New Delhi [India], July 23 (ANI): Chinese artificial intelligence startup Moonshot AI allegedly distilled Anthropic’s Fable model to develop its K3 model and acquired servers equipped with GB300 chips, said Michael Kratsios, director of the White House Office of Science and Technology Policy.
In a post on his official X account, Kratsios accused Moonshot AI of copying Anthropic’s proprietary AI model, while drawing a distinction between legitimate AI distillation and covert efforts to obtain proprietary technology.
He said, US strongly supports the free and fair development of AI adding legitimate distillation can improve AI efficiency. However covert, large-scale efforts to steal proprietary US technology and undermine American research are unacceptable.
“Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable,” he said.
He alleged that Moonshot AI had distilled Anthropic’s Fable model for the development of its K3 model. As per Kratsios, the Chinese startup developed a sophisticated internal platform to conduct large-scale distillation of US AI models, enabling it to rapidly switch between multiple access methods to avoid detection.
“We have information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model. To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection,” he said.
Kratsios further alleged that Moonshot AI had acquired GB300-equipped servers and accessed GB300 systems in Thailand, likely for training its AI models.
“Moonshot AI has also acquired GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models,” he said.
AI distillation, also known as knowledge distillation, is a technique in which a large, complex and computationally expensive model, known as the “teacher”, trains a smaller and faster model. (ANI)
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