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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.
Natural Language Processing with IPUs
... Training Sparse, Large-Scale Language Models on Graphcore's ...
Witnessing the distress caused to communities by armed conflict and forced displacement, Humans in the Loop was established with the big idea of channeling work opportunities to those who need them the most as an alternative to the reliance on humanitarian aid. We currently have a workforce of more than 250 conflict-affected people who are working to power some of the most exciting applications of AI. We, Humans in the Loop are 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. Each year, the company donates part of its profit to finance the core activities of the foundation. Humans in the Loop is recognized as a Global Innovator by Expo 2020 Dubai’s Innovation Impact Grant.
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.