  An occupation is comprised of interconnected tasks, and it is these tasks,
not occupations themselves, that are affected by AI. To evaluate how tasks may
be impacted, previous approaches utilized manual annotations or coarse-grained
matching. Leveraging recent advancements in machine learning, we replace
coarse-grained matching with more precise deep learning approaches. Introducing
the AI Impact (AII) measure, we employ Deep Learning Natural Language
Processing to automatically identify AI patents that may impact various
occupational tasks at scale. Our methodology relies on a comprehensive dataset
of 17,879 task descriptions and quantifies AI's potential impact through
analysis of 24,758 AI patents filed with the United States Patent and Trademark
Office (USPTO) between 2015 and 2022. Our results reveal that some occupations
will potentially be impacted, and that impact is intricately linked to specific
skills. These include not only routine tasks (codified as a series of steps),
as previously thought, but also non-routine ones (e.g., diagnosing health
conditions, programming computers, and tracking flight routes). However, AI's
impact on labour is limited by the fact that some of the occupations affected
are augmented rather than replaced (e.g., neurologists, software engineers, air
traffic controllers), and the sectors affected are experiencing labour
shortages (e.g., IT, Healthcare, Transport).
