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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

Farah Ghizzawi

How a passion for sustainable transportation brought this graduate student from Beirut to Toronto

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U of T Engineering Launches $200-million Campaign to Nurture Innovation and the Next Generation of Engineers

ChemE Professor Named Fellow of International Academy of Food Science and Technology

Women of Influence Magazine Honours Engineering Dean

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