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Emerging tools bring forth fresh approaches to work, and the field of natural science is no different. In natural science, traditional manual, serial, and labour-intensive work is being augmented by automated, parallel, and iterative processes driven by artificial intelligence-based experimental automation and more. To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for...
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ARM is a leading semiconductor company, which supports other producers (such as Apple and Samsung) as an original equipment manufacturer in developing devices like phones and tablets. Additionally, they support manufacturers in exploring new business areas, including education.
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Employees describe the psychological trauma of reading and viewing graphic content, low pay and abrupt dismissals
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Dusseault & Lee: Other than Hawaii's, no education department has publicly focused on policies governing artificial intelligence in the classroom.
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In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our...
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Low-powered mobile devices such as Raspberry Pis and tablets can be used as micro-servers to implement offline Learning Management Systems (LMS). Despite their potential, especially for low-income countries, such as Tanzania, no research is available detailing the affordances of these devices for supporting LMS features. This study investigated the suitability of various low-cost micro-servers for deploying LMSs. It compared the performance of the Raspberry Pi, Android tablet, and Chromebook...
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The repository aims to create an overview and comparison of software used for systematically screening large amounts of textual data using machine learning.
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Abstract Background Evidence synthesis organisations worldwide are trying to meet commissioners’ need for rapid responses to their evidence synthesis commissions. In this project we piloted an intensive process, working to complete an evidence synthesis within six-weeks, rather than the standard lead time of 4-6 months. There were three objectives: 1) To develop a plan for and conduct an evidence synthesis in six weeks or less (“intensive pilot”) 2) To register time used for the intensive...
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The infographic presents different options explored so far for possible modifications that could be implemented in classrooms in Tanzania. The interventions chosen will depend on the conditions of each classroom studied in the ‘Improving Learning Conditions through Classroom Experience’ programme.
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We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured...
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