For AI Risk, 'The Real Answer Has to be Regulation'

An in-depth analysis of the architecture and data used in foundational large language models (LLMs) found that the usage of these models carries significant inherent risks, including the usage of polluted data, a lack of information on the data on which the model was trained, and the opacity of the architecture of the model itself.

The analysis is the work of the Berryville Institute of Machine Learning, a group of security and machine learning experts, which looked at the ways …

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