ByteDance trains massive AI model in bid to rival Anthropic

2 hours ago 1

ByteDance, the parent of viral video platform TikTok, has kept a low profile in its AI development as its models are mostly closed, unlike many of its Chinese peers. Its latest SeeDance model ranks among the most advanced globally in video generation, while its flagship consumer-facing model Doubao is the most popular in China with 324 million monthly active users.

Over the past three years, ByteDance has invested in AI more aggressively than any of the other Chinese tech giants, building out its network of data centers and hiring researchers. It has doubled down on its cloud unit, Volcano Engine, which sells AI solutions to enterprises. ByteDance also has ambitions to develop custom AI chips.

Seed, its model development team led by former Google DeepMind scientist Wu Yonghui, has about 2,000 members in China and overseas. The team includes core researchers, infrastructure engineers, a data labelling team and translators.

Its model development has also implemented a more independent approach that does not involve “distilling” existing models from other labs, according to one of the people. This approach has been in place for more than a year, which some believe has led to its slower development versus rivals.

Model distillation is the process of compressing a large, complex AI model into a smaller, faster one by training the smaller model to copy the knowledge and outputs of the larger teacher.

ByteDance’s management, led by founder Zhang Yiming, believes only independent development can result in a model that outperforms rivals.

Zhang reiterated his stance in an internal meeting two weeks ago, where he told the Seed team to target “world-leading model capabilities” in the long run without getting too worried about falling behind in the near term, according to the person.

Chinese media Latepost and The Information first reported Zhang’s comments from the recent internal meeting.

ByteDance did not respond to a request for comment.

Read Entire Article