As of 2023, China's artificial intelligence (AI) sector has achieved outsized success, with Chinese companies dominating the top five spots in a U.S. government ranking of the most accurate facial recognition technology producers. This success is attributed to the alignment of interests between AI technology and autocratic rulers, with AI being a fundamentally predictive technology and autocratic regimes known for collecting vast amounts of data1. In the field of machine learning (ML), the largest training run reported as of 2023 was for the GPT-3 175B model, consuming 3,640 PetaFLOP days, equivalent to a few days on a top 8 supercomputer. The growth in FLOPs used for ML training over time has been substantial, with increasing access to cloud computing resources and declining costs contributing to this trend.
Before January 1, 2025, will any media report a machine learning training run in China exceeding 10^25 FLOP?
Resolution Criteria:
This question will resolve positively if, before January 1, 2025, there is credible media reporting of a machine learning training run in China that exceeds 10^25 FLOP. The reporting must specify that the run occurred in China and must provide a clear and valid calculation of the FLOP count. A FLOP (floating-point operation) is a measure of computer performance, in this context used to estimate the compute resources used to train a machine learning model. FLOP can be estimated by measuring the FLOPS (floating-point operations per second) consumed by the computers involved and multiplying by the number of seconds each computer was involved in training.
The source of the report must be credible and reputable, such as a recognized media outlet, a government report, or an academic paper. The report must be publicly available and verifiable.
This question will resolve negatively if no such report is made available by January 1, 2025. If a report is released but is later retracted or debunked by a credible source, the question will also resolve negatively.
In the event of conflicting reports, the resolution will be based on the consensus of credible sources. If no consensus is achieved, the question will resolve as ambiguous.