AI’s data collection practices are immature compared with the sophistication of AI model development. Massive data sets often lack clear information about what is in them and where it came from.

In comparison, traditional AI models rely on enormous swaths of prelabeled data sets in a process known as supervised training. The prelabeling is done by humans and is expensive and time-consuming.

- David Talby, John Snow Labs 12. It Reduces Model Bias One impactful way better data annotation improves AI performance is by reducing model bias.

Autodesk, with the release of the AEC Data Model API, is opening up its files and associated data formats to a more granular level of data sharing—allowing the underlying data that makes up a 3D ...

For example, in Meta's flagship open-source model, Llama 3.1 405B, which the company introduced last week, the researchers made extensive use of synthetic data to "fine-tune" the model and to ...