For example, Open AI’s GPT-3 is primarily a language model trained to predict the next words based on natural-language processing. Before M6, most of the large language AI models were trained in English and based on learning involving a single modality. Large, power-hungry AI models are not sustainable in the long run because only giant tech companies can afford to use thousands of TPUs to run over 100-billion-parameter AI models, meaning AI would be accessible only to very few.Ī. That would be too damaging to the environment.

GPT-3, a powerful language model by OpenAI, is estimated to have consumed the same amount of energy in training as a car driving to the moon and back. Google’s landmark AI projects require at least thousands of Tensor Processing Unit (TPU), which are powerful processors that can speed up processing of tasks and can be used for training artificial intelligence. They are thus more intelligent and have greater computer processing power.īut big AI models are very energy-intensive. Compared with traditional models, big AI models have more “neurons” for processing, much like the human brain. What’s behind the race to build big AI models?Ī.

The following transcript has been edited for clarity and brevity Yang Hongxia to understand the importance of this energy-efficient AI, its applications, and the future of green AI models. Last year, the research institute launched M6, the world’s first 10-trillion-parameter pre-training model, designed for multimodality and multitasking.ĭespite its size, M6, or the Multi-Modality to Multi-Modality Multitask Mega-transformer, uses 80% less energy than other trillion-level language models and is 11 times more energy-efficient.Īlizila caught up with DAMO’s AI scientist Dr.

The amount of carbon emissions generated by training one large AI model equals 315 people - or an entire 747 aircraft - traveling roundtrip between San Francisco and New York City, scientists at the University of Massachusetts Amherst calculated.Īs climate change looms, this practice is not sustainable.Įnter Alibaba Group’s DAMO Academy. To be sure, the size of these models is also growing and can now include trillions of parameters, which are configuration variables that are modified as the model learns. According to research lab Open AI, the computing power used in deep learning grew 300,000-fold between 20.

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