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Data science opportunities of large language models for neuroscience and biomedicine.
Bzdok, Danilo; Thieme, Andrew; Levkovskyy, Oleksiy; Wren, Paul; Ray, Thomas; Reddy, Siva.
Afiliação
  • Bzdok D; Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada; TheNeuro - Montreal Neurological Institute (MNI), Department of Biomedical Engineering, McGill University, Montreal, QC, Canada. Electronic address: danilobzdok@gmail.com.
  • Thieme A; Mindstate Design Labs, San Francisco, CA, USA.
  • Levkovskyy O; Mindstate Design Labs, San Francisco, CA, USA.
  • Wren P; Mindstate Design Labs, San Francisco, CA, USA.
  • Ray T; Mindstate Design Labs, San Francisco, CA, USA.
  • Reddy S; Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada; Facebook CIFAR AI Chair; ServiceNow Research.
Neuron ; 112(5): 698-717, 2024 Mar 06.
Article em En | MEDLINE | ID: mdl-38340718
ABSTRACT
Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new modes of investigation in which LLMs can be used to reframe classic neuroscience questions to deliver fresh answers. We reason that LLMs have the potential to (1) enrich neuroscience datasets by adding valuable meta-information, such as advanced text sentiment, (2) summarize vast information sources to overcome divides between siloed neuroscience communities, (3) enable previously unthinkable fusion of disparate information sources relevant to the brain, (4) help deconvolve which cognitive concepts most usefully grasp phenomena in the brain, and much more.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neurociências / Ciência de Dados Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neurociências / Ciência de Dados Idioma: En Ano de publicação: 2024 Tipo de documento: Article