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251-500 Employees
2016
We’re optimistic for a future where people live healthier, fairer, more informed, more sustainable lives. We see a world where our AI technology brings us into a new era of democratized intelligence that everyone can benefit from. Our IPU lets innovators create the next breakthroughs in machine intelligence to enhance human potential. We believe our Intelligence Processing Unit (IPU) technology will become the worldwide standard for machine intelligence compute. The Graphcore IPU is going to be transformative across all industries and sectors with a real potential for positive societal impact from drug discovery and disaster recovery to decarbonization.
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Featured
Natural Language Processing with IPUs
... Training Sparse, Large-Scale Language Models on Graphcore's ...
Sofia, Bulgaria
101-250 Employees
2017
Our goal is to provide digital work and training opportunities in some of the areas of the world which are hit hardest by armed conflict and forced displacement. We provide bounding box annotation for 2D, 3D and video data. We provide both semantic segmentationand instance segmentation, which allow you to get the maximum amount of information out of your data. We are building the next generation of professional humans in the loop from conflict-affected regions and communities. Humans in the Loop is a hybrid social enterprise which is comprised of two entities: a for-profit company which provides employment opportunities to our beneficiaries, and a non-profit foundation which offers training programs to upskill them and support them in their career development. With our trained workforce and tools for automated shape detection, we provide extreme accuracy. By using preset skeletons, we are able to annotate hundreds of facial and pose keypoints on each figure, both on images and videos.
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Featured
Reinforcement learning with human feedback | Humans in the Loop
... Train and improve your LLMs and other large-scale models for language and vision with our trained humans-in-the-loop. Use RLHF for generating examples, ranking outputs, and testing your models for ...
Large Scale Language Models (LSLMs) are a type of artificial neural network that uses a deep learning approach to understand and generate natural language. They are trained on large amounts of text, such as corpora of books and news articles, and can be used for tasks such as language translation, text summarization, sentiment analysis, and question answering. LSLMs typically use a recurrent neural network (RNN) architecture and are composed of multiple layers of neurons connected in a way that allows them to learn and make predictions about language.