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scAIence

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Quantifying AI-infused Science

The recent The recent public release of Artificial Intelligence (AI) that can master natural language has sparked a debate about the capabilities of AI and whether it can produce scientific content. Despite the ongoing debate, AI-... ver más

31/12/2029

UCPH

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-02-22

ERC-2023-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UCPH

2.00M€ | Lider

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Duración del proyecto: 70 meses Fecha Inicio: 2024-02-22
Fecha Fin: 2029-12-31

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The recent The recent public release of Artificial Intelligence (AI) that can master natural language has sparked a debate about the capabilities of AI and whether it can produce scientific content. Despite the ongoing debate, AI-generated written content has already entered the world of science: Papers co-written by AI are published, companies release models to assist scientists with producing scientific content, and a large fraction of scientists use AI-based tools to augment their writing. We will thus inevitably have AI-infused science in our future. The goal of scAIence is to quantify whether, how, and with which implications generative AI is changing how scientists write, communicate, perceive, and diffuse science, and to rigorously explore the opportunities, dangers, and implications of scientists augmenting their science with AI. The key hypothesis of scAIence is that current AI lacks the ability to combine knowledge entities (e.g. references) in the same manner as humans and is unable to replicate the social information (e.g. homophily or recency) present in human scientific output. Testing the implications of this hypothesis is crucial since the scientific enterprise relies heavily on social information present in scientific output data for a wide range of purposes, e.g. metrics definition, information retrieval, and predictions. The scAIence project will deploy a novel computational social science approach, based on a wide array of quantitative disciplines, leveraging large- scale databases of human-generated information and controlled experiments. scAIence will break new ground by (i) introducing the quantitative methods required to understand AI-infused science, (ii) redefining metrics and models to account for AI-generated content in science, and (iii) delivering quantitative scientific insights into how AI is changing the diffusion of science. Taken together, scAIence will lay the scientific foundation for the quantitative study of AI-infused science.

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