Exercise and Its Effects on Gestational Diabetes Mellitus and Related Parameters

Background: Gestational Diabetes Mellitus (GDM) has now become one of the most common and important complication of pregnancy worldwide. There are conflicting results of various studies regarding the role of exercise in reducing the risk of GDM. Therefore, the aim of this study was to determine the effectiveness of exercise on prevention of gestational diabetes. Methods: It is a randomized controlled study directed in the obstetrics and gynecology outpatient clinic of Pakistan Institute of Medical Sciences (PIMS) hospital Islamabad beginning from 6 June 2016 to 5 December 2016 including 170 pregnant women satisfying the inclusion criteria. Group A received routine antenatal care while Group B included the pregnant women that were advised brisk walk for 30 minutes three days per week. At 24-28 week of pregnancy, 75gm oral glucose tolerance test (OGTT) was performed and International association for Diabetes in Pregnancy Study Group (IADPSG) and Hyperglycemia and adverse pregnancy outcome (HAPO) standards, determined GDM. Chi Square was applied for comparing GDM frequency and p-value ≤0.05 was considered as significant. Results: The mean age of the patients was 28.08 ± 4.15 years and mean gestation of pregnancy was 17.18 ± 0.78 weeks. Gestational diabetes was seen in 08 (9.41%) patients of non-exercising group while in exercise group only 01 (1.18%) patient had GDM (p-value 0.016). Conclusion: Moderate exercise during pregnancy decreases the risk of gestational diabetes mellitus and is safe for the mother and the baby. However, more studies are needed to establish recommendations

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc