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alt="Daniela Rus, Director of CSAIL and MIT EECS Professor, was recently named a co-recipient of the 2024 John Scott Award by the Board of Directors of City Trusts (Credit: Rachel Gordon/MIT CSAIL)."
CSAIL article

Daniela Rus, Director of CSAIL and MIT EECS Professor, was recently named a co-recipient of the 2024 John Scott Award by the Board of Directors of City Trusts. This prestigious honor, steeped in historical significance, celebrates scientific innovation at the very location where American independence was signed in Philadelphia, a testament to the enduring connection between scientific progress and human potential.

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alt="Transformers process and generate inputs by “attending” to all previous inputs in each layer, which becomes expensive as the sequence length grows; in contrast, linear transformers maintain a fixed-size memory that is updated recurrently at each time step, allowing it to efficiently process and generate long sequences (Credit: The researchers)."
CSAIL article

Generative AI systems like large language models rely heavily on deep learning - and, in particular, transformers. Transformers make use of an “attention mechanism” for modeling interactions among inputs, which essentially involves doing nonlinear pairwise comparison between inputs and assigning different weights to tokens in a sequence, enabling a prioritization of some over others. The empirical effectiveness of this attention mechanism has led some in the community to claim that attention is “all you need” (the title of the original 2017 Google paper that introduced transformers).