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‘These opportunities are precious’: How U of T Engineering students are gaining global experience abroad

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Inspired by nature, temperature-responsive building facades could help reduce energy use from heating and cooling

Large Language Models (LLMs) have high electricity and water consumption due to the resource requirements of serving them to millions of users. This footprint can be reduced using methods developed by Professor Samin Aref (MIE) and his team, which produce smaller LLMs through quantizing their parameters. (image generated by ChatGPT)

How ‘slimmed-down’ large language models can reduce AI’s environmental and energy footprint

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New research collaboration leverages edge computing to meet defence and security challenges

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Fortified hibiscus beverage aims to reduce iron deficiency in sub-Saharan African women

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Seeing smaller than light: How an advanced microscopy technique can help in the fight against cancer and other diseases

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New CRAFT Tissue Foundry provides infrastructure dedicated to bioengineering innovation