Some months ago, researchers at the University of Massachusetts showed the climate toll of machine learning, especially deep learning. Training Google’s BERT, with its 340 million data parameters, emitted nearly as much carbon as a round-trip flight between the East and West coasts. According to Technology Review, the trend could also accelerate the concentration of AI research into the hands of a few big tech companies. “Under-resourced labs in academia or countries with fewer resources simply don’t have the means to use or develop such computationally expensive models.” (Technology Review, 4 October 2019) In response, some researchers are focused on shrinking the size of existing models without losing their capabilities. The magazine wrote enthusiastically: “Honey, I shrunk the AI” (Technology Review, 4 October 2019) There are advantages not only with regard to the environment and to the access to state-of-the-art AI. According to Technology Review, tiny models will help bring the latest AI advancements to consumer devices. “They avoid the need to send consumer data to the cloud, which improves both speed and privacy. For natural-language models specifically, more powerful text prediction and language generation could improve myriad applications like autocomplete on your phone and voice assistants like Alexa and Google Assistant.” (Technology Review, 4 October 2019)
BY OLIVER BENDEL
