Creating an AI Observer: Generative Semantic Workspaces
<|reference_start|>Creating an AI Observer: Generative Semantic Workspaces: An experienced human Observer reading a document -- such as a crime report -- creates a succinct plot-like $\textit{``Working Memory''}$ comprising different actors, their prototypical roles and states at any point, their evolution over time based on their interactions, and even a map of missing Semantic parts anticipating them in the future. $\textit{An equivalent AI Observer currently does not exist}$. We introduce the $\textbf{[G]}$enerative $\textbf{[S]}$emantic $\textbf{[W]}$orkspace (GSW) -- comprising an $\textit{``Operator''}$ and a $\textit{``Reconciler''}$ -- that leverages advancements in LLMs to create a generative-style Semantic framework, as opposed to a traditionally predefined set of lexicon labels. Given a text segment $C_n$ that describes an ongoing situation, the $\textit{Operator}$ instantiates actor-centric Semantic maps (termed ``Workspace instance'' $\mathcal{W}_n$). The $\textit{Reconciler}$ resolves differences between $\mathcal{W}_n$ and a ``Working memory'' $\mathcal{M}_n^*$ to generate the updated $\mathcal{M}_{n+1}^*$. GSW outperforms well-known baselines on several tasks ($\sim 94\%$ vs. FST, GLEN, BertSRL - multi-sentence Semantics extraction, $\sim 15\%$ vs. NLI-BERT, $\sim 35\%$ vs. QA). By mirroring the real Observer, GSW provides the first step towards Spatial Computing assistants capable of understanding individual intentions and predicting future behavior.<|reference_end|>
@article{holur2024creating,
    title={Creating an AI Observer: Generative Semantic Workspaces},
    author={Pavan Holur, Shreyas Rajesh, David Chong, Vwani Roychowdhury},
    journal={arXiv preprint arXiv:2406.04555},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04555},
    primaryClass={cs.CL cs.AI}
}