition of OOD is often left in vagueness and falls short of the desired notion of OOD in reality. In this paper, we present a new formalization and model the data shifts by taking into account both the invariant and environmental (spurious) features. Under such formalization, we systematically investigate how spurious correlation in the training set impacts OOD detection. Our results suggest that the detection performance is severely worsened when the correlation between spurious features and labels is increased in the training set. We further show insights on detection methods that are more effective in reducing the impact of spurious correlation and provide theoretical analysis on why reliance on environmental features leads to high OOD detection error. Our work aims to facilitate a better understanding of OOD samples and their formalization, as well as the exploration of methods that enhance OOD detection.
Twitter is becoming an increasingly important platform for disseminating information during crisis situations, such as the COVID-19 pandemic. Effective crisis communication on Twitter can shape the public perception of the crisis, influence adherence to preventative measures, and thus affect public health. Influential accounts are particularly important as they reach large audiences quickly. This study identifies influential German-language accounts from almost 3 million German tweets collected between January and May 2020 by constructing a retweet network and calculating PageRank centrality values. We capture the volatility of crisis communication by structuring the analysis into seven stages based on key events during the pandemic and profile influential accounts into roles. Our analysis shows that news and journalist accounts were influential throughout all phases, while government accounts were particularly important shortly before and after the lockdown was instantiated. We discuss implications for crisis communication during health crises and for analyzing long-term crisis data.
The COVID-19 pandemic has shaken the world to its core and has provoked an overnight exodus of developers that normally worked in an office setting to working from home. The magnitude of this shift and the factors that have accompanied this new unplanned work setting go beyond what the software engineering community has previously understood to be remote work. To find out how developers and their productivity were affected, we distributed two surveys (with a combined total of 3,634 responses that answered all required questions) -- weeks apart to understand the presence and prevalence of the benefits, challenges, and opportunities to improve this special circumstance of remote work. From our thematic qualitative analysis and statistical quantitative analysis, we find that there is a dichotomy of developer experiences influenced by many different factors (that for some are a benefit, while for others a challenge). For example, a benefit for some was being close to family members but for others having family members share their working space and interrupting their focus, was a challenge. Our surveys led to powerful narratives from respondents and revealed the scale at which these experiences exist to provide insights as to how the future of (pandemic) remote work can evolve.
Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain and to AI papers, highlighting its broad utility. We make our data, model and search engine publicly available. //challenges.apps.allenai.org/
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.
Over the past few years, we have seen fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. At the same time, the amount of data collected in a wide array of scientific domains is dramatically increasing in both size and complexity. Taken together, this suggests many exciting opportunities for deep learning applications in scientific settings. But a significant challenge to this is simply knowing where to start. The sheer breadth and diversity of different deep learning techniques makes it difficult to determine what scientific problems might be most amenable to these methods, or which specific combination of methods might offer the most promising first approach. In this survey, we focus on addressing this central issue, providing an overview of many widely used deep learning models, spanning visual, sequential and graph structured data, associated tasks and different training methods, along with techniques to use deep learning with less data and better interpret these complex models --- two central considerations for many scientific use cases. We also include overviews of the full design process, implementation tips, and links to a plethora of tutorials, research summaries and open-sourced deep learning pipelines and pretrained models, developed by the community. We hope that this survey will help accelerate the use of deep learning across different scientific domains.
Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data. We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that the models tend to spread too much probability mass over the hypothesis space. Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models. We release both code and multiple human reference translations for two popular benchmarks.
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