In order to learn a new skill or develop expertise in a particular area, one must be exposed to certain types of data. In the context of developing artificial intelligence models, training data is the foundational layer of information which it relies on. The more high quality data that is available and used throughout training, the more capable and adaptable the model is across a suite of tasks.
Last week, FAI published The Data Crunch: Accelerating American AI through Government Data Access. The paper describes the importance of strengthening the data commons, a term that describes freely available and accessible data that can be used to train and improve AI models, specifically by making U.S. government data more accessible.
For the past few years, policymakers in Washington have focused most of their attention on policies that impact access to compute. Examples include but are not limited to the CHIPS and Science Act, the Biden Administration’s diffusion rule (now rescinded), Pax Silica, and the Chip Security Act. The focus on this particular input makes sense, because without this cutting-edge hardware, US firms would not be able to build and deploy AI.
