abstract.tex

\begin{slide}[\slideopts,toc={}]{Abstract}

Today's sequence models (such as large language models) in machine
learning (AI) arose from a blend of principle-based design and
empirical discovery, spanning several fields.
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This talk describes how the ideas could have emerged from an elementary signal-processing
approach.
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This viewpoint offers some features:
\begin{enumerate}
\mpitem Signal processing folks can quickly learn what is happening in a motivated way
\mpitem Machine-learning experts might benefit from signal-processing insights
\mpitem Obvious suggestions for things to try next naturally arise
\end{enumerate}

% \href{https://ccrma.stanford.edu/ccrma-open-house}{[Open House Schedule]}

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\end{slide}