Abstract: Compared to traditional mass production, customized discrete manufacturing—such as furniture panel production—generates massive, heterogeneous, and highly dynamic data throughout its ...
Abstract: Algorithmic text summarization task in natural language processing aims to represent a given text in a shorter and suitable form for a human reader by locating sentences of interest while ...
Late in 2025, we covered the development of an AI system called Evo that was trained on massive numbers of bacterial genomes. So many that, when prompted with sequences from a cluster of related genes ...
Comorbidity—the co-occurrence of multiple diseases in a patient—complicates diagnosis, treatment, and prognosis. Understanding how diseases connect at a molecular level is crucial, especially in aging ...
To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated. MIT Technology Review Explains: Let our writers untangle the complex, messy ...
When we have a dynamo_output_graph from tlparse this can be a helpful reproducer for problems in AOTAutograd. However, Dynamo cannot reliably retrace the output graphs it generates. The biggest ...
Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing the development of drugs. While existing in-silico methods leverage direct ...
When splitting a simple model that contains an nn.Embedding layer into pipeline stages with the torch.distributed.pipelining.pipeline API, the pipeline representation incorrectly calls the embedding ...
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